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Sample records for akari deep field

  1. AKARI/IRC source catalogues and source counts for the IRAC Dark Field, ELAIS North and the AKARI Deep Field South

    Science.gov (United States)

    Davidge, H.; Serjeant, S.; Pearson, C.; Matsuhara, H.; Wada, T.; Dryer, B.; Barrufet, L.

    2017-12-01

    We present the first detailed analysis of three extragalactic fields (IRAC Dark Field, ELAIS-N1, ADF-S) observed by the infrared satellite, AKARI, using an optimized data analysis toolkit specifically for the processing of extragalactic point sources. The InfaRed Camera (IRC) on AKARI complements the Spitzer Space Telescope via its comprehensive coverage between 8-24 μm filling the gap between the Spitzer/IRAC and MIPS instruments. Source counts in the AKARI bands at 3.2, 4.1, 7, 11, 15 and 18 μm are presented. At near-infrared wavelengths, our source counts are consistent with counts made in other AKARI fields and in general with Spitzer/IRAC (except at 3.2 μm where our counts lie above). In the mid-infrared (11 - 18 μm), we find our counts are consistent with both previous surveys by AKARI and the Spitzer peak-up imaging survey with the InfraRed Spectrograph (IRS). Using our counts to constrain contemporary evolutionary models, we find that although the models and counts are in agreement at mid-infrared wavelengths there are inconsistencies at wavelengths shortward of 7 μm, suggesting either a problem with stellar subtraction or indicating the need for refinement of the stellar population models. We have also investigated the AKARI/IRC filters, and find an active galactic nucleus selection criteria out to z < 2 on the basis of AKARI 4.1, 11, 15 and 18 μm colours.

  2. The AKARI FU-HYU galaxy evolution program: first results from the GOODS-N field

    Science.gov (United States)

    Pearson, C. P.; Serjeant, S.; Negrello, M.; Takagi, T.; Jeong, W.-S.; Matsuhara, H.; Wada, T.; Oyabu, S.; Lee, H. M.; Im, M. S.

    2010-05-01

    The AKARI FU-HYU mission program carried out mid-infrared imaging of several well studied Spitzer fields preferentially selecting fields already rich in multi-wavelength data from radio to X-ray wavelengths filling in the wavelength desert between the Spitzer IRAC and MIPS bands. We present the initial results for the FU-HYU survey in the GOODS-N field. We utilize the supreme multiwavelength coverage in the GOODS-N field to produce a multiwavelength catalogue from infrared to ultraviolet wavelengths, containing more than 4393 sources, including photometric redshifts. Using the FU-HYU catalogue we present colour-colour diagrams that map the passage of PAH features through our observation bands. We find that the longer mid-infrared bands from AKARI (IRC-L18W 18 micron band) and Spitzer (MIPS24 24 micron band) provide an accurate measure of the total MIR emission of the sources and therefore their probable total mid-infrared luminosity. We also find that colours incorporating the AKARI IRC-S11 11 micron band produce a bimodal distribution where an excess at 11 microns preferentially selects moderate redshift star-forming galaxies. These powerful colour-colour diagnostics are further used as tools to extract anomalous colour populations, in particular a population of Silicate Break galaxies from the GOODS-N field showing that dusty starbursts can be selected of specific redshift ranges (z = 1.2-1.6) by mid-infrared drop-out techniques. The FU-HYU catalogue will be made publically available to the astronomical community.

  3. The infrared astronomical mission AKARI

    NARCIS (Netherlands)

    Murakami, Hiroshi; Baba, Hajime; Barthel, Peter; Clements, David L.; Cohen, Martin; Doi, Yasuo; Enya, Keigo; Figueredo, Elysandra; Fujishiro, Naofumi; Fujiwara, Hideaki; Fujiwara, Mikio; Garcia-Lario, Pedro; Goto, Tomotsugu; Hasegawa, Sunao; Hibi, Yasunori; Hirao, Takanori; Hiromoto, Norihisa; Hong, Seung Soo; Imai, Koji; Ishigaki, Miho; Ishiguro, Masateru; Ishihara, Daisuke; Ita, Yoshifusa; Jeong, Woong-Seob; Jeong, Kyung Sook; Kaneda, Hidehiro; Kataza, Hirokazu; Kawada, Mitsunobu; Kawai, Toshihide; Kawamura, Akiko; Kessler, Martin F.; Kester, Do; Kii, Tsuneo; Kim, Dong Chan; Kim, Wjung; Kobayashi, Hisato; Koo, Bon Chul; Kwon, Suk Minn; Lee, Hyung Mok; Lorente, Rosario; Makiuti, Sin'itirou; Matsuhara, Hideo; Matsumoto, Toshio; Matsuo, Hiroshi; Matsuura, Shuji; Mueller, Thomas G.; Murakami, Noriko; Nagata, Hirohisa; Nakagawa, Takao; Naoi, Takahiro; Narita, Masanao; Noda, Manabu; Oh, Sang Hoon; Ohnishi, Akira; Ohyama, Youichi; Okada, Yoko; Okuda, Haruyuki; Oliver, Sebastian; Onaka, Takashi; Ootsubo, Takafumi; Oyabu, Shinki; Pak, Sojong; Park, Yong-Sun; Pearson, Chris P.; Rowan-Robinson, Michael; Saito, Toshinobu; Sakon, Itsuki; Salama, Alberto; Sato, Shinji; Savage, Richard S.; Serjeant, Stephen; Shibai, Hiroshi; Shirahata, Mai; Sohn, Jungjoo; Suzuki, Toyoaki; Takagi, Toshinobu; Takahashi, Hidenori; Tanabe, Toshihiko; Takeuchi, Tsutomu T.; Takita, Satoshi; Thomson, Matthew; Uemizu, Kazunori; Ueno, Munetaka; Usui, Fumihiko; Verdugo, Eva; Wada, Takehiko; Wang, Lingyu; Watabe, Toyoki; Watarai, Hidenori; White, Glenn J.; Yamamura, Issei; Yamauchi, Chisato; Yasuda, Akiko

