Design of a Small Modified Minkowski Fractal Antenna for Passive Deep Brain Stimulation Implants

Directory of Open Access Journals (Sweden)

Sara Manafi

2014-01-01

Full Text Available A small planar modified Minkowski fractal antenna is designed and simulated in dual frequency bands (2.4 and 5.8 GHz for wireless energy harvesting by deep brain stimulation (DBS devices. The designed antenna, physically being confined inside a miniaturized structure, can efficiently convert the wireless signals in dual ISM frequency bands to the energy source to recharge the DBS battery or power the pulse generator directly. The performance metrics such as the return loss, the specific absorption rate (SAR, and the radiation pattern within skin and muscle-fat-skin tissues are evaluated for the designed antenna. The gain of the proposed antenna is 3.2 dBi at 2.4 GHz and 4.7 dBi at 5.8 GHz; also the averaged SAR of the antenna in human body tissue is found to be well below the legally allowed limit at both frequency bands. The link budget shows the received power at the distance of 25 cm at 2.4 GHz and 5.8 GHz are around 0.4 mW and 0.04 mW, which can empower the DBS implant. The large operational bandwidth, the physical compactness, and the efficiency in wireless signal reception make this antenna suitable in being used in implanted biomedical devices such as DBS pulse generators.

Compositional Bias in Naïve and Chemically-modified Phage-Displayed Libraries uncovered by Paired-end Deep Sequencing.

Science.gov (United States)

He, Bifang; Tjhung, Katrina F; Bennett, Nicholas J; Chou, Ying; Rau, Andrea; Huang, Jian; Derda, Ratmir

2018-01-19

Understanding the composition of a genetically-encoded (GE) library is instrumental to the success of ligand discovery. In this manuscript, we investigate the bias in GE-libraries of linear, macrocyclic and chemically post-translationally modified (cPTM) tetrapeptides displayed on the M13KE platform, which are produced via trinucleotide cassette synthesis (19 codons) and NNK-randomized codon. Differential enrichment of synthetic DNA {S}, ligated vector {L} (extension and ligation of synthetic DNA into the vector), naïve libraries {N} (transformation of the ligated vector into the bacteria followed by expression of the library for 4.5 hours to yield a "naïve" library), and libraries chemically modified by aldehyde ligation and cysteine macrocyclization {M} characterized by paired-end deep sequencing, detected a significant drop in diversity in {L} → {N}, but only a minor compositional difference in {S} → {L} and {N} → {M}. Libraries expressed at the N-terminus of phage protein pIII censored positively charged amino acids Arg and Lys; libraries expressed between pIII domains N1 and N2 overcame Arg/Lys-censorship but introduced new bias towards Gly and Ser. Interrogation of biases arising from cPTM by aldehyde ligation and cysteine macrocyclization unveiled censorship of sequences with Ser/Phe. Analogous analysis can be used to explore library diversity in new display platforms and optimize cPTM of these libraries.

Outcomes of the DeepWind Conceptual Design

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Borg, Michael; Aagaard Madsen, Helge

2015-01-01

DeepWind has been presented as a novel floating offshore wind turbine concept with cost reduction potentials. Twelve international partners developed a Darrieus type floating turbine with new materials and technologies for deep-sea offshore environment. This paper summarizes results of the 5 MW...... the Deepwind floating 1 kW demonstrator. The 5 MW simulation results, loading and performance are compared to the OC3-NREL 5 MW wind turbine. Finally the paper elaborates the conceptual design on cost modelling....... DeepWind conceptual design. The concept was evaluated at the Hywind test site, described on its few components, in particular on the modified Troposkien blade shape and airfoil design. The feasibility of upscaling from 5 MW to 20 MW is discussed, taking into account the results from testing...

Auditory processing during deep propofol sedation and recovery from unconsciousness

OpenAIRE

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

2006-01-01

Objective 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. Methods 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...

Image-guided modified deep anterior lamellar keratoplasty (DALK) corneal transplant using intraoperative optical coherence tomography

Science.gov (United States)

Tao, Yuankai K.; LaBarbera, Michael; Ehlers, Justis P.; Srivastava, Sunil K.; Dupps, William J.

2015-03-01

Deep anterior lamellar keratoplasty (DALK) is an alternative to full-thickness corneal transplant and has advantages including the absence of allograft rejection; shortened duration of topical corticosteroid treatment and reduced associated risk of glaucoma, cataract, or infection; and enables use of grafts with poor endothelial quality. DALK begins by performing a trephination of approximately 80% stromal thickness, as measured by pachymetry. After removal of the anterior stoma, a needle is inserted into the residual stroma to inject air or viscoelastic to dissect Descemet's membrane. These procedures are inherently difficult and intraoperative rates of Descemet's membrane perforation between 4-39% have been reported. Optical coherence tomography (OCT) provides high-resolution images of tissue microstructures in the cornea, including Descemet's membrane, and allows quantitation of corneal layer thicknesses. Here, we use crosssectional intraoperative OCT (iOCT) measurements of corneal thickness during surgery and a novel micrometeradjustable biopsy punch to precision-cut the stroma down to Descemet's membrane. Our prototype cutting tool allows us to establish a dissection plane at the corneal endothelium interface, mitigates variability in cut-depths as a result of tremor, reduces procedure complexity, and reduces complication rates. iOCT-guided modified DALK procedures were performed on 47 cadaveric porcine eyes by non-experts and achieved a perforation rate of ~5% with a mean corneal dissection time care.

Excess plutonium disposition: The deep borehole option

International Nuclear Information System (INIS)

Ferguson, K.L.

1994-01-01

This report reviews the current status of technologies required for the disposition of plutonium in Very Deep Holes (VDH). It is in response to a recent National Academy of Sciences (NAS) report which addressed the management of excess weapons plutonium and recommended three approaches to the ultimate disposition of excess plutonium: (1) fabrication and use as a fuel in existing or modified reactors in a once-through cycle, (2) vitrification with high-level radioactive waste for repository disposition, (3) burial in deep boreholes. As indicated in the NAS report, substantial effort would be required to address the broad range of issues related to deep bore-hole emplacement. Subjects reviewed in this report include geology and hydrology, design and engineering, safety and licensing, policy decisions that can impact the viability of the concept, and applicable international programs. Key technical areas that would require attention should decisions be made to further develop the borehole emplacement option are identified

Figure-Ground Organization Emerges in a Deep Net with a Feedback Loop

OpenAIRE

Zipser, Karl

2015-01-01

We used a deep net to model how object-specific activation at the high levels of a hierarchical neural network could be fed back to modify representations at lower levels. We first identified a subset of nodes in the uppermost hidden layer that were preferentially activated by images of people. We then ran a procedure to recursively modify an image so as to increase activation of the 'person-selective' nodes. The image was modified by choosing a rectangular region (of random size and position...

Tensional results of non-penetrating deep sclerectomy in the treatment of primary open-angle glaucoma

OpenAIRE

Guedes, Ricardo Augusto Paletta; Guedes, Vanessa Maria Paletta

2004-01-01

OBJETIVO: O presente estudo tem como objetivo avaliar a eficácia, por meio da análise de seus resultados pressóricos, da esclerectomia profunda não penetrante para tratamento cirúrgico do glaucoma primário de ângulo aberto. MÉTODOS: Estudo retrospectivo de 104 olhos operados pela técnica de esclerectomia profunda não penetrante de 1999 a 2002. Nos casos em que havia risco para falência da bolsa filtrante (idade inferior a 45 anos, negros, cirurgia ocular prévia) a mitomicina C foi utilizada. ...

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

Science.gov (United States)

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.

DeepPy: Pythonic deep learning

DEFF Research Database (Denmark)

Larsen, Anders Boesen Lindbo

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

Management of exfoliative glaucoma: challenges and solutions.

Science.gov (United States)

Holló, Gábor; Katsanos, Andreas; Konstas, Anastasios Gp

2015-01-01

Exfoliative glaucoma is the most common type of secondary open-angle glaucoma worldwide. It is characterized by high intraocular pressure (IOP) and worse 24-hour IOP characteristics. In order to minimize progression, treatment of exfoliative glaucoma has to provide a low long-term mean IOP and good 24-hour IOP control. To achieve these goals, fixed-dose combination eye drops, argon and selective laser trabeculoplasty, and various forms of surgery (trabeculectomy, deep sclerectomy, viscocanalostomy, ab interno trabeculotomy, trabecular aspiration, and cataract surgery) all need to be considered during the long-term management of the disease. Since exfoliative glaucoma is a disease of the elderly, and is frequently associated with systemic vascular disease, interdisciplinary consultations are of great clinical importance. These management aspects and the current medical, laser, and surgical results are covered in this review, with a special focus on the needs of the general ophthalmologist.

[A modified technique of liposuction with excision for gynecomastia].

Science.gov (United States)

Yang, Hong-Yan; Xu, Jun; Yan, Xiao-Qing; You, Lei

2007-11-20

To introduce a modified technique of liposuction with excision for gynecomastia. From 2003 to 2007, 32 cases of gynecomastia were treated with the modified technique: the operative region was divided into central and periphery parts, liposuction was performed only in the periphery part and the deep layer of the central part, while the breast gland in the superficial layer of the central part underwent sharp dissection, the subcutaneous tissue of the central part was conserved, and blood supply was reserved and saucer deformity was avoided. Follow-up was conducted for 3.0 - 18.5 months. Normal men breast appearance was achieved. No complication happened such as hematoma, seroma, saucer deformity, and necrosis in nipple and areola. This modified operative technique for gynecomastia proves to be an excellent and effective technique.

Kelvin Wave Influence on the Shallow-to-Deep Transition Over the Amazon

Science.gov (United States)

Rowe, A.; Serra, Y. L.

2017-12-01

The suite of observations from GOAmazon and CHUVA offers a unique opportunity to examine land-based convective processes in the tropics, including the poorly represented shallow-to-deep transition. This study uses these data to investigate impacts of Kelvin waves on the the shallow-to-deep transition over the Central Amazon. The Kelvin waves that propagate over the region often originate over the tropical central and east Pacific, with local generation over the Andes also observed. The observed 15 m s-1 phase speed and 4500 km wave length during the two-year campaign are in agreement with previously published studies of these waves across the tropics. Also in agreement with previous studies, we find the waves are most active during the wet season (November-May) for this region. Using four separate convective event classes (clear-sky, nonprecipitating cumulus congestus, afternoon deep convection, and mesoscale convective systems), we examine how the convection preferentially develops for different phases of the Kelvin waves seen during GOAmazon. We additionally examine surface meteorological variables, the vertical thermodynamic and dynamic structure of the troposphere, vertical moist static stability, integrated column water vapor and liquid water, and surface energy fluxes within the context of these convective classes to identify the important environmental factors contributing to observed periods of enhanced deep convection related to the waves. Results suggest that the waves significantly modify the local environment, such as creating a deep layer of moisture throughout the troposphere, favoring more organized convection in the active than in the suppressed phase of the wave. The significance of wave-related environmental modifications are assessed by comparing local rainfall accumulations during Kelvin wave activity to that when the waves are not present. Future work will further explore the shallow-to-deep transition and its modulation by Kelvin wave activity

Performance Analysis of High-Speed Deep/Shallow Recessed Hybrid Bearing

Directory of Open Access Journals (Sweden)

Lei Wang

2013-01-01

Full Text Available The present paper proposes a theoretical analysis of the performance of deep/shallow recessed hybrid bearing. It is intended that, on the basis of the numerical results drawn from this study, appropriate shallow recess depth and width can be determined for use in the bearing design process. By adopting bulk flow theory, the turbulent Reynolds equation and energy equation are modified and solved numerically including concentrated inertia effects at the recess edge with different depth and width of shallow recess. The results indicate that the load capacity, drag torque increases as the depth of shallow recess is shallower and the width ratio (half angle of deep recess versus half angle of shallow recess is smaller. In contrast, the flow rate decreases as the depth of shallow recess is shallower and the width ratio is smaller. Nevertheless, the appropriate design of the depth and width of shallow recess might well induce the performance of high-speed deep/shallow recessed hybrid bearing.

Simulating deep convection with a shallow convection scheme

Directory of Open Access Journals (Sweden)

C. Hohenegger

2011-10-01

Full Text Available Convective processes profoundly affect the global water and energy balance of our planet but remain a challenge for global climate modeling. Here we develop and investigate the suitability of a unified convection scheme, capable of handling both shallow and deep convection, to simulate cases of tropical oceanic convection, mid-latitude continental convection, and maritime shallow convection. To that aim, we employ large-eddy simulations (LES as a benchmark to test and refine a unified convection scheme implemented in the Single-column Community Atmosphere Model (SCAM. Our approach is motivated by previous cloud-resolving modeling studies, which have documented the gradual transition between shallow and deep convection and its possible importance for the simulated precipitation diurnal cycle.

Analysis of the LES reveals that differences between shallow and deep convection, regarding cloud-base properties as well as entrainment/detrainment rates, can be related to the evaporation of precipitation. Parameterizing such effects and accordingly modifying the University of Washington shallow convection scheme, it is found that the new unified scheme can represent both shallow and deep convection as well as tropical and mid-latitude continental convection. Compared to the default SCAM version, the new scheme especially improves relative humidity, cloud cover and mass flux profiles. The new unified scheme also removes the well-known too early onset and peak of convective precipitation over mid-latitude continental areas.

Deep Echo State Network (DeepESN): A Brief Survey

OpenAIRE

Gallicchio, Claudio; Micheli, Alessio

2017-01-01

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

Putting the Deep Biosphere on the Map for Oceanography Courses: Gas Hydrates As a Case Study for the Deep Biosphere

Science.gov (United States)

Sikorski, J. J.; Briggs, B. R.

2014-12-01

The ocean is essential for life on our planet. It covers 71% of the Earth's surface, is the source of the water we drink, the air we breathe, and the food we eat. Yet, the exponential growth in human population is putting the ocean and thus life on our planet at risk. However, based on student evaluations from our introductory oceanography course it is clear that our students have deficiencies in ocean literacy that impact their ability to recognize that the ocean and humans are inextricably connected. Furthermore, life present in deep subsurface marine environments is also interconnected to the study of the ocean, yet the deep biosphere is not typically covered in undergraduate oceanography courses. In an effort to improve student ocean literacy we developed an instructional module on the deep biosphere focused on gas hydrate deposits. Specifically, our module utilizes Google Earth and cutting edge research about microbial life in the ocean to support three inquiry-based activities that each explore different facets of gas hydrates (i.e. environmental controls, biologic controls, and societal implications). The relevant nature of the proposed module also makes it possible for instructors of introductory geology courses to modify module components to discuss related topics, such as climate, energy, and geologic hazards. This work, which will be available online as a free download, is a solid contribution toward increasing the available teaching resources focused on the deep biosphere for geoscience educators.

Why & When Deep Learning Works: Looking Inside Deep Learnings

OpenAIRE

Ronen, Ronny

2017-01-01

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

Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy.

Science.gov (United States)

Takahashi, Hidenori; Tampo, Hironobu; Arai, Yusuke; Inoue, Yuji; Kawashima, Hidetoshi

2017-01-01

Disease staging involves the assessment of disease severity or progression and is used for treatment selection. In diabetic retinopathy, disease staging using a wide area is more desirable than that using a limited area. We investigated if deep learning artificial intelligence (AI) could be used to grade diabetic retinopathy and determine treatment and prognosis. The retrospective study analyzed 9,939 posterior pole photographs of 2,740 patients with diabetes. Nonmydriatic 45° field color fundus photographs were taken of four fields in each eye annually at Jichi Medical University between May 2011 and June 2015. A modified fully randomly initialized GoogLeNet deep learning neural network was trained on 95% of the photographs using manual modified Davis grading of three additional adjacent photographs. We graded 4,709 of the 9,939 posterior pole fundus photographs using real prognoses. In addition, 95% of the photographs were learned by the modified GoogLeNet. Main outcome measures were prevalence and bias-adjusted Fleiss' kappa (PABAK) of AI staging of the remaining 5% of the photographs. The PABAK to modified Davis grading was 0.64 (accuracy, 81%; correct answer in 402 of 496 photographs). The PABAK to real prognosis grading was 0.37 (accuracy, 96%). We propose a novel AI disease-staging system for grading diabetic retinopathy that involves a retinal area not typically visualized on fundoscopy and another AI that directly suggests treatments and determines prognoses.

Auditory processing during deep propofol sedation and recovery from unconsciousness.

Science.gov (United States)

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.

Modified Approach for Optimization of Real Life Transportation Problem in Neutrosophic Environment

Directory of Open Access Journals (Sweden)

Akanksha Singh

2017-01-01

Full Text Available To the best of our knowledge, there is only one approach for solving neutrosophic cost minimization transportation problems. Since neutrosophic transportation problems are a new area of research, other researchers may be attracted to extend this approach for solving other types of neutrosophic transportation problems like neutrosophic solid transportation problems, neutrosophic time minimization transportation problems, neutrosophic transshipment problems, and so on. However, after a deep study of the existing approach, it is noticed that a mathematical incorrect assumption has been used in these existing approaches; therefore there is a need to modify these existing approaches. Keeping the same in mind, in this paper, the existing approach is modified. Furthermore, the exact results of some existing transportation problems are obtained by the modified approach.

Deep learning and data assimilation for real-time production prediction in natural gas wells

NARCIS (Netherlands)

Loh, K.K.L.; Shoeibi Omrani, P.S.; Linden, R.J.P. van der

2018-01-01

The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties

Complex approach mechanical properties and formability assessment of selected deep-drawing steels

Directory of Open Access Journals (Sweden)

J. Štaba

2009-07-01

Full Text Available The paper analyses the properties of deep-drawing sheets of three grades (Re = 320 to 475 MPa, surface-treated with hot-dip galvanizing, made of microalloyed steels. Deformation properties are assessed using tensile tests, technological Erichsen or cupping tests. These characteristics, as well as the behaviour of the surface layer, are also investigated under dynamic conditions (modified Erichsen test using a drop tester, or using flat bending fatigue tests. Using microscopic analysis the deformation properties of the surface layer are evaluated. The results show the compactness of the surface layer, high deformation characteristics, as well as fatigue properties of the investigated deep-drawing materials, suitable for application in the automotive industry.

Modified big-bubble technique compared to manual dissection deep anterior lamellar keratoplasty in the treatment of keratoconus.

Science.gov (United States)

Knutsson, Karl Anders; Rama, Paolo; Paganoni, Giorgio

2015-08-01

To evaluate the clinical findings and results of manual dissection deep anterior lamellar keratoplasty (DALK) compared to a modified big-bubble DALK technique in eyes affected by keratoconus. Sixty eyes of 60 patients with keratoconus were treated with one of the two surgical techniques manual DALK (n = 30); big-bubble DALK (n = 30). The main outcomes measured were visual acuity, corneal topographic parameters, thickness of residual stroma and endothelial cell density (ECD). Patients were examined postoperatively at 1 month, 6 months, 1 year and 1 month after suture removal. Final best spectacle-corrected visual acuity (BSCVA) measured 1 month after suture removal was 0.11 ± 0.08 LogMAR in the big-bubble group compared to 0.13 ± 0.08 in the manual DALK group (p = 0.227). In patients treated with the big-bubble technique without complications (Descemet's membrane completely bared), the stromal residue was not measureable. Mean stromal residual thickness in the manual DALK group was 30.50 ± 27.60 μm. Data analysis of the manual DALK group demonstrated a significant correlation between BSCVA and residual stromal thickness; lower residual stromal thickness correlated with better BSCVA values (Spearman ρ = 0.509, p = 0.018). Postoperative ECD was similar in both groups at all intervals, with no statistically significant differences. In both groups, ECD loss was only significant during the 1- to 6-month interval (p = 0.001 and p big-bubble DALK and manual DALK groups, respectively). Manual DALK provides comparable results to big-bubble DALK. Big-bubble DALK permits faster visual recovery and is a surgical technique, which can be easily converted to manual DALK in cases of unsuccessful 'big-bubble' formation. © 2015 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Deep iCrawl: An Intelligent Vision-Based Deep Web Crawler

OpenAIRE

R.Anita; V.Ganga Bharani; N.Nityanandam; Pradeep Kumar Sahoo

2011-01-01

The explosive growth of World Wide Web has posed a challenging problem in extracting relevant data. Traditional web crawlers focus only on the surface web while the deep web keeps expanding behind the scene. Deep web pages are created dynamically as a result of queries posed to specific web databases. The structure of the deep web pages makes it impossible for traditional web crawlers to access deep web contents. This paper, Deep iCrawl, gives a novel and vision-based app...

Magnetic molecularly imprinted polymers based on silica modified by deep eutectic solvents for the rapid simultaneous magnetic-based solid-phase extraction of Salvia miltiorrhiza bunge, Glycine max (Linn.) Merr and green tea.

Science.gov (United States)

Li, Guizhen; Wang, Xiaoqin; Row, Kyung Ho

2018-04-01

Novel magnetic molecularly imprinted polymers (MMIPs) with multiple-template based on silica were modified by four types of deep eutectic solvents (DESs) for the rapid simultaneous magnetic solid-phase extraction (MSPE) of tanshinone Ⅰ, tanshinone ⅡA, and cryptotanshinone from Salvia miltiorrhiza bunge; glycitein, genistein, and daidzein from Glycine max (Linn.) Merr; and epicatechin, epigallocatechin gallate, and epicatechin gallate from green tea, respectively. The synthesized materials were characterized by Fourier transform infrared spectroscopy and field emission scanning electron microscopy. Single factor experiments were to explore the relationship between the extraction efficiency and four factors (the sample solution pH, amount of DESs for modification, amount of adsorbent, and extraction time). It was showed that the DES4-MMIPs have better extraction ability than the MMIPs without DESs and the other three DESs-modified MMIPs. The best extraction recoveries with DES4-MMIP were tanshinone Ⅰ (85.57%), tanshinone ⅡA (80.58%), cryptotanshinone (92.12%), glycitein (81.65%), genistein (87.72%), daidzein (92.24%), epicatechin (86.43%), epigallocatechin gallate (80.92%), and epicatechin gallate (93.64%), respectively. The novel multiple-template MMIPs materials modified by DES for the rapid simultaneous MSPE of active compounds were proved to reduce the experimental steps than single-template technique, and increase the extraction efficiency. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Performance Analysis of High-Speed Deep/Shallow Recessed Hybrid Bearing

OpenAIRE

Lei Wang; Shuyun Jiang

2013-01-01

The present paper proposes a theoretical analysis of the performance of deep/shallow recessed hybrid bearing. It is intended that, on the basis of the numerical results drawn from this study, appropriate shallow recess depth and width can be determined for use in the bearing design process. By adopting bulk flow theory, the turbulent Reynolds equation and energy equation are modified and solved numerically including concentrated inertia effects at the recess edge with different depth and widt...

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

Directory of Open Access Journals (Sweden)

A. S. Potapov

2014-09-01

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

Constitutive Behavior and Deep Drawability of Three Aluminum Alloys Under Different Temperatures and Deformation Speeds

Science.gov (United States)

Panicker, Sudhy S.; Prasad, K. Sajun; Basak, Shamik; Panda, Sushanta Kumar

2017-08-01

In the present work, uniaxial tensile tests were carried out to evaluate the stress-strain response of AA2014, AA5052 and AA6082 aluminum alloys at four temperatures: 303, 423, 523 and 623 K, and three strain rates: 0.0022, 0.022 and 0.22 s-1. It was found that the Cowper-Symonds model was not a robust constitutive model, and it failed to predict the flow behavior, particularly the thermal softening at higher temperatures. Subsequently, a comparative study was made on the capability of Johnson-Cook (JC), modified Zerilli-Armstrong (m-ZA), modified Arrhenius (m-ARR) and artificial neural network (ANN) for modeling the constitutive behavior of all the three aluminum alloys under the mentioned strain rates and temperatures. Also, the improvement in formability of the materials was evaluated at an elevated temperature of 623 K in terms of cup height and maximum safe strains by conducting cylindrical cup deep drawing experiments under two different punch speeds of 4 and 400 mm/min. The cup heights increased during warm deep drawing due to thermal softening and increase in failure strains. Also, a small reduction in cup height was observed when the punch speed increased from 4 to 400 mm/min at 623 K. Hence, it was suggested to use high-speed deformation at elevated temperature to reduce both punch load and cycle time during the deep drawing process.

Hardness in high temperature of steels ABNT H11 and ABNT H11 modified with niobium

International Nuclear Information System (INIS)

Goncalves, R.A.

1984-01-01

A method to measure the hardness of metallic materials was developed. The heating was done by the Joule effect heat dissipation in the sample, that is like an electrical resistor. A diamond penetrator with a revolution paraboloid format was used, that assure a linear relation between the load applyed and the penetration deep. Hardness tests were make in the range of 25 - 600 0 C, in the steels ABNT H11 and ABNT H11 modified with niobium, with the simultaneous register of the applied force, penetration deep and temperature. (E.G.) [pt

Current Surgical Options for the Management of Pediatric Glaucoma

Directory of Open Access Journals (Sweden)

Jose Morales

2013-01-01

Full Text Available Currently, there are numerous choices for the treatment of pediatric glaucoma depending on the type of glaucoma, the age of the patient, and other particularities of the condition discussed in this review. Traditionally, goniotomy and trabeculotomy ab externo have been the preferred choices of treatment for congenital glaucoma, and a variety of adult procedures adapted to children have been utilized for other types of pediatric glaucoma with variable results and complications. More recently, seton implantations of different types have become more popular to use in children, and newer techniques have become available including visualized cannulation and opening of Schlemm’s canal, deep sclerectomy, trabectome, and milder more directed cyclodestructive procedures such as endolaser and transcleral diode laser cyclophotocoagulation. This paper reviews the different surgical techniques currently available, their indications, results, and most common complications to allow the surgeon treating these conditions to make a more informed choice in each particular case. Although the outcome of surgical treatment in pediatric glaucoma has improved significantly, its treatment remains challenging.

Deep learning

CERN Document Server

Goodfellow, Ian; Courville, Aaron

2016-01-01

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

Trace Fossil Evidence of Trematode-Bivalve Parasite-Host Interactions in Deep Time.

Science.gov (United States)

Huntley, John Warren; De Baets, Kenneth

2015-01-01

Parasitism is one of the most pervasive phenomena amongst modern eukaryotic life and yet, relative to other biotic interactions, almost nothing is known about its history in deep time. Digenean trematodes (Platyhelminthes) are complex life cycle parasites, which have practically no body fossil record, but induce the growth of characteristic malformations in the shells of their bivalve hosts. These malformations are readily preserved in the fossil record, but, until recently, have largely been overlooked by students of the fossil record. In this review, we present the various malformations induced by trematodes in bivalves, evaluate their distribution through deep time in the phylogenetic and ecological contexts of their bivalve hosts and explore how various taphonomic processes have likely biased our understanding of trematodes in deep time. Trematodes are known to negatively affect their bivalve hosts in a number of ways including castration, modifying growth rates, causing immobilization and, in some cases, altering host behaviour making the host more susceptible to their own predators. Digeneans are expected to be significant agents of natural selection. To that end, we discuss how bivalves may have adapted to their parasites via heterochrony and suggest a practical methodology for testing such hypotheses in deep time. Copyright © 2015 Elsevier Ltd. All rights reserved.

Deep Joint Rain Detection and Removal from a Single Image

OpenAIRE

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

2016-01-01

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

Complex approach mechanical properties and formability assessment of selected deep-drawing steels

OpenAIRE

J. Štaba; M. Buršák

2009-01-01

The paper analyses the properties of deep-drawing sheets of three grades (Re = 320 to 475 MPa), surface-treated with hot-dip galvanizing, made of microalloyed steels. Deformation properties are assessed using tensile tests, technological Erichsen or cupping tests. These characteristics, as well as the behaviour of the surface layer, are also investigated under dynamic conditions (modified Erichsen test using a drop tester), or using flat bending fatigue tests. Using microscopic analysis the d...

On the dragnosis of deep vein thrombosis

International Nuclear Information System (INIS)

Olsson, C.-G.

1979-01-01

Clinical and laboratory diagnostic methods were studied in 301 consecutive patients with suspected deep vein thrombosis (DVT). Unexpectedly, phlebography (the reference method) was found to cause DVT in estimated 48 % of patients without initial DVT. Using a new type of contrast medium, however, no thrombotic complications were found. - Neither clinical examination nor plethysmography were found to give reliable results. Using a modified technique for radioisotope detection, high sensitivity to DVT was found with the 125 I-fibrinogen uptake test (within 2 days) and a newly developed 99 Tcsup(m)-plasmin test (within one hour). Since both tests showed low specificity, they are reliable as screening tests to exclude DVT, but not as independent diagnostic methods. (author)

Deep Incremental Boosting

OpenAIRE

Mosca, Alan; Magoulas, George D

2017-01-01

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

Resorbable and running suture for stable fixation of amniotic membrane multilayers: A useful modification in deep or perforating sterile corneal ulcers

Directory of Open Access Journals (Sweden)

Constantin E. Uhlig

2018-06-01

Full Text Available Purpose: To present a modified technique for secure tightening and fixing of multilayer amniotic membranes in deep or perforating corneal ulcers. Observations: The modified procedure for application and fixation of multilayer amniotic membranes is retrospectively described step by step, and the results of three patients treated with this technique were retrospectively analysed and presented.The modification consists basically in fixing the inlays with one mini-overlay that is sutured intracorneally with resorbable and running Vicryl 10.0, before a corneoscleral overlay is fixed on top conjunctivally with a running nylon 10.0 suture. The resorbable Vicryl suture is left in place permanently. Conclusions and Importance: The method described avoids any risk of destroying or displacing the inlays by removing sutures later. In each of the three patients demonstrated as case reports the cornea remained stable throughout the 3- to 5-month follow-up period. This modified technique represents a very useful auxiliary means of treating deep or perforating non-infectious corneal ulcers. Keywords: Amniotic membrane transplantation, Corneal ulcer, Corneal perforation, Multilayer, Keratoplasty

Deep Super Learner: A Deep Ensemble for Classification Problems

OpenAIRE

Young, Steven; Abdou, Tamer; Bener, Ayse

2018-01-01

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

DeepRT: deep learning for peptide retention time prediction in proteomics

OpenAIRE

Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

2017-01-01

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

Modified Method of Simplest Equation Applied to the Nonlinear Schrödinger Equation

Science.gov (United States)

Vitanov, Nikolay K.; Dimitrova, Zlatinka I.

2018-03-01

We consider an extension of the methodology of the modified method of simplest equation to the case of use of two simplest equations. The extended methodology is applied for obtaining exact solutions of model nonlinear partial differential equations for deep water waves: the nonlinear Schrödinger equation. It is shown that the methodology works also for other equations of the nonlinear Schrödinger kind.

Analyses of the deep borehole drilling status for a deep borehole disposal system

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Youl; Choi, Heui Joo; Lee, Min Soo; Kim, Geon Young; Kim, Kyung Su [KAERI, Daejeon (Korea, Republic of)

2016-05-15

The purpose of disposal for radioactive wastes is not only to isolate them from humans, but also to inhibit leakage of any radioactive materials into the accessible environment. Because of the extremely high level and long-time scale radioactivity of HLW(High-level radioactive waste), a mined deep geological disposal concept, the disposal depth is about 500 m below ground, is considered as the safest method to isolate the spent fuels or high-level radioactive waste from the human environment with the best available technology at present time. Therefore, as an alternative disposal concept, i.e., deep borehole disposal technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general status of deep drilling technologies was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, very preliminary applicability of deep drilling technology for deep borehole disposal analyzed. In this paper, as one of key technologies of deep borehole disposal system, the general status of deep drilling technologies in oil industry, geothermal industry and geo scientific field was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, the very preliminary applicability of deep drilling technology for deep borehole disposal such as relation between depth and diameter, drilling time and feasibility classification was analyzed.

Deep Space Telecommunications

Science.gov (United States)

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

2000-01-01

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

Photobiology of the deep twilight zone and beyond

Science.gov (United States)

Waterman, Talbot H.

1997-02-01

Photobiology in the twilight zone of the deep sea depends on faint light of two, or possibly three, origins: sunlight, bioluminescence and some visible radiation near the bottom associated with hydrothermal vents. The deep twilight zone also contains two quite distinct ecosystems: the vast open ocean pelagic regime far from the shore and the bottom as well as the far less expansive benthic regime with quite different characteristic animals that live on, in or near the sea bo10 Most of the whole ocean's benthic regime with a mean depth over 3000m is well below the twilight zone, which eliminates sunlight as a light source there. Many of the most familiar deepsea animals with their spectacular arrays of dennal light organs and remarkable eyes are from the pelagic 19, 25 The less familiar benthic fishes and crustaceans sometimes have curious internal light organs powered by bacteria13 and occasional incredibly modified eyes.30 With the exception of those on the fishing rods of most female deepsea anglerfish, where the light is produced by symbiotic bacteria, all the numerous light organs of pelagic deepsea fishes are generally believed to manage their own chemiluminescence independent of luminous bacteria.17

Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation

Science.gov (United States)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

Deep learning networks are gaining popularity in many medical image analysis tasks due to their generalized ability to automatically extract relevant features from raw images. However, this can make the learning problem unnecessarily harder requiring network architectures of high complexity. In case of anomaly detection, in particular, there is often sufficient regional difference between the anomaly and the surrounding parenchyma that could be easily highlighted through bottom-up saliency operators. In this paper we propose a new hybrid deep learning network using a combination of raw image and such regional maps to more accurately learn the anomalies using simpler network architectures. Specifically, we modify a deep learning network called U-Net using both the raw and pre-segmented images as input to produce joint encoding (contraction) and expansion paths (decoding) in the U-Net. We present results of successfully delineating subdural and epidural hematomas in brain CT imaging and liver hemangioma in abdominal CT images using such network.

Greedy Deep Dictionary Learning

OpenAIRE

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

2016-01-01

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

DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning.

Science.gov (United States)

Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek; Li, Xiaolin

2017-09-01

Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end-to-end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set. © 2017 Wiley Periodicals, Inc.

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

African Journals Online (AJOL)

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

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

Science.gov (United States)

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.

Offline High pH Reversed-Phase Peptide Fractionation for Deep Phosphoproteome Coverage

DEFF Research Database (Denmark)

Batth, Tanveer S; Olsen, Jesper V

2016-01-01

Protein phosphorylation, a process in which kinases modify serines, threonines, and tyrosines with phosphoryl groups is of major importance in eukaryotic biology. Protein phosphorylation events are key initiators of signaling responses which determine cellular outcomes after environmental...... and metabolic stimuli, and are thus highly regulated. Therefore, studying the mechanism of regulation by phosphorylation, and pinpointing the exact site of phosphorylation on proteins is of high importance. This protocol describes in detail a phosphoproteomics workflow for ultra-deep coverage by fractionating...

Modified Method of Simplest Equation Applied to the Nonlinear Schrödinger Equation

Directory of Open Access Journals (Sweden)

Vitanov Nikolay K.

2018-03-01

Full Text Available We consider an extension of the methodology of the modified method of simplest equation to the case of use of two simplest equations. The extended methodology is applied for obtaining exact solutions of model nonlinear partial differential equations for deep water waves: the nonlinear Schrödinger equation. It is shown that the methodology works also for other equations of the nonlinear Schrödinger kind.

Deep learning with Python

CERN Document Server

Chollet, Francois

2018-01-01

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

Deep learning evaluation using deep linguistic processing

OpenAIRE

Kuhnle, Alexander; Copestake, Ann

2017-01-01

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

Efficacy of two types of palliative sedation therapy defined using intervention protocols: proportional vs. deep sedation.

Science.gov (United States)

Imai, Kengo; Morita, Tatsuya; Yokomichi, Naosuke; Mori, Masanori; Naito, Akemi Shirado; Tsukuura, Hiroaki; Yamauchi, Toshihiro; Kawaguchi, Takashi; Fukuta, Kaori; Inoue, Satoshi

2018-06-01

This study investigated the effect of two types of palliative sedation defined using intervention protocols: proportional and deep sedation. We retrospectively analyzed prospectively recorded data of consecutive cancer patients who received the continuous infusion of midazolam in a palliative care unit. Attending physicians chose the sedation protocol based on each patient's wish, symptom severity, prognosis, and refractoriness of suffering. The primary endpoint was a treatment goal achievement at 4 h: in proportional sedation, the achievement of symptom relief (Support Team Assessment Schedule (STAS) ≤ 1) and absence of agitation (modified Richmond Agitation-Sedation Scale (RASS) ≤ 0) and in deep sedation, the achievement of deep sedation (RASS ≤ - 4). Secondary endpoints included mean scores of STAS and RASS, deep sedation as a result, and adverse events. Among 398 patients who died during the period, 32 received proportional and 18 received deep sedation. The treatment goal achievement rate was 68.8% (22/32, 95% confidence interval 52.7-84.9) in the proportional sedation group vs. 83.3% (15/18, 66.1-100) in the deep sedation group. STAS decreased from 3.8 to 0.8 with proportional sedation at 4 h vs. 3.7 to 0.3 with deep sedation; RASS decreased from + 1.2 to - 1.7 vs. + 1.4 to - 3.7, respectively. Deep sedation was needed as a result in 31.3% (10/32) of the proportional sedation group. No fatal events that were considered as probably or definitely related to the intervention occurred. The two types of intervention protocol well reflected the treatment intention and expected outcomes. Further, large-scale cohort studies are promising.

The routine use of modified Borelli's lactritmel agar (MBLA).

Science.gov (United States)

Kaminski, G W

1985-07-01

The original formula of Borelli's lactritmel agar (BLA)(3) which contains wheat flour, milk and honey, has been modified by replacing the wheat flour with dehydrated Bacto Corn Meal Agar (Difco) and by slightly altering the concentrations of the milk and honey. The modified medium (MBLA) is less turbid, less particulate, and easier to prepare than BLA. Although Trichophyton rubrum usually produces a wine-red pigment with BLA, most strains initially produce a yellow pigment, with the red pigment developing later. The corn meal in MBLA reduces this tendency and stimulates the early formation of deep wine red pigment, MBLA enhances sporulation of dermatophytes and various fungi which fail to sporulate on other media, and maintains characteristic growth without developing pleomorphic degeneration. It has been used routinely since 1972 as a reliable aid to the differentiation of T. rubrum and T. mentagrophytes. Since 1975 selective MBLA has been used as a routine primary isolation medium for dermatophytes, and has proved to be most useful.

Computer assisted strain-gauge plethysmography is a practical method of excluding deep venous thrombosis

International Nuclear Information System (INIS)

Goddard, A.J.P.; Chakraverty, S.; Wright, J.

2001-01-01

AIM: To evaluate a computed strain-gauge plethysmograph (CSGP) as a screening tool to exclude above knee deep venous thrombosis (DVT). METHODS: The first phase took place in the Radiology department. One hundred and forty-nine patients had both Doppler ultrasound and CSGP performed. Discordant results were resolved by venography where possible. The second phase took place in an acute medical admissions ward using a modified protocol. A further 173 patients had both studies performed. The results were collated and analysed. RESULTS: Phase 1. The predictive value of a negative CSGP study was 98%. There were two false-negative CSGP results (false-negative rate 5%), including one equivocal CSGP study which had deep venous thrombosis on ultrasound examination. Two patients thought to have thrombus on ultrasound proved not to have acute thrombus on venography. Phase 2. The negative predictive value of CSGP using a modified protocol was 97%. There were two definite and one possible false-negative studies (false-negative rate 4-7%). CONCLUSION: Computer strain-gauge plethysmograph can provide a simple, cheap and effective method of excluding lower limb DVT. However, its use should be rigorously assessed in each hospital in which it is used. Goddard, A.J.P., Chakraverty, S. and Wright, J. (2001)

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Science.gov (United States)

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Deep frying

NARCIS (Netherlands)

Koerten, van K.N.

2016-01-01

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

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene

2018-05-02

Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient\\'s phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

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

Science.gov (United States)

Price, Leigh C.

1978-01-01

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

Magnetically tunable oil droplet lens of deep-sea shrimp

Science.gov (United States)

Iwasaka, M.; Hirota, N.; Oba, Y.

2018-05-01

In this study, the tunable properties of a bio-lens from a deep-sea shrimp were investigated for the first time using magnetic fields. The skin of the shrimp exhibited a brilliantly colored reflection of incident white light. The light reflecting parts and the oil droplets in the shrimp's skin were observed in a glass slide sample cell using a digital microscope that operated in the bore of two superconducting magnets (maximum strengths of 5 and 13 T). In the ventral skin of the shrimp, which contained many oil droplets, some comparatively large oil droplets (50 to 150 μm in diameter) were present. A distinct response to magnetic fields was found in these large oil droplets. Further, the application of the magnetic fields to the sample cell caused a change in the size of the oil droplets. The phenomena observed in this work indicate that the oil droplets of deep sea shrimp can act as lenses in which the optical focusing can be modified via the application of external magnetic fields. The results of this study will make it possible to fabricate bio-inspired soft optical devices in future.

Deep learning in bioinformatics.

Science.gov (United States)

Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

2017-09-01

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

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

Li, Yu

2017-12-23

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

Deep learning relevance

DEFF Research Database (Denmark)

Lioma, Christina; Larsen, Birger; Petersen, Casper

2016-01-01

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

Early- and late-stage morphea subtypes with deep tissue involvement is treatable with Abatacept (Orencia).

Science.gov (United States)

Adeeb, Fahd; Anjum, Shakeel; Hodnett, Philip; Kashif, Ahmad; Brady, Mary; Morrissey, Siobhan; Devlin, Joseph; Fraser, Alexander Duncan

2017-06-01

This case series explores the potential efficacy of Abatacept in patients presenting with morphea subtypes and deep tissue involvement. Three patients with established morphea subtypes and deep tissue involvement and with no contraindication to Abatacept were included in this prospective open-label study. The index patient was exceptionally severely affected with a mean Modified Rodnan Skin Score (MRSS) of 38/51. At baseline, whole-body MRI and skin biopsy were performed which confirmed classical deposition of dense fibrous tissue in the appropriate layer of the skin. MRSS was performed independently by three clinicians and VAS scores (10cm) were measured at baseline for Patient Global Disease Activity (PGDA), Patient Global Pain (PGP), Patient Day Pain (PDP), Patient Night Pain (PNP), and Physician Global Disease Activity (PhGDA). Patients 2 and 3 were similarly screened at baseline except for MRI. Patients were commenced on Abatacept as per body weight (10mg/kg) given intravenously with concomitant tapering dose of oral prednisolone. All three were re-assessed at 6 months and the index case was further re-assessed at 18 months. All patients tolerated the Abatacept well and showed dramatic improvement. The index patient's clinical signs and symptoms, whole-body MRI, and mean Modified Rodnan Skin Score improved dramatically from baseline by 37% at 6 months and by 74% at 18 months. There were no clinically significant adverse outcomes noted. We present three cases, one with exceptionally severe disease, which demonstrated excellent clinical response to Abatacept. Abatacept is a promising option for the treatment of severe or resistant morphea, especially in those with deep tissue involvement. Copyright © 2017 Elsevier Inc. All rights reserved.

STIMULATION TECHNOLOGIES FOR DEEP WELL COMPLETIONS

Energy Technology Data Exchange (ETDEWEB)

Stephen Wolhart

2003-06-01

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

Deep learning in TMVA Benchmarking Benchmarking TMVA DNN Integration of a Deep Autoencoder

CERN Document Server

Huwiler, Marc

2017-01-01

The TMVA library in ROOT is dedicated to multivariate analysis, and in partic- ular oers numerous machine learning algorithms in a standardized framework. It is widely used in High Energy Physics for data analysis, mainly to perform regression and classication. To keep up to date with the state of the art in deep learning, a new deep learning module was being developed this summer, oering deep neural net- work, convolutional neural network, and autoencoder. TMVA did not have yet any autoencoder method, and the present project consists in implementing the TMVA autoencoder class based on the deep learning module. It also includes some bench- marking performed on the actual deep neural network implementation, in comparison to the Keras framework with Tensorflow and Theano backend.

Farming system context drives the value of deep wheat roots in semi-arid environments.

Science.gov (United States)

Lilley, Julianne M; Kirkegaard, John A

2016-06-01

The capture of subsoil water by wheat roots can make a valuable contribution to grain yield on deep soils. More extensive root systems can capture more water, but leave the soil in a drier state, potentially limiting water availability to subsequent crops. To evaluate the importance of these legacy effects, a long-term simulation analysis at eight sites in the semi-arid environment of Australia compared the yield of standard wheat cultivars with cultivars that were (i) modified to have root systems which extract more water at depth and/or (ii) sown earlier to increase the duration of the vegetative period and hence rooting depth. We compared simulations with and without annual resetting of soil water to investigate the legacy effects of drier subsoils related to modified root systems. Simulated mean yield benefits from modified root systems declined from 0.1-0.6 t ha(-1) when annually reset, to 0-0.2 t ha(-1) in the continuous simulation due to a legacy of drier soils (mean 0-32mm) at subsequent crop sowing. For continuous simulations, predicted yield benefits of >0.2 t ha(-1) from more extensive root systems were rare (3-10% of years) at sites with shallow soils (<1.0 m), but occurred in 14-44% of years at sites with deeper soils (1.6-2.5 m). Earlier sowing had a larger impact than modified root systems on water uptake (14-31 vs 2-17mm) and mean yield increase (up to 0.7 vs 0-0.2 t ha(-1)) and the benefits occurred on deep and shallow soils and in more years (9-79 vs 3-44%). Increasing the proportion of crops in the sequence which dry the subsoil extensively has implications for the farming system productivity, and the crop sequence must be managed tactically to optimize overall system benefits. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Deep subsurface microbial processes

Science.gov (United States)

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

1995-01-01

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

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

Science.gov (United States)

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.

Pathogenesis of deep endometriosis.

Science.gov (United States)

Gordts, Stephan; Koninckx, Philippe; Brosens, Ivo

2017-12-01

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

Genetically modified foods and social concerns.

Science.gov (United States)

Maghari, Behrokh Mohajer; Ardekani, Ali M

2011-07-01

Biotechnology is providing us with a wide range of options for how we can use agricultural and commercial forestry lands. The cultivation of genetically modified (GM) crops on millions of hectares of lands and their injection into our food chain is a huge global genetic experiment involving all living beings. Considering the fast pace of new advances in production of genetically modified crops, consumers, farmers and policymakers worldwide are challenged to reach a consensus on a clear vision for the future of world food supply. The current food biotechnology debate illustrates the serious conflict between two groups: 1) Agri-biotech investors and their affiliated scientists who consider agricultural biotechnology as a solution to food shortage, the scarcity of environmental resources and weeds and pests infestations; and 2) independent scientists, environmentalists, farmers and consumers who warn that genetically modified food introduces new risks to food security, the environment and human health such as loss of biodiversity; the emergence of superweeds and superpests; the increase of antibiotic resistance, food allergies and other unintended effects. This article reviews major viewpoints which are currently debated in the food biotechnology sector in the world. It also lays the ground-work for deep debate on benefits and risks of Biotech-crops for human health, ecosystems and biodiversity. In this context, although some regulations exist, there is a need for continuous vigilance for all countries involved in producing genetically engineered food to follow the international scientific bio-safety testing guidelines containing reliable pre-release experiments and post-release track of transgenic plants to protect public health and avoid future environmental harm.

DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

OpenAIRE

Kim, Hanjoo; Park, Jaehong; Jang, Jaehee; Yoon, Sungroh

2016-01-01

The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operation...

Treatment of ischial pressure sores using a modified gracilis myofasciocutaneous flap.

Science.gov (United States)

Lin, Haodong; Hou, Chunlin; Chen, Aimin; Xu, Zhen

2010-04-01

Despite the availability of a variety of flap reconstruction options, ischial pressure sores continue to be the most difficult pressure sores to treat. This article describes a successful surgical procedure for the coverage of ischial ulcers using a modified gracilis myofasciocutaneous flap. From August 2000 to April 2004, 12 patients with ischial sores were enrolled in the study. All patients underwent early aggressive surgical debridement followed by surgical reconstruction with a modified gracilis myofasciocutaneous flap. The follow-up period ranged from 13 to 86 months, with a mean of 44 months. Overall, 91.7% of the flaps (11 of 12) survived primarily. Partial flap necrosis occurred in one patient. Primary wound healing occurred without complications at both the donor and recipient sites in all cases. In one patient, grade II ischial pressure sores recurred 13 months after the operation. There was no recurrence in other 11 patients. A modified gracilis myofasciocutaneous flap provides a good cover for ischial pressure sores. Because it is easy to use and has favorable results, it can be used in the primary treatment for large and deep ischial pressure sores. Copyright Thieme Medical Publishers.

Search for nonstandard neutrino interactions with IceCube DeepCore

Science.gov (United States)

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.; Argüelles, C.; Auffenberg, J.; Axani, S.; Bagherpour, H.; Bai, X.; Barron, J. P.; 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.; Bourbeau, E.; Bourbeau, J.; Bradascio, F.; Braun, J.; Brayeur, L.; Brenzke, M.; Bretz, H.-P.; Bron, S.; Brostean-Kaiser, J.; Burgman, A.; Carver, T.; Casey, J.; 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.; DeLaunay, J. J.; 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.; Dvorak, E.; Eberhardt, B.; Ehrhardt, T.; Eichmann, B.; Eller, P.; Evenson, P. A.; Fahey, S.; Fazely, A. R.; Felde, J.; Filimonov, K.; Finley, C.; Flis, S.; Franckowiak, A.; Friedman, E.; Fuchs, T.; Gaisser, T. K.; Gallagher, J.; Gerhardt, L.; Ghorbani, K.; Giang, W.; Glauch, T.; Glüsenkamp, T.; Goldschmidt, A.; Gonzalez, J. G.; Grant, D.; Griffith, Z.; Haack, C.; Hallgren, A.; Halzen, F.; Hanson, K.; Hebecker, D.; Heereman, D.; Helbing, K.; Hellauer, R.; Hickford, S.; Hignight, J.; Hill, G. C.; Hoffman, K. D.; Hoffmann, R.; Hokanson-Fasig, B.; Hoshina, K.; Huang, F.; Huber, M.; Hultqvist, K.; Hünnefeld, M.; In, S.; Ishihara, A.; Jacobi, E.; Japaridze, G. S.; Jeong, M.; Jero, K.; Jones, B. J. P.; Kalaczynski, 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.; Kirby, C.; Kiryluk, J.; Kittler, T.; Klein, S. R.; Kohnen, G.; Koirala, R.; Kolanoski, H.; Köpke, L.; Kopper, C.; Kopper, S.; Koschinsky, J. P.; Koskinen, D. J.; Kowalski, M.; Krings, K.; Kroll, M.; Krückl, G.; 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.; Liu, Q. R.; Lu, L.; Lünemann, J.; Luszczak, W.; Madsen, J.; Maggi, G.; Mahn, K. B. M.; Mancina, S.; 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.; Moore, R. W.; Moulai, M.; Nahnhauer, R.; Nakarmi, P.; 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.; Pepper, J. A.; Pérez de los Heros, C.; Pieloth, D.; Pinat, E.; Plum, M.; Price, P. B.; Przybylski, G. T.; Raab, C.; Rädel, L.; Rameez, M.; Rawlins, K.; Rea, I. C.; Reimann, R.; Relethford, B.; Relich, M.; Resconi, E.; Rhode, W.; Richman, M.; Robertson, S.; Rongen, M.; Rott, C.; Ruhe, T.; Ryckbosch, D.; Rysewyk, D.; Sälzer, T.; Sanchez Herrera, S. E.; Sandrock, A.; Sandroos, J.; Santander, M.; Sarkar, S.; Sarkar, S.; Satalecka, K.; Schlunder, P.; Schmidt, T.; Schneider, A.; Schoenen, S.; Schöneberg, S.; Schumacher, L.; Seckel, D.; Seunarine, S.; Soedingrekso, J.; Soldin, D.; Song, M.; Spiczak, G. M.; Spiering, C.; Stachurska, J.; Stamatikos, M.; Stanev, T.; Stasik, A.; Stettner, J.; Steuer, A.; Stezelberger, T.; Stokstad, R. G.; Stößl, A.; Strotjohann, N. L.; Stuttard, T.; Sullivan, G. W.; Sutherland, M.; 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.; Turley, C. F.; Ty, B.; Unger, E.; Usner, M.; Vandenbroucke, J.; Van Driessche, W.; van Eijndhoven, N.; Vanheule, S.; van Santen, J.; Vehring, M.; Vogel, E.; Vraeghe, M.; Walck, C.; Wallace, A.; Wallraff, M.; Wandler, F. D.; Wandkowsky, N.; Waza, A.; Weaver, C.; Weiss, M. J.; Wendt, C.; Werthebach, J.; Westerhoff, S.; Whelan, B. J.; Wiebe, K.; Wiebusch, C. H.; Wille, L.; Williams, D. R.; Wills, L.; Wolf, M.; Wood, J.; Wood, T. R.; Woolsey, E.; Woschnagg, K.; Xu, D. L.; Xu, X. W.; Xu, Y.; Yanez, J. P.; Yodh, G.; Yoshida, S.; Yuan, T.; Zoll, M.; IceCube Collaboration

2018-04-01

As atmospheric neutrinos propagate through the Earth, vacuumlike oscillations are modified by Standard Model neutral- and charged-current interactions with electrons. Theories beyond the Standard Model introduce heavy, TeV-scale bosons that can produce nonstandard neutrino interactions. These additional interactions may modify the Standard Model matter effect producing a measurable deviation from the prediction for atmospheric neutrino oscillations. The result described in this paper constrains nonstandard interaction parameters, building upon a previous analysis of atmospheric muon-neutrino disappearance with three years of IceCube DeepCore data. The best fit for the muon to tau flavor changing term is ɛμ τ=-0.0005 , with a 90% C.L. allowed range of -0.0067 <ɛμ τ<0.0081 . This result is more restrictive than recent limits from other experiments for ɛμ τ. Furthermore, our result is complementary to a recent constraint on ɛμ τ using another publicly available IceCube high-energy event selection. Together, they constitute the world's best limits on nonstandard interactions in the μ -τ sector.

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert

2018-01-01

phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Science.gov (United States)

Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

2018-02-01

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

None

2003-09-30

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

DeepQA: improving the estimation of single protein model quality with deep belief networks.

Science.gov (United States)

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Deep Brain Electrical Stimulation in Epilepsy

Science.gov (United States)

Rocha, Luisa L.

2008-11-01

The deep brain electrical stimulation has been used for the treatment of neurological disorders such as Parkinson's disease, chronic pain, depression and epilepsy. Studies carried out in human brain indicate that the application of high frequency electrical stimulation (HFS) at 130 Hz in limbic structures of patients with intractable temporal lobe epilepsy abolished clinical seizures and significantly decreased the number of interictal spikes at focus. The anticonvulsant effects of HFS seem to be more effective in patients with less severe epilepsy, an effect associated with a high GABA tissue content and a low rate of cell loss. In addition, experiments using models of epilepsy indicate that HFS (pulses of 60 μs width at 130 Hz at subthreshold current intensity) of specific brain areas avoids the acquisition of generalized seizures and enhances the postictal seizure suppression. HFS is also able to modify the status epilepticus. It is concluded that the effects of HFS may be a good strategy to reduce or avoid the epileptic activity.

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

Stephen Wolhart

2005-06-30

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

DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

Science.gov (United States)

Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

2016-09-01

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

DeepBase: annotation and discovery of microRNAs and other noncoding RNAs from deep-sequencing data.

Science.gov (United States)

Yang, Jian-Hua; Qu, Liang-Hu

2012-01-01

Recent advances in high-throughput deep-sequencing technology have produced large numbers of short and long RNA sequences and enabled the detection and profiling of known and novel microRNAs (miRNAs) and other noncoding RNAs (ncRNAs) at unprecedented sensitivity and depth. In this chapter, we describe the use of deepBase, a database that we have developed to integrate all public deep-sequencing data and to facilitate the comprehensive annotation and discovery of miRNAs and other ncRNAs from these data. deepBase provides an integrative, interactive, and versatile web graphical interface to evaluate miRBase-annotated miRNA genes and other known ncRNAs, explores the expression patterns of miRNAs and other ncRNAs, and discovers novel miRNAs and other ncRNAs from deep-sequencing data. deepBase also provides a deepView genome browser to comparatively analyze these data at multiple levels. deepBase is available at http://deepbase.sysu.edu.cn/.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

Energy Technology Data Exchange (ETDEWEB)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

International Nuclear Information System (INIS)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu

2016-01-01

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea

Deep Mapping and Spatial Anthropology

Directory of Open Access Journals (Sweden)

Les Roberts

2016-01-01

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

Deep Vein Thrombosis

African Journals Online (AJOL)

OWNER

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

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

Science.gov (United States)

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

2017-12-05

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

Habitat structure modified by an invasive grass enhances inundation withstanding in a salt-marsh wolf spider

OpenAIRE

Pétillon, J.; Lambeets, K.; Montaigne, W.; Maelfait, J.-P.; Bonte, D.

2010-01-01

Vegetation and underground structures are known to influence flood avoidance and flood resistance in invertebrates. In bimonthly-flooded European salt marshes, recent invasions by the nitrophilous grass Elymus athericus strongly modified usual habitat structure, notably by the production of a deep litter layer. Consequently, invaded habitats provide more interstitial spaces that may act as a refuge during flood events. By using both controlled and field designs, we tested whether invaded habi...

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

Science.gov (United States)

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

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

Science.gov (United States)

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.

Deep learning for image classification

Science.gov (United States)

McCoppin, Ryan; Rizki, Mateen

2014-06-01

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

Deep learning for computational chemistry

Energy Technology Data Exchange (ETDEWEB)

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

2017-03-08

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

Deep learning for computational chemistry.

Science.gov (United States)

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

2017-06-15

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

What Really is Deep Learning Doing?

OpenAIRE

Xiong, Chuyu

2017-01-01

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

Deep level transient spectroscopic investigation of phosphorus-doped silicon by self-assembled molecular monolayers.

Science.gov (United States)

Gao, Xuejiao; Guan, Bin; Mesli, Abdelmadjid; Chen, Kaixiang; Dan, Yaping

2018-01-09

It is known that self-assembled molecular monolayer doping technique has the advantages of forming ultra-shallow junctions and introducing minimal defects in semiconductors. In this paper, we report however the formation of carbon-related defects in the molecular monolayer-doped silicon as detected by deep-level transient spectroscopy and low-temperature Hall measurements. The molecular monolayer doping process is performed by modifying silicon substrate with phosphorus-containing molecules and annealing at high temperature. The subsequent rapid thermal annealing drives phosphorus dopants along with carbon contaminants into the silicon substrate, resulting in a dramatic decrease of sheet resistance for the intrinsic silicon substrate. Low-temperature Hall measurements and secondary ion mass spectrometry indicate that phosphorus is the only electrically active dopant after the molecular monolayer doping. However, during this process, at least 20% of the phosphorus dopants are electrically deactivated. The deep-level transient spectroscopy shows that carbon-related defects are responsible for such deactivation.

Taoism and Deep Ecology.

Science.gov (United States)

Sylvan, Richard; Bennett, David

1988-01-01

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

deepTools2: a next generation web server for deep-sequencing data analysis.

Science.gov (United States)

Ramírez, Fidel; Ryan, Devon P; Grüning, Björn; Bhardwaj, Vivek; Kilpert, Fabian; Richter, Andreas S; Heyne, Steffen; Dündar, Friederike; Manke, Thomas

2016-07-08

We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Linking white matter and deep gray matter alterations in premanifest Huntington disease

Directory of Open Access Journals (Sweden)

Andreia V. Faria

2016-01-01

Full Text Available Huntington disease (HD is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i regions of interest surrounding these structures, using (ii tractography-based analysis, and using (iii whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores, and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be

Is Multitask Deep Learning Practical for Pharma?

Science.gov (United States)

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

The effect of deep cryogenic treatments on the mechanical properties of an AISI H13 steel

Energy Technology Data Exchange (ETDEWEB)

Pérez, Marcos, E-mail: marcosperezrd@gmail.com; Belzunce, Francisco Javier

2015-01-29

Cryogenic treatments are considered a good way to reduce the retained austenite content and improve the performance of tool steels. Four different heat treatments, two of which included a deep cryogenic stage, were applied in this study to an H13 tool steel, subsequently determining its mechanical properties by means of tensile, hardness and fracture toughness tests. Furthermore, scanning electron microscopy and X-ray diffraction analysis were performed to gain an insight into the microstructural evolution of these heat treatments during all the stages. It was concluded that the application of a deep cryogenic treatment to H13 steel induces higher thermal stresses and structural defects, producing a dispersed network of fine carbides after the subsequent tempering stages, which were responsible for a significant improvement in the fracture toughness of this steel without modifying other mechanical properties. Although the application of a deep cryogenic treatment reduces the retained austenite content, there is a minimum innate content which cannot be transformed by heat treatment. Nevertheless, this austenite is hence believed to be stable enough and should not transform during the normal service life of forging dies.

The effect of deep cryogenic treatments on the mechanical properties of an AISI H13 steel

International Nuclear Information System (INIS)

Pérez, Marcos; Belzunce, Francisco Javier

2015-01-01

Cryogenic treatments are considered a good way to reduce the retained austenite content and improve the performance of tool steels. Four different heat treatments, two of which included a deep cryogenic stage, were applied in this study to an H13 tool steel, subsequently determining its mechanical properties by means of tensile, hardness and fracture toughness tests. Furthermore, scanning electron microscopy and X-ray diffraction analysis were performed to gain an insight into the microstructural evolution of these heat treatments during all the stages. It was concluded that the application of a deep cryogenic treatment to H13 steel induces higher thermal stresses and structural defects, producing a dispersed network of fine carbides after the subsequent tempering stages, which were responsible for a significant improvement in the fracture toughness of this steel without modifying other mechanical properties. Although the application of a deep cryogenic treatment reduces the retained austenite content, there is a minimum innate content which cannot be transformed by heat treatment. Nevertheless, this austenite is hence believed to be stable enough and should not transform during the normal service life of forging dies

Scaled momentum spectra in deep inelastic scattering at HERA

International Nuclear Information System (INIS)

Abramowicz, H.; Abt, I.; Adamczyk, L.

2009-12-01

Charged particle production has been studied in neutral current deep inelastic ep scattering with the ZEUS detector at HERA using an integrated luminosity of 0.44 fb -1 . Distributions of scaled momenta in the Breit frame are presented for particles in the current fragmentation region. The evolution of these spectra with the photon virtuality, Q 2 , is described in the kinematic region 10 2 2 . Next-to-leading-order and modified leading-log-approximation QCD calculations as well as predictions from Monte Carlo models are compared to the data. The results are also compared to e + e - annihilation data. The dependences of the pseudorapidity distribution of the particles on Q 2 and on the energy in the γp system, W, are presented and interpreted in the context of the hypothesis of limiting fragmentation. (orig.)

DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian

Directory of Open Access Journals (Sweden)

Chao Li

2017-02-01

Full Text Available Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.. Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way.

Invited talk: Deep Learning Meets Physics

CERN Multimedia

CERN. Geneva

2018-01-01

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

Deep geothermics

International Nuclear Information System (INIS)

Anon.

1995-01-01

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

Stable architectures for deep neural networks

Science.gov (United States)

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.

Acrylamide in deep-fried snacks of India.

Science.gov (United States)

Shamla, L; Nisha, P

2014-01-01

Acrylamide content in deep-fried snacks from 20 different production sites of South Indian province of Kerala (80 samples representing 4 important product categories) were determined using a modified high performance liquid chromatography (HPLC)-diode array detector (DAD) method. The limit of detection and the limit of quantification for this method were 1.04 and 3.17 μg/kg, respectively. The mean recoveries of acrylamide obtained by using spiked samples ranged between 90% and 103%, which shows good extraction efficiency. Acrylamide concentrations in the four groups of snacks ranged from 82.0 to 4245.6 µg/kg for potato chips, 46.2-2431.4 µg/kg for jack chips, 24.8-1959.8 µg/kg for sweet plantain chips and 14.7-1690.5 µg/kg for plantain chips. These are the most widely consumed snacks in South India, and the results revealed reasonable levels of acrylamide in these foods, which indicated the general risk of consumer exposure.

Development of Deep-tow Autonomous Cable Seismic (ACS) for Seafloor Massive Sulfides (SMSs) Exploration.

Science.gov (United States)

Asakawa, Eiichi; Murakami, Fumitoshi; Tsukahara, Hitoshi; Saito, Shutaro; Lee, Sangkyun; Tara, Kenji; Kato, Masafumi; Jamali Hondori, Ehsan; Sumi, Tomonori; Kadoshima, Kazuyuki; Kose, Masami

2017-04-01

Within the EEZ of Japan, numerous surveys exploring ocean floor resources have been conducted. The exploration targets are gas hydrates, mineral resources (manganese, cobalt or rare earth) and especially seafloor massive sulphide (SMS) deposits. These resources exist in shallow subsurface areas in deep waters (>1500m). For seismic explorations very high resolution images are required. These cannot be effectively obtained with conventional marine seismic techniques. Therefore we have been developing autonomous seismic survey systems which record the data close to the seafloor to preserve high frequency seismic energy. Very high sampling rate (10kHz) and high accurate synchronization between recording systems and shot time are necessary. We adopted Cs-base atomic clock considering its power consumption. At first, we developed a Vertical Cable Seismic (VCS) system that uses hydrophone arrays moored vertically from the ocean bottom to record close to the target area. This system has been successfully applied to SMS exploration. Specifically it fixed over known sites to assess the amount of reserves with the resultant 3D volume. Based on the success of VCS, we modified the VCS system to use as a more efficient deep-tow seismic survey system. Although there are other examples of deep-tow seismic systems, signal transmission cables present challenges in deep waters. We use our autonomous recording system to avoid these problems. Combining a high frequency piezoelectric source (Sub Bottom Profiler:SBP) that automatically shots with a constant interval, we achieve the high resolution deep-tow seismic without data transmission/power cable to the board. Although the data cannot be monitored in real-time, the towing system becomes very simple. We have carried out survey trial, which showed the systems utility as a high-resolution deep-tow seismic survey system. Furthermore, the frequency ranges of deep-towed source (SBP) and surface towed sparker are 700-2300Hz and 10-200Hz

Deep Seawater Intrusion Enhanced by Geothermal Through Deep Faults in Xinzhou Geothermal Field in Guangdong, China

Science.gov (United States)

Lu, G.; Ou, H.; Hu, B. X.; Wang, X.

2017-12-01

This study investigates abnormal sea water intrusion from deep depth, riding an inland-ward deep groundwater flow, which is enhanced by deep faults and geothermal processes. The study site Xinzhou geothermal field is 20 km from the coast line. It is in southern China's Guangdong coast, a part of China's long coastal geothermal belt. The geothermal water is salty, having fueled an speculation that it was ancient sea water retained. However, the perpetual "pumping" of the self-flowing outflow of geothermal waters might alter the deep underground flow to favor large-scale or long distant sea water intrusion. We studied geochemical characteristics of the geothermal water and found it as a mixture of the sea water with rain water or pore water, with no indication of dilution involved. And we conducted numerical studies of the buoyancy-driven geothermal flow in the deep ground and find that deep down in thousand meters there is favorable hydraulic gradient favoring inland-ward groundwater flow, allowing seawater intrude inland for an unusually long tens of kilometers in a granitic groundwater flow system. This work formed the first in understanding geo-environment for deep ground water flow.

Deep Learning in Neuroradiology.

Science.gov (United States)

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

2018-02-01

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

Laboratory simulation of an oxidative disturbance in a deep granitic environment

International Nuclear Information System (INIS)

Trotignon, L.; Michaud, V.; Lartigue, J.E.

2000-01-01

Granite formations are a potential host environment for a deep nuclear waste repository. Such a repository (typical depth 500 m) will remain open during a period of several years and then be closed to isolate the waste. The chemical stability of the waste and the migration behavior of several radionuclides will depend on the chemistry and circulation of deep groundwaters. Among the critical parameters involved in radionuclide retention processes, the pH and Eh of the groundwaters play a key role. Before site excavation, conditions are usually reducing and slightly alkaline. The intrusion of oxygen due to the opening of galleries modifies the mineral/solution equilibria as well as microbial ecosystems. After several years, radionuclide retention properties along fractures intersecting the galleries will be modified. After site closure, reducing conditions will progressively be restored. To gain a better understanding of processes involved in the reactivity of dissolved oxygen in deep crystalline media, ANDRA (Agence Nationale pour la Gestion des Dechets Redioactifs), SKB (Svensk Karnbranslehantering AB) and JNC (Japan Nuclear Cycle Development Institute) have organized and supported a dedicated project (REX project, for Redox Experiment) including: - an in situ experiment performed in the Aspo Hard Rock Laboratory; a fracture surface isolated in an experimental chamber interacts with water containing variable dissolved O 2 concentrations while physicochemical parameters of the solution are monitored (Royal Institute of Technology, Geosigma); - laboratory studies of the contribution of micro-organisms and fracture minerals to dissolved oxygen uptake (Goteborg, Sheffield and Bradford Universities, British Geological Survey); - construction of a replica of the in situ experiment at Cadarache (CEA, CNRS). The concept of this replica is to reproduce the in situ experiment for its preparation and dimensioning, and also to assess the possibility of finding a link between

Performance Assessment of a Plate Beam Splitter for Deep-Ultraviolet Raman Measurements with a Spatial Heterodyne Raman Spectrometer.

Science.gov (United States)

Lamsal, Nirmal; Angel, S Michael

2017-06-01

In earlier works, we demonstrated a high-resolution spatial heterodyne Raman spectrometer (SHRS) for deep-ultraviolet (UV) Raman measurements, and showed its ability to measure UV light-sensitive compounds using a large laser spot size. We recently modified the SHRS by replacing the cube beam splitter (BS) with a custom plate beam splitter with higher light transmission, an optimized reflectance/transmission ratio, higher surface flatness, and better refractive index homogeneity than the cube beam splitter. Ultraviolet Raman measurements were performed using a SHRS modified to use the plate beam splitter and a matching compensator plate and compared to the previously described cube beam splitter setup. Raman spectra obtained using the modified SHRS exhibit much higher signals and signal-to-noise (S/N) ratio and show fewer spectral artifacts. In this paper, we discuss the plate beam splitter SHRS design features, the advantages over previous designs, and discuss some general SHRS issues such as spectral bandwidth, S/N ratio characteristics, and optical efficiency.

Temperature impacts on deep-sea biodiversity.

Science.gov (United States)

Yasuhara, Moriaki; Danovaro, Roberto

2016-05-01

Temperature is considered to be a fundamental factor controlling biodiversity in marine ecosystems, but precisely what role temperature plays in modulating diversity is still not clear. The deep ocean, lacking light and in situ photosynthetic primary production, is an ideal model system to test the effects of temperature changes on biodiversity. Here we synthesize current knowledge on temperature-diversity relationships in the deep sea. Our results from both present and past deep-sea assemblages suggest that, when a wide range of deep-sea bottom-water temperatures is considered, a unimodal relationship exists between temperature and diversity (that may be right skewed). It is possible that temperature is important only when at relatively high and low levels but does not play a major role in the intermediate temperature range. Possible mechanisms explaining the temperature-biodiversity relationship include the physiological-tolerance hypothesis, the metabolic hypothesis, island biogeography theory, or some combination of these. The possible unimodal relationship discussed here may allow us to identify tipping points at which on-going global change and deep-water warming may increase or decrease deep-sea biodiversity. Predicted changes in deep-sea temperatures due to human-induced climate change may have more adverse consequences than expected considering the sensitivity of deep-sea ecosystems to temperature changes. © 2014 Cambridge Philosophical Society.

Carbon Dioxide Capture by Deep Eutectic Solvent Impregnated Sea Mango Activated Carbon

Science.gov (United States)

Zulkurnai, N. Z.; Ali, U. F. Md.; Ibrahim, N.; Manan, N. S. Abdul

2018-03-01

The increment amount of the CO2 emission by years has become a major concern worldwide due to the global warming issue. However, the influence modification of activated carbon (AC) has given a huge revolution in CO2 adsorption capture compare to the unmodified AC. In the present study, the Deep Eutectic Solvent (DES) modified surface AC was used for Carbon Dioxide (CO2) capture in the fixed-bed column. The AC underwent pre-carbonization and carbonization processes at 519.8 °C, respectively, with flowing of CO2 gas and then followed by impregnation with 53.75% phosphoric acid (H3PO4) at 1:2 precursor-to-activant ratios. The prepared AC known as sea mango activated carbon (SMAC) was impregnated with DES at 1:2 solid-to-liquid ratio. The DES is composing of choline chloride and urea with ratio 1:2 choline chloride to urea. The optimum adsorption capacity of SMAC was 33.46 mgco2/gsol and 39.40 mgco2/gsol for DES modified AC (DESAC).

Extreme Longevity in Proteinaceous Deep-Sea Corals

Energy Technology Data Exchange (ETDEWEB)

Roark, E B; Guilderson, T P; Dunbar, R B; Fallon, S J; Mucciarone, D A

2009-02-09

Deep-sea corals are found on hard substrates on seamounts and continental margins world-wide at depths of 300 to {approx}3000 meters. Deep-sea coral communities are hotspots of deep ocean biomass and biodiversity, providing critical habitat for fish and invertebrates. Newly applied radiocarbon age date from the deep water proteinaceous corals Gerardia sp. and Leiopathes glaberrima show that radial growth rates are as low as 4 to 35 {micro}m yr{sup -1} and that individual colony longevities are on the order of thousands of years. The management and conservation of deep sea coral communities is challenged by their commercial harvest for the jewelry trade and damage caused by deep water fishing practices. In light of their unusual longevity, a better understanding of deep sea coral ecology and their interrelationships with associated benthic communities is needed to inform coherent international conservation strategies for these important deep-sea ecosystems.

New optimized drill pipe size for deep-water, extended reach and ultra-deep drilling

Energy Technology Data Exchange (ETDEWEB)

Jellison, Michael J.; Delgado, Ivanni [Grant Prideco, Inc., Hoston, TX (United States); Falcao, Jose Luiz; Sato, Ademar Takashi [PETROBRAS, Rio de Janeiro, RJ (Brazil); Moura, Carlos Amsler [Comercial Perfuradora Delba Baiana Ltda., Rio de Janeiro, RJ (Brazil)

2004-07-01

A new drill pipe size, 5-7/8 in. OD, represents enabling technology for Extended Reach Drilling (ERD), deep water and other deep well applications. Most world-class ERD and deep water wells have traditionally been drilled with 5-1/2 in. drill pipe or a combination of 6-5/8 in. and 5-1/2 in. drill pipe. The hydraulic performance of 5-1/2 in. drill pipe can be a major limitation in substantial ERD and deep water wells resulting in poor cuttings removal, slower penetration rates, diminished control over well trajectory and more tendency for drill pipe sticking. The 5-7/8 in. drill pipe provides a significant improvement in hydraulic efficiency compared to 5-1/2 in. drill pipe and does not suffer from the disadvantages associated with use of 6-5/8 in. drill pipe. It represents a drill pipe assembly that is optimized dimensionally and on a performance basis for casing and bit programs that are commonly used for ERD, deep water and ultra-deep wells. The paper discusses the engineering philosophy behind 5-7/8 in. drill pipe, the design challenges associated with development of the product and reviews the features and capabilities of the second-generation double-shoulder connection. The paper provides drilling case history information on significant projects where the pipe has been used and details results achieved with the pipe. (author)

Deep Reinforcement Learning: An Overview

OpenAIRE

Li, Yuxi

2017-01-01

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

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

Science.gov (United States)

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.

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture.

Science.gov (United States)

Chen, C L Philip; Liu, Zhulin

2018-01-01

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.

Deep Unfolding for Topic Models.

Science.gov (United States)

Chien, Jen-Tzung; Lee, Chao-Hsi

2018-02-01

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

Transforniceal Lateral Deep Bone Decompression—A Modified Technique to Prevent Postoperative Diplopia in Patients with Disfiguring Exophthalmos Due to Dysthyroid Orbitopathy

Directory of Open Access Journals (Sweden)

Shu-Lang Liao

2006-01-01

Conclusion: Transforniceal lateral deep bone decompression produces less new-onset, persistent diplopia than traditional inferomedial wall decompression, and provides good cosmesis by using a hidden small incisional wound. This approach appears to be a safe and effective procedure for patients with disfiguring exophthalmos, especially for Asian patients without crease fold.

Applying ultrasonic in-line inspection technology in a deep water environment: exploring the challenges

Energy Technology Data Exchange (ETDEWEB)

Thielager, N.; Nadler, M.; Pieske, M.; Beller, M. [NDT Systems and Services AG, Stutensee (Germany)

2009-12-19

The demand for higher inspection accuracies of in-line inspection tools (ILI tools) is permanently growing. As integrity assessment procedures are being refined, detection performances, sizing accuracies and confidence levels regarding detection and sizing play an ever increasing role. ILI tools utilizing conventional ultrasound technology are at the forefront of technology and fulfill the market requirements regarding sizing accuracies and the ability to provide quantitative measurements of wall thickness as well as crack inspection capabilities. Data from ultrasonic tools is ideally suited for advanced integrity assessment applications and run comparisons. Making this technology available for a deep-water environment of heavy wall, high pressures and temperatures comes with a wide range of challenges which have to be addressed. This paper will introduce developments recently made in order to adapt and modify ultrasonic in-line inspection tools for the application in a heavy wall, high pressure and high temperature environment as encountered in deep offshore pipelines. The paper will describe necessary design modifications and new conceptual approaches especially regarding tool electronics, cables, connectors and the sensor carrier. A tool capable of deep-water inspection with a pressure bearing capability of 275 bar will be introduced and data from inspection runs will be presented. As an outlook, the paper will also discuss future inspection requirements for offshore pipelines with maximum pressure values of up to 500 bar. (author)

Docker Containers for Deep Learning Experiments

OpenAIRE

Gerke, Paul K.

2017-01-01

Deep learning is a powerful tool to solve problems in the area of image analysis. The dominant compute platform for deep learning is Nvidia’s proprietary CUDA, which can only be used together with Nvidia graphics cards. The nivida-docker project allows exposing Nvidia graphics cards to docker containers and thus makes it possible to run deep learning experiments in docker containers.In our department, we use deep learning to solve problems in the area of medical image analysis and use docker ...

Auxiliary Deep Generative Models

DEFF Research Database (Denmark)

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

2016-01-01

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

Accelerating Deep Learning with Shrinkage and Recall

OpenAIRE

Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

2016-01-01

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

Consolidated Deep Actor Critic Networks (DRAFT)

NARCIS (Netherlands)

Van der Laan, T.A.

2015-01-01

The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with

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

OpenAIRE

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

2017-01-01

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

Analysis of large optical ground stations for deep-space optical communications

Science.gov (United States)

Garcia-Talavera, M. Reyes; Rivera, C.; Murga, G.; Montilla, I.; Alonso, A.

2017-11-01

Inter-satellite and ground to satellite optical communications have been successfully demonstrated over more than a decade with several experiments, the most recent being NASA's lunar mission Lunar Atmospheric Dust Environment Explorer (LADEE). The technology is in a mature stage that allows to consider optical communications as a high-capacity solution for future deep-space communications [1][2], where there is an increasing demand on downlink data rate to improve science return. To serve these deep-space missions, suitable optical ground stations (OGS) have to be developed providing large collecting areas. The design of such OGSs must face both technical and cost constraints in order to achieve an optimum implementation. To that end, different approaches have already been proposed and analyzed, namely, a large telescope based on a segmented primary mirror, telescope arrays, and even the combination of RF and optical receivers in modified versions of existing Deep-Space Network (DSN) antennas [3][4][5]. Array architectures have been proposed to relax some requirements, acting as one of the key drivers of the present study. The advantages offered by the array approach are attained at the expense of adding subsystems. Critical issues identified for each implementation include their inherent efficiency and losses, as well as its performance under high-background conditions, and the acquisition, pointing, tracking, and synchronization capabilities. It is worth noticing that, due to the photon-counting nature of detection, the system performance is not solely given by the signal-to-noise ratio parameter. To start with the analysis, first the main implications of the deep space scenarios are summarized, since they are the driving requirements to establish the technical specifications for the large OGS. Next, both the main characteristics of the OGS and the potential configuration approaches are presented, getting deeper in key subsystems with strong impact in the

Deep Feature Consistent Variational Autoencoder

OpenAIRE

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

2016-01-01

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

Modification of deep waters in Marguerite Bay, western Antarctic Peninsula, caused by topographic overflows

Science.gov (United States)

Venables, Hugh J.; Meredith, Michael P.; Brearley, J. Alexander

2017-05-01

Circumpolar Deep Water (CDW) intrudes from the mid-layers of the Antarctic Circumpolar Current onto the shelf of the western Antarctic Peninsula, providing a source of heat and nutrients to the regional ocean. It is well known that CDW is modified as it flows across the shelf, but the mechanisms responsible for this are not fully known. Here, data from underwater gliders with high spatial resolution are used to demonstrate the importance of detailed bathymetry in inducing multiple local mixing events. Clear evidence for overflows is observed in the glider data as water flows along a deep channel with multiple transverse ridges. The ridges block the densest waters, with overflowing water descending several hundred metres to fill subsequent basins. This vertical flow leads to entrainment of overlying colder and fresher water in localised mixing events. Initially this process leads to an increase in bottom temperatures due to the temperature maximum waters descending to greater depths. After several ridges, however, the mixing is sufficient to remove the temperature maximum completely and the entrainment of colder thermocline waters to depth reduces the bottom temperature, to approximately the same as in the source region of Marguerite Trough. Similarly, it is shown that deep waters of Palmer Deep are warmer than at the same depth at the shelf break. The exact details of the transformations observed are heavily dependent on the local bathymetry and water column structure, but glacially-carved troughs and shallow sills are a common feature of the bathymetry of polar shelves, and these types of processes may be a factor in determining the hydrographic conditions close to the coast across a wider area.