    2007-01-01

    AKARI, the first Japanese satellite dedicated to infrared astronomy, was launched on 2006 February 21, and started observations in May of the same year. AKARI has a 68.5 cm cooled telescope, together with two focal-plane instruments, which survey the sky in six wavelength bands from mid- to

  4. First Results from the AKARI FU-HYU Mission Program

    Science.gov (United States)

    Pearson, C.; Serjeant, S.; Takagi, T.; Jeong, W.-S.; Negrello, M.; Matsuhara, H.; Wada, T.; Oyabu, S.; Lee, H. M.; Im, M.

    2009-12-01

    The AKARI FU-HYU mission program has carried out mid-infrared imaging of several well studied Spitzer fields. This imaging fills in the wavelength coverage lacking from the Spitzer surveys and gives an extremely high scientific return for minimal input for AKARI. We select fields already rich in multi-wavelength data from radio to X-ray wavelengths and present the results from our initial analysis in the GOODS-N field. We utilize the comprehansive multiwavelength coverage in the GOODS-N field to produce a multiwavelength catalogue from infrared to ultraviolet wavelengths including photometric redshifts. Using the FU-HYU catalogue we present colour-colour diagrams that map the passage of PAH features through our observation bands. These colour-colours diagrams are used as tools to extract anomalous colour populations, in particular a population of Silicate Break galaxies from the GOODS-N field.

  5. The AKARI IRC asteroid flux catalogue: updated diameters and albedos

    Science.gov (United States)

    Alí-Lagoa, V.; Müller, T. G.; Usui, F.; Hasegawa, S.

    2018-05-01

    The AKARI IRC all-sky survey provided more than twenty thousand thermal infrared observations of over five thousand asteroids. Diameters and albedos were obtained by fitting an empirically calibrated version of the standard thermal model to these data. After the publication of the flux catalogue in October 2016, our aim here is to present the AKARI IRC all-sky survey data and discuss valuable scientific applications in the field of small body physical properties studies. As an example, we update the catalogue of asteroid diameters and albedos based on AKARI using the near-Earth asteroid thermal model (NEATM). We fit the NEATM to derive asteroid diameters and, whenever possible, infrared beaming parameters. We fit groups of observations taken for the same object at different epochs of the survey separately, so we compute more than one diameter for approximately half of the catalogue. We obtained a total of 8097 diameters and albedos for 5170 asteroids, and we fitted the beaming parameter for almost two thousand of them. When it was not possible to fit the beaming parameter, we used a straight line fit to our sample's beaming parameter-versus-phase angle plot to set the default value for each fit individually instead of using a single average value. Our diameters agree with stellar-occultation-based diameters well within the accuracy expected for the model. They also match the previous AKARI-based catalogue at phase angles lower than 50°, but we find a systematic deviation at higher phase angles, at which near-Earth and Mars-crossing asteroids were observed. The AKARI IRC All-sky survey is an essential source of information about asteroids, especially the large ones, since, it provides observations at different observation geometries, rotational coverages and aspect angles. For example, by comparing in more detail a few asteroids for which dimensions were derived from occultations, we discuss how the multiple observations per object may already provide three

  6. The Far-Infrared Surveyor (FIS) for AKARI

    NARCIS (Netherlands)