Patients' expectations in subthalamic nucleus deep brain stimulation surgery for Parkinson disease.

Science.gov (United States)

Hasegawa, Harutomo; Samuel, Michael; Douiri, Abdel; Ashkan, Keyoumars

2014-12-01

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an established treatment for patients with advanced Parkinson disease. However, some patients feel less satisfied with the outcome of surgery. We sought to study the relationship between expectations, satisfaction, and outcome in STN DBS for Parkinson disease. Twenty-two consecutive patients undergoing STN DBS completed a modified 39-item Parkinson disease questionnaire (PDQ-39) preoperatively and 6 months postoperatively. A satisfaction questionnaire accompanied the postoperative questionnaire. Patients expected a significant improvement from surgery preoperatively: preoperative score (median PDQ-39 summary score [interquartile range]): 37.0 (9.5), expected postoperative score: 13.0 (8.0), P Parkinson disease. Copyright © 2014 Elsevier Inc. All rights reserved.

How Stressful Is "Deep Bubbling"?

Science.gov (United States)

Tyrmi, Jaana; Laukkanen, Anne-Maria

2017-03-01

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

Evaluation of the DeepWind concept

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Borg, Michael; Gonzales Seabra, Luis Alberto

The report describes the DeepWind 5 MW conceptual design as a baseline for results obtained in the scientific and technical work packages of the DeepWind project. A comparison of DeepWi nd with existing VAWTs and paper projects are carried out and the evaluation of the concept in terms of cost...

Simulator Studies of the Deep Stall

Science.gov (United States)

White, Maurice D.; Cooper, George E.

1965-01-01

Simulator studies of the deep-stall problem encountered with modern airplanes are discussed. The results indicate that the basic deep-stall tendencies produced by aerodynamic characteristics are augmented by operational considerations. Because of control difficulties to be anticipated in the deep stall, it is desirable that adequate safeguards be provided against inadvertent penetrations.

Deep Learning

DEFF Research Database (Denmark)

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

2018-01-01

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

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

Science.gov (United States)

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

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

Deep Learning and Its Applications in Biomedicine.

Science.gov (United States)

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 Water Acoustics

Science.gov (United States)

2016-06-28

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

Overview of deep learning in medical imaging.

Science.gov (United States)

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

Deep, diverse and definitely different: unique attributes of the world's largest ecosystem

Directory of Open Access Journals (Sweden)

E. Ramirez-Llodra

2010-09-01

-water corals have an increased productivity through specific physical processes, such as topographic modification of currents and enhanced transport of particles and detrital matter. Because of its unique abiotic attributes, the deep sea hosts a specialized fauna. Although there are no phyla unique to deep waters, at lower taxonomic levels the composition of the fauna is distinct from that found in the upper ocean. Amongst other characteristic patterns, deep-sea species may exhibit either gigantism or dwarfism, related to the decrease in food availability with depth. Food limitation on the seafloor and water column is also reflected in the trophic structure of heterotrophic deep-sea communities, which are adapted to low energy availability. In most of these heterotrophic habitats, the dominant megafauna is composed of detritivores, while filter feeders are abundant in habitats with hard substrata (e.g. mid-ocean ridges, seamounts, canyon walls and coral reefs. Chemoautotrophy through symbiotic relationships is dominant in reducing habitats.

Deep-sea biodiversity is among of the highest on the planet, mainly composed of macro and meiofauna, with high evenness. This is true for most of the continental margins and abyssal plains with hot spots of diversity such as seamounts or cold-water corals. However, in some ecosystems with particularly "extreme" physicochemical processes (e.g. hydrothermal vents, biodiversity is low but abundance and biomass are high and the communities are dominated by a few species. Two large-scale diversity patterns have been discussed for deep-sea benthic communities. First, a unimodal relationship between diversity and depth is observed, with a peak at intermediate depths (2000–3000 m, although this is not universal and particular abiotic processes can modify the trend. Secondly, a poleward trend of decreasing diversity has been discussed, but this remains controversial and studies with larger and more robust data sets are needed. Because of

WFIRST: Science from Deep Field Surveys

Science.gov (United States)

Koekemoer, Anton; Foley, Ryan; WFIRST Deep Field Working Group

2018-01-01

WFIRST will enable deep field imaging across much larger areas than those previously obtained with Hubble, opening up completely new areas of parameter space for extragalactic deep fields including cosmology, supernova and galaxy evolution science. The instantaneous field of view of the Wide Field Instrument (WFI) is about 0.3 square degrees, which would for example yield an Ultra Deep Field (UDF) reaching similar depths at visible and near-infrared wavelengths to that obtained with Hubble, over an area about 100-200 times larger, for a comparable investment in time. Moreover, wider fields on scales of 10-20 square degrees could achieve depths comparable to large HST surveys at medium depths such as GOODS and CANDELS, and would enable multi-epoch supernova science that could be matched in area to LSST Deep Drilling fields or other large survey areas. Such fields may benefit from being placed on locations in the sky that have ancillary multi-band imaging or spectroscopy from other facilities, from the ground or in space. The WFIRST Deep Fields Working Group has been examining the science considerations for various types of deep fields that may be obtained with WFIRST, and present here a summary of the various properties of different locations in the sky that may be considered for future deep fields with WFIRST.

Effects of semantic context on access to words of low imageability in deep-phonological dysphasia: a treatment case study.

Science.gov (United States)

McCarthy, Laura Mary; Kalinyak-Fliszar, Michelene; Kohen, Francine; Martin, Nadine

2017-01-01

Deep dysphasia is a relatively rare subcategory of aphasia, characterised by word repetition impairment and a profound auditory-verbal short-term memory (STM) limitation. Repetition of words is better than nonwords (lexicality effect) and better for high-image than low-image words (imageability effect). Another related language impairment profile is phonological dysphasia, which includes all of the characteristics of deep dysphasia except for the occurrence of semantic errors in single word repetition. The overlap in symptoms of deep and phonological dysphasia has led to the hypothesis that they share the same root cause, impaired maintenance of activated representation of words, but that they differ in severity of that impairment, with deep dysphasia being more severe. We report a single-subject multiple baseline, multiple probe treatment study of a person who presented with a pattern of repetition that was consistent with the continuum of deep-phonological dysphasia: imageability and lexicality effects in repetition of single and multiple words and semantic errors in repetition of multiple-word utterances. The aim of this treatment study was to improve access to and repetition of low-imageability words by embedding them in modifier-noun phrases that enhanced their imageability. The treatment involved repetition of abstract noun pairs. We created modifier-abstract noun phrases that increased the semantic and syntactic cohesiveness of the words in the pair. For example, the phrases "long distance" and "social exclusion" were developed to improve repetition of the abstract pair "distance-exclusion". The goal of this manipulation was to increase the probability of accessing lexical and semantic representations of abstract words in repetition by enriching their semantic -syntactic context. We predicted that this increase in accessibility would be maintained when the words were repeated as pairs, but without the contextual phrase. Treatment outcomes indicated that

TOPIC MODELING: CLUSTERING OF DEEP WEBPAGES

OpenAIRE

Muhunthaadithya C; Rohit J.V; Sadhana Kesavan; E. Sivasankar

2015-01-01

The internet is comprised of massive amount of information in the form of zillions of web pages.This information can be categorized into the surface web and the deep web. The existing search engines can effectively make use of surface web information.But the deep web remains unexploited yet. Machine learning techniques have been commonly employed to access deep web content.

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

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

2017-01-01

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

Building Program Vector Representations for Deep Learning

OpenAIRE

Mou, Lili; Li, Ge; Liu, Yuxuan; Peng, Hao; Jin, Zhi; Xu, Yan; Zhang, Lu

2014-01-01

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, whi...

Scaled momentum spectra in deep inelastic scattering at HERA

Energy Technology Data Exchange (ETDEWEB)

Abramowicz, H. [Tel Aviv Univ. (Israel). Raymond and Beverly Sackler Faculty of Exact Sciences; University College London (United Kingdom); Max Planck Inst., Munich (Germany); Abt, I. [Max-Planck-Inst. fuer Physik, Muenchen (Germany); Adamczyk, L. [AGH-Univ. of Science and Technology, Cracow (PL). Faculty of Physics and Applied Computer Science] (and others)

2009-12-15

Charged particle production has been studied in neutral current deep inelastic ep scattering with the ZEUS detector at HERA using an integrated luminosity of 0.44 fb{sup -1}. Distributions of scaled momenta in the Breit frame are presented for particles in the current fragmentation region. The evolution of these spectra with the photon virtuality, Q{sup 2}, is described in the kinematic region 10modified leading-log-approximation QCD calculations as well as predictions from Monte Carlo models are compared to the data. The results are also compared to e{sup +}e{sup -} annihilation data. The dependences of the pseudorapidity distribution of the particles on Q{sup 2} and on the energy in the {gamma}p system, W, are presented and interpreted in the context of the hypothesis of limiting fragmentation. (orig.)

[Deep vein thrombosis prophylaxis.

Science.gov (United States)

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

2013-01-01

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

An Unsupervised Deep Hyperspectral Anomaly Detector

Directory of Open Access Journals (Sweden)

Ning Ma

2018-02-01

Full Text Available Hyperspectral image (HSI based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD, local RX detector (LRXD and the-state-of-the-art Collaborative Representation detector (CRD.

Contemporary deep recurrent learning for recognition

Science.gov (United States)

Iftekharuddin, K. M.; Alam, M.; Vidyaratne, L.

2017-05-01

Large-scale feed-forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time, potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial expression and character recognition tasks using novel deep recurrent model and compares recognition performance with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed deep SRN based recognition framework in a humanoid robotic platform called NAO.

Towards deep learning with segregated dendrites.

Science.gov (United States)

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

2017-12-05

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

The deep ocean under climate change

Science.gov (United States)

Levin, Lisa A.; Le Bris, Nadine

2015-11-01

The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems.

Dynamic Analysis of Thick Plates Including Deep Beams on Elastic Foundations Using Modified Vlasov Model

Directory of Open Access Journals (Sweden)

Korhan Ozgan

2013-01-01

Full Text Available Dynamic analysis of foundation plate-beam systems with transverse shear deformation is presented using modified Vlasov foundation model. Finite element formulation of the problem is derived by using an 8-node (PBQ8 finite element based on Mindlin plate theory for the plate and a 2-node Hughes element based on Timoshenko beam theory for the beam. Selective reduced integration technique is used to avoid shear locking problem for the evaluation of the stiffness matrices for both the elements. The effect of beam thickness, the aspect ratio of the plate and subsoil depth on the response of plate-beam-soil system is analyzed. Numerical examples show that the displacement, bending moments and shear forces are changed significantly by adding the beams.

NATURAL GAS RESOURCES IN DEEP SEDIMENTARY BASINS

Energy Technology Data Exchange (ETDEWEB)

Thaddeus S. Dyman; Troy Cook; Robert A. Crovelli; Allison A. Henry; Timothy C. Hester; Ronald C. Johnson; Michael D. Lewan; Vito F. Nuccio; James W. Schmoker; Dennis B. Riggin; Christopher J. Schenk

2002-02-05

From a geological perspective, deep natural gas resources are generally defined as resources occurring in reservoirs at or below 15,000 feet, whereas ultra-deep gas occurs below 25,000 feet. From an operational point of view, ''deep'' is often thought of in a relative sense based on the geologic and engineering knowledge of gas (and oil) resources in a particular area. Deep gas can be found in either conventionally-trapped or unconventional basin-center accumulations that are essentially large single fields having spatial dimensions often exceeding those of conventional fields. Exploration for deep conventional and unconventional basin-center natural gas resources deserves special attention because these resources are widespread and occur in diverse geologic environments. In 1995, the U.S. Geological Survey estimated that 939 TCF of technically recoverable natural gas remained to be discovered or was part of reserve appreciation from known fields in the onshore areas and State waters of the United. Of this USGS resource, nearly 114 trillion cubic feet (Tcf) of technically-recoverable gas remains to be discovered from deep sedimentary basins. Worldwide estimates of deep gas are also high. The U.S. Geological Survey World Petroleum Assessment 2000 Project recently estimated a world mean undiscovered conventional gas resource outside the U.S. of 844 Tcf below 4.5 km (about 15,000 feet). Less is known about the origins of deep gas than about the origins of gas at shallower depths because fewer wells have been drilled into the deeper portions of many basins. Some of the many factors contributing to the origin of deep gas include the thermal stability of methane, the role of water and non-hydrocarbon gases in natural gas generation, porosity loss with increasing thermal maturity, the kinetics of deep gas generation, thermal cracking of oil to gas, and source rock potential based on thermal maturity and kerogen type. Recent experimental simulations

Deep smarts.

Science.gov (United States)

Leonard, Dorothy; Swap, Walter

2004-09-01

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

Chemistry and origin of deep ground water in crystalline rocks; Kemi och genes av djupa grundvatten i kristallint berg

Energy Technology Data Exchange (ETDEWEB)

Lagerblad, B [Swedish Cement and Concrete Research Inst., Stockholm (Sweden)

1995-11-01

This report discusses the interactions between water and crystalline rocks and its consequences for the chemical composition of the water. It also treats how flows of different types of water are modified by the rock, and the possible consequences for the ground water near a nuclear waste repository. The focus of the work is the changes in composition that ground water gets at deep levels in the rock. Data from Finnsjoen and Aespoe in Sweden show higher salinity in deep rock, which has been interpreted as a result of marine inflow of water during glaciation. Data from other, deeper boreholes in Finland, Canada, Russia, England and Sweden show that the increasing salinity is a rule and very high at great depths, higher than marine water can produce. Therefore, the deep waters from Finnsjoen and Aespoe are probably very old, and the high salinity a result from geological processes. Differing cation and isotopic composition than seawater also indicate geologic water. Differing theories on the origin of the ground water should be regarded in the safety analysis for a repository. 36 refs, 3 figs, 1 tab.

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

Science.gov (United States)

Sadeghi, Zahra

2016-09-01

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

Modified Kelvin Equations for Capillary Condensation in Narrow and Wide Grooves

Science.gov (United States)

Malijevský, Alexandr; Parry, Andrew O.

2018-03-01

We consider the location and order of capillary condensation transitions occurring in deep grooves of width L and depth D . For walls that are completely wet by liquid (contact angle θ =0 ) the transition is continuous and its location is not sensitive to the depth of the groove. However, for walls that are partially wet by liquid, where the transition is first order, we show that the pressure at which it occurs is determined by a modified Kelvin equation characterized by an edge contact angle θE describing the shape of the meniscus formed at the top of the groove. The dependence of θE on the groove depth D relies, in turn, on whether corner menisci are formed at the bottom of the groove in the low density gaslike phase. While for macroscopically wide grooves these are always present when θ condensation transition is different depending on whether the contact angle is greater or less than a universal value θ*≈31 °. Our arguments are supported by detailed microscopic density functional theory calculations that show that the modified Kelvin equation remains highly accurate even when L and D are of the order of tens of molecular diameters.

Climate, carbon cycling, and deep-ocean ecosystems.

Science.gov (United States)

Smith, K L; Ruhl, H A; Bett, B J; Billett, D S M; Lampitt, R S; Kaufmann, R S

2009-11-17

Climate variation affects surface ocean processes and the production of organic carbon, which ultimately comprises the primary food supply to the deep-sea ecosystems that occupy approximately 60% of the Earth's surface. Warming trends in atmospheric and upper ocean temperatures, attributed to anthropogenic influence, have occurred over the past four decades. Changes in upper ocean temperature influence stratification and can affect the availability of nutrients for phytoplankton production. Global warming has been predicted to intensify stratification and reduce vertical mixing. Research also suggests that such reduced mixing will enhance variability in primary production and carbon export flux to the deep sea. The dependence of deep-sea communities on surface water production has raised important questions about how climate change will affect carbon cycling and deep-ocean ecosystem function. Recently, unprecedented time-series studies conducted over the past two decades in the North Pacific and the North Atlantic at >4,000-m depth have revealed unexpectedly large changes in deep-ocean ecosystems significantly correlated to climate-driven changes in the surface ocean that can impact the global carbon cycle. Climate-driven variation affects oceanic communities from surface waters to the much-overlooked deep sea and will have impacts on the global carbon cycle. Data from these two widely separated areas of the deep ocean provide compelling evidence that changes in climate can readily influence deep-sea processes. However, the limited geographic coverage of these existing time-series studies stresses the importance of developing a more global effort to monitor deep-sea ecosystems under modern conditions of rapidly changing climate.

Deep brain stimulation for phantom limb pain.

Science.gov (United States)

Bittar, Richard G; Otero, Sofia; Carter, Helen; Aziz, Tipu Z

2005-05-01

Phantom limb pain is an often severe and debilitating phenomenon that has been reported in up to 85% of amputees. Its pathophysiology is poorly understood. Peripheral and spinal mechanisms are thought to play a role in pain modulation in affected individuals; however central mechanisms are also likely to be of importance. The neuromatrix theory postulates a genetically determined representation of body image, which is modified by sensory input to create a neurosignature. Persistence of the neurosignature may be responsible for painless phantom limb sensations, whereas phantom limb pain may be due to abnormal reorganisation within the neuromatrix. This study assessed the clinical outcome of deep brain stimulation of the periventricular grey matter and somatosensory thalamus for the relief of chronic neuropathic pain associated with phantom limb in three patients. These patients were assessed preoperatively and at 3 month intervals postoperatively. Self-rated visual analogue scale pain scores assessed pain intensity, and the McGill Pain Questionnaire assessed the quality of the pain. Quality of life was assessed using the EUROQOL EQ-5D scale. Periventricular gray stimulation alone was optimal in two patients, whilst a combination of periventricular gray and thalamic stimulation produced the greatest degree of relief in one patient. At follow-up (mean 13.3 months) the intensity of pain was reduced by 62% (range 55-70%). In all three patients, the burning component of the pain was completely alleviated. Opiate intake was reduced in the two patients requiring morphine sulphate pre-operatively. Quality of life measures indicated a statistically significant improvement. This data supports the role for deep brain stimulation in patients with phantom limb pain. The medical literature relating to the epidemiology, pathogenesis, and treatment of this clinical entity is reviewed in detail.

The deep ocean under climate change.

Science.gov (United States)

Levin, Lisa A; Le Bris, Nadine

2015-11-13

The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems. Copyright © 2015, American Association for the Advancement of Science.

SEDS: THE SPITZER EXTENDED DEEP SURVEY. SURVEY DESIGN, PHOTOMETRY, AND DEEP IRAC SOURCE COUNTS

International Nuclear Information System (INIS)

Ashby, M. L. N.; Willner, S. P.; Fazio, G. G.; Huang, J.-S.; Hernquist, L.; Hora, J. L.; Arendt, R.; Barmby, P.; Barro, G.; Faber, S.; Guhathakurta, P.; Bell, E. F.; Bouwens, R.; Cattaneo, A.; Croton, D.; Davé, R.; Dunlop, J. S.; Egami, E.; Finlator, K.; Grogin, N. A.

2013-01-01

The Spitzer Extended Deep Survey (SEDS) is a very deep infrared survey within five well-known extragalactic science fields: the UKIDSS Ultra-Deep Survey, the Extended Chandra Deep Field South, COSMOS, the Hubble Deep Field North, and the Extended Groth Strip. SEDS covers a total area of 1.46 deg 2 to a depth of 26 AB mag (3σ) in both of the warm Infrared Array Camera (IRAC) bands at 3.6 and 4.5 μm. Because of its uniform depth of coverage in so many widely-separated fields, SEDS is subject to roughly 25% smaller errors due to cosmic variance than a single-field survey of the same size. SEDS was designed to detect and characterize galaxies from intermediate to high redshifts (z = 2-7) with a built-in means of assessing the impact of cosmic variance on the individual fields. Because the full SEDS depth was accumulated in at least three separate visits to each field, typically with six-month intervals between visits, SEDS also furnishes an opportunity to assess the infrared variability of faint objects. This paper describes the SEDS survey design, processing, and publicly-available data products. Deep IRAC counts for the more than 300,000 galaxies detected by SEDS are consistent with models based on known galaxy populations. Discrete IRAC sources contribute 5.6 ± 1.0 and 4.4 ± 0.8 nW m –2 sr –1 at 3.6 and 4.5 μm to the diffuse cosmic infrared background (CIB). IRAC sources cannot contribute more than half of the total CIB flux estimated from DIRBE data. Barring an unexpected error in the DIRBE flux estimates, half the CIB flux must therefore come from a diffuse component.

Modified cyanobacteria

Science.gov (United States)

Vermaas, Willem F J.

2014-06-17

Disclosed is a modified photoautotrophic bacterium comprising genes of interest that are modified in terms of their expression and/or coding region sequence, wherein modification of the genes of interest increases production of a desired product in the bacterium relative to the amount of the desired product production in a photoautotrophic bacterium that is not modified with respect to the genes of interest.

The deep lymphatic anatomy of the hand.

Science.gov (United States)

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

2018-04-03

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

Normal endothelial function after meals rich in olive or safflower oil previously used for deep frying.

Science.gov (United States)

Williams, M J; Sutherland, W H; McCormick, M P; Yeoman, D; de Jong, S A; Walker, R J

2001-06-01

Polyunsaturated fats are more susceptible to oxidation during heating than monounsaturated fats but their effects on endothelial function when heated are unknown. The aim of this study was to compare the effect of meals rich in heat-modified safflower and olive oils on postprandial flow-mediated endothelium-dependent dilation (EDD) in healthy men. Flow-mediated EDD and glyceryltrinitrate-induced endothelium-independent dilation of the brachial artery were investigated in 14 subjects before and 4 hours after meals rich in olive oil and safflower oil used hourly for deep-frying for 8 hours in a double-blind crossover study design. There were high levels of lipid oxidation products (peroxides and carbonyls) in both heated oils. Plasma triglycerides were markedly increased at 4 hours after heated olive oil (1.26 +/- 0.43 vs 2.06 +/- 0.97 mmol/L) and heated safflower oil (1.44 +/- 0.63 vs 1.99 +/- 0.88 mmol/L). There was no change in EDD between fasting and postprandial studies and the response during the postprandial period was not significantly (p = 0.51) different between the meals (heated olive oil: 4.9 +/- 2.2% vs 4.9 +/- 2.5%; heated safflower oil: 5.1 +/- 3.1% vs 5.6 +/- 3.4%). Meals rich in olive and safflower oils previously used for deep frying and containing high levels of lipid oxidation products increase postprandial serum triglycerides without affecting endothelial function. These findings suggest that relatively short-term use of these vegetable oils for frying may not adversely affect postprandial endothelial function when foods containing the heat-modified oils are consumed.

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Science.gov (United States)

Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

2018-02-08

Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

Deep inelastic electron and muon scattering

International Nuclear Information System (INIS)

Taylor, R.E.

1975-07-01

From the review of deep inelastic electron and muon scattering it is concluded that the puzzle of deep inelastic scattering versus annihilation was replaced with the challenge of the new particles, that the evidence for the simplest quark-algebra models of deep inelastic processes is weaker than a year ago. Definite evidence of scale breaking was found but the specific form of that scale breaking is difficult to extract from the data. 59 references

Fast, Distributed Algorithms in Deep Networks

Science.gov (United States)

2016-05-11

shallow networks, additional work will need to be done in order to allow for the application of ADMM to deep nets. The ADMM method allows for quick...Quock V Le, et al. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, pages 1223–1231, 2012. [11] Ken-Ichi...A TRIDENT SCHOLAR PROJECT REPORT NO. 446 Fast, Distributed Algorithms in Deep Networks by Midshipman 1/C Ryan J. Burmeister, USN

Deep Learning from Crowds

DEFF Research Database (Denmark)

Rodrigues, Filipe; Pereira, Francisco Camara

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

Deep learning methods for protein torsion angle prediction.

Science.gov (United States)

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.

Deep Learning in Gastrointestinal Endoscopy.

Science.gov (United States)

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

2016-01-01

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

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

Science.gov (United States)

Mills, Kyle; Tamblyn, Isaac

2018-03-01

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

Neuromorphic Deep Learning Machines

OpenAIRE

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

2017-01-01

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

Bacterial Community Response in Deep Faroe-Shetland Channel Sediments Following Hydrocarbon Entrainment With and Without Dispersant Addition

Directory of Open Access Journals (Sweden)

Luis J. Perez Calderon

2018-05-01

Full Text Available Deep sea oil exploration is increasing and presents environmental challenges for deep ocean ecosystems. Marine oil spills often result in contamination of sediments with oil; following the Deepwater Horizon (DwH disaster up to 31% of the released oil entrained in the water column was deposited as oily residues on the seabed. Although the aftermath of DwH was studied intensely, lessons learned may not be directly transferable to other deep-sea hydrocarbon exploration areas, such as the Faroe-Shetland Channel (FSC which comprises cold temperatures and a unique hydrodynamic regime. Here, transport of hydrocarbons into deep FSC sediments, subsequent responses in benthic microbial populations and effects of dispersant application on hydrocarbon fate and microbial communities were investigated. Sediments from 1,000 m in the FSC were incubated at 0°C for 71 days after addition of a 20-hydrocarbon component oil-sediment aggregate. Dispersant was added periodically from day 4. An additional set of cores using sterilized and homogenized sediment was analyzed to evaluate the effects of sediment matrix modification on hydrocarbon entrainment. Sediment layers were independently analyzed for hydrocarbon content by gas chromatography with flame ionization detection and modeled with linear mixed effects models. Oil was entrained over 4 cm deep into FSC sediments after 42 days and dispersant effectiveness on hydrocarbon removal from sediment to the water column decreased with time. Sterilizing and homogenizing sediment resulted in hydrocarbon transport over 4 cm into sediments after 7 days. Significant shifts in bacterial populations were observed (DGGE profiling in response to hydrocarbon exposure after 42 days and below 2 cm deep. Dispersant application resulted in an accelerated and modified shift in bacterial communities. Bacterial 16S rRNA gene sequencing of oiled sediments revealed dominance of Colwellia and of Fusibacter when dispersant was applied over

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

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.

Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-06-15

Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested.

Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

International Nuclear Information System (INIS)

Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su

2016-01-01

Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested

Toolkits and Libraries for Deep Learning.

Science.gov (United States)

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

2017-08-01

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

The roles of MCDW and deep water iron supply in sustaining a recurrent phytoplankton bloom on central Pennell Bank (Ross Sea)

Science.gov (United States)

Kustka, Adam B.; Kohut, Josh T.; White, Angelicque E.; Lam, Phoebe J.; Milligan, Allen J.; Dinniman, Michael S.; Mack, Stefanie; Hunter, Elias; Hiscock, Michael R.; Smith, Walker O.; Measures, Chris I.

2015-11-01

During January-February 2011 standing stocks of phytoplankton (chl a) in the Pennell Bank region of the Ross Sea were variable over 10-100 km spatial scales. One area of elevated chl a on central Pennell Bank (CPB) appeared to be a recurrent mid-summer feature. The western flank (WF) of Pennell Bank had pronounced signatures of Modified Circumpolar Deep Water (MCDW). We evaluated the spatial extent of Fe limitation and net primary production and tested whether MCDW may provide elevated amounts of Fe to the CPB region, through a combination of in situ measurements, shipboard incubations and a horizontally resolved physical model. Regional fluxes of dissolved Fe from deep to surface waters were compared to calculated Fe demands. Low in situ variable to maximum fluorescence (Fv/Fm; 0.24-0.37) and surface water dissolved Fe concentrations (~0.12-0.21 nM) were suggestive of widespread limitation, corroborated by the consistent responses (Fv/Fm, growth, and nutrient removal ratios) of incubation treatments to Fe addition. MCDW from the WF region had lower dissolved Fe concentrations than that measured in CDW (Circumpolar Deep Water), which suggests on-shelf modification with Fe deplete surface waters and is consistent with the lack of stimulation due to incubation amendments with filtered MCDW. Model results and empirical data suggest MCDW from the WF region is further modified and mixed en route to the CPB region, leading to both the erosion of the canonical MCDW signature and an elevated dissolved Fe inventory of CPB region deep water. This suggests the addition of Fe possibly via diagenesis, as suggested by Marsay et al. (2014). Calculated deep water supply rates to the surface waters of CPB were ~0.18-0.43 m d-1, while calculated rates at the WF or northern Pennell Bank (NPB) regions were negative. The CPB populations exhibited ~4.5-fold higher net production rates compared to those in the WF and NPB regions and required 520-3200 nmol Fe m-2 d-1. The modeled vertical

Deep-sea coral research and technology program: Alaska deep-sea coral and sponge initiative final report

Science.gov (United States)

Rooper, Chris; Stone, Robert P.; Etnoyer, Peter; Conrath, Christina; Reynolds, Jennifer; Greene, H. Gary; Williams, Branwen; Salgado, Enrique; Morrison, Cheryl L.; Waller, Rhian G.; Demopoulos, Amanda W.J.

2017-01-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska’s marine waters. In some places, such as the central and western Aleutian Islands, deep-sea coral and sponge resources can be extremely diverse and may rank among the most abundant deep-sea coral and sponge communities in the world. Many different species of fishes and invertebrates are associated with deep-sea coral and sponge communities in Alaska. Because of their biology, these benthic invertebrates are potentially impacted by climate change and ocean acidification. Deepsea coral and sponge ecosystems are also vulnerable to the effects of commercial fishing activities. Because of the size and scope of Alaska’s continental shelf and slope, the vast majority of the area has not been visually surveyed for deep-sea corals and sponges. NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) sponsored a field research program in the Alaska region between 2012–2015, referred to hereafter as the Alaska Initiative. The priorities for Alaska were derived from ongoing data needs and objectives identified by the DSCRTP, the North Pacific Fishery Management Council (NPFMC), and Essential Fish Habitat-Environmental Impact Statement (EFH-EIS) process.This report presents the results of 15 projects conducted using DSCRTP funds from 2012-2015. Three of the projects conducted as part of the Alaska deep-sea coral and sponge initiative included dedicated at-sea cruises and fieldwork spread across multiple years. These projects were the eastern Gulf of Alaska Primnoa pacifica study, the Aleutian Islands mapping study, and the Gulf of Alaska fish productivity study. In all, there were nine separate research cruises carried out with a total of 109 at-sea days conducting research. The remaining projects either used data and samples collected by the three major fieldwork projects or were piggy-backed onto existing research programs at the Alaska Fisheries Science Center (AFSC).

Image Captioning with Deep Bidirectional LSTMs

OpenAIRE

Wang, Cheng; Yang, Haojin; Bartz, Christian; Meinel, Christoph

2016-01-01

This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are propose...

An overview of latest deep water technologies

International Nuclear Information System (INIS)

Anon.

1995-01-01

The 8th Deep Offshore Technology Conference (DOT VIII, Rio de Janeiro, October 30 - November 3, 1995) has brought together renowned specialists in deep water development projects, as well as managers from oil companies and engineering/service companies to discuss state-of-the-art technologies and ongoing projects in the deep offshore. This paper is a compilation of the session summaries about sub sea technologies, mooring and dynamic positioning, floaters (Tension Leg Platforms (TLP) and Floating Production Storage and Off loading (FPSO)), pipelines and risers, exploration and drilling, and other deep water techniques. (J.S.)

Deep learning in neural networks: an overview.

Science.gov (United States)

Schmidhuber, Jürgen

2015-01-01

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

Combining shallow and deep processing for a robust, fast, deep-linguistic dependency parser

OpenAIRE

Schneider, G

2004-01-01

This paper describes Pro3Gres, a fast, robust, broad-coverage parser that delivers deep-linguistic grammatical relation structures as output, which are closer to predicate-argument structures and more informative than pure constituency structures. The parser stays as shallow as is possible for each task, combining shallow and deep-linguistic methods by integrating chunking and by expressing the majority of long-distance dependencies in a context-free way. It combines statistical and rule-base...

DeepVel: Deep learning for the estimation of horizontal velocities at the solar surface

Science.gov (United States)

Asensio Ramos, A.; Requerey, I. S.; Vitas, N.

2017-07-01

Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are perpendicular to the line of sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel, every time step, and at three different heights in the atmosphere from just two consecutive continuum images. We confront DeepVel with local correlation tracking, pointing out that they give very similar results in the time and spatially averaged cases. We use the network to study the evolution in height of the horizontal velocity field in fragmenting granules, supporting the buoyancy-braking mechanism for the formation of integranular lanes in these granules. We also show that DeepVel can capture very small vortices, so that we can potentially expand the scaling cascade of vortices to very small sizes and durations. The movie attached to Fig. 3 is available at http://www.aanda.org

Deep Learning in Drug Discovery.

Science.gov (United States)

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

2016-01-01

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

Iris Transponder-Communications and Navigation for Deep Space

Science.gov (United States)

Duncan, Courtney B.; Smith, Amy E.; Aguirre, Fernando H.

2014-01-01

The Jet Propulsion Laboratory has developed the Iris CubeSat compatible deep space transponder for INSPIRE, the first CubeSat to deep space. Iris is 0.4 U, 0.4 kg, consumes 12.8 W, and interoperates with NASA's Deep Space Network (DSN) on X-Band frequencies (7.2 GHz uplink, 8.4 GHz downlink) for command, telemetry, and navigation. This talk discusses the Iris for INSPIRE, it's features and requirements; future developments and improvements underway; deep space and proximity operations applications for Iris; high rate earth orbit variants; and ground requirements, such as are implemented in the DSN, for deep space operations.

Risk factors for deep surgical site infection following operative treatment of ankle fractures.

Science.gov (United States)

Ovaska, Mikko T; Mäkinen, Tatu J; Madanat, Rami; Huotari, Kaisa; Vahlberg, Tero; Hirvensalo, Eero; Lindahl, Jan

2013-02-20

Surgical site infection is one of the most common complications following ankle fracture surgery. These infections are associated with substantial morbidity and lead to increased resource utilization. Identification of risk factors is crucial for developing strategies to prevent these complications. We performed an age and sex-matched case-control study to identify patient and surgery-related risk factors for deep surgical site infection following operative ankle fracture treatment. We identified 1923 ankle fracture operations performed in 1915 patients from 2006 through 2009. A total of 131 patients with deep infection were identified and compared with an equal number of uninfected control patients. Risk factors for infection were determined with use of conditional logistic regression analysis. The incidence of deep infection was 6.8%. Univariate analysis showed diabetes (odds ratio [OR] = 2.2, 95% confidence interval [CI] = 1.0, 4.9), alcohol abuse (OR = 3.8, 95% CI = 1.6, 9.4), fracture-dislocation (OR = 2.0, 95% CI = 1.2, 3.5), and soft-tissue injury (a Tscherne grade of ≥1) (OR = 2.6, 95% CI = 1.3, 5.3) to be significant patient-related risk factors for infection. Surgery-related risk factors were suboptimal timing of prophylactic antibiotics (OR = 1.9, 95% CI = 1.0, 3.4), difficulties encountered during surgery, (OR = 2.1, 95% CI = 1.1, 4.0), wound complications (OR = 4.8, 95% CI = 1.6, 14.0), and fracture malreduction (OR = 3.4, 95% CI = 1.3, 9.2). Independent risk factors for infection identified by multivariable analyses were tobacco use (OR = 3.7, 95% CI = 1.6, 8.5) and a duration of surgery of more than ninety minutes (OR = 2.5, 95% CI = 1.1, 5.7). Cast application in the operating room was independently associated with a decreased infection rate (OR = 0.4, 95% CI = 0.2, 0.8). We identified several modifiable risk factors for deep surgical site infection following operative treatment of ankle fractures.

Context and Deep Learning Design

Science.gov (United States)

Boyle, Tom; Ravenscroft, Andrew

2012-01-01

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

Deep Generative Models for Molecular Science

DEFF Research Database (Denmark)

Jørgensen, Peter Bjørn; Schmidt, Mikkel Nørgaard; Winther, Ole

2018-01-01

Generative deep machine learning models now rival traditional quantum-mechanical computations in predicting properties of new structures, and they come with a significantly lower computational cost, opening new avenues in computational molecular science. In the last few years, a variety of deep...... generative models have been proposed for modeling molecules, which differ in both their model structure and choice of input features. We review these recent advances within deep generative models for predicting molecular properties, with particular focus on models based on the probabilistic autoencoder (or...

Too Deep or Not Too Deep?: A Propensity-Matched Comparison of the Analgesic Effects of a Superficial Versus Deep Serratus Fascial Plane Block for Ambulatory Breast Cancer Surgery.

Science.gov (United States)

Abdallah, Faraj W; Cil, Tulin; MacLean, David; Madjdpour, Caveh; Escallon, Jaime; Semple, John; Brull, Richard

2018-07-01

Serratus fascial plane block can reduce pain following breast surgery, but the question of whether to inject the local anesthetic superficial or deep to the serratus muscle has not been answered. This cohort study compares the analgesic benefits of superficial versus deep serratus plane blocks in ambulatory breast cancer surgery patients at Women's College Hospital between February 2014 and December 2016. We tested the joint hypothesis that deep serratus block is noninferior to superficial serratus block for postoperative in-hospital (pre-discharge) opioid consumption and pain severity. One hundred sixty-six patients were propensity matched among 2 groups (83/group): superficial and deep serratus blocks. The cohort was used to evaluate the effect of blocks on postoperative oral morphine equivalent consumption and area under the curve for rest pain scores. We considered deep serratus block to be noninferior to superficial serratus block if it were noninferior for both outcomes, within 15 mg morphine and 4 cm·h units margins. Other outcomes included intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and incidence of postoperative nausea and vomiting. Deep serratus block was associated with postoperative morphine consumption and pain scores area under the curve that were noninferior to those of the superficial serratus block. Intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and postoperative nausea and vomiting were not different between blocks. The postoperative in-hospital analgesia associated with deep serratus block is as effective (within an acceptable margin) as superficial serratus block following ambulatory breast cancer surgery. These new findings are important to inform both current clinical practices and future prospective studies.

STELLAR STRUCTURE AND TESTS OF MODIFIED GRAVITY

International Nuclear Information System (INIS)

Chang, Philip; Hui, Lam

2011-01-01

Theories that attempt to explain cosmic acceleration by modifying gravity typically introduces a long-range scalar force that needs to be screened on small scales. One common screening mechanism is the chameleon, where the scalar force is screened in environments with a sufficiently deep gravitational potential, but acts unimpeded in regions with a shallow gravitational potential. This leads to a variation in the overall gravitational G with environment. We show that such a variation can occur within a star itself, significantly affecting its evolution and structure, provided that the host galaxy is unscreened. The effect is most pronounced for red giants, which would be smaller by a factor of tens of percent and thus hotter by hundreds of Kelvin, depending on the parameters of the underlying scalar-tensor theory. Careful measurements of these stars in suitable environments (nearby dwarf galaxies not associated with groups or clusters) would provide constraints on the chameleon mechanism that are four orders of magnitude better than current large-scale structure limits and two orders of magnitude better than present solar system tests.

Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging

Science.gov (United States)

Errico, Claudia; Pierre, Juliette; Pezet, Sophie; Desailly, Yann; Lenkei, Zsolt; Couture, Olivier; Tanter, Mickael

2015-11-01

Non-invasive imaging deep into organs at microscopic scales remains an open quest in biomedical imaging. Although optical microscopy is still limited to surface imaging owing to optical wave diffusion and fast decorrelation in tissue, revolutionary approaches such as fluorescence photo-activated localization microscopy led to a striking increase in resolution by more than an order of magnitude in the last decade. In contrast with optics, ultrasonic waves propagate deep into organs without losing their coherence and are much less affected by in vivo decorrelation processes. However, their resolution is impeded by the fundamental limits of diffraction, which impose a long-standing trade-off between resolution and penetration. This limits clinical and preclinical ultrasound imaging to a sub-millimetre scale. Here we demonstrate in vivo that ultrasound imaging at ultrafast frame rates (more than 500 frames per second) provides an analogue to optical localization microscopy by capturing the transient signal decorrelation of contrast agents—inert gas microbubbles. Ultrafast ultrasound localization microscopy allowed both non-invasive sub-wavelength structural imaging and haemodynamic quantification of rodent cerebral microvessels (less than ten micrometres in diameter) more than ten millimetres below the tissue surface, leading to transcranial whole-brain imaging within short acquisition times (tens of seconds). After intravenous injection, single echoes from individual microbubbles were detected through ultrafast imaging. Their localization, not limited by diffraction, was accumulated over 75,000 images, yielding 1,000,000 events per coronal plane and statistically independent pixels of ten micrometres in size. Precise temporal tracking of microbubble positions allowed us to extract accurately in-plane velocities of the blood flow with a large dynamic range (from one millimetre per second to several centimetres per second). These results pave the way for deep non

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

Science.gov (United States)

He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

2017-03-01

Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

The effect of scandium addition on microstructure and mechanical properties of Al–Si–Mg alloy: A multi-refinement modifier

Energy Technology Data Exchange (ETDEWEB)

Xu, Cong, E-mail: xucong55555@gmail.com [Key Laboratory of Aerospace Advanced Materials and Performance of Ministry of Education, School of Material Science and Engineering, Beihang University, Beijing 100191 (China); Xiao, Wenlong, E-mail: wlxiao@buaa.edu.cn [Key Laboratory of Aerospace Advanced Materials and Performance of Ministry of Education, School of Material Science and Engineering, Beihang University, Beijing 100191 (China); Hanada, Shuji [Institute for Materials Research, Tohoku University, Sendai 980-8577 (Japan); Yamagata, Hiroshi [Center for Advanced Die Engineering and Technology, Gifu University, 1-1 Yanagido, Gifu City, Gifu 501-1193 (Japan); Ma, Chaoli [Key Laboratory of Aerospace Advanced Materials and Performance of Ministry of Education, School of Material Science and Engineering, Beihang University, Beijing 100191 (China)

2015-12-15

Effect of scandium (Sc) additions on the microstructure, mechanical properties and fracture behavior of Al–Si–Mg casting alloy (F357) were systematically investigated. It was found that Sc addition caused a multi-refining efficiency on the microstructure of as-cast F357 alloy, including refinement of grains and secondary dendrite arm spacing (SDAS), modification of eutectic Si and harmless disposal of β-Al{sub 5}FeSi phase. Subsequent T6 heat treatment had further induced the complete spheroidization of eutectic Si and precipitation of fine secondary Al{sub 3}Sc dispersoids in the Sc modified alloys. Thus the mechanical properties, especially the ductility, were significantly enhanced by the addition of Sc combined with the heat treatment. The highest ultimate tensile strength, yield strength and elongation were achieved in 0.8 wt.% Sc modified F357 alloy combined with T6 heat treatment. Furthermore, fractographic examinations indicated that the ductile fracture mechanism served as a dominate role in the modified alloys due to the formation of fine, deep and uniformly distributed dimples. - Highlights: • Detailed characterization of the multi-refining microstructure of Sc modified F357 alloy was performed. • The multi-refinement was proposed to refine grain and SDAS, modify eutectic Si and β-phase. • Sc modifier combined with T6 treatment is effective in improving tensile properties. • Modification of eutectic Si in F357 alloy with Sc is consistent with the IIT mechanism.

Molecular analysis of deep subsurface bacteria

International Nuclear Information System (INIS)

Jimenez Baez, L.E.

1989-09-01

Deep sediments samples from site C10a, in Appleton, and sites, P24, P28, and P29, at the Savannah River Site (SRS), near Aiken, South Carolina were studied to determine their microbial community composition, DNA homology and mol %G+C. Different geological formations with great variability in hydrogeological parameters were found across the depth profile. Phenotypic identification of deep subsurface bacteria underestimated the bacterial diversity at the three SRS sites, since bacteria with the same phenotype have different DNA composition and less than 70% DNA homology. Total DNA hybridization and mol %G+C analysis of deep sediment bacterial isolates suggested that each formation is comprised of different microbial communities. Depositional environment was more important than site and geological formation on the DNA relatedness between deep subsurface bacteria, since more 70% of bacteria with 20% or more of DNA homology came from the same depositional environments. Based on phenotypic and genotypic tests Pseudomonas spp. and Acinetobacter spp.-like bacteria were identified in 85 million years old sediments. This suggests that these microbial communities might have been adapted during a long period of time to the environmental conditions of the deep subsurface

Magnetic graphene oxide modified with choline chloride-based deep eutectic solvent for the solid-phase extraction of protein

Energy Technology Data Exchange (ETDEWEB)

Huang, Yanhua; Wang, Yuzhi, E-mail: wyzss@hnu.edu.cn; Pan, Qi; Wang, Ying; Ding, Xueqin; Xu, Kaijia; Li, Na; Wen, Qian

2015-06-02

Highlights: • A strategy for extraction of protein based on DES-coated magnetic graphene oxide. • The deep eutectic solvents were based on choline chloride. • Bovine serum albumin was used as the analyte. • The material prepared works for the acidic but not the basic or the neutral proteins. - Abstract: Four kinds of green deep eutectic solvents (DESs) based on choline chloride (ChCl) have been synthesized and coated on the surface of magnetic graphene oxide (Fe{sub 3}O{sub 4}@GO) to form Fe{sub 3}O{sub 4}@GO-DES for the magnetic solid-phase extraction of protein. X-ray diffraction (XRD), vibrating sample magnetometer (VSM), Fourier transform infrared spectrometry (FTIR), field emission scanning electron microscopy (FESEM) and thermal gravimetric analysis (TGA) were employed to characterize Fe{sub 3}O{sub 4}@GO-DES, and the results indicated the successful preparation of Fe{sub 3}O{sub 4}@GO-DES. The UV–vis spectrophotometer was used to measure the concentration of protein after extraction. Single factor experiments proved that the extraction amount was influenced by the types of DESs, solution temperature, solution ionic strength, extraction time, protein concentration and the amount of Fe{sub 3}O{sub 4}@GO-DES. Comparison of Fe{sub 3}O{sub 4}@GO and Fe{sub 3}O{sub 4}@GO-DES was carried out by extracting bovine serum albumin, ovalbumin, bovine hemoglobin and lysozyme. The experimental results showed that the proposed Fe{sub 3}O{sub 4}@GO-DES performs better than Fe{sub 3}O{sub 4}@GO in the extraction of acidic protein. Desorption of protein was carried out by eluting the solid extractant with 0.005 mol L{sup −1} Na{sub 2}HPO{sub 4} contained 1 mol L{sup −1} NaCl. The obtained elution efficiency was about 90.9%. Attributed to the convenient magnetic separation, the solid extractant could be easily recycled.

Joint Training of Deep Boltzmann Machines

OpenAIRE

Goodfellow, Ian; Courville, Aaron; Bengio, Yoshua

2012-01-01

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

Discovery deep space optical communications (DSOC) transceiver

Science.gov (United States)

Roberts, W. Thomas

2017-02-01

NASA's 22 cm diameter Deep Space Optical Communications (DSOC) Transceiver is designed to provide a bidirectional optical link between a spacecraft in the inner solar system and an Earth-based optical ground station. This design, optimized for operation across a wide range of illumination conditions, is focused on minimizing blinding from stray light, and providing reliable, accurate attitude information to point its narrow communication beam accurately to the future location of the ground terminal. Though our transceiver will transmit in the 1550 nm waveband and receive in the 1064 nm waveband, the system design relies heavily on reflective optical elements, extending flexibility to be modified for use at different wavebands. The design makes use of common path propagation among transmit, receive and pointing verification optical channels to maintain precise alignment among its components, and to naturally correct for element misalignment resulting from launch or thermal element perturbations. This paper presents the results of trade studies showing the evolution of the design, unique operational characteristics of the design, elements that help to maintain minimal stray light contamination, and preliminary results from development and initial testing of a functional aluminum test model.

Deep boreholes; Tiefe Bohrloecher

Energy Technology Data Exchange (ETDEWEB)

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

2016-02-15

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

Gout: a review of non-modifiable and modifiable risk factors

Science.gov (United States)

MacFarlane, Lindsey A.; Kim, Seoyoung C.

2014-01-01

Gout is a common inflammatory arthritis triggered by the crystallization of uric acid within the joints. Gout affects millions worldwide and has an increasing prevalence. Recent research has been carried out to better qualify and quantify the risk factors predisposing individuals to gout. These can largely be broken into non-modifiable risk factors such as sex, age, race, and genetics, and modifiable risk factors such as diet and lifestyle. Increasing knowledge of factors predisposing certain individuals to gout could potentially lead to improved preventive practices. This review summarizes the non-modifiable and modifiable risk factors associated with development of gout. PMID:25437279

Detection of Genetically Modified Food: Has Your Food Been Genetically Modified?

Science.gov (United States)

Brandner, Diana L.

2002-01-01

Explains the benefits and risks of genetically-modified foods and describes methods for genetically modifying food. Presents a laboratory experiment using a polymerase chain reaction (PCR) test to detect foreign DNA in genetically-modified food. (Contains 18 references.) (YDS)

Updates on the development of Deep Blue aerosol algorithm for constructing consistent long-term data records from MODIS to VIIRS

Science.gov (United States)

Hsu, N. Y. C.; Sayer, A. M.; Lee, J.; Kim, W. V.

2017-12-01

The impacts of natural and anthropogenic sources of air pollution on climate and human health have continued to gain attention from the scientific community. In order to facilitate these effects, high quality consistent long-term global aerosol data records from satellites are essential. Several EOS-era instruments (e.g., SeaWiFS, MODIS, and MISR) are able to provide such information with a high degree of fidelity. However, with the aging MODIS sensors and the launch of the VIIRS instrument on Suomi NPP in late 2011, the continuation of long-term aerosol data records suitable for climate studies from MODIS to VIIRS is needed urgently. Recently, we have successfully modified our MODIS Deep Blue algorithm to process the VIIRS data. Extensive works were performed in refining the surface reflectance determination scheme to account for the wavelength differences between MODIS and VIIRS. Better aerosol models (including non-spherical dust) are also now implemented in our VIIRS algorithm compared to the MODIS C6 algorithm. We will show the global (land and ocean) distributions of various aerosol products from Version 1 of the VIIRS Deep Blue data set. The preliminary validation results of these new VIIRS Deep Blue aerosol products using data from AERONET sunphotometers over land and ocean will be discussed. We will also compare the monthly averaged Deep Blue aerosol optical depth (AOD) from VIIRS with the MODIS C6 products to investigate if any systematic biases may exist between MODIS C6 and VIIRS AOD. The Version 1 VIIRS Deep Blue aerosol products are currently scheduled to be released to the public in 2018.

DeepLoc: prediction of protein subcellular localization using deep learning

DEFF Research Database (Denmark)

Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2017-01-01

The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

Pre-cementation of deep shaft

Science.gov (United States)

Heinz, W. F.

1988-12-01

Pre-cementation or pre-grouting of deep shafts in South Africa is an established technique to improve safety and reduce water ingress during shaft sinking. The recent completion of several pre-cementation projects for shafts deeper than 1000m has once again highlighted the effectiveness of pre-grouting of shafts utilizing deep slimline boreholes and incorporating wireline technique for drilling and conventional deep borehole grouting techniques for pre-cementation. Pre-cementation of deep shaft will: (i) Increase the safety of shaft sinking operation (ii) Minimize water and gas inflow during shaft sinking (iii) Minimize the time lost due to additional grouting operations during sinking of the shaft and hence minimize costly delays and standing time of shaft sinking crews and equipment. (iv) Provide detailed information of the geology of the proposed shaft site. Informations on anomalies, dykes, faults as well as reef (gold bearing conglomerates) intersections can be obtained from the evaluation of cores of the pre-cementation boreholes. (v) Provide improved rock strength for excavations in the immediate vicinity of the shaft area. The paper describes pre-cementation techniques recently applied successfully from surface and some conclusions drawn for further considerations.

Applications of Deep Learning in Biomedicine.

Science.gov (United States)

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

2016-05-02

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

Deep Complementary Bottleneck Features for Visual Speech Recognition

NARCIS (Netherlands)

Petridis, Stavros; Pantic, Maja

Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

Producing deep-water hydrocarbons

International Nuclear Information System (INIS)

Pilenko, Thierry

2011-01-01

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

Deep inelastic processes and the parton model

International Nuclear Information System (INIS)

Altarelli, G.

The lecture was intended as an elementary introduction to the physics of deep inelastic phenomena from the point of view of theory. General formulae and facts concerning inclusive deep inelastic processes in the form: l+N→l'+hadrons (electroproduction, neutrino scattering) are first recalled. The deep inelastic annihilation e + e - →hadrons is then envisaged. The light cone approach, the parton model and their relation are mainly emphasized

Life Support for Deep Space and Mars

Science.gov (United States)

Jones, Harry W.; Hodgson, Edward W.; Kliss, Mark H.

2014-01-01

How should life support for deep space be developed? The International Space Station (ISS) life support system is the operational result of many decades of research and development. Long duration deep space missions such as Mars have been expected to use matured and upgraded versions of ISS life support. Deep space life support must use the knowledge base incorporated in ISS but it must also meet much more difficult requirements. The primary new requirement is that life support in deep space must be considerably more reliable than on ISS or anywhere in the Earth-Moon system, where emergency resupply and a quick return are possible. Due to the great distance from Earth and the long duration of deep space missions, if life support systems fail, the traditional approaches for emergency supply of oxygen and water, emergency supply of parts, and crew return to Earth or escape to a safe haven are likely infeasible. The Orbital Replacement Unit (ORU) maintenance approach used by ISS is unsuitable for deep space with ORU's as large and complex as those originally provided in ISS designs because it minimizes opportunities for commonality of spares, requires replacement of many functional parts with each failure, and results in substantial launch mass and volume penalties. It has become impractical even for ISS after the shuttle era, resulting in the need for ad hoc repair activity at lower assembly levels with consequent crew time penalties and extended repair timelines. Less complex, more robust technical approaches may be needed to meet the difficult deep space requirements for reliability, maintainability, and reparability. Developing an entirely new life support system would neglect what has been achieved. The suggested approach is use the ISS life support technologies as a platform to build on and to continue to improve ISS subsystems while also developing new subsystems where needed to meet deep space requirements.

Automatic Segmentation and Deep Learning of Bird Sounds

NARCIS (Netherlands)

Koops, Hendrik Vincent; Van Balen, J.M.H.; Wiering, F.

2015-01-01

We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as

Deep Learning: A Primer for Radiologists.

Science.gov (United States)

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Science.gov (United States)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Stratification-Based Outlier Detection over the Deep Web.

Science.gov (United States)

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

Deep Learning and Bayesian Methods

Directory of Open Access Journals (Sweden)

Prosper Harrison B.

2017-01-01

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

Eric Davidson and deep time.

Science.gov (United States)

Erwin, Douglas H

2017-10-13

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

More Far-Side Deep Moonquake Nests Discovered

Science.gov (United States)

Nakamura, Y.; Jackson, John A.; Jackson, Katherine G.

2004-01-01

As reported last year, we started to reanalyze the seismic data acquired from 1969 to 1977 with a network of stations established on the Moon during the Apollo mission. The reason for the reanalysis was because recent advances in computer technology make it possible to employ much more sophisticated analysis techniques than was possible previously. The primary objective of the reanalysis was to search for deep moonquakes on the far side of the Moon and, if found, to use them to infer the structure of the Moon's deep interior, including a possible central core. The first step was to identify any new deep moonquakes that escaped our earlier search by applying a combination of waveform cross-correlation and single-link cluster analysis, and then to see if any of them are from previously unknown nests of deep moonquakes. We positively identified 7245 deep moonquakes, more than a five-fold increase from the previous 1360. We also found at least 88 previously unknown deep-moonquake nests. The question was whether any of these newly discovered nets were on the far side of the Moon, and we now report that our analysis of the data indicates that some of them are indeed on the far side.

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

Directory of Open Access Journals (Sweden)

Peter Christiansen

2016-11-01

Full Text Available 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.

Learning Transferable Features with Deep Adaptation Networks

OpenAIRE

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

2015-01-01

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

Theory of deep inelastic lepton-hadron scattering

International Nuclear Information System (INIS)

Geyer, B.; Robaschik, D.; Wieczorek, E.

1979-01-01

The description of deep inelastic lepton-nucleon scattering in the lowest order of the electromagnetic and weak coupling constants leads to a study of virtual Compton amplitudes and their absorptive parts. Some aspects of quantum chromodynamics are discussed. Deep inelastic scattering enables a central quantity of quantum field theory, namely the light cone behaviour of the current commutator. The moments of structure functions are used for the description of deep inelastic scattering. (author)

DeepQA: Improving the estimation of single protein model quality with deep belief networks

OpenAIRE

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-01-01

Background Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. Results We introduce a novel single-model quality assessment method DeepQA based on deep belie...

DeepDive: Declarative Knowledge Base Construction.

Science.gov (United States)

De Sa, Christopher; Ratner, Alex; Ré, Christopher; Shin, Jaeho; Wang, Feiran; Wu, Sen; Zhang, Ce

2016-03-01

The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.

Pathways to deep decarbonization - 2015 report

International Nuclear Information System (INIS)

Ribera, Teresa; Colombier, Michel; Waisman, Henri; Bataille, Chris; Pierfederici, Roberta; Sachs, Jeffrey; Schmidt-Traub, Guido; Williams, Jim; Segafredo, Laura; Hamburg Coplan, Jill; Pharabod, Ivan; Oury, Christian

2015-12-01

In September 2015, the Deep Decarbonization Pathways Project published the Executive Summary of the Pathways to Deep Decarbonization: 2015 Synthesis Report. The full 2015 Synthesis Report was launched in Paris on December 3, 2015, at a technical workshop with the Mitigation Action Plans and Scenarios (MAPS) program. The Deep Decarbonization Pathways Project (DDPP) is a collaborative initiative to understand and show how individual countries can transition to a low-carbon economy and how the world can meet the internationally agreed target of limiting the increase in global mean surface temperature to less than 2 degrees Celsius (deg. C). Achieving the 2 deg. C limit will require that global net emissions of greenhouse gases (GHG) approach zero by the second half of the century. In turn, this will require a profound transformation of energy systems by mid-century through steep declines in carbon intensity in all sectors of the economy, a transition we call 'deep decarbonization'

Generation of binary holograms for deep scenes captured with a camera and a depth sensor

Science.gov (United States)

Leportier, Thibault; Park, Min-Chul

2017-01-01

This work presents binary hologram generation from images of a real object acquired from a Kinect sensor. Since hologram calculation from a point-cloud or polygon model presents a heavy computational burden, we adopted a depth-layer approach to generate the holograms. This method enables us to obtain holographic data of large scenes quickly. Our investigations focus on the performance of different methods, iterative and noniterative, to convert complex holograms into binary format. Comparisons were performed to examine the reconstruction of the binary holograms at different depths. We also propose to modify the direct binary search algorithm to take into account several reference image planes. Then, deep scenes featuring multiple planes of interest can be reconstructed with better efficiency.

Conjunction of 2D and 3D modified flow solvers for simulating spatio-temporal wind induced hydrodynamics in the Caspian Sea

Science.gov (United States)

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.

Deep drilling KLX 02. Drilling and documentation of a 1700 m deep borehole at Laxemar, Sweden

Energy Technology Data Exchange (ETDEWEB)

Andersson, O [VBB VIAK AB, Malmoe (Sweden)

1994-08-01

In this report the preparation and execution of the deep core drilling KLX 02 is described. The hole was drilled with the wireline methods, NQ dimension (diameter 76 mm), to a final depth of 1700.5 m. Prior to core drilling a diameter 215 mm pilot hole was pre drilled to 200 m with controlled hammer drilling (DTH). In this hole casing and air-lift equipment was installed with the aim to support the circulation of drilling fluid. During core drilling there was a measurement of major drilling parameters and drilling fluid in and out of hole. As a fluid tracer uranine was used. Each 300 m of core drilling air-lift pump tests were performed. After completion a flow-meter log was run to finalize the project phase. It can be concluded that both the pre drilling and core drilling methods used proved to be successful. No severe technical problem occurred. However, potential risks have been pointed at in the report. The air-lift system functioned only partly and has to be modified for further use. Also the technique for monitoring of drilling parameters needs improvement as does the method for air-lift pump tests with packer. The organisation model for planning and realization functioned satisfactory and can be recommended for similar future projects. 9 refs, numerous tabs and figs.

Deep drilling KLX 02. Drilling and documentation of a 1700 m deep borehole at Laxemar, Sweden

International Nuclear Information System (INIS)

Andersson, O.

1994-08-01

In this report the preparation and execution of the deep core drilling KLX 02 is described. The hole was drilled with the wireline methods, NQ dimension (diameter 76 mm), to a final depth of 1700.5 m. Prior to core drilling a diameter 215 mm pilot hole was pre drilled to 200 m with controlled hammer drilling (DTH). In this hole casing and air-lift equipment was installed with the aim to support the circulation of drilling fluid. During core drilling there was a measurement of major drilling parameters and drilling fluid in and out of hole. As a fluid tracer uranine was used. Each 300 m of core drilling air-lift pump tests were performed. After completion a flow-meter log was run to finalize the project phase. It can be concluded that both the pre drilling and core drilling methods used proved to be successful. No severe technical problem occurred. However, potential risks have been pointed at in the report. The air-lift system functioned only partly and has to be modified for further use. Also the technique for monitoring of drilling parameters needs improvement as does the method for air-lift pump tests with packer. The organisation model for planning and realization functioned satisfactory and can be recommended for similar future projects. 9 refs, numerous tabs and figs

Text feature extraction based on deep learning: a review.

Science.gov (United States)

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

A New CT-Guided Modified Trocar Technique for Drainage of Difficult Locations Abscesses

Energy Technology Data Exchange (ETDEWEB)

Tyng, Chiang J., E-mail: chiangjengtyng@gmail.com; Amoedo, Maurício K.; Bohrer, Yves; Bitencourt, Almir G. V.; Barbosa, Paula N. V.; Almeida, Maria Fernanda A.; Zurstrassen, Charles E. [AC Camargo Cancer Center, Department of Imaging (Brazil); Coimbra, Felipe J. F.; Costa, Wilson L. da [AC Camargo Cancer Center, Department of Abdominal Surgery (Brazil); Chojniak, Rubens [AC Camargo Cancer Center, Department of Imaging (Brazil)

2017-05-15

PurposeComputed tomography (CT) is commonly used to guide drainage of deep-seated abdominal fluid collections. However, in some cases, these collections seem to be inaccessible due to surrounding organs or their being in difficult locations. The aim of this study is to describe a modified Trocar technique to drain collections in difficult locations, especially those in the subphrenic space, without passing through intervening organs.Materials and MethodsThis retrospective case series study describes seven inpatients who underwent CT-guided drainage using a modified Trocar technique for abscesses that are difficult to access percutaneously. All patients provided written informed consent prior to the procedure. After placement of a 12–14F catheter inside the peritoneum, the Trocar stylet was removed so that the tip of the catheter became blunt and flexible to avoid injury to organs and structures in the catheter route, and the catheter was slowly advanced towards the collection using CT guidance and tactile sensation. After reaching the target, the stylet was reintroduced to enter the abscess wall.ResultsAll procedures were performed using an anterior abdominal wall access with adequate catheter positioning and resulted in clinical status improvement in the days after the drainage. No complications related to the procedure were identified in any of the patients.ConclusionsThe modified Trocar technique for percutaneous CT-guided drainage of abdominal abscesses may be feasible for lesions that are difficult to access with conventional methods.

Deep-seated sarcomas of the penis

Directory of Open Access Journals (Sweden)

Alberto A. Antunes

2005-06-01

Full Text Available Mesenchymal neoplasias represent 5% of tumors affecting the penis. Due to the rarity of such tumors, there is no agreement concerning the best method for staging and managing these patients. Sarcomas of the penis can be classified as deep-seated if they derive from the structures forming the spongy body and the cavernous bodies. Superficial lesions are usually low-grade and show a small tendency towards distant metastasis. In contrast, deep-seated lesions usually show behavior that is more aggressive and have poorer prognosis. The authors report 3 cases of deep-seated primary sarcomas of the penis and review the literature on this rare and aggressive neoplasia.

In Brief: Deep-sea observatory

Science.gov (United States)

Showstack, Randy

2008-11-01

The first deep-sea ocean observatory offshore of the continental United States has begun operating in the waters off central California. The remotely operated Monterey Accelerated Research System (MARS) will allow scientists to monitor the deep sea continuously. Among the first devices to be hooked up to the observatory are instruments to monitor earthquakes, videotape deep-sea animals, and study the effects of acidification on seafloor animals. ``Some day we may look back at the first packets of data streaming in from the MARS observatory as the equivalent of those first words spoken by Alexander Graham Bell: `Watson, come here, I need you!','' commented Marcia McNutt, president and CEO of the Monterey Bay Aquarium Research Institute, which coordinated construction of the observatory. For more information, see http://www.mbari.org/news/news_releases/2008/mars-live/mars-live.html.

Deep Learning for Computer Vision: A Brief Review

Science.gov (United States)

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

Deep Learning for Computer Vision: A Brief Review

Directory of Open Access Journals (Sweden)

Athanasios Voulodimos

2018-01-01

Full Text Available 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.

Deep Learning for Computer Vision: A Brief Review.

Science.gov (United States)

Voulodimos, Athanasios; 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.

A Rare Case of Deep Digital Flexor Tendinopathy following Centesis of the Navicular Bursa

Directory of Open Access Journals (Sweden)

Tim J. Froydenlund

2017-10-01

Full Text Available Navicular bursa (NB centesis is a common diagnostic and therapeutic procedure in equine practice. This case report documents the clinical, diagnostic imaging and histological findings in a horse with a suspected iatrogenic deep digital flexor tendon (DDFT injury following centesis of the NB via a modified distal plantar approach (placement of two needles in a weight bearing position. Although it cannot be proven with absolute certainty, the authors believe that this is the first reported case where NB centesis is the likely cause of a DDFT lesion, and with magnetic resonance imaging performed both pre- and post-centesis. With this potential, though rare, complication of the procedure, alternative tendon sparing injection techniques should be considered prior to NB centesis in certain cases.

Is deep dreaming the new collage?

Science.gov (United States)

Boden, Margaret A.

2017-10-01

Deep dreaming (DD) can combine and transform images in surprising ways. But, being based in deep learning (DL), it is not analytically understood. Collage is an art form that is constrained along various dimensions. DD will not be able to generate collages until DL can be guided in a disciplined fashion.

Density functionals from deep learning

OpenAIRE

McMahon, Jeffrey M.

2016-01-01

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

A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

OpenAIRE

Wang, Haohan; Raj, Bhiksha

2015-01-01

This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...

Assessment of deep geological environment condition

International Nuclear Information System (INIS)

Bae, Dae Seok; Han, Kyung Won; Joen, Kwan Sik

2003-05-01

The main tasks of geoscientific study in the 2nd stage was characterized focusing mainly on a near-field condition of deep geologic environment, and aimed to generate the geologic input data for a Korean reference disposal system for high level radioactive wastes and to establish site characterization methodology, including neotectonic features, fracture systems and mechanical properties of plutonic rocks, and hydrogeochemical characteristics. The preliminary assessment of neotectonics in the Korean peninsula was performed on the basis of seismicity recorded, Quarternary faults investigated, uplift characteristics studied on limited areas, distribution of the major regional faults and their characteristics. The local fracture system was studied in detail from the data obtained from deep boreholes in granitic terrain. Through this deep drilling project, the geometrical and hydraulic properties of different fracture sets are statistically analysed on a block scale. The mechanical properties of intact rocks were evaluated from the core samples by laboratory testing and the in-situ stress conditions were estimated by a hydro fracturing test in the boreholes. The hydrogeochemical conditions in the deep boreholes were characterized based on hydrochemical composition and isotopic signatures and were attempted to assess the interrelation with a major fracture system. The residence time of deep groundwater was estimated by C-14 dating. For the travel time of groundwater between the boreholes, the methodology and equipment for tracer test were established

Deep Carbon Observatory investigates Carbon from Crust to Core: An Academic Record of the History of Deep Carbon Science

Science.gov (United States)

Mitton, S. A.

2017-12-01

Carbon plays an unparalleled role in our lives: as the element of life, as the basis of most of society's energy, as the backbone of most new materials, and as the central focus in efforts to understand Earth's variable and uncertain climate. Yet in spite of carbon's importance, scientists remain largely ignorant of the physical, chemical, and biological behavior of many of Earth's carbon-bearing systems. The Deep Carbon Observatory (DCO) is a global research program to transform our understanding of carbon in Earth. At its heart, DCO is a community of scientists, from biologists to physicists, geoscientists to chemists, and many others whose work crosses these disciplinary lines, forging a new, integrative field of deep carbon science. As a historian of science, I specialise in the history of planetary science and astronomy since 1900. This is directed toward understanding of the history of the steps on the road to discovering the internal dynamics of our planet. Within a framework that describes the historical background to the new field of Earth System Science, I present the first history of deep carbon science. This project will identifies the key discoveries of deep carbon science. It will assess the impact of new knowledge on geochemistry, geodynamics, and geobiology. The project will lead to publication, in book form in 2019, of an illuminating narrative that will highlight the engaging human stories of many remarkable scientists and natural philosophers from whom we have learned about the complexity of Earth's internal world. On this journey of discovery we will encounter not just the pioneering researchers of deep carbon science, but also their institutions, their instrumental inventiveness, and their passion for exploration. The book is organised thematically around the four communities of the Deep Carbon Observatory: Deep Life, Extreme Physics and Chemistry, Reservoirs and Fluxes, and Deep Energy. The presentation has a gallery and list of Deep Carbon

Deep Energy Retrofit

DEFF Research Database (Denmark)

Zhivov, Alexander; Lohse, Rüdiger; Rose, Jørgen

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

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

Directory of Open Access Journals (Sweden)

Chengdong Li

2018-01-01

Full Text Available To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity. In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN, the generalized radial basis function neural network (GRBFNN, the extreme learning machine (ELM, and the support vector regressor (SVR are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption

Deep-well injection of radioactive waste in Russia

International Nuclear Information System (INIS)

Hoek, J.

1998-01-01

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

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.

2014-11-05

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer\\'s properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.

2014-01-01

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

Identification of a BRCA2-Specific Modifier Locus at 6p24 Related to Breast Cancer Risk

DEFF Research Database (Denmark)

Gaudet, Mia M; Kuchenbaecker, Karoline B; Vijai, Joseph

2013-01-01

of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer...... carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part...

DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.

Science.gov (United States)

Park, Bo-Yong; Lee, Mi Ji; Lee, Seung-Hak; Cha, Jihoon; Chung, Chin-Sang; Kim, Sung Tae; Park, Hyunjin

2018-01-01

Migraineurs show an increased load of white matter hyperintensities (WMHs) and more rapid deep WMH progression. Previous methods for WMH segmentation have limited efficacy to detect small deep WMHs. We developed a new fully automated detection pipeline, DEWS (DEep White matter hyperintensity Segmentation framework), for small and superficially-located deep WMHs. A total of 148 non-elderly subjects with migraine were included in this study. The pipeline consists of three components: 1) white matter (WM) extraction, 2) WMH detection, and 3) false positive reduction. In WM extraction, we adjusted the WM mask to re-assign misclassified WMHs back to WM using many sequential low-level image processing steps. In WMH detection, the potential WMH clusters were detected using an intensity based threshold and region growing approach. For false positive reduction, the detected WMH clusters were classified into final WMHs and non-WMHs using the random forest (RF) classifier. Size, texture, and multi-scale deep features were used to train the RF classifier. DEWS successfully detected small deep WMHs with a high positive predictive value (PPV) of 0.98 and true positive rate (TPR) of 0.70 in the training and test sets. Similar performance of PPV (0.96) and TPR (0.68) was attained in the validation set. DEWS showed a superior performance in comparison with other methods. Our proposed pipeline is freely available online to help the research community in quantifying deep WMHs in non-elderly adults.

Deep-Sea Corals: A New Oceanic Archive

National Research Council Canada - National Science Library

Adkins, Jess

1998-01-01

Deep-sea corals are an extraordinary new archive of deep ocean behavior. The species Desmophyllum cristagalli is a solitary coral composed of uranium rich, density banded aragonite that I have calibrated for several paleoclimate tracers...

Challenging oil bioremediation at deep-sea hydrostatic pressure

Directory of Open Access Journals (Sweden)

Alberto Scoma

2016-08-01

Full Text Available The Deepwater Horizon (DWH accident has brought oil contamination of deep-sea environments to worldwide attention. The risk for new deep-sea spills is not expected to decrease in the future, as political pressure mounts to access deep-water fossil reserves, and poorly tested technologies are used to access oil. This also applies to the response to oil-contamination events, with bioremediation the only (biotechnology presently available to combat deep-sea spills. Many questions about the fate of petroleum-hydrocarbons at deep-sea remain unanswered, as much as the main constraints limiting bioremediation under increased hydrostatic pressures and low temperatures. The microbial pathways fueling oil take up are unclear, and the mild upregulation observed for beta-oxidation-related genes in both water and sediments contrasts with the high amount of alkanes present in the spilled-oil. The fate of solid alkanes (tar and that of hydrocarbons degradation rates was largely overlooked, as the reason why the most predominant hydrocarbonoclastic genera were not enriched at deep-sea, despite being present at hydrocarbon seeps at the Gulf of Mexico. This mini-review aims at highlighting the missing information in the field, proposing a holistic approach where in situ and ex situ studies are integrated to reveal the principal mechanisms accounting for deep-sea oil bioremediation.

Deep Crustal Melting and the Survival of Continental Crust

Science.gov (United States)

Whitney, D.; Teyssier, C. P.; Rey, P. F.; Korchinski, M.

2017-12-01

Plate convergence involving continental lithosphere leads to crustal melting, which ultimately stabilizes the crust because it drives rapid upward flow of hot deep crust, followed by rapid cooling at shallow levels. Collision drives partial melting during crustal thickening (at 40-75 km) and/or continental subduction (at 75-100 km). These depths are not typically exceeded by crustal rocks that are exhumed in each setting because partial melting significantly decreases viscosity, facilitating upward flow of deep crust. Results from numerical models and nature indicate that deep crust moves laterally and then vertically, crystallizing at depths as shallow as 2 km. Deep crust flows en masse, without significant segregation of melt into magmatic bodies, over 10s of kms of vertical transport. This is a major mechanism by which deep crust is exhumed and is therefore a significant process of heat and mass transfer in continental evolution. The result of vertical flow of deep, partially molten crust is a migmatite dome. When lithosphere is under extension or transtension, the deep crust is solicited by faulting of the brittle upper crust, and the flow of deep crust in migmatite domes traverses nearly the entire thickness of orogenic crust in Recognition of the importance of migmatite (gneiss) domes as archives of orogenic deep crust is applicable to determining the chemical and physical properties of continental crust, as well as mechanisms and timescales of crustal differentiation.

Deep Learning and Bayesian Methods

OpenAIRE

Prosper Harrison B.

2017-01-01

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

The dynamics of biogeographic ranges in the deep sea.

Science.gov (United States)

McClain, Craig R; Hardy, Sarah Mincks

2010-12-07

Anthropogenic disturbances such as fishing, mining, oil drilling, bioprospecting, warming, and acidification in the deep sea are increasing, yet generalities about deep-sea biogeography remain elusive. Owing to the lack of perceived environmental variability and geographical barriers, ranges of deep-sea species were traditionally assumed to be exceedingly large. In contrast, seamount and chemosynthetic habitats with reported high endemicity challenge the broad applicability of a single biogeographic paradigm for the deep sea. New research benefiting from higher resolution sampling, molecular methods and public databases can now more rigorously examine dispersal distances and species ranges on the vast ocean floor. Here, we explore the major outstanding questions in deep-sea biogeography. Based on current evidence, many taxa appear broadly distributed across the deep sea, a pattern replicated in both the abyssal plains and specialized environments such as hydrothermal vents. Cold waters may slow larval metabolism and development augmenting the great intrinsic ability for dispersal among many deep-sea species. Currents, environmental shifts, and topography can prove to be dispersal barriers but are often semipermeable. Evidence of historical events such as points of faunal origin and climatic fluctuations are also evident in contemporary biogeographic ranges. Continued synthetic analysis, database construction, theoretical advancement and field sampling will be required to further refine hypotheses regarding deep-sea biogeography.

Survey on deep learning for radiotherapy.

Science.gov (United States)

Meyer, Philippe; Noblet, Vincent; Mazzara, Christophe; Lallement, Alex

2018-05-17

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Hello World Deep Learning in Medical Imaging.

Science.gov (United States)

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.

Deep Neuromuscular Blockade Improves Laparoscopic Surgical Conditions

DEFF Research Database (Denmark)

Rosenberg, Jacob; Herring, W Joseph; Blobner, Manfred

2017-01-01

INTRODUCTION: Sustained deep neuromuscular blockade (NMB) during laparoscopic surgery may facilitate optimal surgical conditions. This exploratory study assessed whether deep NMB improves surgical conditions and, in doing so, allows use of lower insufflation pressures during laparoscopic cholecys...

Development of Hydro-Mechanical Deep Drawing

DEFF Research Database (Denmark)

Zhang, Shi-Hong; Danckert, Joachim

1998-01-01

The hydro-mechanical deep-drawing process is reviewed in this article. The process principles and features are introduced and the developments of the hydro-mechanical deep-drawing process in process performances, in theory and in numerical simulation are described. The applications are summarized....... Some other related hydraulic forming processes are also dealt with as a comparison....

76 FR 66078 - Notice of Industry Workshop on Technical and Regulatory Challenges in Deep and Ultra-Deep Outer...

Science.gov (United States)

2011-10-25

...-0087] Notice of Industry Workshop on Technical and Regulatory Challenges in Deep and Ultra-Deep Outer... discussions expected to help identify Outer Continental Shelf (OCS) challenges and technologies associated... structured venue for consultation among offshore deepwater oil and gas industry and regulatory experts in...

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

OpenAIRE

Lea-Anne Henry; Sankha S. Mukherjee; Neil M. Roberston; Laurence De Clippele; J. Murray Roberts

2016-01-01

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

Evolutionary process of deep-sea bathymodiolus mussels.

Science.gov (United States)

Miyazaki, Jun-Ichi; de Oliveira Martins, Leonardo; Fujita, Yuko; Matsumoto, Hiroto; Fujiwara, Yoshihiro

2010-04-27

Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI) and NADH dehydrogenase subunit 4 (ND4) genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of symbiosis in that nutritional adaptation to the deep sea proceeded from extracellular

Ultra Deep Wave Equation Imaging and Illumination

Energy Technology Data Exchange (ETDEWEB)

Alexander M. Popovici; Sergey Fomel; Paul Sava; Sean Crawley; Yining Li; Cristian Lupascu

2006-09-30

In this project we developed and tested a novel technology, designed to enhance seismic resolution and imaging of ultra-deep complex geologic structures by using state-of-the-art wave-equation depth migration and wave-equation velocity model building technology for deeper data penetration and recovery, steeper dip and ultra-deep structure imaging, accurate velocity estimation for imaging and pore pressure prediction and accurate illumination and amplitude processing for extending the AVO prediction window. Ultra-deep wave-equation imaging provides greater resolution and accuracy under complex geologic structures where energy multipathing occurs, than what can be accomplished today with standard imaging technology. The objective of the research effort was to examine the feasibility of imaging ultra-deep structures onshore and offshore, by using (1) wave-equation migration, (2) angle-gathers velocity model building, and (3) wave-equation illumination and amplitude compensation. The effort consisted of answering critical technical questions that determine the feasibility of the proposed methodology, testing the theory on synthetic data, and finally applying the technology for imaging ultra-deep real data. Some of the questions answered by this research addressed: (1) the handling of true amplitudes in the downward continuation and imaging algorithm and the preservation of the amplitude with offset or amplitude with angle information required for AVO studies, (2) the effect of several imaging conditions on amplitudes, (3) non-elastic attenuation and approaches for recovering the amplitude and frequency, (4) the effect of aperture and illumination on imaging steep dips and on discriminating the velocities in the ultra-deep structures. All these effects were incorporated in the final imaging step of a real data set acquired specifically to address ultra-deep imaging issues, with large offsets (12,500 m) and long recording time (20 s).

U.V. repair in deep-sea bacteria

International Nuclear Information System (INIS)

Lutz, L.; Yayanos, A.A.

1986-01-01

Exposure of cells to light of less than 320 nanometers wavelengths may lead to lethal lesions and perhaps carcinogenesis. Many organisms have evolved mechanisms to repair U.V. light-induced damage. Organisms such as deep-sea bacteria are presumably never exposed to U.V. light and perhaps occasionally to visible from bioluminescence. Thus, the repair of U.V. damage in deep-sea bacterial DNA might be inefficient and repair by photoreactivation unlikely. The bacteria utilized in this investigation are temperature sensitive and barophilic. Four deep-sea isolates were chosen for this study: PE-36 from 3584 m, CNPT-3 from 5782 m, HS-34 from 5682 m, and MT-41 from 10,476 m, all are from the North Pacific ocean. The deep-sea extends from 1100 m to depths greater than 7000 m. It is a region of relatively uniform conditions. The temperature ranges from 5 to -1 0 C. There is no solar light in the deep-sea. Deep-sea bacteria are sensitive to U.V. light; in fact more sensitive than a variety of terrestrial and sea-surface bacteria. All four isolates demonstrate thymine dimer repair. Photoreactivation was observed in only MT-41. The other strains from shallower depths displayed no photoreactivation. The presence of DNA sequences homologous to the rec A, uvr A, B, and C and phr genes of E. coli have been examined by Southern hybridization techniques

Diabetic retinopathy screening using deep neural network.

Science.gov (United States)

Ramachandran, Nishanthan; Hong, Sheng Chiong; Sime, Mary J; Wilson, Graham A

2017-09-07

There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

Extracting Databases from Dark Data with DeepDive.

Science.gov (United States)

Zhang, Ce; Shin, Jaeho; Ré, Christopher; Cafarella, Michael; Niu, Feng

2016-01-01

DeepDive is a system for extracting relational databases from dark data : the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data - scientific papers, Web classified ads, customer service notes, and so on - were instead in a relational database, it would give analysts a massive and valuable new set of "big data." DeepDive is distinctive when compared to previous information extraction systems in its ability to obtain very high precision and recall at reasonable engineering cost; in a number of applications, we have used DeepDive to create databases with accuracy that meets that of human annotators. To date we have successfully deployed DeepDive to create data-centric applications for insurance, materials science, genomics, paleontologists, law enforcement, and others. The data unlocked by DeepDive represents a massive opportunity for industry, government, and scientific researchers. DeepDive is enabled by an unusual design that combines large-scale probabilistic inference with a novel developer interaction cycle. This design is enabled by several core innovations around probabilistic training and inference.

Deep groundwater chemistry

International Nuclear Information System (INIS)

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

1987-06-01

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

Preface: Deep Slab and Mantle Dynamics

Science.gov (United States)

Suetsugu, Daisuke; Bina, Craig R.; Inoue, Toru; Wiens, Douglas A.

2010-11-01

We are pleased to publish this special issue of the journal Physics of the Earth and Planetary Interiors entitled "Deep Slab and Mantle Dynamics". This issue is an outgrowth of the international symposium "Deep Slab and Mantle Dynamics", which was held on February 25-27, 2009, in Kyoto, Japan. This symposium was organized by the "Stagnant Slab Project" (SSP) research group to present the results of the 5-year project and to facilitate intensive discussion with well-known international researchers in related fields. The SSP and the symposium were supported by a Grant-in-Aid for Scientific Research (16075101) from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese Government. In the symposium, key issues discussed by participants included: transportation of water into the deep mantle and its role in slab-related dynamics; observational and experimental constraints on deep slab properties and the slab environment; modeling of slab stagnation to constrain its mechanisms in comparison with observational and experimental data; observational, experimental and modeling constraints on the fate of stagnant slabs; eventual accumulation of stagnant slabs on the core-mantle boundary and its geodynamic implications. This special issue is a collection of papers presented in the symposium and other papers related to the subject of the symposium. The collected papers provide an overview of the wide range of multidisciplinary studies of mantle dynamics, particularly in the context of subduction, stagnation, and the fate of deep slabs.

Biological response modifiers

Energy Technology Data Exchange (ETDEWEB)

Weller, R.E.

1991-10-01

Much of what used to be called immunotherapy is now included in the term biological response modifiers. Biological response modifiers (BRMs) are defined as those agents or approaches that modify the relationship between the tumor and host by modifying the host's biological response to tumor cells with resultant therapeutic effects.'' Most of the early work with BRMs centered around observations of spontaneous tumor regression and the association of tumor regression with concurrent bacterial infections. The BRM can modify the host response in the following ways: Increase the host's antitumor responses through augmentation and/or restoration of effector mechanisms or mediators of the host's defense or decrease the deleterious component by the host's reaction; Increase the host's defenses by the administration of natural biologics (or the synthetic derivatives thereof) as effectors or mediators of an antitumor response; Augment the host's response to modified tumor cells or vaccines, which might stimulate a greater response by the host or increase tumor-cell sensitivity to an existing response; Decrease the transformation and/or increase differentiation (maturation) of tumor cells; or Increase the ability of the host to tolerate damage by cytotoxic modalities of cancer treatment.

Obstetrical complications of endometriosis, particularly deep endometriosis.

Science.gov (United States)

Leone Roberti Maggiore, Umberto; Inversetti, Annalisa; Schimberni, Matteo; Viganò, Paola; Giorgione, Veronica; Candiani, Massimo

2017-12-01

Over the past few years, a new topic in the field of endometriosis has emerged: the potential impact of the disease on pregnancy outcomes. This review aims to summarize in detail the available evidence on the relationship between endometriosis, particularly deep endometriosis (DE), and obstetrical outcomes. Acute complications of DE, such as spontaneous hemoperitoneum, bowel perforation, and uterine rupture, may occur during pregnancy. Although these events represent life-threatening conditions, they are rare and unpredictable. Therefore, the current literature does not support any kind of prophylactic surgery before pregnancy to prevent such complications. Results on the impact of DE on obstetrical outcomes are debatable and characterized by several limitations, including small sample size, lack of adjustment for confounders, lack of adequate control subjects, and other methodologic flaws. For these reasons, it is not possible to draw conclusions on this topic. The strongest evidence shows that DE is associated with higher rates of placenta previa; for other obstetrical outcomes, such as miscarriage, intrauterine growth restriction, preterm birth and hypertensive disorders, results are controversial. Although it is unlikely that surgery of DE may modify the impact of the disease on the course of pregnancy, no study has yet investigated this issue. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Harnessing the Deep Web: Present and Future

OpenAIRE

Madhavan, Jayant; Afanasiev, Loredana; Antova, Lyublena; Halevy, Alon

2009-01-01

Over the past few years, we have built a system that has exposed large volumes of Deep-Web content to Google.com users. The content that our system exposes contributes to more than 1000 search queries per-second and spans over 50 languages and hundreds of domains. The Deep Web has long been acknowledged to be a major source of structured data on the web, and hence accessing Deep-Web content has long been a problem of interest in the data management community. In this paper, we report on where...

Zooplankton at deep Red Sea brine pools

KAUST Repository

Kaartvedt, Stein

2016-03-02

The deep-sea anoxic brines of the Red Sea comprise unique, complex and extreme habitats. These environments are too harsh for metazoans, while the brine–seawater interface harbors dense microbial populations. We investigated the adjacent pelagic fauna at two brine pools using net tows, video records from a remotely operated vehicle and submerged echosounders. Waters just above the brine pool of Atlantis II Deep (2000 m depth) appeared depleted of macrofauna. In contrast, the fauna appeared to be enriched at the Kebrit Deep brine–seawater interface (1466 m).

How to study deep roots - and why it matters

OpenAIRE

Maeght, Jean-Luc; Rewald, B.; Pierret, Alain

2013-01-01

The drivers underlying the development of deep root systems, whether genetic or environmental, are poorly understood but evidence has accumulated that deep rooting could be a more widespread and important trait among plants than commonly anticipated from their share of root biomass. Even though a distinct classification of "deep roots" is missing to date, deep roots provide important functions for individual plants such as nutrient and water uptake but can also shape plant communities by hydr...

Benchmarking State-of-the-Art Deep Learning Software Tools

OpenAIRE

Shi, Shaohuai; Wang, Qiang; Xu, Pengfei; Chu, Xiaowen

2016-01-01

Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Training a deep network is usually a very time-consuming process. To address the computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training time. However, different tools exhibit different features and running performance when training ...

High-Redshift Radio Galaxies from Deep Fields

Indian Academy of Sciences (India)

2016-01-27

Jan 27, 2016 ... High-Redshift Radio Galaxies from Deep Fields ... Here we present results from the deep 150 MHz observations of LBDS-Lynx field, which has been imaged at 327, ... Articles are also visible in Web of Science immediately.

Deep Learning Microscopy

KAUST Repository

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

2017-01-01

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

Pencil graphite electrodes for improved electrochemical detection of oleuropein by the combination of Natural Deep Eutectic Solvents and graphene oxide.

Science.gov (United States)

Gomez, Federico J V; Spisso, Adrian; Fernanda Silva, María

2017-11-01

A novel methodology is presented for the enhanced electrochemical detection of oleuropein in complex plant matrices by Graphene Oxide Pencil Grahite Electrode (GOPGE) in combination with a buffer modified with a Natural Deep Eutectic Solvent, containing 10% (v/v) of Lactic acid, Glucose and H 2 O (LGH). The electrochemical behavior of oleuropein in the modified-working buffer was examined using differential pulse voltammetry. The combination of both modifications, NADES modified buffer and nanomaterial modified electrode, LGH-GOPGE, resulted on a signal enhancement of 5.3 times higher than the bare electrode with unmodified buffer. A calibration curve of oleuropein was performed between 0.10 to 37 μM and a good linearity was obtained with a correlation coefficient of 0.989. Detection and quantification limits of the method were obtained as 30 and 102 nM, respectively. In addition, precision studies indicated that the voltammetric method was sufficiently repeatable, %RSD 0.01 and 3.16 (n = 5) for potential and intensity, respectively. Finally, the proposed electrochemical sensor was successfully applied to the determination of oleuropein in an olive leaf extract prepared by ultrasound-assisted extraction. The results obtained with the proposed electrochemical sensor were compared with Capillary Zone Electrophoresis analysis with satisfactory results. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Deep-sea fungi

Digital Repository Service at National Institute of Oceanography (India)

Raghukumar, C; Damare, S.R.

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

Deep Trawl Dataset

Data.gov (United States)

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

Modified Allergens for Immunotherapy.

Science.gov (United States)

Satitsuksanoa, Pattraporn; Głobińska, Anna; Jansen, Kirstin; van de Veen, Willem; Akdis, Mübeccel

2018-02-16

During the past few decades, modified allergens have been developed for use in allergen-specific immunotherapy (AIT) with the aim to improve efficacy and reduce adverse effects. This review aims to provide an overview of the different types of modified allergens, their mechanism of action and their potential for improving AIT. In-depth research in the field of allergen modifications as well as the advance of recombinant DNA technology have paved the way for improved diagnosis and research on human allergic diseases. A wide range of structurally modified allergens has been generated including allergen peptides, chemically altered allergoids, adjuvant-coupled allergens, and nanoparticle-based allergy vaccines. These modified allergens show promise for the development of AIT regimens with improved safety and long-term efficacy. Certain modifications ensure reduced IgE reactivity and retained T cell reactivity, which facilities induction of immune tolerance to the allergen. To date, multiple clinical trials have been performed using modified allergens. Promising results were obtained for the modified cat, grass and birch pollen, and house dust mite allergens. The use of modified allergens holds promise for improving AIT efficacy and safety. There is however a need for larger clinical studies to reliably assess the added benefit for the patient of using modified allergens for AIT.

Deep learning with convolutional neural network in radiology.

Science.gov (United States)

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

2018-04-01

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Therapeutic deep brain stimulation in Parkinsonian rats directly influences motor cortex.

Science.gov (United States)

Li, Qian; Ke, Ya; Chan, Danny C W; Qian, Zhong-Ming; Yung, Ken K L; Ko, Ho; Arbuthnott, Gordon W; Yung, Wing-Ho

2012-12-06

Much recent discussion about the origin of Parkinsonian symptoms has centered around the idea that they arise with the increase of beta frequency waves in the EEG. This activity may be closely related to an oscillation between subthalamic nucleus (STN) and globus pallidus. Since STN is the target of deep brain stimulation, it had been assumed that its action is on the nucleus itself. By means of simultaneous recordings of the firing activities from populations of neurons and the local field potentials in the motor cortex of freely moving Parkinsonian rats, this study casts doubt on this assumption. Instead, we found evidence that the corrective action is upon the cortex, where stochastic antidromic spikes originating from the STN directly modify the firing probability of the corticofugal projection neurons, destroy the dominance of beta rhythm, and thus restore motor control to the subjects, be they patients or rodents. Copyright © 2012 Elsevier Inc. All rights reserved.

Photon diffractive dissociation in deep inelastic scattering

International Nuclear Information System (INIS)

Ryskin, M.G.

1990-01-01

The new ep-collider HERA gives us the possibility to study the diffractive dissociation of virtual photon in deep inelastic ep-collision. The process of photon dissociation in deep inelastic scattering is the most direct way to measure the value of triple-pomeron vertex G 3P . It was shown that the value of the correct bare vertex G 3P may more than 4 times exceeds its effective value measuring in the triple-reggeon region and reaches the value of about 40-50% of the elastic pp-pomeron vertex. On the contrary in deep inelastic processes the perpendicular momenta q t of the secondary particles are large enough. Thus in deep inelastic reactions one can measure the absolute value of G 3P vertex in the most direct way and compare its value and q t dependence with the leading log QCD predictions

The fabrication of silicon nanostructures by local gallium implantation and cryogenic deep reactive ion etching

International Nuclear Information System (INIS)

Chekurov, N; Grigoras, K; Franssila, S; Tittonen, I; Peltonen, A

2009-01-01

We show that gallium-ion-implanted silicon serves as an etch mask for fabrication of high aspect ratio nanostructures by cryogenic plasma etching (deep reactive ion etching). The speed of focused ion beam (FIB) patterning is greatly enhanced by the fact that only a thin approx. 30 nm surface layer needs to be modified to create a mask for the etching step. Etch selectivity between gallium-doped and undoped material is at least 1000:1, greatly decreasing the mask erosion problems. The resolution of the combined FIB-DRIE process is 20 lines μm -1 with the smallest masked feature size of 40 nm. The maximum achieved aspect ratio is 15:1 (e.g. 600 nm high pillars 40 nm in diameter).

Ubiquitous healthy diatoms in the deep sea confirm deep carbon injection by the biological pump

KAUST Repository

Agusti, Susana

2015-07-09

The role of the ocean as a sink for CO2 is partially dependent on the downward transport of phytoplankton cells packaged within fast-sinking particles. However, whether such fast-sinking mechanisms deliver fresh organic carbon down to the deep bathypelagic sea and whether this mechanism is prevalent across the ocean requires confirmation. Here we report the ubiquitous presence of healthy photosynthetic cells, dominated by diatoms, down to 4,000 m in the deep dark ocean. Decay experiments with surface phytoplankton suggested that the large proportion (18%) of healthy photosynthetic cells observed, on average, in the dark ocean, requires transport times from a few days to a few weeks, corresponding to sinking rates (124–732 m d−1) comparable to those of fast-sinking aggregates and faecal pellets. These results confirm the expectation that fast-sinking mechanisms inject fresh organic carbon into the deep sea and that this is a prevalent process operating across the global oligotrophic ocean.

Ubiquitous healthy diatoms in the deep sea confirm deep carbon injection by the biological pump

KAUST Repository

Agusti, Susana; Gonzá lez-Gordillo, J. I.; Vaqué , D.; Estrada, M.; Cerezo, M. I.; Salazar, G.; Gasol, J. M.; Duarte, Carlos M.

2015-01-01

The role of the ocean as a sink for CO2 is partially dependent on the downward transport of phytoplankton cells packaged within fast-sinking particles. However, whether such fast-sinking mechanisms deliver fresh organic carbon down to the deep bathypelagic sea and whether this mechanism is prevalent across the ocean requires confirmation. Here we report the ubiquitous presence of healthy photosynthetic cells, dominated by diatoms, down to 4,000 m in the deep dark ocean. Decay experiments with surface phytoplankton suggested that the large proportion (18%) of healthy photosynthetic cells observed, on average, in the dark ocean, requires transport times from a few days to a few weeks, corresponding to sinking rates (124–732 m d−1) comparable to those of fast-sinking aggregates and faecal pellets. These results confirm the expectation that fast-sinking mechanisms inject fresh organic carbon into the deep sea and that this is a prevalent process operating across the global oligotrophic ocean.

The deep universe

CERN Document Server

Sandage, AR; Longair, MS

1995-01-01

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

Deep Vein Thrombosis

Centers for Disease Control (CDC) Podcasts

2012-04-05

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

Deep inelastic scattering

International Nuclear Information System (INIS)

Aubert, J.J.

1982-01-01

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

A choline derivate-modified nanoprobe for glioma diagnosis using MRI

Science.gov (United States)

Li, Jianfeng; Huang, Shixian; Shao, Kun; Liu, Yang; An, Sai; Kuang, Yuyang; Guo, Yubo; Ma, Haojun; Wang, Xuxia; Jiang, Chen

2013-04-01

Gadolinium (Gd) chelate contrast-enhanced magnetic resonance imaging (MRI) is a preferred method of glioma detection and preoperative localisation because it offers high spatial resolution and non-invasive deep tissue penetration. Gd-based contrast agents, such as Gd-diethyltriaminepentaacetic acid (DTPA-Gd, Magnevist), are widely used clinically for tumor diagnosis. However, the Gd-based MRI approach is limited for patients with glioma who have an uncompromised blood-brain barrier (BBB). Moreover, the rapid renal clearance and non-specificity of such contrast agents further hinders their prevalence. We present a choline derivate (CD)-modified nanoprobe with BBB permeability, glioma specificity and a long blood half-life. Specific accumulation of the nanoprobe in gliomas and subsequent MRI contrast enhancement are demonstrated in vitro in U87 MG cells and in vivo in a xenograft nude model. BBB and glioma dual targeting by this nanoprobe may facilitate precise detection of gliomas with an uncompromised BBB and may offer better preoperative and intraoperative tumor localization.

Outcomes of the DeepWind conceptual design

NARCIS (Netherlands)

Paulsen, US; Borg, M.; Madsen, HA; Pedersen, TF; Hattel, J; Ritchie, E.; Simao Ferreira, C.; Svendsen, H.; Berthelsen, P.A.; Smadja, C.

2015-01-01

DeepWind has been presented as a novel floating offshore wind turbine concept with cost reduction potentials. Twelve international partners developed a Darrieus type floating turbine with new materials and technologies for deep-sea offshore environment. This paper summarizes results of the 5 MW

Earthquakes - a danger to deep-lying repositories?

International Nuclear Information System (INIS)

2012-03-01

This booklet issued by the Swiss National Cooperative for the Disposal of Radioactive Waste NAGRA takes a look at geological factors concerning earthquakes and the safety of deep-lying repositories for nuclear waste. The geological processes involved in the occurrence of earthquakes are briefly looked at and the definitions for magnitude and intensity of earthquakes are discussed. Examples of damage caused by earthquakes are given. The earthquake situation in Switzerland is looked at and the effects of earthquakes on sub-surface structures and deep-lying repositories are discussed. Finally, the ideas proposed for deep-lying geological repositories for nuclear wastes are discussed

DRREP: deep ridge regressed epitope predictor.

Science.gov (United States)

Sher, Gene; Zhi, Degui; Zhang, Shaojie

2017-10-03

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

Parallel Distributed Processing Theory in the Age of Deep Networks.

Science.gov (United States)

Bowers, Jeffrey S

2017-12-01

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory. Copyright © 2017. Published by Elsevier Ltd.

DEEP VADOSE ZONE TREATABILITY TEST PLAN

International Nuclear Information System (INIS)

Chronister, G.B.; Truex, M.J.

2009-01-01

(sm b ullet) Treatability test plan published in 2008 (sm b ullet) Outlines technology treatability activities for evaluating application of in situ technologies and surface barriers to deep vadose zone contamination (technetium and uranium) (sm b ullet) Key elements - Desiccation testing - Testing of gas-delivered reactants for in situ treatment of uranium - Evaluating surface barrier application to deep vadose zone - Evaluating in situ grouting and soil flushing

Deep Learning in Visual Computing and Signal Processing

OpenAIRE

Xie, Danfeng; Zhang, Lei; Bai, Li

2017-01-01

Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply...

Deep Visual Attention Prediction

Science.gov (United States)

Wang, Wenguan; Shen, Jianbing

2018-05-01

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

High-pressure hydrogen respiration in hydrothermal vent samples from the deep biosphere

Science.gov (United States)

Morgan-Smith, D.; Schrenk, M. O.

2013-12-01

Cultivation of organisms from the deep biosphere has met with many challenges, chief among them the ability to replicate this extreme environment in a laboratory setting. The maintenance of in situ pressure levels, carbon sources, and gas concentrations are important, intertwined factors which may all affect the growth of subsurface microorganisms. Hydrogen in particular is of great importance in hydrothermal systems, but in situ hydrogen concentrations are largely disregarded in attempts to culture from these sites. Using modified Hungate-type culture tubes (Bowles et al. 2011) within pressure-retaining vessels, which allow for the dissolution of higher concentrations of gas than is possible with other culturing methods, we have incubated hydrothermal chimney and hydrothermally-altered rock samples from the Lost City and Mid-Cayman Rise hydrothermal vent fields. Hydrogen concentrations up to 15 mmol/kg have been reported from Lost City (Kelley et al. 2005), but data are not yet available from the recently-discovered Mid-Cayman site, and the elevated concentration of 30 mmol/kg is being used in all incubations. We are using a variety of media types to enrich for various metabolic pathways including iron and sulfur reduction under anoxic or microaerophilic conditions. Incubations are being carried out at atmospheric (0.1 MPa), in situ (9, 23, or 50 MPa, depending on site), and elevated (50 MPa) pressure levels. Microbial cell concentrations, taxonomic diversity, and metabolic activities are being monitored during the course of these experiments. These experiments will provide insight into the relationships between microbial activities, pressure, and gas concentrations typical of deep biosphere environments. Results will inform further culturing studies from both fresh and archived samples. References cited: Bowles, M.W., Samarkin, V.A., Joye, S.B. 2011. Improved measurement of microbial activity in deep-sea sediments at in situ pressure and methane concentration

Deep Brain Stimulation of the internal globus pallidus in refractory Tourette Syndrome.

Science.gov (United States)

Smeets, A Y J M; Duits, A A; Plantinga, B R; Leentjens, A F G; Oosterloo, M; Visser-Vandewalle, V; Temel, Y; Ackermans, L

2016-03-01

Deep Brain Stimulation in psychiatric disorders is becoming an increasingly performed surgery. At present, seven different targets have been stimulated in Tourette Syndrome, including the internal globus pallidus. We describe the effects on tics and comorbid behavioral disorders of Deep Brain Stimulation of the anterior internal globus pallidus in five patients with refractory Tourette Syndrome. This study was performed as an open label study with follow-up assessment between 12 and 38 months. Patients were evaluated twice, one month before surgery and at long-term follow-up. Primary outcome was tic severity, assessed by several scales. Secondary outcomes were comorbid behavioral disorders, mood and cognition. The final position of the active contacts of the implanted electrodes was investigated and side effects were reported. Three males and two females were included with a mean age of 41.6 years (SD 9.7). The total post-operative score on the Yale Global Tic Severity Scale was significantly lower than the pre-operative score (42.2±4.8 versus 12.8±3.8, P=0.043). There was also a significant reduction on the modified Rush Video-Based Tic Rating Scale (13.0±2.0 versus 7.0±1.6, P=0.041) and in the total number of video-rated tics (259.6±107.3 versus 49.6±24.8, P=0.043). No significant difference on the secondary outcomes was found, however, there was an improvement on an individual level for obsessive-compulsive behavior. The final position of the active contacts was variable in our sample and no relationship between position and stimulation effects could be established. Our study suggests that Deep Brain Stimulation of the anterior internal globus pallidus is effective in reducing tic severity, and possibly also obsessive-compulsive behavior, in refractory Tourette patients without serious adverse events or side-effects. Copyright © 2016 Elsevier B.V. All rights reserved.

Deep processes in non-relativistic confining potentials

International Nuclear Information System (INIS)

Fishbane, P.M.; Grisaru, M.T.

1978-01-01

The authors study deep inelastic and hard scattering processes for non-relativistic particles confined in deep potentials. The mechanisms by which the effects of confinement disappear and the particles scatter as if free are useful in understanding the analogous results for a relativistic field theory. (Auth.)

Deep Learning in Medical Image Analysis.

Science.gov (United States)

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

pathways to deep decarbonization - 2014 report

International Nuclear Information System (INIS)

Sachs, Jeffrey; Guerin, Emmanuel; Mas, Carl; Schmidt-Traub, Guido; Tubiana, Laurence; Waisman, Henri; Colombier, Michel; Bulger, Claire; Sulakshana, Elana; Zhang, Kathy; Barthelemy, Pierre; Spinazze, Lena; Pharabod, Ivan

2014-09-01

The Deep Decarbonization Pathways Project (DDPP) is a collaborative initiative to understand and show how individual countries can transition to a low-carbon economy and how the world can meet the internationally agreed target of limiting the increase in global mean surface temperature to less than 2 degrees Celsius (deg. C). Achieving the 2 deg. C limit will require that global net emissions of greenhouse gases (GHG) approach zero by the second half of the century. This will require a profound transformation of energy systems by mid-century through steep declines in carbon intensity in all sectors of the economy, a transition we call 'deep decarbonization.' Successfully transition to a low-carbon economy will require unprecedented global cooperation, including a global cooperative effort to accelerate the development and diffusion of some key low carbon technologies. As underscored throughout this report, the results of the DDPP analyses remain preliminary and incomplete. The DDPP proceeds in two phases. This 2014 report describes the DDPP's approach to deep decarbonization at the country level and presents preliminary findings on technically feasible pathways to deep decarbonization, utilizing technology assumptions and timelines provided by the DDPP Secretariat. At this stage we have not yet considered the economic and social costs and benefits of deep decarbonization, which will be the topic for the next report. The DDPP is issuing this 2014 report to the UN Secretary-General Ban Ki-moon in support of the Climate Leaders' Summit at the United Nations on September 23, 2014. This 2014 report by the Deep Decarbonization Pathway Project (DDPP) summarizes preliminary findings of the technical pathways developed by the DDPP Country Research Partners with the objective of achieving emission reductions consistent with limiting global warming to less than 2 deg. C., without, at this stage, consideration of economic and social costs and benefits. The DDPP is a knowledge

Evolutionary process of deep-sea bathymodiolus mussels.

Directory of Open Access Journals (Sweden)

Jun-Ichi Miyazaki

Full Text Available BACKGROUND: Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. METHODOLOGY/PRINCIPAL FINDING: We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI and NADH dehydrogenase subunit 4 (ND4 genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. CONCLUSIONS/SIGNIFICANCE: The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of

Deep water recycling through time.

Science.gov (United States)

Magni, Valentina; Bouilhol, Pierre; van Hunen, Jeroen

2014-11-01

We investigate the dehydration processes in subduction zones and their implications for the water cycle throughout Earth's history. We use a numerical tool that combines thermo-mechanical models with a thermodynamic database to examine slab dehydration for present-day and early Earth settings and its consequences for the deep water recycling. We investigate the reactions responsible for releasing water from the crust and the hydrated lithospheric mantle and how they change with subduction velocity ( v s ), slab age ( a ) and mantle temperature (T m ). Our results show that faster slabs dehydrate over a wide area: they start dehydrating shallower and they carry water deeper into the mantle. We parameterize the amount of water that can be carried deep into the mantle, W (×10 5 kg/m 2 ), as a function of v s (cm/yr), a (Myrs), and T m (°C):[Formula: see text]. We generally observe that a 1) 100°C increase in the mantle temperature, or 2) ∼15 Myr decrease of plate age, or 3) decrease in subduction velocity of ∼2 cm/yr all have the same effect on the amount of water retained in the slab at depth, corresponding to a decrease of ∼2.2×10 5 kg/m 2 of H 2 O. We estimate that for present-day conditions ∼26% of the global influx water, or 7×10 8 Tg/Myr of H 2 O, is recycled into the mantle. Using a realistic distribution of subduction parameters, we illustrate that deep water recycling might still be possible in early Earth conditions, although its efficiency would generally decrease. Indeed, 0.5-3.7 × 10 8 Tg/Myr of H 2 O could still be recycled in the mantle at 2.8 Ga. Deep water recycling might be possible even in early Earth conditions We provide a scaling law to estimate the amount of H 2 O flux deep into the mantle Subduction velocity has a a major control on the crustal dehydration pattern.

On Modified Bar recursion

DEFF Research Database (Denmark)

Oliva, Paulo Borges

2002-01-01

Modified bar recursion is a variant of Spector's bar recursion which can be used to give a realizability interpretation of the classical axiom of dependent choice. This realizability allows for the extraction of witnesses from proofs of forall-exists-formulas in classical analysis. In this talk I...... shall report on results regarding the relationship between modified and Spector's bar recursion. I shall also show that a seemingly weak form of modified bar recursion is as strong as "full" modified bar recursion in higher types....

Stratification-Based Outlier Detection over the Deep Web

OpenAIRE

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S.; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribu...

Deep neural networks to enable real-time multimessenger astrophysics

Science.gov (United States)

George, Daniel; Huerta, E. A.

2018-02-01

Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field of research, there is a pressing need to increase the depth and speed of the algorithms used to enable these ground-breaking discoveries. We introduce Deep Filtering—a new scalable machine learning method for end-to-end time-series signal processing. Deep Filtering is based on deep learning with two deep convolutional neural networks, which are designed for classification and regression, to detect gravitational wave signals in highly noisy time-series data streams and also estimate the parameters of their sources in real time. Acknowledging that some of the most sensitive algorithms for the detection of gravitational waves are based on implementations of matched filtering, and that a matched filter is the optimal linear filter in Gaussian noise, the application of Deep Filtering using whitened signals in Gaussian noise is investigated in this foundational article. The results indicate that Deep Filtering outperforms conventional machine learning techniques, achieves similar performance compared to matched filtering, while being several orders of magnitude faster, allowing real-time signal processing with minimal resources. Furthermore, we demonstrate that Deep Filtering can detect and characterize waveform signals emitted from new classes of eccentric or spin-precessing binary black holes, even when trained with data sets of only quasicircular binary black hole waveforms. The results presented in this article, and the recent use of deep neural networks for the identification of optical transients in telescope data, suggests that deep learning can facilitate real-time searches of gravitational wave sources and their electromagnetic and astroparticle counterparts. In the subsequent article, the framework introduced herein is directly applied to identify and characterize gravitational wave events in real LIGO data.

Avalanches of sediment form deep-marine depositions

NARCIS (Netherlands)

Pohl, Florian|info:eu-repo/dai/nl/34309424X

2017-01-01

The deep ocean is the largest sedimentary system basin on the planet. It serves as the primary storage point for all terrestrially weathered sediment that makes it beyond the near-shore environment. These deep-marine offshore deposits have become a focus of attention in exploration due to the

Equivalent drawbead performance in deep drawing simulations

NARCIS (Netherlands)

Meinders, Vincent T.; Geijselaers, Hubertus J.M.; Huetink, Han

1999-01-01

Drawbeads are applied in the deep drawing process to improve the control of the material flow during the forming operation. In simulations of the deep drawing process these drawbeads can be replaced by an equivalent drawbead model. In this paper the usage of an equivalent drawbead model in the

Deep web search: an overview and roadmap

NARCIS (Netherlands)

Tjin-Kam-Jet, Kien; Trieschnigg, Rudolf Berend; Hiemstra, Djoerd

2011-01-01

We review the state-of-the-art in deep web search and propose a novel classification scheme to better compare deep web search systems. The current binary classification (surfacing versus virtual integration) hides a number of implicit decisions that must be made by a developer. We make these

Three-dimensional ultrasonography in the diagnosis of deep endometriosis.

Science.gov (United States)

Guerriero, Stefano; Saba, Luca; Ajossa, Silvia; Peddes, Cristina; Angiolucci, Marco; Perniciano, Maura; Melis, Gian Benedetto; Alcázar, Juan Luis

2014-06-01

In the use of 'tenderness-guided' transvaginal ultrasound, is the diagnostic accuracy of three-dimensional (3D) ultrasonography better than two-dimensional (2D) ultrasonography in the identification of deep endometriosis? Three-dimensional ultrasonography has a significantly higher diagnostic accuracy in the diagnosis of posterior locations of deep endometriosis without intestinal involvement, such as the uterosacral ligaments, vaginal and rectovaginal endometriosis. The only previous study of the diagnosis of posterior compartment endometriosis reported an poor sensitivity of 3D ultrasonography for uterosacral and sigmoid colon involvement. This diagnostic test study included 202 patients scheduled for surgery because of clinical suspicion of deep pelvic endometriosis and was carried out between January 2009 and September 2012. Modified transvaginal ultrasonography was performed on all of the women by a single examiner. Two locations of deep endometriosis were considered: intestinal involvement and other posterior lesions (including vaginal location, rectovaginal septum and uterosacral ligaments). Once the 2D ultrasonography had been performed, the 3D acquisition was performed and the obtained volume was stored. To avoid the risk of recall bias, the same operator evaluated the 3D volumes 6 months after the last examination using virtual navigation to provide a presumptive diagnosis of the presence and localization of deep endometriosis. In addition, to evaluate the reproducibility of 3D, two operators with different levels of expertise performed a retrospective review of 3D volumes from a random sample of 35 patients, twice, 1 week apart to also assess intraobserver agreement. The diagnostic performance of both tests was expressed as area under the receiver-operating characteristics curve (AUC), sensitivity, specificity, positive and negative predictive values, positive (LR+) and negative (LR-) likelihood ratios, with their respective 95% confidence interval (CI

Age-dependent mixing of deep-sea sediments

International Nuclear Information System (INIS)

Smith, C.R.; Maggaard, L.; Pope, R.H.; DeMaster, D.J.

1993-01-01

Rates of bioturbation measured in deep-sea sediments commonly are tracer dependent; in particular, shorter lived radiotracers (such as 234 Th) often yield markedly higher diffusive mixing coefficients than their longer-lived counterparts (e.g., 210 Pb). At a single station in the 1,240-m deep Santa Catalina Basin, the authors document a strong negative correlation between bioturbation rate and tracer half-life. Sediment profiles of 234 Th (half-life = 24 days) yield an average mixing coefficient (60 cm 2 y -1 ) two orders of magnitude greater than that for 210 Pb (half-life = 22 y, mean mixing coefficient = 0.4 cm 2 y -1 ). A similar negative relationship between mixing rate and tracer time scale is observed at thirteen other deep-sea sites in which multiple radiotracers have been used to assess diffusive mixing rates. This relationship holds across a variety of radiotracer types and time scales. The authors hypothesize that this negative relationship results from age-dependent mixing, a process in which recently sedimented, food-rich particles are ingested and mixed at higher rates by deposit feeders than are older, food-poor particles. Results from an age-dependent mixing model demonstrate that this process indeed can yield the bioturbation-rate vs. tracer-time-scale correlations observed in deep-sea sediments. Field data on mixing rates of recently sedimented particles, as well as the radiotracer activity of deep-sea deposit feeders, provide strong support for the age-dependent mixing model. The presence of age-dependent mixing in deep-sea sediments may have major implications for diagenetic modeling, requiring a match between the characteristic time scales of mixing tracers and modeled reactants. 102 refs., 6 figs., 5 tabs

Deep-sea Hexactinellida (Porifera) of the Weddell Sea

Science.gov (United States)

Janussen, Dorte; Tabachnick, Konstantin R.; Tendal, Ole S.

2004-07-01

New Hexactinellida from the deep Weddel Sea are described. This moderately diverse hexactinellid fauna includes 14 species belonging to 12 genera, of which five species and one subgenus are new to science: Periphragella antarctica n. sp., Holascus pseudostellatus n. sp., Caulophacus (Caulophacus) discohexactinus n. sp., C. ( Caulodiscus) brandti n. sp., C. ( Oxydiscus) weddelli n. sp., and C. ( Oxydiscus) n. subgen. So far, 20 hexactinellid species have been reported from the deep Weddell Sea, 15 are known from the northern part and 10 only from here, while 10 came from the southern area, and five of these only from there. However, this apparent high "endemism" of Antarctic hexactinellid sponges is most likely the result of severe undersampling of the deep-sea fauna. We find no reason to believe that a division between an oceanic and a more continental group of species exists. The current poor database indicates that a substantial part of the deep hexactinellid fauna of the Weddell Sea is shared with other deep-sea regions, but it does not indicate a special biogeographic relationship with any other ocean.

Deepwater Program: Lophelia II, continuing ecological research on deep-sea corals and deep-reef habitats in the Gulf of Mexico

Science.gov (United States)

Demopoulos, Amanda W.J.; Ross, Steve W.; Kellogg, Christina A.; Morrison, Cheryl L.; Nizinski, Martha S.; Prouty, Nancy G.; Bourque, Jill R.; Galkiewicz, Julie P.; Gray, Michael A.; Springmann, Marcus J.; Coykendall, D. Katharine; Miller, Andrew; Rhode, Mike; Quattrini, Andrea; Ames, Cheryl L.; Brooke, Sandra D.; McClain Counts, Jennifer; Roark, E. Brendan; Buster, Noreen A.; Phillips, Ryan M.; Frometa, Janessy

2017-12-11

The deep sea is a rich environment composed of diverse habitat types. While deep-sea coral habitats have been discovered within each ocean basin, knowledge about the ecology of these habitats and associated inhabitants continues to grow. This report presents information and results from the Lophelia II project that examined deep-sea coral habitats in the Gulf of Mexico. The Lophelia II project focused on Lophelia pertusa habitats along the continental slope, at depths ranging from 300 to 1,000 meters. The chapters are authored by several scientists from the U.S. Geological Survey, National Oceanic and Atmospheric Administration, University of North Carolina Wilmington, and Florida State University who examined the community ecology (from microbes to fishes), deep-sea coral age, growth, and reproduction, and population connectivity of deep-sea corals and inhabitants. Data from these studies are presented in the chapters and appendixes of the report as well as in journal publications. This study was conducted by the Ecosystems Mission Area of the U.S. Geological Survey to meet information needs identified by the Bureau of Ocean Energy Management.

Deep learning quick reference useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

CERN Document Server

Bernico, Michael

2018-01-01

This book is a practical guide to applying deep neural networks including MLPs, CNNs, LSTMs, and more in Keras and TensorFlow. Packed with useful hacks to solve real-world challenges along with the supported math and theory around each topic, this book will be a quick reference for training and optimize your deep neural networks.

Deep inelastic scattering

International Nuclear Information System (INIS)

Zakharov, V.I.

1977-01-01

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

The DEEP-South: Scheduling and Data Reduction Software System

Science.gov (United States)

Yim, Hong-Suh; Kim, Myung-Jin; Bae, Youngho; Moon, Hong-Kyu; Choi, Young-Jun; Roh, Dong-Goo; the DEEP-South Team

2015-08-01

The DEep Ecliptic Patrol of the Southern sky (DEEP-South), started in October 2012, is currently in test runs with the first Korea Microlensing Telescope Network (KMTNet) 1.6 m wide-field telescope located at CTIO in Chile. While the primary objective for the DEEP-South is physical characterization of small bodies in the Solar System, it is expected to discover a large number of such bodies, many of them previously unknown.An automatic observation planning and data reduction software subsystem called "The DEEP-South Scheduling and Data reduction System" (the DEEP-South SDS) is currently being designed and implemented for observation planning, data reduction and analysis of huge amount of data with minimum human interaction. The DEEP-South SDS consists of three software subsystems: the DEEP-South Scheduling System (DSS), the Local Data Reduction System (LDR), and the Main Data Reduction System (MDR). The DSS manages observation targets, makes decision on target priority and observation methods, schedules nightly observations, and archive data using the Database Management System (DBMS). The LDR is designed to detect moving objects from CCD images, while the MDR conducts photometry and reconstructs lightcurves. Based on analysis made at the LDR and the MDR, the DSS schedules follow-up observation to be conducted at other KMTNet stations. In the end of 2015, we expect the DEEP-South SDS to achieve a stable operation. We also have a plan to improve the SDS to accomplish finely tuned observation strategy and more efficient data reduction in 2016.

A Modeling Study of Deep Water Renewal in the Red Sea

Science.gov (United States)

Yao, F.; Hoteit, I.

2016-02-01

Deep water renewal processes in the Red Sea are examined in this study using a 50-year numerical simulation from 1952-2001. The deep water in the Red Sea below the thermocline ( 200 m) exhibits a near-uniform vertical structure in temperature and salinity, but geochemical tracer distributions, such as 14C and 3He, and dissolved oxygen concentrations indicate that the deep water is renewed on time scales as short as 36 years. The renewal process is accomplished through a deep overturning cell that consists of a southward bottom current and a northward returning current at depths of 400-600 m. Three sources regions are proposed for the formation of the deep water, including two deep outflows from the Gulfs of Aqaba and Suez and winter deep convections in the northern Red Sea. The MITgcm (MIT general circulation model), which has been used to simulate the shallow overturning circulations in the Red Sea, is configured in this study with increased resolutions in the deep water. During the 50 years of simulation, artificial passive tracers added in the model indicate that the deep water in the Red Sea was only episodically renewed during some anomalously cold years; two significant episodes of deep water renewal are reproduced in the winters of 1983 and 1992, in accordance with reported historical hydrographic observations. During these renewal events, deep convections reaching the bottom of the basin occurred, which further facilitated deep sinking of the outflows from the Gulfs of Aqaba and Suez. Ensuing spreading of the newly formed deep water along the bottom caused upward displacements of thermocline, which may have profound effects on the water exchanges in the Strait of Bab el Mandeb between the Red Sea and the Gulf of Aden and the functioning of the ecosystem in the Red Sea by changing the vertical distributions of nutrients.

Deep Borehole Field Test Research Activities at LBNL

Energy Technology Data Exchange (ETDEWEB)

Dobson, Patrick [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Tsang, Chin-Fu [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Kneafsey, Timothy [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Borglin, Sharon [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Piceno, Yvette [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Andersen, Gary [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Nakagawa, Seiji [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Nihei, Kurt [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Rutqvist, Jonny [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Doughty, Christine [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Reagan, Matthew [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2016-08-19

The goal of the U.S. Department of Energy Used Fuel Disposition’s (UFD) Deep Borehole Field Test is to drill two 5 km large-diameter boreholes: a characterization borehole with a bottom-hole diameter of 8.5 inches and a field test borehole with a bottom-hole diameter of 17 inches. These boreholes will be used to demonstrate the ability to drill such holes in crystalline rocks, effectively characterize the bedrock repository system using geophysical, geochemical, and hydrological techniques, and emplace and retrieve test waste packages. These studies will be used to test the deep borehole disposal concept, which requires a hydrologically isolated environment characterized by low permeability, stable fluid density, reducing fluid chemistry conditions, and an effective borehole seal. During FY16, Lawrence Berkeley National Laboratory scientists conducted a number of research studies to support the UFD Deep Borehole Field Test effort. This work included providing supporting data for the Los Alamos National Laboratory geologic framework model for the proposed deep borehole site, conducting an analog study using an extensive suite of geoscience data and samples from a deep (2.5 km) research borehole in Sweden, conducting laboratory experiments and coupled process modeling related to borehole seals, and developing a suite of potential techniques that could be applied to the characterization and monitoring of the deep borehole environment. The results of these studies are presented in this report.

Deep Borehole Field Test Research Activities at LBNL

International Nuclear Information System (INIS)

Dobson, Patrick; Tsang, Chin-Fu; Kneafsey, Timothy; Borglin, Sharon; Piceno, Yvette; Andersen, Gary; Nakagawa, Seiji; Nihei, Kurt; Rutqvist, Jonny; Doughty, Christine; Reagan, Matthew

2016-01-01

The goal of the U.S. Department of Energy Used Fuel Disposition's (UFD) Deep Borehole Field Test is to drill two 5 km large-diameter boreholes: a characterization borehole with a bottom-hole diameter of 8.5 inches and a field test borehole with a bottom-hole diameter of 17 inches. These boreholes will be used to demonstrate the ability to drill such holes in crystalline rocks, effectively characterize the bedrock repository system using geophysical, geochemical, and hydrological techniques, and emplace and retrieve test waste packages. These studies will be used to test the deep borehole disposal concept, which requires a hydrologically isolated environment characterized by low permeability, stable fluid density, reducing fluid chemistry conditions, and an effective borehole seal. During FY16, Lawrence Berkeley National Laboratory scientists conducted a number of research studies to support the UFD Deep Borehole Field Test effort. This work included providing supporting data for the Los Alamos National Laboratory geologic framework model for the proposed deep borehole site, conducting an analog study using an extensive suite of geoscience data and samples from a deep (2.5 km) research borehole in Sweden, conducting laboratory experiments and coupled process modeling related to borehole seals, and developing a suite of potential techniques that could be applied to the characterization and monitoring of the deep borehole environment. The results of these studies are presented in this report.

The biomass of the deep-sea benthopelagic plankton

Science.gov (United States)

Wishner, K. F.

1980-04-01

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

Evolving Deep Networks Using HPC

Energy Technology Data Exchange (ETDEWEB)

Young, Steven R. [ORNL, Oak Ridge; Rose, Derek C. [ORNL, Oak Ridge; Johnston, Travis [ORNL, Oak Ridge; Heller, William T. [ORNL, Oak Ridge; Karnowski, thomas P. [ORNL, Oak Ridge; Potok, Thomas E. [ORNL, Oak Ridge; Patton, Robert M. [ORNL, Oak Ridge; Perdue, Gabriel [Fermilab; Miller, Jonathan [Santa Maria U., Valparaiso

2017-01-01

While a large number of deep learning networks have been studied and published that produce outstanding results on natural image datasets, these datasets only make up a fraction of those to which deep learning can be applied. These datasets include text data, audio data, and arrays of sensors that have very different characteristics than natural images. As these “best” networks for natural images have been largely discovered through experimentation and cannot be proven optimal on some theoretical basis, there is no reason to believe that they are the optimal network for these drastically different datasets. Hyperparameter search is thus often a very important process when applying deep learning to a new problem. In this work we present an evolutionary approach to searching the possible space of network hyperparameters and construction that can scale to 18, 000 nodes. This approach is applied to datasets of varying types and characteristics where we demonstrate the ability to rapidly find best hyperparameters in order to enable practitioners to quickly iterate between idea and result.

Desalination Economic Evaluation Program (DEEP). User's manual

International Nuclear Information System (INIS)

2000-01-01

DEEP (formerly named ''Co-generation and Desalination Economic Evaluation'' Spreadsheet, CDEE) has been developed originally by General Atomics under contract, and has been used in the IAEA's feasibility studies. For further confidence in the software, it was validated in March 1998. After that, a user friendly version has been issued under the name of DEEP at the end of 1998. DEEP output includes the levelised cost of water and power, a breakdown of cost components, energy consumption and net saleable power for each selected option. Specific power plants can be modelled by adjustment of input data including design power, power cycle parameters and costs

Characterization of majority and minority carrier deep levels in p-type GaN:Mg grown by molecular beam epitaxy using deep level optical spectroscopy

International Nuclear Information System (INIS)

Armstrong, A.; Caudill, J.; Ringel, S. A.; Corrion, A.; Poblenz, C.; Mishra, U. K.; Speck, J. S.

2008-01-01

Deep level defects in p-type GaN:Mg grown by molecular beam epitaxy were characterized using steady-state photocapacitance and deep level optical spectroscopy (DLOS). Low frequency capacitance measurements were used to alleviate dispersion effects stemming from the deep Mg acceptor. Use of DLOS enabled a quantitative survey of both deep acceptor and deep donor levels, the latter being particularly important due to the limited understanding of minority carrier states for p-type GaN. Simultaneous electron and hole photoemissions resulted in a convoluted deep level spectrum that was decoupled by emphasizing either majority or minority carrier optical emission through control of the thermal filling time conditions. In this manner, DLOS was able to resolve and quantify the properties of deep levels residing near both the conduction and valence bandedges in the same sample. Bandgap states through hole photoemission were observed at E v +3.05 eV, E v +3.22 eV and E v +3.26 eV. Additionally, DLOS revealed levels at E c -3.24 eV and E c -2.97 eV through electron emission to the conduction band with the former attributed to the Mg acceptor itself. The detected deep donor concentration is less than 2% of activated [Mg] and demonstrates the excellent quality of the film

DeepFlavour in CMS

CERN Multimedia

CERN. Geneva

2017-01-01

Flavour-tagging of jets is an important task in collider based high energy physics and a field where machine learning tools are applied by all major experiments. A new tagger (DeepFlavour) was developed and commissioned in CMS that is based on an advanced machine learning procedure. A deep neural network is used to do multi-classification of jets that origin from a b-quark, two b-quarks, a c-quark, two c-quarks or light colored particles (u, d, s-quark or gluon). The performance was measured in both, data and simulation. The talk will also include the measured performance of all taggers in CMS. The different taggers and results will be discussed and compared with some focus on details of the newest tagger.

Deep bite malocclusion: exploration of the skeletal and dental factors

International Nuclear Information System (INIS)

Bhateja, N.K.; Fida, M.; Shaikh, A.

2016-01-01

Correction of deep bite is crucial for maintenance of dental hard and soft tissue structures and for prevention of temporomandibular joint disorders. Exploration of underlying skeletal and dental factors is essential for efficient and individualized treatment planning. To date etiological factors of dental and skeletal deep bite have not been explored in Pakistani orthodontic patients. The objectives of this study were to explore frequencies of dental and skeletal etiological factors in deep bite patients and to determine correlations amongst dental and skeletal etiological factors of deep bite. Methods: The study included a total of 113 subjects (males=35; females=78) with no craniofacial syndromes or prior orthodontic treatment. Pre-treatment orthodontic records were used to evaluate various dental and skeletal parameters. Descriptive statistics of each parameter were calculated. The various study parameters were correlated using Pearson's Correlation. Results: Deep curve of Spee was most frequently seen factor of dental deep bite (72.6%), followed by increased coronal length of upper incisors (28.3%), retroclined upper incisors (17.7%), retroclined lower incisors (8%) and increased coronal length of lower incisors (5.3%). Decreased gonial angle was most commonly found factor of skeletal deep bite (43.4%), followed by decreased mandibular plane angle (27.4%) and maxillary plane's clockwise rotation (26.5%). Frankfort mandibular plane angle and gonial angle showed a strong positive correlation (r=0.66, p=0.000). Conclusions: Reduced gonial angle is most frequently seen skeletal factor, signifying the importance of angulation and growth of ramus in development of deep bite. Deep curve of Spee is most frequently seen dental etiological component in deep bite subjects, hence signifying the importance of intruding the lower anterior teeth. (author)

Preliminary discussion on deep-sourced uranium metallogenesis and deep prospecting

International Nuclear Information System (INIS)

Huang Shijie

2006-01-01

Prospecting for hydrothermal type uranium deposits should be aimed at medium-to large-sized deposits, and be guided by mantle-sourced, superimposed, deep-sourced metallogenic theory and the establishment of a multifactor, composite, deep-sourced metallogenic model. The author suggests that hydrothermal uranium deposits may be classified into three genetic types, i.e. hydrothermal circulation concentration, postmagmatic hydrothermal and mantle fluid concentration. These types of uranium deposits are characterized by their own metallogenic features and are concentrated in the same mineralization-concentrated area forming a metallogenic series. Large-sized uranium ore fields and rich-large uranium deposits are usually closely associated with mantle-sourced metallogenesis and the formation of such uranium ore fields and deposits is characterized by specific and unique regional geologic environments. Recognition criteria of mantle-sourced metallogenesis are preliminarily proposed in the paper. It is pointed out that prospecting in the future should follow the metallogenic model proper for the specific genetic type, and the establishment of operable prospecting model to realize the model-guided prospecting. (authors)

Deep Ocean Contribution to Sea Level Rise

Science.gov (United States)

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

2017-12-01

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

Deep learning for SAR image formation

Science.gov (United States)

Mason, Eric; Yonel, Bariscan; Yazici, Birsen

2017-04-01

The recent success of deep learning has lead to growing interest in applying these methods to signal processing problems. This paper explores the applications of deep learning to synthetic aperture radar (SAR) image formation. We review deep learning from a perspective relevant to SAR image formation. Our objective is to address SAR image formation in the presence of uncertainties in the SAR forward model. We present a recurrent auto-encoder network architecture based on the iterative shrinkage thresholding algorithm (ISTA) that incorporates SAR modeling. We then present an off-line training method using stochastic gradient descent and discuss the challenges and key steps of learning. Lastly, we show experimentally that our method can be used to form focused images in the presence of phase uncertainties. We demonstrate that the resulting algorithm has faster convergence and decreased reconstruction error than that of ISTA.

Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network

Directory of Open Access Journals (Sweden)

Ira zulfa

2017-07-01

Full Text Available Sentiment analysis is a computational research of opinion sentiment and emotion which is expressed in textual mode. Twitter becomes the most popular communication device among internet users. Deep Learning is a new area of machine learning research. It aims to move machine learning closer to its main goal, artificial intelligence. The purpose of deep learning is to change the manual of engineering with learning. At its growth, deep learning has algorithms arrangement that focus on non-linear data representation. One of the machine learning methods is Deep Belief Network (DBN. Deep Belief Network (DBN, which is included in Deep Learning method, is a stack of several algorithms with some extraction features that optimally utilize all resources. This study has two points. First, it aims to classify positive, negative, and neutral sentiments towards the test data. Second, it determines the classification model accuracy by using Deep Belief Network method so it would be able to be applied into the tweet classification, to highlight the sentiment class of training data tweet in Bahasa Indonesia. Based on the experimental result, it can be concluded that the best method in managing tweet data is the DBN method with an accuracy of 93.31%, compared with  Naive Bayes method which has an accuracy of 79.10%, and SVM (Support Vector Machine method with an accuracy of 92.18%.

Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition

OpenAIRE

Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao

2016-01-01

A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...

Research Proposal for Distributed Deep Web Search

NARCIS (Netherlands)

Tjin-Kam-Jet, Kien

2010-01-01

This proposal identifies two main problems related to deep web search, and proposes a step by step solution for each of them. The first problem is about searching deep web content by means of a simple free-text interface (with just one input field, instead of a complex interface with many input

Short-term Memory of Deep RNN

OpenAIRE

Gallicchio, Claudio

2018-01-01

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

Biodiversity loss from deep-sea mining

OpenAIRE

C. L. Van Dover; J. A. Ardron; E. Escobar; M. Gianni; K. M. Gjerde; A. Jaeckel; D. O. B. Jones; L. A. Levin; H. Niner; L. Pendleton; C. R. Smith; T. Thiele; P. J. Turner; L. Watling; P. P. E. Weaver

2017-01-01

The emerging deep-sea mining industry is seen by some to be an engine for economic development in the maritime sector. The International Seabed Authority (ISA) – the body that regulates mining activities on the seabed beyond national jurisdiction – must also protect the marine environment from harmful effects that arise from mining. The ISA is currently drafting a regulatory framework for deep-sea mining that includes measures for environmental protection. Responsible mining increasingly stri...

Deep Predictive Models in Interactive Music

OpenAIRE

Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

2018-01-01

Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

THE TEMPORAL CHANGES IN THE EMISSION SPECTRUM OF COMET 9P/TEMPEL 1 AFTER DEEP IMPACT

International Nuclear Information System (INIS)

Jackson, William M.; Yang Xueliang; Shi Xiaoyu; Cochran, Anita L.

2009-01-01

The time dependence of the changes in the emission spectra of Comet 9P/Tempel 1 after Deep Impact is derived and discussed. This was a unique event because for the first time it gave astronomers the opportunity to follow the time history of the formation and decay of O( 1 S), OH, CN, C 2 , C 3 , NH, and NH 2 . Least-squares fits of a modified Haser model with constraints using known rate constants were fit to the observed data. In the case of OH, a simple two-step Haser model provides a reasonable fit to the observations. Fitting the emissions from O( 1 S), CN, C 2 , C 3 , NH, and NH 2 requires the addition of a delayed component to a regular two- or three-step Haser model. From this information, a picture of the Deep Impact encounter emerges where there is an initial formation of gas and dust, which is responsible for the prompt emission that occurs right after impact. A secondary source of gas starts later after impact when the initial dust has dissipated enough so that solar radiation can reach the surface of freshly exposed material. The implications of this and other results are discussed in terms of the structure and composition of the comet's nucleus.

Recent changes in the deep-water fish populations of Lake Michigan

Science.gov (United States)

Moffett, James W.

1957-01-01

The deep-water fish fauna of Lake Michigan consisted of lake trout (Salvelinus namaycush), burbot (Lota lota maculosa), seven species of chubs or deep-water ciscoes (Leucichthys spp.), and the deep-water sculpin (Myoxocephalus quadricornis). Other species occupied the deep-water zone but were not typically part of the fauna.

Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

Science.gov (United States)

Burt, Jeremy R; Torosdagli, Neslisah; Khosravan, Naji; RaviPrakash, Harish; Mortazi, Aliasghar; Tissavirasingham, Fiona; Hussein, Sarfaraz; Bagci, Ulas

2018-04-10

Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

Stable isotope geochemistry of deep sea cherts

Energy Technology Data Exchange (ETDEWEB)

Kolodny, Y; Epstein, S [California Inst. of Tech., Pasadena (USA). Div. of Geological Sciences

1976-10-01

Seventy four samples of DSDP (Deep Sea Drilling Project) recovered cherts of Jurassic to Miocene age from varying locations, and 27 samples of on-land exposed cherts were analyzed for the isotopic composition of their oxygen and hydrogen. These studies were accompanied by mineralogical analyses and some isotopic analyses of the coexisting carbonates. delta/sup 18/0 of chert ranges between 27 and 39 parts per thousand relative to SMOW, delta/sup 18/0 of porcellanite - between 30 and 42 parts per thousand. The consistent enrichment of opal-CT in porcellanites in /sup 18/0 with respect to coexisting microcrystalline quartz in chert is probably a reflection of a different temperature (depth) of diagenesis of the two phases. delta/sup 18/0 of deep sea cherts generally decrease with increasing age, indicating an overall cooling of the ocean bottom during the last 150 m.y. A comparison of this trend with that recorded by benthonic foraminifera (Douglas et al., Initial Reports of the Deep Sea Drilling Project; 32:509(1975)) indicates the possibility of delta/sup 18/0 in deep sea cherts not being frozen in until several tens of millions of years after deposition. Cherts of any Age show a spread of delta/sup 18/0 values, increasing diagenesis being reflected in a lowering of delta/sup 18/0. Drusy quartz has the lowest delta/sup 18/0 values. On land exposed cherts are consistently depleted in /sup 18/0 in comparison to their deep sea time equivalent cherts. Water extracted from deep sea cherts ranges between 0.5 and 1.4 wt%. deltaD of this water ranges between -78 and -95 parts per thousand and is not a function of delta/sup 18/0 of the cherts (or the temperature of their formation).

Distributed deep learning networks among institutions for medical imaging.

Science.gov (United States)

Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

2018-03-29

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

Deep learning for studies of galaxy morphology

Science.gov (United States)

Tuccillo, D.; Huertas-Company, M.; Decencière, E.; Velasco-Forero, S.

2017-06-01

Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.

Deep-Learning-Based Drug-Target Interaction Prediction.

Science.gov (United States)

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

Deep inelastic scattering near the Coulomb barrier

International Nuclear Information System (INIS)

Gehring, J.; Back, B.; Chan, K.

1995-01-01

Deep inelastic scattering was recently observed in heavy ion reactions at incident energies near and below the Coulomb barrier. Traditional models of this process are based on frictional forces and are designed to predict the features of deep inelastic processes at energies above the barrier. They cannot be applied at energies below the barrier where the nuclear overlap is small and friction is negligible. The presence of deep inelastic scattering at these energies requires a different explanation. The first observation of deep inelastic scattering near the barrier was in the systems 124,112 Sn + 58,64 Ni by Wolfs et al. We previously extended these measurements to the system 136 Xe + 64 Ni and currently measured the system 124 Xe + 58 Ni. We obtained better statistics, better mass and energy resolution, and more complete angular coverage in the Xe + Ni measurements. The cross sections and angular distributions are similar in all of the Sn + Ni and Xe + Ni systems. The data are currently being analyzed and compared with new theoretical calculations. They will be part of the thesis of J. Gehring

Deep Phenotyping: Deep Learning For Temporal Phenotype/Genotype Classification

OpenAIRE

Najafi, Mohammad; Namin, Sarah; Esmaeilzadeh, Mohammad; Brown, Tim; Borevitz, Justin

2017-01-01

High resolution and high throughput, genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. Complex developmental phenotypes are observed by imaging a variety of accessions in different environment conditions, however extracting the genetically heritable traits is challenging. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long-Short Term Memories (LSTMs), h...

Deep imitation learning for 3D navigation tasks.

Science.gov (United States)

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

Characterization of modified clinoptilolite

International Nuclear Information System (INIS)

Novosad, J.; Jandl, J.; Woollins, J.D.

1992-01-01

Samples of clinoptilolite were modified using insoluble hexacyanoferrate from aqueous solution. The modified samples were characterized by elemental analysis, powder X-ray diffraction, solid state NMR and vibrational spectroscopy. The sorption properties of modified clinoptilolite were studied, too. Higher affinity for 137 Cs sorption in comparison with the natural clinoptilolite has been proved. (author) 5 refs.; 3 figs.; 2 tabs

Detecting atrial fibrillation by deep convolutional neural networks.

Science.gov (United States)

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Description of new systems for acquiring in situ data from deep wells

International Nuclear Information System (INIS)

Peyrus, J.C.; Vinson, J.M.

1984-02-01

The studies being made by CEA/IPSN in respect of storage sites in a granite medium require a knowledge of the physico-chemical parameters of the water in the geological formation, since these waters are the principal vectors of hydrogeological transfers. In order to attain this objective, the design, construction and operation of two new experimental systems for sampling water in a deep well without modifying the pressure of the fluid as well was undertaken, at the same time as the measurement of pH, oxidation-reduction potential and water temperature of the aqueous medium. A pressure-compensated combined pH-Eh electrode for completely representative measurements down to 2000 metres was studied. The first results obtained at the Auriat site in France show confrontation with water belonging to the sodium bicarbonate facies, which is rich in CO 2 , CH 4 and H 2 , and that pH and Eh vary as a function of the mineralogical nature of the granite and tectonic fractures

Denaturing of single electrospun fibrinogen fibers studied by deep ultraviolet fluorescence microscopy.

Science.gov (United States)

Kim, Jeongyong; Song, Hugeun; Park, Inho; Carlisle, Christine R; Bonin, Keith; Guthold, Martin

2011-03-01

Deep ultraviolet (DUV) microscopy is a fluorescence microscopy technique to image unlabeled proteins via the native fluorescence of some of their amino acids. We constructed a DUV fluorescence microscope, capable of 280 nm wavelength excitation by modifying an inverted optical microscope. Moreover, we integrated a nanomanipulator-controlled micropipette into this instrument for precise delivery of picoliter amounts of fluid to selected regions of the sample. In proof-of-principle experiments, we used this instrument to study, in situ, the effect of a denaturing agent on the autofluorescence intensity of single, unlabeled, electrospun fibrinogen nanofibers. Autofluorescence emission from the nanofibers was excited at 280 nm and detected at ∼350 nm. A denaturant solution was discretely applied to small, select sections of the nanofibers and a clear local reduction in autofluorescence intensity was observed. This reduction is attributed to the dissolution of the fibers and the unfolding of proteins in the fibers. Copyright © 2010 Wiley-Liss, Inc.

Deep Space Habitat Project

Data.gov (United States)

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

Deep Water Survey Data

Data.gov (United States)

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

Benchmarking Deep Learning Models on Large Healthcare Datasets.

Science.gov (United States)

Purushotham, Sanjay; Meng, Chuizheng; Che, Zhengping; Liu, Yan

2018-06-04

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models. Copyright © 2018 Elsevier Inc. All rights reserved.

Investigation of deep levels in GaInNAs

International Nuclear Information System (INIS)

Abulfotuh, F.; Balcioglu, A.; Friedman, D.; Geisz, J.; Kurtz, S.

1999-01-01

This paper presents and discusses the first Deep-Level transient spectroscopy (DLTS) data obtained from measurements carried out on both Schottky barriers and homojunction devices of GaInNAs. The effect of N and In doping on the electrical properties of the GaNInAs devices, which results in structural defects and interface states, has been investigated. Moreover, the location and densities of deep levels related to the presence of N, In, and N+In are identified and correlated with the device performance. The data confirmed that the presence of N alone creates a high density of shallow hole traps related to the N atom and structural defects in the device. Doping by In, if present alone, also creates low-density deep traps (related to the In atom and structural defects) and extremely deep interface states. On the other hand, the co-presence of In and N eliminates both the interface states and levels related to structural defects. However, the device still has a high density of the shallow and deep traps that are responsible for the photocurrent loss in the GaNInAs device, together with the possible short diffusion length. copyright 1999 American Institute of Physics

Investigation of Deep Levels in GaInNas

International Nuclear Information System (INIS)

Balcioglu, A.; Friedman, D.; Abulfotuh, F.; Geisz, J.; Kurtz, S.

1998-01-01

This paper presents and discusses the first Deep-Level transient spectroscopy (DLTS) data obtained from measurements carried out on both Schottky barriers and homojunction devices of GaInNAs. The effect of N and In doping on the electrical properties of the GaNInAs devices, which results in structural defects and interface states, has been investigated. Moreover, the location and densities of deep levels related to the presence of N, In, and N+In are identified and correlated with the device performance. The data confirmed that the presence of N alone creates a high density of shallow hole traps related to the N atom and structural defects in the device. Doping by In, if present alone, also creates low-density deep traps (related to the In atom and structural defects) and extremely deep interface states. On the other hand, the co-presence of In and N eliminates both the interface states and levels related to structural defects. However, the device still has a high density of the shallow and deep traps that are responsible for the photocurrent loss in the GaNInAs device, together with the possible short diffusion length

Predicting Process Behaviour using Deep Learning

OpenAIRE

Evermann, Joerg; Rehse, Jana-Rebecca; Fettke, Peter

2016-01-01

Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real da...

Strategic Technologies for Deep Space Transport

Science.gov (United States)

Litchford, Ronald J.

2016-01-01

Deep space transportation capability for science and exploration is fundamentally limited by available propulsion technologies. Traditional chemical systems are performance plateaued and require enormous Initial Mass in Low Earth Orbit (IMLEO) whereas solar electric propulsion systems are power limited and unable to execute rapid transits. Nuclear based propulsion and alternative energetic methods, on the other hand, represent potential avenues, perhaps the only viable avenues, to high specific power space transport evincing reduced trip time, reduced IMLEO, and expanded deep space reach. Here, key deep space transport mission capability objectives are reviewed in relation to STMD technology portfolio needs, and the advanced propulsion technology solution landscape is examined including open questions, technical challenges, and developmental prospects. Options for potential future investment across the full compliment of STMD programs are presented based on an informed awareness of complimentary activities in industry, academia, OGAs, and NASA mission directorates.

A deep learning / neuroevolution hybrid for visual control

DEFF Research Database (Denmark)

Poulsen, Andreas Precht; Thorhauge, Mark; Funch, Mikkel Hvilshj

2017-01-01

This paper presents a deep learning / neuroevolution hybrid approach called DLNE, which allows FPS bots to learn to aim & shoot based only on high-dimensional raw pixel input. The deep learning component is responsible for visual recognition and translating raw pixels to compact feature...... representations, while the evolving network takes those features as inputs to infer actions. The results suggest that combining deep learning and neuroevolution in a hybrid approach is a promising research direction that could make complex visual domains directly accessible to networks trained through evolution....

Assisted Diagnosis Research Based on Improved Deep Autoencoder

Directory of Open Access Journals (Sweden)

Ke Zhang-Han

2017-01-01

Full Text Available Deep Autoencoder has the powerful ability to learn features from large number of unlabeled samples and a small number of labeled samples. In this work, we have improved the network structure of the general deep autoencoder and applied it to the disease auxiliary diagnosis. We have achieved a network by entering the specific indicators and predicting whether suffering from liver disease, the network using real physical examination data for training and verification. Compared with the traditional semi-supervised machine learning algorithm, deep autoencoder will get higher accuracy.

An adaptive deep-coupled GNSS/INS navigation system with hybrid pre-filter processing

Science.gov (United States)

Wu, Mouyan; Ding, Jicheng; Zhao, Lin; Kang, Yingyao; Luo, Zhibin

2018-02-01

The deep-coupling of a global navigation satellite system (GNSS) with an inertial navigation system (INS) can provide accurate and reliable navigation information. There are several kinds of deeply-coupled structures. These can be divided mainly into coherent and non-coherent pre-filter based structures, which have their own strong advantages and disadvantages, especially in accuracy and robustness. In this paper, the existing pre-filters of the deeply-coupled structures are analyzed and modified to improve them firstly. Then, an adaptive GNSS/INS deeply-coupled algorithm with hybrid pre-filters processing is proposed to combine the advantages of coherent and non-coherent structures. An adaptive hysteresis controller is designed to implement the hybrid pre-filters processing strategy. The simulation and vehicle test results show that the adaptive deeply-coupled algorithm with hybrid pre-filters processing can effectively improve navigation accuracy and robustness, especially in a GNSS-challenged environment.

Revised estimate for the radiocarbon age of North Atlantic deep water

International Nuclear Information System (INIS)

Broecker, W.S.

1979-01-01

The extent to which the admixture of water of Antarctic origin influences the 14 C/C ratio in North Atlantic deep water (NADW) has been heretofore underestimated. When this correction is properly made, a ventilation time for the deep western Atlantic is reduced to only about 100 years. The production rate of the northern component of NADW entering the western basin must be of the order of 30 Sv. If this northern component water is assumed to be the major supplier of new 14 C to the deep sea, the carbon isotope ventilation time of the world deep ocean must be of the order of 900 years. However, since the new deep waters formed around the perimeter of the Antarctic are thought to enter the deep sea at a rate of about 20 Sv, the water ventilation time for the deep sea is of the order of 550 years

Urea impedimetric biosensing using electrospun nanofibers modified with zinc oxide nanoparticles

Science.gov (United States)

Migliorini, Fernanda L.; Sanfelice, Rafaela C.; Mercante, Luiza A.; Andre, Rafaela S.; Mattoso, Luiz H. C.; Correa, Daniel. S.

2018-06-01

Reliable analytical techniques to evaluate dairy products, including milk, are of outmost importance to ensure food safety against contaminants. Among possible substances employed as adulterants in milk, urea raises deep concern due to its harmful effects to consumer's health. In the present study, a biosensing platform was developed to be applied in the electrochemical detection of urea. The sensing platform was fabricated using polymeric electrospun nanofibers of polyamide 6 (PA6) and polypyrrole (PPy) deposited onto fluorine doped tin oxide (FTO) electrodes, which were then modified with zinc oxide nanoparticles (ZnO). This material showed excellent properties for the immobilization of urease enzyme, conferring the FTO/PA6/PPy/ZnO/urease electrode high sensitivity for urea detection within the concentration range between 0.1 and 250 mg dL-1 with a limit of detection of 0.011 mg dL-1. The results achieved evidence the potential of electrospun nanofibers-based electrodes for applications in biosensors aiming at dairy products analysis.

Training Deep Spiking Neural Networks Using Backpropagation.

Science.gov (United States)

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

The deep cerebral stimulation of the under thalamic nucleus modifies the cerebral metabolism in {sup 18}FDG-Tep of obsessive compulsive patients; La stimulation cerebrale profonde du noyau sous thalamique modifie le metabolisme cerebral en 18FDG-TEP des patients obsessionnels compulsifs

Energy Technology Data Exchange (ETDEWEB)

Le Jeune, F.; Garin, E. [Service de medecine nucleaire, centre Eugene-Marquis, Rennes, (France); Verin, M.; Peron, J. [service de neurologie, CHU Pontchaillou, Rennes, (France); Mallet, L.; Yelnik, J. [Inserm, Avenir Team, Behavior, Emotion and Basal Ganglia, IFR 70, Pitie-Salpetriere, Paris, (France); Kreps, M.O. [Inserm U796, service de psychiatrie, hopital Sainte-Anne, Paris, (France); Drapier, D.; Millet, B. [service de psychiatrie adulte, centre hospitalier Guillaume-Regnier, Rennes, (France)

2009-05-15

The aim of this work was to find again this orbito-frontal hyper metabolism among the resistant obsessive compulsive disorder patients that are going to benefit of a deep cerebral stimulation of the under thalamus nucleus and to demonstrate that this new therapy approach leads a reduction of the metabolism in this area in correlation with the clinical improvement. It is about the first study realized in isotopic functional imaging on ten resistant compulsive disorder patients treated by bilateral deep cerebral stimulation of the under thalamus nucleus. It shows that the treatment efficiency is in relation with a reduction of the glucide metabolism in the right orbito-frontal cortex. It suggests equally that the under thalamus nucleus would be functionally linked to the orbito-frontal cortex. (N.C.)

The deep Canary poleward undercurrent

Science.gov (United States)

Velez-Belchi, P. J.; Hernandez-Guerra, A.; González-Pola, C.; Fraile, E.; Collins, C. A.; Machín, F.

2012-12-01

Poleward undercurrents are well known features in Eastern Boundary systems. In the California upwelling system (CalCEBS), the deep poleward flow has been observed along the entire outer continental shelf and upper-slope, using indirect methods based on geostrophic estimates and also using direct current measurements. The importance of the poleward undercurrents in the CalCEBS, among others, is to maintain its high productivity by means of the transport of equatorial Pacific waters all the way northward to Vancouver Island and the subpolar gyre but there is also concern about the low oxygen concentration of these waters. However, in the case of the Canary Current Eastern Boundary upwelling system (CanCEBS), there are very few observations of the poleward undercurrent. Most of these observations are short-term mooring records, or drifter trajectories of the upper-slope flow. Hence, the importance of the subsurface poleward flow in the CanCEBS has been only hypothesized. Moreover, due to the large differences between the shape of the coastline and topography between the California and the Canary Current system, the results obtained for the CalCEBS are not completely applicable to the CanCEBS. In this study we report the first direct observations of the continuity of the deep poleward flow of the Canary Deep Poleward undercurrent (CdPU) in the North-Africa sector of the CanCEBS, and one of the few direct observations in the North-Africa sector of the Canary Current eastern boundary. The results indicate that the Canary Island archipelago disrupts the deep poleward undercurrent even at depths where the flow is not blocked by the bathymetry. The deep poleward undercurrent flows west around the eastern-most islands and north east of the Conception Bank to rejoin the intermittent branch that follows the African slope in the Lanzarote Passage. This hypothesis is consistent with the AAIW found west of Lanzarote, as far as 17 W. But also, this hypothesis would be coherent

Performance of deep geothermal energy systems

Science.gov (United States)

Manikonda, Nikhil

Geothermal energy is an important source of clean and renewable energy. This project deals with the study of deep geothermal power plants for the generation of electricity. The design involves the extraction of heat from the Earth and its conversion into electricity. This is performed by allowing fluid deep into the Earth where it gets heated due to the surrounding rock. The fluid gets vaporized and returns to the surface in a heat pipe. Finally, the energy of the fluid is converted into electricity using turbine or organic rankine cycle (ORC). The main feature of the system is the employment of side channels to increase the amount of thermal energy extracted. A finite difference computer model is developed to solve the heat transport equation. The numerical model was employed to evaluate the performance of the design. The major goal was to optimize the output power as a function of parameters such as thermal diffusivity of the rock, depth of the main well, number and length of lateral channels. The sustainable lifetime of the system for a target output power of 2 MW has been calculated for deep geothermal systems with drilling depths of 8000 and 10000 meters, and a financial analysis has been performed to evaluate the economic feasibility of the system for a practical range of geothermal parameters. Results show promising an outlook for deep geothermal systems for practical applications.

Deep-Sea Microbes: Linking Biogeochemical Rates to -Omics Approaches

Science.gov (United States)

Herndl, G. J.; Sintes, E.; Bayer, B.; Bergauer, K.; Amano, C.; Hansman, R.; Garcia, J.; Reinthaler, T.

2016-02-01

Over the past decade substantial progress has been made in determining deep ocean microbial activity and resolving some of the enigmas in understanding the deep ocean carbon flux. Also, metagenomics approaches have shed light onto the dark ocean's microbes but linking -omics approaches to biogeochemical rate measurements are generally rare in microbial oceanography and even more so for the deep ocean. In this presentation, we will show by combining metagenomics, -proteomics and biogeochemical rate measurements on the bulk and single-cell level that deep-sea microbes exhibit characteristics of generalists with a large genome repertoire, versatile in utilizing substrate as revealed by metaproteomics. This is in striking contrast with the apparently rather uniform dissolved organic matter pool in the deep ocean. Combining the different -omics approaches with metabolic rate measurements, we will highlight some major inconsistencies and enigmas in our understanding of the carbon cycling and microbial food web structure in the dark ocean.

Multiscale deep features learning for land-use scene recognition

Science.gov (United States)

Yuan, Baohua; Li, Shijin; Li, Ning

2018-01-01

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

Deep-water subsea lifting operations

Energy Technology Data Exchange (ETDEWEB)

Nestegaard, Arne; Boee, Tormod

2010-07-01

Significant costs are related to marine operations in the installation phase of deep water subsea field developments. In order to establish safe operational criteria and procedures for the installation, detailed planning is necessary, including numerical modelling and analysis of the environmental conditions and hydrodynamic loads on the installed object as well as the installation equipment. This paper presents recommendations for modelling and analysis of deep water subsea lifting operations developed for the new DNV RP-H103 [1]. During installation of subsea structures, the highest dynamic forces are most often encountered in the splash zone. Recommendations for estimation of maximum forces will be presented. For small structures and tools, installation through the moon pool of a small installation vessel is often preferred. Calculation methods for loading on structures installed through a moon pool will be presented. During intervention or installation in deep water a significant amplification of amplitude and forces can be experienced when the frequency range of vertical crane tip motion coincides with the natural vertical oscillation of the lift wire and load. Vertical resonance may reduce the operability of the operation. Simplified calculation methods for such operations are presented. (Author)

Deep-Elaborative Learning of Introductory Management Accounting for Business Students

Science.gov (United States)

Choo, Freddie; Tan, Kim B.

2005-01-01

Research by Choo and Tan (1990; 1995) suggests that accounting students, who engage in deep-elaborative learning, have a better understanding of the course materials. The purposes of this paper are: (1) to describe a deep-elaborative instructional approach (hereafter DEIA) that promotes deep-elaborative learning of introductory management…

Microbially-mediated fluorescent organic matter transformations in the deep ocean

DEFF Research Database (Denmark)

Aparicio, Fran L.; Nieto-Cid, Mar; Borrull, Encarna

2015-01-01

The refractory nature of marine dissolved organic matter (DOM) increases while it travels from surface waters to the deep ocean. This resistant fraction is in part composed of fluorescent humic-like material, which is relatively difficult to metabolize by deep water prokaryotes, and it can also b....... These findings contribute to the understanding of FDOM variability in deep waters and provide valuable information for studies where fluorescent compounds are used in order to track water masses and/or microbial processes.......The refractory nature of marine dissolved organic matter (DOM) increases while it travels from surface waters to the deep ocean. This resistant fraction is in part composed of fluorescent humic-like material, which is relatively difficult to metabolize by deep water prokaryotes, and it can also...

Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

Science.gov (United States)

Ubbens, Jordan R.; Stavness, Ian

2017-01-01

Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana. PMID:28736569

Deep-brain-stimulation does not impair deglutition in Parkinson's disease.

Science.gov (United States)

Lengerer, Sabrina; Kipping, Judy; Rommel, Natalie; Weiss, Daniel; Breit, Sorin; Gasser, Thomas; Plewnia, Christian; Krüger, Rejko; Wächter, Tobias

2012-08-01

A large proportion of patients with Parkinson's disease develop dysphagia during the course of the disease. Dysphagia in Parkinson's disease affects different phases of deglutition, has a strong impact on quality of life and may cause severe complications, i.e., aspirational pneumonia. So far, little is known on how deep-brain-stimulation of the subthalamic nucleus influences deglutition in PD. Videofluoroscopic swallowing studies on 18 patients with Parkinson's disease, which had been performed preoperatively, and postoperatively with deep-brain-stimulation-on and deep-brain-stimulation-off, were analyzed retrospectively. The patients were examined in each condition with three consistencies (viscous, fluid and solid). The 'New Zealand index for multidisciplinary evaluation of swallowing (NZIMES) Subscale One' for qualitative and 'Logemann-MBS-Parameters' for quantitative evaluation were assessed. Preoperatively, none of the patients presented with clinically relevant signs of dysphagia. While postoperatively, the mean daily levodopa equivalent dosage was reduced by 50% and deep-brain-stimulation led to a 50% improvement in motor symptoms measured by the UPDRS III, no clinically relevant influence of deep-brain-stimulation-on swallowing was observed using qualitative parameters (NZIMES). However quantitative parameters (Logemann scale) found significant changes of pharyngeal parameters with deep-brain-stimulation-on as compared to preoperative condition and deep-brain-stimulation-off mostly with fluid consistency. In Parkinson patients without dysphagia deep-brain-stimulation of the subthalamic nucleus modulates the pharyngeal deglutition phase but has no clinically relevant influence on deglutition. Further studies are needed to test if deep-brain-stimulation is a therapeutic option for patients with swallowing disorders. Copyright © 2012 Elsevier Ltd. All rights reserved.

Deep venous thrombosis of the upper extremity. A review

DEFF Research Database (Denmark)

Klitfod, Lotte; Broholm, R; Baekgaard, N

2013-01-01

Upper extremity deep venous thrombosis (UEDVT) occurs either spontaneously, as a consequence of strenuous upper limb activity (also known as the Paget-Schroetter syndrome) or secondary to an underlying cause. Primary and secondary UEDVT differs in long-term sequelae and mortality. This review...... to the condition. Malignancy and therapeutic interventions are major risk factors for the secondary deep vein thrombosis in combination with the patient's characteristics, comorbidities and prior history of deep vein thrombosis. Complications: recurrent deep venous thrombosis, pulmonary embolism and Post....... Treatment modalities and strategies: the treatment modalities include anticoagulation therapy, catheter-directed thrombolysis, surgical decompression, percutaneous transluminal angioplasty and stenting and they may be combined. However, the optimal treatment and timing of treatment remains controversial...

Approximate Inference and Deep Generative Models

CERN Multimedia

CERN. Geneva

2018-01-01

Advances in deep generative models are at the forefront of deep learning research because of the promise they offer for allowing data-efficient learning, and for model-based reinforcement learning. In this talk I'll review a few standard methods for approximate inference and introduce modern approximations which allow for efficient large-scale training of a wide variety of generative models. Finally, I'll demonstrate several important application of these models to density estimation, missing data imputation, data compression and planning.

Deep Learning for Video Game Playing

OpenAIRE

Justesen, Niels; Bontrager, Philip; Togelius, Julian; Risi, Sebastian

2017-01-01

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces...

Deep Learning For Sequential Pattern Recognition

OpenAIRE

Safari, Pooyan

2013-01-01

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM) In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with ...

Deep mycoses in Amazon region.

Science.gov (United States)

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

1988-09-01

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

Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

Science.gov (United States)

Maskey, M.; Ramachandran, R.; Miller, J.

2017-12-01

Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

Deep learning—Accelerating Next Generation Performance Analysis Systems?

Directory of Open Access Journals (Sweden)

Heike Brock

2018-02-01

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

Plastic microfibre ingestion by deep-sea organisms

Science.gov (United States)

Taylor, M. L.; Gwinnett, C.; Robinson, L. F.; Woodall, L. C.

2016-09-01

Plastic waste is a distinctive indicator of the world-wide impact of anthropogenic activities. Both macro- and micro-plastics are found in the ocean, but as yet little is known about their ultimate fate and their impact on marine ecosystems. In this study we present the first evidence that microplastics are already becoming integrated into deep-water organisms. By examining organisms that live on the deep-sea floor we show that plastic microfibres are ingested and internalised by members of at least three major phyla with different feeding mechanisms. These results demonstrate that, despite its remote location, the deep sea and its fragile habitats are already being exposed to human waste to the extent that diverse organisms are ingesting microplastics.

Deep Learning Microscopy

KAUST Repository

Rivenson, Yair

2017-05-12

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

Deep groundwater quantity and quality in the southwestern US

Science.gov (United States)

Kang, M.; Ayars, J. E.; Jackson, R. B.

2017-12-01

Groundwater demands are growing in many arid regions and adaptation through the use of non-traditional resources during extreme droughts is increasingly common. One such resource is deep groundwater, which we define as deeper than 300 m and up to several kilometer-depths. Although deep groundwater has been studied in the context of oil and gas, geothermal, waste disposal, and other uses, it remains poorly characterized, especially for the purposes of human consumption and irrigation uses. Therefore, we evaluate deep groundwater quantity and quality within these contexts. We compile and analyze data from water management agencies and oil and gas-based sources for the southwestern US, with a detailed look at California's Central Valley. We also use crop tolerance thresholds to evaluate deep groundwater quality for irrigation purposes. We find fresh and usable groundwater volume estimates in California's Central Valley to increase by three- and four-fold respectively when depths of up to 3 km are considered. Of the ten basins in the southwestern US with the most data, we find that the Great Basin has the greatest proportions of fresh and usable deep groundwater. Given the potentially large deep groundwater volumes, it is important to characterize the resource, guard against subsidence where extracted, and protect it for use in decades and centuries to come.

An Ensemble of Deep Support Vector Machines for Image Categorization

NARCIS (Netherlands)

Abdullah, Azizi; Veltkamp, Remco C.; Wiering, Marco

2009-01-01

This paper presents the deep support vector machine (D-SVM) inspired by the increasing popularity of deep belief networks for image recognition. Our deep SVM trains an SVM in the standard way and then uses the kernel activations of support vectors as inputs for training another SVM at the next

Deep water characteristics and circulation in the South China Sea

Science.gov (United States)

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

2018-04-01

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

Focused Crawling of the Deep Web Using Service Class Descriptions

Energy Technology Data Exchange (ETDEWEB)

Rocco, D; Liu, L; Critchlow, T

2004-06-21

Dynamic Web data sources--sometimes known collectively as the Deep Web--increase the utility of the Web by providing intuitive access to data repositories anywhere that Web access is available. Deep Web services provide access to real-time information, like entertainment event listings, or present a Web interface to large databases or other data repositories. Recent studies suggest that the size and growth rate of the dynamic Web greatly exceed that of the static Web, yet dynamic content is often ignored by existing search engine indexers owing to the technical challenges that arise when attempting to search the Deep Web. To address these challenges, we present DynaBot, a service-centric crawler for discovering and clustering Deep Web sources offering dynamic content. DynaBot has three unique characteristics. First, DynaBot utilizes a service class model of the Web implemented through the construction of service class descriptions (SCDs). Second, DynaBot employs a modular, self-tuning system architecture for focused crawling of the DeepWeb using service class descriptions. Third, DynaBot incorporates methods and algorithms for efficient probing of the Deep Web and for discovering and clustering Deep Web sources and services through SCD-based service matching analysis. Our experimental results demonstrate the effectiveness of the service class discovery, probing, and matching algorithms and suggest techniques for efficiently managing service discovery in the face of the immense scale of the Deep Web.

State of HIV in the US Deep South.

Science.gov (United States)

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.

Quantitative phenotyping via deep barcode sequencing.

Science.gov (United States)

Smith, Andrew M; Heisler, Lawrence E; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J; Chee, Mark; Roth, Frederick P; Giaever, Guri; Nislow, Corey

2009-10-01

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or "Bar-seq," outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that approximately 20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

Gene expression in the deep biosphere.

Science.gov (United States)

Orsi, William D; Edgcomb, Virginia P; Christman, Glenn D; Biddle, Jennifer F

2013-07-11

Scientific ocean drilling has revealed a deep biosphere of widespread microbial life in sub-seafloor sediment. Microbial metabolism in the marine subsurface probably has an important role in global biogeochemical cycles, but deep biosphere activities are not well understood. Here we describe and analyse the first sub-seafloor metatranscriptomes from anaerobic Peru Margin sediment up to 159 metres below the sea floor, represented by over 1 billion complementary DNA (cDNA) sequence reads. Anaerobic metabolism of amino acids, carbohydrates and lipids seem to be the dominant metabolic processes, and profiles of dissimilatory sulfite reductase (dsr) transcripts are consistent with pore-water sulphate concentration profiles. Moreover, transcripts involved in cell division increase as a function of microbial cell concentration, indicating that increases in sub-seafloor microbial abundance are a function of cell division across all three domains of life. These data support calculations and models of sub-seafloor microbial metabolism and represent the first holistic picture of deep biosphere activities.

Exploration in the Deep water Niger Delta: Technical to Business Perspectives

International Nuclear Information System (INIS)

Feeley, M.H.

2002-01-01

Prolific source rocks, high quality deep water reservoirs and a high technical success rate in finding hydrocarbons make the Nigeria deep water one of the top exploration opportunities in the world. Several major discoveries have resulted from exploration on blocks awarded in 1993. Enthusiastic participation by industry in the 2000 Tender Round clearly indicates the continuing appeal of deep water exploration in Nigeria.Commercially, challenges still exist in the Nigerian deep water. Industry has spent more than $2 Billion USD on exploration and appraisal, yet only a handful of developments are moving forward to development. First oil from the deep water is not expected until 2004, 11 years after acreage award and 8 years after discovery. Tougher economic terms, OPEC quota constraints, an abundance of deep water gas, lengthy approval processes and high up-front bonus and exploration costs challenge the economic returns on deep water gas, lengthy approval processes and high up-front bonus and exploration costs challenge the economic returns on deep water investments. Will deep water exploration, development and production deliver the financial returns industry expected when it signed up for the blocks 10 years ago? What are the indications for the 2000 Tender Round blocks?A good explorer learns form experience. What can be learned technically and commercially by looking back over the results of the last 10 years of exploration in Nigeria's deep water? A perspective is provided on the successes, the failures and the challenges to be overcome in realizing the commercial potential of the basin

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

Science.gov (United States)

Purkey, S. G.; Llovel, W.

2017-12-01

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

Performance Evaluation of Deep Learning Tools in Docker Containers

OpenAIRE

Xu, Pengfei; Shi, Shaohuai; Chu, Xiaowen

2017-01-01

With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to adapt to new hardware features and software libraries, which bring a big challenge for end users and system administrators. To address this problem, container techniques are widely used to simplify the deployment and management of deep learning software. However, it remains unknown whether container techniques brin...

Warming combined with more extreme precipitation regimes modifies the water sources used by trees.

Science.gov (United States)

Grossiord, Charlotte; Sevanto, Sanna; Dawson, Todd E; Adams, Henry D; Collins, Adam D; Dickman, Lee T; Newman, Brent D; Stockton, Elizabeth A; McDowell, Nate G

2017-01-01

The persistence of vegetation under climate change will depend on a plant's capacity to exploit water resources. We analyzed water source dynamics in piñon pine and juniper trees subjected to precipitation reduction, atmospheric warming, and to both simultaneously. Piñon and juniper exhibited different and opposite shifts in water uptake depth in response to experimental stress and background climate over 3 yr. During a dry summer, juniper responded to warming with a shift to shallow water sources, whereas piñon pine responded to precipitation reduction with a shift to deeper sources in autumn. In normal and wet summers, both species responded to precipitation reduction, but juniper increased deep water uptake and piñon increased shallow water uptake. Shifts in the utilization of water sources were associated with reduced stomatal conductance and photosynthesis, suggesting that belowground compensation in response to warming and water reduction did not alleviate stress impacts for gas exchange. We have demonstrated that predicted climate change could modify water sources of trees. Warming impairs juniper uptake of deep sources during extended dry periods. Precipitation reduction alters the uptake of shallow sources following extended droughts for piñon. Shifts in water sources may not compensate for climate change impacts on tree physiology. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

The Iceland Deep Drilling Project (IDDP): (I) Status and Future Plans.

Science.gov (United States)

Elders, W. A.; Fridleifsson, G. O.; Bird, D. K.; Schiffman, P.; Zierenberg, R.; Reed, M. H.

2006-12-01

The IDDP represents a challenging step forward in the worldwide development of geothermal energy by assessing the potential of power production from natural supercritical fluids. A feasibility study in 2003 concluded that in order to reach fluids at temperatures of >400°C drilling to depths of 4 to 5 km is necessary, but the resultant superheated steam should have a power output ten times that of conventional subcritical steam with the same volumetric flow rate. A consortium of leading Icelandic energy companies together with a government agency, the Icelandic Energy Authority, is carrying out the IDDP. In late 2003 a member of the consortium offered a planned exploratory well to the IDDP for deepening. This is in a geothermal system that produces hydrothermally modified seawater on the Reykjanes peninsula, in southern Iceland, where the Mid-Atlantic Ridge comes on land. Processes at depth at Reykjanes should be similar to those responsible for black smokers on ocean spreading centers. This well reached 3.1 km in February 2005, and research on the downhole samples began. Unfortunately the well became plugged during a flow test and was abandoned in February 2006 after attempts to recondition it failed. This led to the IDDP deciding to move the site for the first deep borehole to Krafla, near the northern end of the central rift zone of Iceland, within a volcanic caldera that has had recent volcanic activity. The Krafla geothermal system has higher temperature gradients than at Reykjanes but produces hydrothermally modified meteoric water with magmatic gases. The drill site chosen is near an existing well that encountered 340°C at only 2.5 km depth. It will be rotary drilled with spot coring to 3.5 km depth, and then deepened to ~4.5 km, using continuous wireline coring for scientific purposes. However, given the competition for drilling rigs internationally, and the year-long lead times in obtaining specialized well casings, it will be a year before IDDP begins

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

Science.gov (United States)

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.

Active Cooling of Oil after Deep-frying.

Science.gov (United States)

Totani, Nagao; Yasaki, Naoko; Doi, Rena; Hasegawa, Etsuko

2017-10-01

Oil used for deep-frying is often left to stand after cooking. A major concern is oxidation during standing that might be avoidable, especially in the case of oil used repeatedly for commercial deep-frying as this involves large volumes that are difficult to cool in a conventional fryer. This paper describes a method to minimize oil oxidation. French fries were deep-fried and the oil temperature decreased in a manner typical for a commercial deep-fryer. The concentration of polar compounds generated from thermally oxidized oil remarkably increased at temperature higher than 100°C but little oxidation occurred below 60°C. Heating the oil showed that the peroxide and polar compound content did not increase when the oil was actively cooled using a running water-cooled Graham-type condenser system to cool the oil from 180°C to room temperature within 30 min. When French fries were fried and the oil was then immediately cooled using the condenser, the polar compound content during cooling did not increase. Our results demonstrate that active cooling of heated oil is simple and quite effective for inhibiting oxidation.

Deep sedation during pneumatic reduction of intussusception.

Science.gov (United States)

Ilivitzki, Anat; Shtark, Luda Glozman; Arish, Karin; Engel, Ahuva

2012-05-01

Pneumatic reduction of intussusception under fluoroscopic guidance is a routine procedure. The unsedated child may resist the procedure, which may lengthen its duration and increase the radiation dose. We use deep sedation during the procedure to overcome these difficulties. The purpose of this study was to summarize our experience with deep sedation during fluoroscopic reduction of intussusception and assess the added value and complication rate of deep sedation. All children with intussusception who underwent pneumatic reduction in our hospital between January 2004 and June 2011 were included in this retrospective study. Anesthetists sedated the children using propofol. The fluoroscopic studies, ultrasound (US) studies and the childrens' charts were reviewed. One hundred thirty-one attempted reductions were performed in 119 children, of which 121 (92%) were successful and 10 (8%) failed. Two perforations (1.5%) occurred during attempted reduction. Average fluoroscopic time was 1.5 minutes. No complication to sedation was recorded. Deep sedation with propofol did not add any complication to the pneumatic reduction. The fluoroscopic time was short. The success rate of reduction was high,raising the possibility that sedation is beneficial, possibly by smooth muscle relaxation.

Optimizing interplanetary trajectories with deep space maneuvers

Science.gov (United States)

Navagh, John

1993-09-01

Analysis of interplanetary trajectories is a crucial area for both manned and unmanned missions of the Space Exploration Initiative. A deep space maneuver (DSM) can improve a trajectory in much the same way as a planetary swingby. However, instead of using a gravitational field to alter the trajectory, the on-board propulsion system of the spacecraft is used when the vehicle is not near a planet. The purpose is to develop an algorithm to determine where and when to use deep space maneuvers to reduce the cost of a trajectory. The approach taken to solve this problem uses primer vector theory in combination with a non-linear optimizing program to minimize Delta(V). A set of necessary conditions on the primer vector is shown to indicate whether a deep space maneuver will be beneficial. Deep space maneuvers are applied to a round trip mission to Mars to determine their effect on the launch opportunities. Other studies which were performed include cycler trajectories and Mars mission abort scenarios. It was found that the software developed was able to locate quickly DSM's which lower the total Delta(V) on these trajectories.

Deep inelastic scattering near the Coulomb barrier

Energy Technology Data Exchange (ETDEWEB)

Gehring, J.; Back, B.; Chan, K. [and others

1995-08-01

Deep inelastic scattering was recently observed in heavy ion reactions at incident energies near and below the Coulomb barrier. Traditional models of this process are based on frictional forces and are designed to predict the features of deep inelastic processes at energies above the barrier. They cannot be applied at energies below the barrier where the nuclear overlap is small and friction is negligible. The presence of deep inelastic scattering at these energies requires a different explanation. The first observation of deep inelastic scattering near the barrier was in the systems {sup 124,112}Sn + {sup 58,64}Ni by Wolfs et al. We previously extended these measurements to the system {sup 136}Xe + {sup 64}Ni and currently measured the system {sup 124}Xe + {sup 58}Ni. We obtained better statistics, better mass and energy resolution, and more complete angular coverage in the Xe + Ni measurements. The cross sections and angular distributions are similar in all of the Sn + Ni and Xe + Ni systems. The data are currently being analyzed and compared with new theoretical calculations. They will be part of the thesis of J. Gehring.

Magnetic Solid-phase Extraction with Fe₃O₄/Molecularly Imprinted Polymers Modified by Deep Eutectic Solvents and Ionic Liquids for the Rapid Purification of Alkaloid Isomers (Theobromine and Theophylline) from Green Tea.

Science.gov (United States)

Li, Guizhen; Wang, Xiaoqin; Row, Kyung Ho

2017-06-25

Different kinds of deep eutectic solvents (DES) based on choline chloride (ChCl) and ionic liquids (ILs) based on 1-methylimidazole were used to modify Fe3O4/molecularly imprinted polymers (Fe3O4/MIPs), and the resulting materials were applied for the rapid purification of alkaloid isomers (theobromine and theophylline) from green tea with magnetic solid-phase extraction (M-SPE). The M-SPE procedure was optimized using the response surface methodology (RSM) to analyze the maximum conditions. The materials were characterized by Fourier transform infrared spectroscopy (FI-IR) and field emission scanning electron microscopy (FE-SEM). Compared to the ILs-Fe3O4/MIPs, the DESs-Fe3O4/MIPs were developed for the stronger recognition and higher recoveries of the isomers (theophylline and theobromine) from green tea, particularly DES-7-Fe3O4/MIPs. With RSM, the optimal recovery condition for theobromine and theophylline in the M-SPE were observed with ratio of methanol (80%) as the washing solution, methanol/acetic acid (HAc) (8:2) as the eluent at pH 3, and an eluent volume of 4 mL. The practical recoveries of theobromine and theophylline in green tea were 92.27% and 87.51%, respectively, with a corresponding actual extraction amount of 4.87 mg•g-1 and 5.07 mg•g-1. Overall, the proposed approach with the high affinity of Fe3O4/MIPs might offer a novel method for the purification of complex isomer samples.

Applying a punch with microridges in multistage deep drawing processes.

Science.gov (United States)

Lin, Bor-Tsuen; Yang, Cheng-Yu

2016-01-01

The developers of high aspect ratio components aim to minimize the processing stages in deep drawing processes. This study elucidates the application of microridge punches in multistage deep drawing processes. A microridge punch improves drawing performance, thereby reducing the number of stages required in deep forming processes. As an example, the original eight-stage deep forming process for a copper cylindrical cup with a high aspect ratio was analyzed by finite element simulation. Microridge punch designs were introduced in Stages 4 and 7 to replace the original punches. In addition, Stages 3 and 6 were eliminated. Finally, these changes were verified through experiments. The results showed that the microridge punches reduced the number of deep drawing stages yielding similar thickness difference percentages. Further, the numerical and experimental results demonstrated good consistency in the thickness distribution.

Shifted-modified Chebyshev filters

OpenAIRE

ŞENGÜL, Metin

2013-01-01

This paper introduces a new type of filter approximation method that utilizes shifted-modified Chebyshev filters. Construction of the new filters involves the use of shifted-modified Chebyshev polynomials that are formed using the roots of conventional Chebyshev polynomials. The study also includes 2 tables containing the shifted-modified Chebyshev polynomials and the normalized element values for the low-pass prototype filters up to degree 6. The transducer power gain, group dela...

Deep inelastic lepton scattering

International Nuclear Information System (INIS)

Nachtmann, O.

1977-01-01

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

Gamma-rays from deep inelastic collisions

International Nuclear Information System (INIS)

Stephens, F.S.

1979-01-01

The γ-rays associated with deep inelastic collisions can give information about the magnitude and orientation of the angular momentum transferred in these events. In this review, special emphasis is placed on understanding the origin and nature of these γ-rays in order to avoid some of the ambiguities that can arise. The experimental information coming from these γ-ray studies is reviewed, and compared briefly with that obtained by other methods and also with the expectations from current models for deep inelastic collisions. 15 figures

Omni-directional reflectors for deep blue LED using symmetric autocloning method

Science.gov (United States)

Chen, Sheng-Hui; Chen, Chun-Ko; Huang, Yu-Chia; Lee, Cheng-Chung

2013-03-01

Omni-directional reflectors (ODRs) for deep blue LED were designed and fabricated using symmetric autocloning method. The symmetric stack multi-layers for the reflectors were designed by finite-difference time-domain simulation. The fabricating process of ODR is combined with the techniques of anodic aluminum oxide (AAO) process and autocloning method. The two-dimensional structure template of nano-channel array was grown using AAO with the period of 150 nm. Then the shaping layer was deposited on the AAO template by evaporation deposition. Besides, the ion etching was applied to modify the apex angle to the triangle shape at 100°. Finally, the sub/(0.5TiO2 SiO2 0.5TiO2)8 multi-layer stack was deposited on the shaping layer using autocloning method to achieve the ODR. The results show the reflective spectra of ODR at the incident angles of 0, 30, 45, and 60° had high values within the range 400-450 nm. Besides, the central wavelength shifting is not obvious which is very good for keeping the color of LED stable.

Genetic diversity of archaea in deep-sea hydrothermal vent environments.

Science.gov (United States)

Takai, K; Horikoshi, K

1999-08-01

Molecular phylogenetic analysis of naturally occurring archaeal communities in deep-sea hydrothermal vent environments was carried out by PCR-mediated small subunit rRNA gene (SSU rDNA) sequencing. As determined through partial sequencing of rDNA clones amplified with archaea-specific primers, the archaeal populations in deep-sea hydrothermal vent environments showed a great genetic diversity, and most members of these populations appeared to be uncultivated and unidentified organisms. In the phylogenetic analysis, a number of rDNA sequences obtained from deep-sea hydrothermal vents were placed in deep lineages of the crenarchaeotic phylum prior to the divergence of cultivated thermophilic members of the crenarchaeota or between thermophilic members of the euryarchaeota and members of the methanogen-halophile clade. Whole cell in situ hybridization analysis suggested that some microorganisms of novel phylotypes predicted by molecular phylogenetic analysis were likely present in deep-sea hydrothermal vent environments. These findings expand our view of the genetic diversity of archaea in deep-sea hydrothermal vent environments and of the phylogenetic organization of archaea.

Automated analysis of high-content microscopy data with deep learning.

Science.gov (United States)

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Bipolar gene flow in deep-sea benthic foraminifera

DEFF Research Database (Denmark)

Pawlowski, J.; Fahrni, J.; Lecroq, B.

2007-01-01

Despite its often featureless appearance, the deep-ocean floor includes some of the most diverse habitats on Earth. However, the accurate assessment of global deep-sea diversity is impeded by a paucity of data on the geographical ranges of bottom-dwelling species, particularly at the genetic leve...

DeepWind - from Idea to 5 MW Concept

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Aagaard Madsen, Helge; Kragh, Knud Abildgaard

2014-01-01

The DeepWind concept has been described previously on challenges and potentials, this new offshore floating technology can offer to the wind industry [1]. The paper describes state of the art design improvements, new simulation results of the DeepWind floating vertical axis wind turbine concept...

Modified SEAGULL

Science.gov (United States)

Salas, M. D.; Kuehn, M. S.

1994-01-01

Original version of program incorporated into program SRGULL (LEW-15093) for use on National Aero-Space Plane project, its duty being to model forebody, inlet, and nozzle portions of vehicle. However, real-gas chemistry effects in hypersonic flow fields limited accuracy of that version, because it assumed perfect-gas properties. As a result, SEAGULL modified according to real-gas equilibrium-chemistry methodology. This program analyzes two-dimensional, hypersonic flows of real gases. Modified version of SEAGULL maintains as much of original program as possible, and retains ability to execute original perfect-gas version.

Geotechnical aspects of deep ocean radioactive waste disposal

International Nuclear Information System (INIS)

Freeman, T.J.

1990-01-01

The methods that might be used to bury radioactive waste in the deep ocean, and their likely effect on the sediment barrier, have been the subject of an international research program performed during the last ten years. This paper reviews the geotechnical aspects of deep ocean disposal and discusses how far the research performed has gone towards providing the information needed to assess this form of disposal. Considerable progress has been made during the course of the international program towards understanding the processes involved in the emplacement of heat generating waste (HGW) into the deep ocean bed and the subsequent interactions between the waste and the sediments. These processes do not appear to have a deleterious effect on the barrier properties of the sediments, and it is concluded that it is likely that HGW could be emplaced in the deep ocean in such a way that the seabed would provide an effective containment for the radionuclides

Deep Neural Network-Based Chinese Semantic Role Labeling

Institute of Scientific and Technical Information of China (English)

ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

2017-01-01

A recent trend in machine learning is to use deep architec-tures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network - based model is designed and optimized, particularly for smart phone applications. Ex-periment results on all the NLP sub - tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requir-ing real-time response, highlighting the potential of the pro-posed solution for practical NLP systems.

Recursive Monte Carlo method for deep-penetration problems

International Nuclear Information System (INIS)

Goldstein, M.; Greenspan, E.

1980-01-01

The Recursive Monte Carlo (RMC) method developed for estimating importance function distributions in deep-penetration problems is described. Unique features of the method, including the ability to infer the importance function distribution pertaining to many detectors from, essentially, a single M.C. run and the ability to use the history tape created for a representative region to calculate the importance function in identical regions, are illustrated. The RMC method is applied to the solution of two realistic deep-penetration problems - a concrete shield problem and a Tokamak major penetration problem. It is found that the RMC method can provide the importance function distributions, required for importance sampling, with accuracy that is suitable for an efficient solution of the deep-penetration problems considered. The use of the RMC method improved, by one to three orders of magnitude, the solution efficiency of the two deep-penetration problems considered: a concrete shield problem and a Tokamak major penetration problem. 8 figures, 4 tables

Deep Learning in the Automotive Industry: Applications and Tools

OpenAIRE

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

2017-01-01

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

Microbiological characterization of deep geological compartments

International Nuclear Information System (INIS)

Barsotti, V.; Sergeant, C.; Vesvres, M.H.; Coulon, S.; Joulian, C.; Garrido, F.; Ollivier, B.

2012-01-01

Document available in extended abstract form only. Microbial life in deep sediments and Earth's crust is now acknowledged by the scientific world. The deep subsurface biosphere contributes significantly to fundamental biogeochemical processes. However, despite great advances in geo-microbiological studies, deep terrestrial ecosystems are microbiologically poorly understood, mainly due to their inaccessibility. The drilling down to the base of the Triassic (1980 meters deep) in the geological formations of the eastern Paris Basin performed by ANDRA (EST433) in 2008 provides us a good opportunity to explore the deep biosphere. We conditioned the samples on the coring site, in as aseptic conditions as possible. In addition to storage at atmospheric pressure, a portion of the four Triassic samples was placed in a 190 bars pressurized chamber to investigate the influence of the conservation pressure factor on the found microflora. In parallel, in order to evaluate a potential bacterial contamination of the cores by the drilling fluids, samples of mud just before each sample drilling were taken and analyzed. The microbial exploration can be divided in two parts: - A cultural approach in different culture media for metabolic groups as methanogens, fermenters and sulphate reducing bacteria to stimulate their growth and to isolate microbial cells still viable. - A molecular approach by direct extraction of genomic DNA from the geological samples to explore a larger biodiversity. The limits are here the difficulties to extract DNA from these low biomass containing rocks. After comparison and optimization of several DNA extraction methods, the bacterial diversity present in rock cores was analyzed using DGGE (Denaturating Gel Gradient Electrophoresis) and cloning. The detailed results of all these investigations will be presented: - Despite all 400 cultural conditions experimented (with various media, salinities, temperatures, conservation pressure, agitation), no viable and

Postoperative deep venous thrombosis in Japan. Incidence and prophylaxis

International Nuclear Information System (INIS)

Inada, K.; Shirai, N.; Hayashi, M.; Matsumoto, K.; Hirose, M.

1983-01-01

The incidence of postoperative deep venous thrombosis was investigated using the iodine-125-fibrinogen method in 256 patients undergoing major surgery. Deep venous thrombosis was found in 49 patients (15.3 percent), and nonfatal pulmonary embolism developed in one of seven patients in whom the thrombus extended to the popliteal vein. The same investigation was performed in 110 patients who wore a graduated compression stocking on one leg, with the other leg serving as a control. Deep venous thrombosis was found in 4 of 110 stockinged legs (3.6 percent) and in 16 of 110 control legs (14.5 percent). The incidence of deep venous thrombosis decreased significantly in patients who wore the stocking. An increase in femoral venous flow velocity was found in the stockinged legs by the Doppler method. The mean velocity of venous return by xenon-133 clearance was significantly greater in the stockinged legs than in the control legs. These findings were considered to support the efficacy of graduated compression stockings for the prevention of deep venous thrombosis

Deep Learning in Open Source Learning Streams

DEFF Research Database (Denmark)

Kjærgaard, Thomas

2016-01-01

This chapter presents research on deep learning in a digital learning environment and raises the question if digital instructional designs can catalyze deeper learning than traditional classroom teaching. As a theoretical point of departure the notion of ‘situated learning’ is utilized...... and contrasted to the notion of functionalistic learning in a digital context. The mechanism that enables deep learning in this context is ‘The Open Source Learning Stream’. ‘The Open Source Learning Stream’ is the notion of sharing ‘learning instances’ in a digital space (discussion board, Facebook group......, unistructural, multistructural or relational learning. The research concludes that ‘The Open Source Learning Stream’ can catalyze deep learning and that there are four types of ‘Open Source Learning streams’; individual/ asynchronous, individual/synchronous, shared/asynchronous and shared...

Pathways to deep decarbonization - Interim 2014 Report

International Nuclear Information System (INIS)

2014-01-01

The interim 2014 report by the Deep Decarbonization Pathways Project (DDPP), coordinated and published by IDDRI and the Sustainable Development Solutions Network (SDSN), presents preliminary findings of the pathways developed by the DDPP Country Research Teams with the objective of achieving emission reductions consistent with limiting global warming to less than 2 deg. C. The DDPP is a knowledge network comprising 15 Country Research Teams and several Partner Organizations who develop and share methods, assumptions, and findings related to deep decarbonization. Each DDPP Country Research Team has developed an illustrative road-map for the transition to a low-carbon economy, with the intent of taking into account national socio-economic conditions, development aspirations, infrastructure stocks, resource endowments, and other relevant factors. The interim 2014 report focuses on technically feasible pathways to deep decarbonization

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.

Science.gov (United States)

Hohman, Fred; Hodas, Nathan; Chau, Duen Horng

2017-05-01

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as "black-boxes" due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user's data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation

Energy Technology Data Exchange (ETDEWEB)

Hohman, Frederick M.; Hodas, Nathan O.; Chau, Duen Horng

2017-05-30

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as “black-boxes” due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user’s data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

Genetically modified foods and allergy.

Science.gov (United States)

Lee, T H; Ho, H K; Leung, T F

2017-06-01

2015 marked the 25th anniversary of the commercial use and availability of genetically modified crops. The area of planted biotech crops cultivated globally occupies a cumulative two billion hectares, equivalent to twice the land size of China or the United States. Foods derived from genetically modified plants are widely consumed in many countries and genetically modified soybean protein is extensively used in processed foods throughout the industrialised countries. Genetically modified food technology offers a possible solution to meet current and future challenges in food and medicine. Yet there is a strong undercurrent of anxiety that genetically modified foods are unsafe for human consumption, sometimes fuelled by criticisms based on little or no firm evidence. This has resulted in some countries turning away food destined for famine relief because of the perceived health risks of genetically modified foods. The major concerns include their possible allergenicity and toxicity despite the vigorous testing of genetically modified foods prior to marketing approval. It is imperative that scientists engage the public in a constructive evidence-based dialogue to address these concerns. At the same time, improved validated ways to test the safety of new foods should be developed. A post-launch strategy should be established routinely to allay concerns. Mandatory labelling of genetically modified ingredients should be adopted for the sake of transparency. Such ingredient listing and information facilitate tracing and recall if required.

Waste disposal in the deep ocean: An overview

International Nuclear Information System (INIS)

O'Connor, T.P.; Kester, D.R.; Burt, W.V.; Capuzzo, J.M.; Park, P.K.; Duedall, I.W.

1985-01-01

Incineration at sea, industrial and sewage waste disposal in the surface mixing zone, and disposal of low-level nuclear wastes, obsolete munitions, and nerve gas onto the seafloor have been the main uses of the deep sea for waste management. In 1981 the wastes disposed of in the deep sea consisted of 48 X 10/sup 4/ t of liquid industrial wastes and 2 X 10/sup 4/ t of sewage sludge by the United States; 1.5 X 10/sup 4/ t (solids) of sewage sludge by the Federal Republic of German; 5300 t of liquid industrial wastes by Denmark; 99 t of solid industrial wastes by the United Kingdom; and 9400 t of low-level radioactive wastes by several European countries. Also in 1981 at-sea incineration of slightly more than 10/sup 5/ t of organic wastes from Belgium, France, the Federal Republic of Germany, the Netherlands, Norway, Sweden, and the United Kingdom was carried out in the North Sea. Unique oceanographic features of the deep sea include its large dilution capacity; the long residence time of deep-sea water (on the order of 10/sup 2/ y); low biological productivity in the surface water of the open ocean (≅50 g m/sup -2/ of carbon per year); the existence of an oxygen minimum zone at several hundred meters deep in the mid-latitudes; and the abyssal-clay regions showing sedimentary records of tens of millions of years of slow, uninterrupted deposition of fine-grained clay. Any deep-sea waste disposal strategy must take into account oceanic processes and current scientific knowledge in order to attain a safe solution that will last for centuries

Deep borehole disposal of high-level radioactive waste.

Energy Technology Data Exchange (ETDEWEB)

Stein, Joshua S.; Freeze, Geoffrey A.; Brady, Patrick Vane; Swift, Peter N.; Rechard, Robert Paul; Arnold, Bill Walter; Kanney, Joseph F.; Bauer, Stephen J.

2009-07-01

Preliminary evaluation of deep borehole disposal of high-level radioactive waste and spent nuclear fuel indicates the potential for excellent long-term safety performance at costs competitive with mined repositories. Significant fluid flow through basement rock is prevented, in part, by low permeabilities, poorly connected transport pathways, and overburden self-sealing. Deep fluids also resist vertical movement because they are density stratified. Thermal hydrologic calculations estimate the thermal pulse from emplaced waste to be small (less than 20 C at 10 meters from the borehole, for less than a few hundred years), and to result in maximum total vertical fluid movement of {approx}100 m. Reducing conditions will sharply limit solubilities of most dose-critical radionuclides at depth, and high ionic strengths of deep fluids will prevent colloidal transport. For the bounding analysis of this report, waste is envisioned to be emplaced as fuel assemblies stacked inside drill casing that are lowered, and emplaced using off-the-shelf oilfield and geothermal drilling techniques, into the lower 1-2 km portion of a vertical borehole {approx}45 cm in diameter and 3-5 km deep, followed by borehole sealing. Deep borehole disposal of radioactive waste in the United States would require modifications to the Nuclear Waste Policy Act and to applicable regulatory standards for long-term performance set by the US Environmental Protection Agency (40 CFR part 191) and US Nuclear Regulatory Commission (10 CFR part 60). The performance analysis described here is based on the assumption that long-term standards for deep borehole disposal would be identical in the key regards to those prescribed for existing repositories (40 CFR part 197 and 10 CFR part 63).

Deep Belief Nets for Topic Modeling

DEFF Research Database (Denmark)

Maaløe, Lars; Arngren, Morten; Winther, Ole

2015-01-01

-formative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep be-lief nets (DBN) that both find low-dimensional latent representations for documents....... Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an on-line publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance...

Un paseo por la Deep Web

OpenAIRE

Ortega Castillo, Carlos

2018-01-01

Este documento busca presentar una mirada técnica e inclusiva a algunas de las tecnologías de interconexión desarrolladas en la DeepWeb, primero desde un punto de vista teórico y después con una breve introducción práctica. La desmitificación de los procesos desarrollados bajo la DeepWeb, brinda herramientas a los usuarios para esclarecer y construir nuevos paradigmas de sociedad, conocimiento y tecnología que aporten al desarrollo responsable de este tipo de redes y contribuyan al crecimi...

AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling.

Science.gov (United States)

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2016-09-01

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC.

Deep-inelastic electron-proton diffraction

International Nuclear Information System (INIS)

Dainton, J.B.

1995-11-01

Recent measurements by the H1 collaboration at HERA of the cross section for deep-inelastic electron-proton scattering in which the proton interacts with minimal energy transfer and limited 4-momentum transfer squared are presented in the form of the contribution F 2 D(3) to the proton structure function F 2 . By parametrising the cross section phenomenologically in terms of a leading effective Regge pole exchange and comparing the result with a similar parametrisation of hadronic pp physics, the proton interaction is demonstrated to be dominantly of a diffractive nature. The quantitative interpretation of the parametrisation in terms of the properties of an effective leading Regge pole exchange, the pomeron (IP), shows that there is no evidence for a 'harder' BFKL-motivated IP in such deep-inelastic proton diffraction. The total contribution of proton diffraction to deep-inelastic electron-proton scattering is measured to be ∝10% and to be rather insensitive to Bjorken-x and Q 2 . A first measurement of the partonic structure of diffractive exchange is presented. It is shown to be readily interpreted in terms of the exchange of gluons, and to suggest that the bulk of diffractive momentum transfer is carried by a leading gluon. (orig.)

Evaluation of deep drawing force under different friction conditions

OpenAIRE

Lăzărescu Lucian; Banabic Dorel

2017-01-01

The purpose of this study is to investigate the variation of the required punch load during the deep drawing process under different friction conditions. In this regards, several deep-drawing tests of cylindrical cups were conducted under four friction conditions at the tool–blank interface. The first case was the dry deep-drawing, considered as a reference friction condition, while in the other three cases hydraulic oil, lithium-based grease and animal fat were used as lubricants. For each f...

Barbabos Deep-Water Sponges

NARCIS (Netherlands)

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

1988-01-01

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

Storage tests on irradiated deep-frozen chickens

International Nuclear Information System (INIS)

Gruenewald, T.

1975-01-01

Salmonellae infections in deep-frozen roasting chicken can be dealt with by ionising radiation as this process involves hardly any heating of the product. Deep-frozen chickens irradiated with doses up to 800 krad were stored at -30 0 C for two years and were regularly submitted to sensory tests. There was no significant difference in quality between the irradiated samples and the non-irradiated controls. (orig.) [de

Viral infections as controlling factors for the deep biosphere? (Invited)

Science.gov (United States)

Engelen, B.; Engelhardt, T.; Sahlberg, M.; Cypionka, H.

2009-12-01

The marine deep biosphere represents the largest biotope on Earth. Throughout the last years, we have obtained interesting insights into its microbial community composition. However, one component that was completely overlooked so far is the viral inventory of deep-subsurface sediments. While viral infections were identified to have a major impact on the benthic microflora of deep-sea surface sediments (Danavaro et al. 2008), no studies were performed on deep-biosphere samples, so far. As grazers probably play only a minor role in anoxic and highly compressed deep sediments, viruses might be the main “predators” for indigenous microorganisms. Furthermore, the release of cell components, called “the viral shunt”, could have a major impact on the deep biosphere in providing labile organic compounds to non-infected microorganisms in these generally nutrient depleted sediments. However, direct counting of viruses in sediments is highly challenging due to the small size of viruses and the high background of small particles. Even molecular surveys using “universal” PCR primers that target phage-specific genes fail due to the vast phage diversity. One solution for this problem is the lysogenic viral life cycle as many bacteriophages integrate their DNA into the host genome. It is estimated that up to 70% of cultivated bacteria contain prophages within their genome. Therefore, culture collections (Batzke et al. 2007) represent an archive of the viral composition within the respective habitat. These prophages can be induced to become free phage particles in stimulation experiments in which the host cells are set under certain stress situations such as a treatment with UV exposure or DNA-damaging antibiotics. The study of the viral component within the deep biosphere offers to answer the following questions: To which extent are deep-biosphere populations controlled by viral infections? What is the inter- and intra-specific diversity and the host-specific viral

Deep vein thrombosis: a clinical review

Directory of Open Access Journals (Sweden)

Kesieme EB

2011-04-01

Full Text Available Emeka Kesieme1, Chinenye Kesieme2, Nze Jebbin3, Eshiobo Irekpita1, Andrew Dongo11Department of Surgery, Irrua Specialist Teaching Hospital, Irrua, Nigeria; 2Department of Paediatrics, Irrua Specialist Teaching Hospital, Irrua, Nigeria; 3Department of Surgery, University of Port Harcourt Teaching Hospital, Port-Harcourt, NigeriaBackground: Deep vein thrombosis (DVT is the formation of blood clots (thrombi in the deep veins. It commonly affects the deep leg veins (such as the calf veins, femoral vein, or popliteal vein or the deep veins of the pelvis. It is a potentially dangerous condition that can lead to preventable morbidity and mortality.Aim: To present an update on the causes and management of DVT.Methods: A review of publications obtained from Medline search, medical libraries, and Google.Results: DVT affects 0.1% of persons per year. It is predominantly a disease of the elderly and has a slight male preponderance. The approach to making a diagnosis currently involves an algorithm combining pretest probability, D-dimer testing, and compression ultrasonography. This will guide further investigations if necessary. Prophylaxis is both mechanical and pharmacological. The goals of treatment are to prevent extension of thrombi, pulmonary embolism, recurrence of thrombi, and the development of complications such as pulmonary hypertension and post-thrombotic syndrome.Conclusion: DVT is a potentially dangerous condition with a myriad of risk factors. Prophylaxis is very important and can be mechanical and pharmacological. The mainstay of treatment is anticoagulant therapy. Low-molecular-weight heparin, unfractionated heparin, and vitamin K antagonists have been the treatment of choice. Currently anticoagulants specifically targeting components of the common pathway have been recommended for prophylaxis. These include fondaparinux, a selective indirect factor Xa inhibitor and the new oral selective direct thrombin inhibitors (dabigatran and selective

A Deep Hydrographic Section Across the Tasman Sea.

Science.gov (United States)

1985-09-01

the same cruise, TC1, as that on which the magneto- telluric moorings (plus a RANRL recording current-meter) were deployed. A small number of deep...that of Wyrtki (1961) who described the different water masses of this area and the northward movement of deep waters from Antarctica. Boland and

Search for sterile neutrinos with IceCube DeepCore

Energy Technology Data Exchange (ETDEWEB)

Terliuk, Andrii [DESY, Platanenallee 6, 15738 Zeuthen (Germany); Collaboration: IceCube-Collaboration

2016-07-01

The DeepCore detector is a sub-array of the IceCube Neutrino Observatory that lowers the energy threshold for neutrino detection down to approximately 10 GeV. DeepCore is used for a variety of studies including atmospheric neutrino oscillations. The standard three-neutrino oscillation paradigm is tested using the DeepCore detector by searching for an additional light, sterile neutrino with a mass on the order of 1 eV. Sterile neutrinos do not interact with the ordinary matter, however they can be mixed with the three active neutrino states. Such mixture changes the picture of standard neutrino oscillations for atmospheric neutrinos with energies below 100 GeV. The capabilities of DeepCore detector to measure such sterile neutrino mixing will be presented in this talk.

Plant Species Identification by Bi-channel Deep Convolutional Networks

Science.gov (United States)

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

2018-04-01

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

Conceptual model for concrete long time degradation in a deep nuclear waste repository

Energy Technology Data Exchange (ETDEWEB)

Lagerblad, B; Traegaardh, J [Swedish Cement and Concrete Research Inst., Stockholm (Sweden)

1994-02-01

This report is mainly a state-of-the-art report of concrete long time durability in the environment expected in a deep site underground nuclear waste repository in Swedish crystalline bedrock. The report treats how the concrete and the surrounding groundwater will interact and how they will be affected by cement chemistry, type of aggregate etc. The different mechanisms for concrete alteration treated include sulphate attack, carbonation, chloride attack, alkali-silica reaction and leaching phenomena. In a long time perspective, the chemical alterations in concrete is mainly governed by the surrounding groundwater composition. After closure the composition of the groundwater will change character from a modified meteoric to a saline composition. Therefore two different simulated groundwater compositions have been used in modelling the chemical interaction between concrete and groundwater. The report also includes a study of old and historical concrete which show observations concerning recrystallization phenomena in concrete. 72 refs, 39 figs.

Conceptual model for concrete long time degradation in a deep nuclear waste repository

International Nuclear Information System (INIS)

Lagerblad, B.; Traegaardh, J.

1994-02-01

This report is mainly a state-of-the-art report of concrete long time durability in the environment expected in a deep site underground nuclear waste repository in Swedish crystalline bedrock. The report treats how the concrete and the surrounding groundwater will interact and how they will be affected by cement chemistry, type of aggregate etc. The different mechanisms for concrete alteration treated include sulphate attack, carbonation, chloride attack, alkali-silica reaction and leaching phenomena. In a long time perspective, the chemical alterations in concrete is mainly governed by the surrounding groundwater composition. After closure the composition of the groundwater will change character from a modified meteoric to a saline composition. Therefore two different simulated groundwater compositions have been used in modelling the chemical interaction between concrete and groundwater. The report also includes a study of old and historical concrete which show observations concerning recrystallization phenomena in concrete. 72 refs, 39 figs

Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Science.gov (United States)

Midya, Abhishek; Chakraborty, Jayasree; Pak, Linda M.; Zheng, Jian; Jarnagin, William R.; Do, Richard K. G.; Simpson, Amber L.

2018-02-01

Liver cancer is the second leading cause of cancer-related death worldwide.1 Hepatocellular carcinoma (HCC) is the most common primary liver cancer accounting for approximately 80% of cases. Intrahepatic cholangiocarcinoma (ICC) is a rare liver cancer, arising in patients with the same risk factors as HCC, but treatment options and prognosis differ. The diagnosis of HCC is based primarily on imaging but distinguishing between HCC and ICC is challenging due to common radiographic features.2-4 The aim of the present study is to classify HCC and ICC in portal venous phase CT. 107 patients with resected ICC and 116 patients with resected HCC were included in our analysis. We developed a deep neural network by modifying a pre-trained Inception network by retraining the final layers. The proposed method achieved the best accuracy and area under the receiver operating characteristics curve of 69.70% and 0.72, respectively on the test data.

Cytotoxic macrolides from a new species of the deep-water marine sponge Leiodermatium.

Science.gov (United States)

Sandler, Joel S; Colin, Patrick L; Kelly, Michelle; Fenical, William

2006-09-15

Chemical investigation of a new species of the deep-water marine sponge Leiodermatium, collected by manned submersible at a depth of 740 feet in Palau, resulted in the isolation of two cytotoxic macrolides, leiodolides A (1) and B (2). The leiodolides represent the first members of a new class of 19-membered ring macrolides, incorporating several unique functional groups including a conjugated oxazole ring, a bromine substituent, and an alpha-hydroxy-alpha-methyl carboxylic acid side-chain terminus. The structures of these new metabolites were established by spectroscopic analysis, chemical modification, and degradation. The relative and absolute stereochemistries at most chiral centers were assigned on detailed interpretation of spectroscopic data, coupled with chemical degradation and application of the modified Mosher ester method. Leiodolide A showed significant cytotoxicity (average GI(50) = 2.0 microM) in the National Cancer Institute's 60 cell line panel with enhanced activity against HL-60 leukemia and OVCAR-3 ovarian cancer cell lines.

Natural deep eutectic solvents as the major mobile phase components in high-performance liquid chromatography-searching for alternatives to organic solvents.

Science.gov (United States)

Sutton, Adam T; Fraige, Karina; Leme, Gabriel Mazzi; da Silva Bolzani, Vanderlan; Hilder, Emily F; Cavalheiro, Alberto J; Arrua, R Dario; Funari, Cristiano Soleo

2018-06-01

Over the past six decades, acetonitrile (ACN) has been the most employed organic modifier in reversed-phase high-performance liquid chromatography (RP-HPLC), followed by methanol (MeOH). However, from the growing environmental awareness that leads to the emergence of "green analytical chemistry," new research has emerged that includes finding replacements to problematic ACN because of its low sustainability. Deep eutectic solvents (DES) can be produced from an almost infinite possible combinations of compounds, while being a "greener" alternative to organic solvents in HPLC, especially those prepared from natural compounds called natural DES (NADES). In this work, the use of three NADES as the main organic component in RP-HPLC, rather than simply an additive, was explored and compared to the common organic solvents ACN and MeOH but additionally to the greener ethanol for separating two different mixtures of compounds, one demonstrating the elution of compounds with increasing hydrophobicity and the other comparing molecules of different functionality and molar mass. To utilize NADES as an organic modifier and overcome their high viscosity monolithic columns, temperatures at 50 °C and 5% ethanol in the mobile phase were used. NADES are shown to give chromatographic performances in between those observed for ACN and MeOH when eluotropic strength, resolution, and peak capacity were taken into consideration, while being less environmentally impactful as shown by the HPLC-Environmental Assessment Tool (HPLC-EAT) metric. With the development of proper technologies, DES could open a new class of mobile phases increasing the possibilities of new separation selectivities while reducing the environmental impact of HPLC analyses. Graphical abstract Natural deep eutectic solvents versus traditional solvents in HPLC.

Sanford Underground Research Facility - The United State's Deep Underground Research Facility

Science.gov (United States)

Vardiman, D.

2012-12-01

The 2.5 km deep Sanford Underground Research Facility (SURF) is managed by the South Dakota Science and Technology Authority (SDSTA) at the former Homestake Mine site in Lead, South Dakota. The US Department of Energy currently supports the development of the facility using a phased approach for underground deployment of experiments as they obtain an advanced design stage. The geology of the Sanford Laboratory site has been studied during the 125 years of operations at the Homestake Mine and more recently as part of the preliminary geotechnical site investigations for the NSF's Deep Underground Science and Engineering Laboratory project. The overall geology at DUSEL is a well-defined stratigraphic sequence of schist and phyllites. The three major Proterozoic units encountered in the underground consist of interbedded schist, metasediments, and amphibolite schist which are crosscut by Tertiary rhyolite dikes. Preliminary geotechnical site investigations included drift mapping, borehole drilling, borehole televiewing, in-situ stress analysis, laboratory analysis of core, mapping and laser scanning of new excavations, modeling and analysis of all geotechnical information. The investigation was focused upon the determination if the proposed site rock mass could support the world's largest (66 meter diameter) deep underground excavation. While the DUSEL project has subsequently been significantly modified, these data are still available to provide a baseline of the ground conditions which may be judiciously extrapolated throughout the entire Proterozoic rock assemblage for future excavations. Recommendations for facility instrumentation and monitoring were included in the preliminary design of the DUSEL project design and include; single and multiple point extensometers, tape extensometers and convergence measurements (pins), load cells and pressure cells, smart cables, inclinometers/Tiltmeters, Piezometers, thermistors, seismographs and accelerometers, scanners (laser

Shear Strengthening of RC Deep Beam Using Externally Bonded GFRP Fabrics

Science.gov (United States)

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

2018-06-01

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

Deep-sea ciliates: Recorded diversity and experimental studies on pressure tolerance

Science.gov (United States)

Schoenle, Alexandra; Nitsche, Frank; Werner, Jennifer; Arndt, Hartmut

2017-10-01

Microbial eukaryotes play an important role in biogeochemical cycles not only in productive surface waters but also in the deep sea. Recent studies based on metagenomics report deep-sea protistan assemblages totally different from continental slopes and shelf waters. To give an overview about the ciliate fauna recorded from the deep sea we summarized the available information on ciliate occurrence in the deep sea. Our literature review revealed that representatives of the major phylogenetic groups of ciliates were recorded from the deep sea (> 1000 m depth): Karyorelictea, Heterotrichea, Spirotrichea (Protohypotrichia, Euplotia, Oligotrichia, Choreotrichia, Hypotrichia), Armophorea (Armophorida), Litostomatea (Haptoria), Conthreep (Phyllopharyngea incl. Cyrtophoria, Chonotrichia, Suctoria; Nassophorea incl. Microthoracida, Synhymeniida, Nassulida; Colpodea incl. Bursariomorphida, Cyrtolophosidida; Prostomatea; Plagiopylea incl. Plagiopylida, Odontostomatida; Oligohymenophorea incl. Peniculia, Scuticociliatia, Hymenostomatia, Apostomatia, Peritrichia, Astomatia). Species occurring in both habitats, deep sea and shallow water, are rarely found to our knowledge to date. This indicates a high deep-sea specific ciliate fauna. Our own studies of similar genotypes (SSU rDNA and cox1 gene) revealed that two small scuticociliate species (Pseudocohnilembus persalinus and Uronema sp.) could be isolated from surface as well as deep waters (2687 m, 5276 m, 5719 m) of the Pacific. The adaptation to deep-sea conditions was investigated by exposing the ciliate isolates directly or stepwise to different hydrostatic pressures ranging from 1 to 550 atm at temperatures of 2 °C and 13 °C. Although the results indicated no general barophilic behavior, all four isolated strains survived the highest established pressure. A better survival at 550 atm could be observed for the lower temperature. Among microbial eukaryotes, ciliates should be considered as a diverse and potentially

The hadronic final state in deep inelastic scattering at HERA

International Nuclear Information System (INIS)

Lanius, P.

1994-10-01

Global properties of the hadronic final state of deep inelastic scattering events recorded in 1992 with the H1 detector at HERA, are investigated. The data are corrected for detector effects and can be compared directly with QCD phenomenology and calculations based on BFKL dynamics. The measurement of the energy flows in the laboratory frame and in the hadronic centre of mass system reveal large discrepancies between the data and the different model predictions, indicating the failure of models based on Altarelli-Parisi evolution at low χ. However, these energy flow results are found to agree fairly well with theoretical predictions derived from Lipatov (BFKL) evolution. In the hadronic centre of mass frame the longitudinal and transverse momentum components of charged particles are measured. The longitudinal component exhibits scaling with W and allows comparison with lower energy lepton-nucleon scattering data as well as with e + e - data from LEP. For the 1993 data, studies of the charged particle energy spectra in the Breit frame are undertaken. This measurement allows a first tentative look at predictions from the Modified Leading Logarithmic Approximation for the target region, a region that to-date unexplored has been unexplored. (orig.)

North Atlantic deep water formation and AMOC in CMIP5 models

Directory of Open Access Journals (Sweden)

C. Heuzé

2017-07-01

Full Text Available Deep water formation in climate models is indicative of their ability to simulate future ocean circulation, carbon and heat uptake, and sea level rise. Present-day temperature, salinity, sea ice concentration and ocean transport in the North Atlantic subpolar gyre and Nordic Seas from 23 CMIP5 (Climate Model Intercomparison Project, phase 5 models are compared with observations to assess the biases, causes and consequences of North Atlantic deep convection in models. The majority of models convect too deep, over too large an area, too often and too far south. Deep convection occurs at the sea ice edge and is most realistic in models with accurate sea ice extent, mostly those using the CICE model. Half of the models convect in response to local cooling or salinification of the surface waters; only a third have a dynamic relationship between freshwater coming from the Arctic and deep convection. The models with the most intense deep convection have the warmest deep waters, due to a redistribution of heat through the water column. For the majority of models, the variability of the Atlantic Meridional Overturning Circulation (AMOC is explained by the volumes of deep water produced in the subpolar gyre and Nordic Seas up to 2 years before. In turn, models with the strongest AMOC have the largest heat export to the Arctic. Understanding the dynamical drivers of deep convection and AMOC in models is hence key to realistically forecasting Arctic oceanic warming and its consequences for the global ocean circulation, cryosphere and marine life.

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

Energy Technology Data Exchange (ETDEWEB)

Pantenburg, F.J. E-mail: pantenburg@imt.fzk.de; Mohr, J

2001-07-21

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 {mu}m 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.

Deep shaft high rate aerobic digestion: laboratory and pilot plant performance

Energy Technology Data Exchange (ETDEWEB)

Tran, F; Gannon, D

1981-01-01

The Deep Shaft is essentially an air-lift reactor, sunk deep in the ground (100-160 m); the resulting high hydrostatic pressure together with very efficient mixing in the shaft provide extremely high O transfer efficiencies (O.T.E.) of less than or equal to 90% vs. 4-20% in other aerators. This high O.T.E. suggests real potential for Deep-Shaft technology in the aerobic digestion of sludges and animal wastes: with conventional aerobic digesters an O.T.E. over 8% is extremely difficult to achieve. Laboratory and pilot plant Deep-Shaft aerobic digester studies carried out at Eco-Research's Pointe Claire, Quebec laboratories, and at the Paris, Ontario pilot Deep-Shaft digester are described.

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

Pantenburg, F.J.; Mohr, J.

2001-01-01

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

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

CERN Document Server

Pantenburg, F J

2001-01-01

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 mu m 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.

Deep Ecology Education: Learning from Its Vaisnava Roots

Science.gov (United States)

Haigh, Martin

2006-01-01

Deep ecology arises from the personal intuition that one's self is part of the world's environmental wholeness. This awareness may be constructed upon scientific foundations but it is more commonly thought a spiritual concept. Deep ecology pedagogy emerges from its three-step process of ecological Self-realization. This paper traces the roots of…

Cognitive Implications of Deep Gray Matter Iron in Multiple Sclerosis.

Science.gov (United States)

Fujiwara, E; Kmech, J A; Cobzas, D; Sun, H; Seres, P; Blevins, G; Wilman, A H

2017-05-01

Deep gray matter iron accumulation is increasingly recognized in association with multiple sclerosis and can be measured in vivo with MR imaging. The cognitive implications of this pathology are not well-understood, especially vis-à-vis deep gray matter atrophy. Our aim was to investigate the relationships between cognition and deep gray matter iron in MS by using 2 MR imaging-based iron-susceptibility measures. Forty patients with multiple sclerosis (relapsing-remitting, n = 16; progressive, n = 24) and 27 healthy controls were imaged at 4.7T by using the transverse relaxation rate and quantitative susceptibility mapping. The transverse relaxation rate and quantitative susceptibility mapping values and volumes (atrophy) of the caudate, putamen, globus pallidus, and thalamus were determined by multiatlas segmentation. Cognition was assessed with the Brief Repeatable Battery of Neuropsychological Tests. Relationships between cognition and deep gray matter iron were examined by hierarchic regressions. Compared with controls, patients showed reduced memory ( P processing speed ( P = .02) and smaller putamen ( P deep gray matter iron accumulation in the current multiple sclerosis cohort. Atrophy and iron accumulation in deep gray matter both have negative but separable relationships to cognition in multiple sclerosis. © 2017 by American Journal of Neuroradiology.

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

Science.gov (United States)

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.

A trial of scheduled deep brain stimulation for Tourette syndrome: moving away from continuous deep brain stimulation paradigms.

Science.gov (United States)

Okun, Michael S; Foote, Kelly D; Wu, Samuel S; Ward, Herbert E; Bowers, Dawn; Rodriguez, Ramon L; Malaty, Irene A; Goodman, Wayne K; Gilbert, Donald M; Walker, Harrison C; Mink, Jonathan W; Merritt, Stacy; Morishita, Takashi; Sanchez, Justin C

2013-01-01

To collect the information necessary to design the methods and outcome variables for a larger trial of scheduled deep brain stimulation (DBS) for Tourette syndrome. We performed a small National Institutes of Health-sponsored clinical trials planning study of the safety and preliminary efficacy of implanted DBS in the bilateral centromedian thalamic region. The study used a cranially contained constant-current device and a scheduled, rather than the classic continuous, DBS paradigm. Baseline vs 6-month outcomes were collected and analyzed. In addition, we compared acute scheduled vs acute continuous vs off DBS. A university movement disorders center. Five patients with implanted DBS. A 50% improvement in the Yale Global Tic Severity Scale (YGTSS) total score. RESULTS Participating subjects had a mean age of 34.4 (range, 28-39) years and a mean disease duration of 28.8 years. No significant adverse events or hardware-related issues occurred. Baseline vs 6-month data revealed that reductions in the YGTSS total score did not achieve the prestudy criterion of a 50% improvement in the YGTSS total score on scheduled stimulation settings. However, statistically significant improvements were observed in the YGTSS total score (mean [SD] change, -17.8 [9.4]; P=.01), impairment score (-11.3 [5.0]; P=.007), and motor score (-2.8 [2.2]; P=.045); the Modified Rush Tic Rating Scale Score total score (-5.8 [2.9]; P=.01); and the phonic tic severity score (-2.2 [2.6]; P=.04). Continuous, off, and scheduled stimulation conditions were assessed blindly in an acute experiment at 6 months after implantation. The scores in all 3 conditions showed a trend for improvement. Trends for improvement also occurred with continuous and scheduled conditions performing better than the off condition. Tic suppression was commonly seen at ventral (deep) contacts, and programming settings resulting in tic suppression were commonly associated with a subjective feeling of calmness. This study provides

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.

Science.gov (United States)

Wang, Guan; Sun, Yu; Wang, Jianxin

2017-01-01

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.

Facial expression recognition based on improved deep belief networks

Science.gov (United States)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

A Deep-Sea Simulation.

Science.gov (United States)

Montes, Georgia E.

1997-01-01

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

Deep sky observing an astronomical tour

CERN Document Server

Coe, Steven R

2016-01-01

This updated second edition has all of the information needed for your successful forays into deep sky observing. Coe uses his years of experience to give detailed practical advice about how to find the best observing site, how to make the most of the time spent there, and what equipment and instruments to take along. There are comprehensive lists of deep sky objects of all kinds, along with Steve's own observations describing how they look through telescopes with apertures ranging from 4 inches to 36 inches (0.1 - 0.9 meters). Binocular observing also gets its due, while the lists of objects have been amended to highlight only the best targets. A new index makes finding targets easier than ever before, while the selection of viewing targets has been revised from the first edition. Most of all, this book is all about how to enjoy astronomy. The author's enthusiasm and sense of wonder shine through every page as he invites you along on a tour of some of the most beautiful and fascinating sites in the deep ...

Deep levels in silicon–oxygen superlattices

International Nuclear Information System (INIS)

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

2016-01-01

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

La modified TiO{sub 2} photoanode and its effect on DSSC performance: A comparative study of doping and surface treatment on deep and surface charge trapping

Energy Technology Data Exchange (ETDEWEB)

Ako, Rajour Tanyi [Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, BE1410, Negara Brunei Darussalam (Brunei Darussalam); Ekanayake, Piyasiri, E-mail: piyasiri.ekanayake@ubd.edu.bn [Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, BE1410, Negara Brunei Darussalam (Brunei Darussalam); Centre for Advanced Material and Energy Sciences (CAMES), Universiti Brunei Darussalam, Jalan Tungku Link, BE1410, Negara Brunei Darussalam (Brunei Darussalam); Tan, Ai Ling [Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, BE1410, Negara Brunei Darussalam (Brunei Darussalam); Young, David James [Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, BE1410, Negara Brunei Darussalam (Brunei Darussalam); Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, Queensland, 4558 (Australia); Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research - A*STAR, #08-03, 2 Fusionopolis Way, Innovis, 138634 (Singapore)

2016-04-01

The effect of Lanthanum ions (La{sup 3+}) on charge trapping in dye-sensitized solar cell (DSSC) photoanodes has been investigated with doped and surface-treated TiO{sub 2} nanoparticles. Doped nanoparticles consisting of 0.5 mol.% Mg and La co-doped TiO{sub 2}, 0.5 mol.% Mg doped TiO{sub 2} and pure TiO{sub 2} were synthesized by the sol gel method. Surface-treated nanoparticles of Mg doped TiO{sub 2} and pure TiO{sub 2} were prepared by ball milling in 0.05 M aqueous La{sup 3+} solution. All materials were analyzed by XRD, XPS and UV–Vis DRS. Cell performance, surface free energy state changes and electron injection efficiency of DSSCs based on these nanoparticles were evaluated using current –voltage measurements, EIS and Incident photon to current conversion efficiency. Doped materials had La and Mg ions incorporated into the TiO{sub 2} lattice, while no lattice changes were observed for the surface-treated materials. Less visible light was absorbed by treated oxides compared with doped oxide samples. The overall power conversion efficiencies (PCE) of DSSC photoanodes based on doped materials were twice those of photoanodes fabricated from treated nanoparticles. Doping establishes deep traps that reduce the recombination of electron–hole (e–h) pairs. Conversely, the presence of absorbed oxygen in treated materials enhances e–h recombination with electrolyte at surface trap sites. - Highlights: • DSSC performance is investigated using photoanodes of doped and La{sup 3+} surface treated TiO{sub 2}. • TiO{sub 2} and Mg–TiO{sub 2} treated with La{sup 3+} absorbed less visible light. • A high concentration of absorbed oxygen on surface treated oxides reduced band bending. • Increased surface free energy in the modified DSSC anodes is caused more by Mg{sup 2+} at Ti{sup 4+} than by La{sup 3+} at the surfaces. • Near surface charge traps due to La{sup 3+} treatment promotes e–h recombination.

Deep surface rolling for fatigue life enhancement of laser clad aircraft aluminium alloy

Energy Technology Data Exchange (ETDEWEB)

Zhuang, W., E-mail: wyman.zhuang@dsto.defence.gov.au [Aerospace Division, Defence Science and Technology Organisation, 506 Lorimer Street, Fishermans Bend, Victoria 3207 (Australia); Liu, Q.; Djugum, R.; Sharp, P.K. [Aerospace Division, Defence Science and Technology Organisation, 506 Lorimer Street, Fishermans Bend, Victoria 3207 (Australia); Paradowska, A. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW 2232 (Australia)

2014-11-30

Highlights: • Deep surface rolling as a post-repair enhancement technology was applied to the laser cladded 7075-T651 aluminium alloy specimens that simulated corrosion damage blend-out repair. • The residual stresses induced by the deep surface rolling process were measured. • The deep surface rolling process can introduce deep and high magnitude compressive residual stresses beyond the laser clad and substrate interface. • Spectrum fatigue test showed the fatigue life was significantly increased by deep surface rolling. - Abstract: Deep surface rolling can introduce deep compressive residual stresses into the surface of aircraft metallic structure to extend its fatigue life. To develop cost-effective aircraft structural repair technologies such as laser cladding, deep surface rolling was considered as an advanced post-repair surface enhancement technology. In this study, aluminium alloy 7075-T651 specimens with a blend-out region were first repaired using laser cladding technology. The surface of the laser cladding region was then treated by deep surface rolling. Fatigue testing was subsequently conducted for the laser clad, deep surface rolled and post-heat treated laser clad specimens. It was found that deep surface rolling can significantly improve the fatigue life in comparison with the laser clad baseline repair. In addition, three dimensional residual stresses were measured using neutron diffraction techniques. The results demonstrate that beneficial compressive residual stresses induced by deep surface rolling can reach considerable depths (more than 1.0 mm) below the laser clad surface.

Deep surface rolling for fatigue life enhancement of laser clad aircraft aluminium alloy

International Nuclear Information System (INIS)

Zhuang, W.; Liu, Q.; Djugum, R.; Sharp, P.K.; Paradowska, A.

2014-01-01

Highlights: • Deep surface rolling as a post-repair enhancement technology was applied to the laser cladded 7075-T651 aluminium alloy specimens that simulated corrosion damage blend-out repair. • The residual stresses induced by the deep surface rolling process were measured. • The deep surface rolling process can introduce deep and high magnitude compressive residual stresses beyond the laser clad and substrate interface. • Spectrum fatigue test showed the fatigue life was significantly increased by deep surface rolling. - Abstract: Deep surface rolling can introduce deep compressive residual stresses into the surface of aircraft metallic structure to extend its fatigue life. To develop cost-effective aircraft structural repair technologies such as laser cladding, deep surface rolling was considered as an advanced post-repair surface enhancement technology. In this study, aluminium alloy 7075-T651 specimens with a blend-out region were first repaired using laser cladding technology. The surface of the laser cladding region was then treated by deep surface rolling. Fatigue testing was subsequently conducted for the laser clad, deep surface rolled and post-heat treated laser clad specimens. It was found that deep surface rolling can significantly improve the fatigue life in comparison with the laser clad baseline repair. In addition, three dimensional residual stresses were measured using neutron diffraction techniques. The results demonstrate that beneficial compressive residual stresses induced by deep surface rolling can reach considerable depths (more than 1.0 mm) below the laser clad surface

Bank of Weight Filters for Deep CNNs

Science.gov (United States)

2016-11-22

very large even on the best available hardware . In some studies in transfer learning it has been observed that the network learnt on one task can be...CNNs. Keywords: CNN, deep learning , neural networks, transfer learning , bank of weigh filters, BWF 1. Introduction Object recognition is an important...of CNNs (or, in general, of deep neural networks) is that feature generation part is fused with the classifier part and both parts are learned together

Exploring frontiers of the deep biosphere through scientific ocean drilling

Science.gov (United States)

Inagaki, F.; D'Hondt, S.; Hinrichs, K. U.

2015-12-01

Since the first deep biosphere-dedicated Ocean Drilling Program (ODP) Leg 201 using the US drill ship JOIDES Resolution in 2002, scientific ocean drilling has offered unique opportunities to expand our knowledge of the nature and extent of the deep biosphere. The latest estimate of the global subseafloor microbial biomass is ~1029cells, accounting for 4 Gt of carbon and ~1% of the Earth's total living biomass. The subseafloor microbial communities are evolutionarily diverse and their metabolic rates are extraordinarily slow. Nevertheless, accumulating activity most likely plays a significant role in elemental cycles over geological time. In 2010, during Integrated Ocean Drilling Program (IODP) Expedition 329, the JOIDES Resolutionexplored the deep biosphere in the open-ocean South Pacific Gyre—the largest oligotrophic province on our planet. During Expedition 329, relatively high concentrations of dissolved oxygen and significantly low biomass of microbial populations were observed in the entire sediment column, indicating that (i) there is no limit to life in open-ocean sediment and (ii) a significant amount of oxygen reaches through the sediment to the upper oceanic crust. This "deep aerobic biosphere" inhabits the sediment throughout up to ~37 percent of the world's oceans. The remaining ~63 percent of the oceans is comprised of higher productivity areas that contain the "deep anaerobic biosphere". In 2012, during IODP Expedition 337, the Japanese drill ship Chikyu explored coal-bearing sediments down to 2,466 meters below the seafloor off the Shimokita Peninsula, Japan. Geochemical and microbiological analyses consistently showed the occurrence of methane-producing communities associated with the coal beds. Cell concentrations in deep sediments were notably lower than those expected from the global regression line, implying that the bottom of the deep biosphere is approached in these beds. Taxonomic composition of the deep coal-bearing communities profoundly

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

Directory of Open Access Journals (Sweden)

Chumachenko О.І.

2016-12-01

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

Species distribution models of tropical deep-sea snappers.

Directory of Open Access Journals (Sweden)

Céline Gomez

Full Text Available Deep-sea fisheries provide an important source of protein to Pacific Island countries and territories that are highly dependent on fish for food security. However, spatial management of these deep-sea habitats is hindered by insufficient data. We developed species distribution models using spatially limited presence data for the main harvested species in the Western Central Pacific Ocean. We used bathymetric and water temperature data to develop presence-only species distribution models for the commercially exploited deep-sea snappers Etelis Cuvier 1828, Pristipomoides Valenciennes 1830, and Aphareus Cuvier 1830. We evaluated the performance of four different algorithms (CTA, GLM, MARS, and MAXENT within the BIOMOD framework to obtain an ensemble of predicted distributions. We projected these predictions across the Western Central Pacific Ocean to produce maps of potential deep-sea snapper distributions in 32 countries and territories. Depth was consistently the best predictor of presence for all species groups across all models. Bathymetric slope was consistently the poorest predictor. Temperature at depth was a good predictor of presence for GLM only. Model precision was highest for MAXENT and CTA. There were strong regional patterns in predicted distribution of suitable habitat, with the largest areas of suitable habitat (> 35% of the Exclusive Economic Zone predicted in seven South Pacific countries and territories (Fiji, Matthew & Hunter, Nauru, New Caledonia, Tonga, Vanuatu and Wallis & Futuna. Predicted habitat also varied among species, with the proportion of predicted habitat highest for Aphareus and lowest for Etelis. Despite data paucity, the relationship between deep-sea snapper presence and their environments was sufficiently strong to predict their distribution across a large area of the Pacific Ocean. Our results therefore provide a strong baseline for designing monitoring programs that balance resource exploitation and

Deep challenges for China's war on water pollution.

Science.gov (United States)

Han, Dongmei; Currell, Matthew J; Cao, Guoliang

2016-11-01

China's Central government has released an ambitious plan to tackle the nation's water pollution crisis. However, this is inhibited by a lack of data, particularly for groundwater. We compiled and analyzed water quality classification data from publicly available government sources, further revealing the scale and extent of the crisis. We also compiled nitrate data in shallow and deep groundwater from a range of literature sources, covering 52 of China's groundwater systems; the most comprehensive national-scale assessment yet. Nitrate pollution at levels exceeding the US EPA's maximum contaminant level (10 mg/L NO 3 N) occurs at the 90th percentile in 25 of 36 shallow aquifers and 10 out of 37 deep or karst aquifers. Isotopic compositions of groundwater nitrate (δ 15 N and δ 18 O NO3 values ranging from -14.9‰ to 35.5‰ and -8.1‰ to 51.0‰, respectively) indicate many nitrate sources including soil nitrogen, agricultural fertilizers, untreated wastewater and/or manure, and locally show evidence of de-nitrification. From these data, it is clear that contaminated groundwater is ubiquitous in deep aquifers as well as shallow groundwater (and surface water). Deep aquifers contain water recharged tens of thousands of years before present, long before widespread anthropogenic nitrate contamination. This groundwater has therefore likely been contaminated due to rapid bypass flow along wells or other conduits. Addressing the issue of well condition is urgently needed to stop further pollution of China's deep aquifers, which are some of China's most important drinking water sources. China's new 10-point Water Pollution Plan addresses previous shortcomings, however, control and remediation of deep groundwater pollution will take decades of sustained effort. Copyright © 2016. Published by Elsevier Ltd.

Volume fracturing of deep shale gas horizontal wells

Directory of Open Access Journals (Sweden)

Tingxue Jiang

2017-03-01

Full Text Available Deep shale gas reservoirs buried underground with depth being more than 3500 m are characterized by high in-situ stress, large horizontal stress difference, complex distribution of bedding and natural cracks, and strong rock plasticity. Thus, during hydraulic fracturing, these reservoirs often reveal difficult fracture extension, low fracture complexity, low stimulated reservoir volume (SRV, low conductivity and fast decline, which hinder greatly the economic and effective development of deep shale gas. In this paper, a specific and feasible technique of volume fracturing of deep shale gas horizontal wells is presented. In addition to planar perforation, multi-scale fracturing, full-scale fracture filling, and control over extension of high-angle natural fractures, some supporting techniques are proposed, including multi-stage alternate injection (of acid fluid, slick water and gel and the mixed- and small-grained proppant to be injected with variable viscosity and displacement. These techniques help to increase the effective stimulated reservoir volume (ESRV for deep gas production. Some of the techniques have been successfully used in the fracturing of deep shale gas horizontal wells in Yongchuan, Weiyuan and southern Jiaoshiba blocks in the Sichuan Basin. As a result, Wells YY1HF and WY1HF yielded initially 14.1 × 104 m3/d and 17.5 × 104 m3/d after fracturing. The volume fracturing of deep shale gas horizontal well is meaningful in achieving the productivity of 50 × 108 m3 gas from the interval of 3500–4000 m in Phase II development of Fuling and also in commercial production of huge shale gas resources at a vertical depth of less than 6000 m.

A plugging solution for cementing deep oil and gas wells

Energy Technology Data Exchange (ETDEWEB)

Angelopulo, O K; Bakshutov, V S; Bikbau, M Ya; Danyushevskiy, V S; Ilyukhin, V V; Khydyrov, M B; Lobov, L L; Nikolayeva, M K; Nikulin, V Ya; Nudelman, B I

1983-01-01

In order to use the solution in a temperature range of -10 to +250 degrees in conditions of salt agression, a plugging solution for cementing deep oil and gas wells, which contains a ground clinker, a mineral additive, a slaking liquid and a modifier additive, is made up in the following manner: ground chlorosilicate clinker is used as the clinker (alynite portland cement) (100 parts); a mixture of gypsum and limestone with peat or cope in a 1 to 3 to 4 to 3 to 8 ratio is used as the mineral additive (5 to 65 parts); the slaking liquid is water of a 1 to 2.5 percent aqueous solution of electrolytes of CaC/sub 12/ or K/sub 2/CO/sub 3/ (42 to 115.5 parts), while the modifier additive is an aqueous solution of liquid glass or polyacrylate (0.05 to 17.55 parts). The solution contains an analyte clinker of the following mineral composition, in parts by weight: Ca/sub 3/Si0/sub 4/C1/sub 2/ (alynite), 100; Ca/sub 2/Si0/sub 3/C1/sub 2/, 2.2 to 33.2; 12CuO with 7A1/sub 2/0/sub 3/, 2.2 to 25.0; CuO with 5A1/sub 2/0/sub 3/, 1.1 to 8.3; CuO with 2A10/sub 2/, 1.1 to 8.3; 4CaO with A1/sub 2/0/sub 3/ with Fe/sub 2/0/sub 3/, 15.6 to 25.0; 2CuO with Fe/sub 2/0/sub 3/, 0.1 to 5.0; a glass phase of 0.3 to 25.0 and free CuO, 0.6 to 2.5; and total MgO, 0.3 to 2.5.

Validation of a high-power, time-resolved, near-infrared spectroscopy system for measurement of superficial and deep muscle deoxygenation during exercise.

Science.gov (United States)

Koga, Shunsaku; Barstow, Thomas J; Okushima, Dai; Rossiter, Harry B; Kondo, Narihiko; Ohmae, Etsuko; Poole, David C

2015-06-01