    Kawada, Mitsunobu; Baba, Hajime; Barthel, Peter D.; Clements, David; Cohen, Martin; Doi, Yasuo; Figueredo, Elysandra; Fujiwara, Mikio; Goto, Tomotsugu; Hasegawa, Sunao; Hibi, Yasunori; Hirao, Takanori; Hiromoto, Norihisa; Jeong, Woong-Seob; Kaneda, Hidehiro; Kawai, Toshihide; Kawamura, Akiko; Kester, Do; Kii, Tsuneo; Kobayashi, Hisato; Kwon, Suk Minn; Lee, Hyung Mok; Makiuti, Sin'itirou; Matsuo, Hiroshi; Matsuura, Shuji; Mueller, Thomas G.; Murakami, Noriko; Nagata, Hirohisa; Nakagawa, Takao; Narita, Masanao; Noda, Manabu; Oh, Sang Hoon; Okada, Yoko; Okuda, Haruyuki; Oliver, Sebastian; Ootsubo, Takafumi; Pak, Soojong; Park, Yong-Sun; Pearson, Chris P.; Rowan-Robinson, Michael; Saito, Toshinobu; Salama, Alberto; Sato, Shinji; Savage, Richard S.; Serjeant, Stephen; Shibai, Hiroshi; Shirahata, Mai; Sohn, Jungjoo; Suzuki, Toyoaki; Takagi, Toshinobu; Takahashi, Hidenori; Thomson, Matthew; Usui, Fumihiko; Verdugo, Eva; Watabe, Toyoki; White, Glenn J.; Wang, Lingyu; Yamamura, Issei; Yamauchi, Chisato; Yasuda, Akiko

    2007-01-01

    The Far-Infrared Surveyor (FIS) is one of two focal-plane instruments on the AKARI satellite. FIS has four photometric bands at 65, 90, 140, and 160 mu m, and uses two kinds of array detectors. The FIS arrays and optics are designed to sweep the sky with high spatial resolution and redundancy. The

  7. Dust color temperature distribution of two FIR cavities at IRIS and AKARI maps

    Science.gov (United States)

    Jha, A. K.; Aryal, B.

    2018-04-01

    By systematically searching the region of far infrared loops, we found a number of huge cavity-like dust structures at 60 μ m and 100 μ m IRIS maps. By checking these with AKARI maps (90 μ m and 140 μ m), two new cavity-like structures (sizes ˜ 2.7 pc × 0.8 pc and ˜ 1.8 pc × 1 pc) located at R.A. (J2000)=14h41m23s and Dec. (J2000)=-64°04^' }17^' }' }} and R.A. (J2000)=05h05m35s and Dec. (J2000)=- 69°35^' } 25^' }' }} were selected for the study. The difference in the average dust color temperatures calculated using IRIS and AKARI maps of the cavity candidates were found to be 3.2± 0.9 K and 4.1± 1.2 K, respectively. Interestingly, the longer wavelength AKARI map gives larger values of dust color temperature than that of the shorter wavelength IRIS maps. Possible explanation of the results will be presented.

  8. The infrared luminosity function of AKARI 90 μm galaxies in the local Universe

    Science.gov (United States)

    Kilerci Eser, Ece; Goto, Tomotsugu

    2018-03-01

    Local infrared (IR) luminosity functions (LFs) are necessary benchmarks for high-redshift IR galaxy evolution studies. Any accurate IR LF evolution studies require accordingly accurate local IR LFs. We present IR galaxy LFs at redshifts of z ≤ 0.3 from AKARI space telescope, which performed an all-sky survey in six IR bands (9, 18, 65, 90, 140, and 160 μm) with 10 times better sensitivity than its precursor Infrared Astronomical Satellite. Availability of 160 μm filter is critically important in accurately measuring total IR luminosity of galaxies, covering across the peak of the dust emission. By combining data from Wide-field Infrared Survey Explorer (WISE), Sloan Digital Sky Survey (SDSS) Data Release 13 (DR 13), six-degree Field Galaxy Survey and the 2MASS Redshift Survey, we created a sample of 15 638 local IR galaxies with spectroscopic redshifts, factor of 7 larger compared to previously studied AKARI-SDSS sample. After carefully correcting for volume effects in both IR and optical, the obtained IR LFs agree well with previous studies, but comes with much smaller errors. Measured local IR luminosity density is ΩIR = 1.19 ± 0.05 × 108L⊙ Mpc-3. The contributions from luminous IR galaxies and ultraluminous IR galaxies to ΩIR are very small, 9.3 per cent and 0.9 per cent, respectively. There exists no future all-sky survey in far-IR wavelengths in the foreseeable future. The IR LFs obtained in this work will therefore remain an important benchmark for high-redshift studies for decades.

  9. 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.

  10. EFFICIENT SELECTION AND CLASSIFICATION OF INFRARED EXCESS EMISSION STARS BASED ON AKARI AND 2MASS DATA

    Energy Technology Data Exchange (ETDEWEB)

    Huang Yafang; Li Jinzeng [National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012 (China); Rector, Travis A. [University of Alaska, 3211 Providence Drive, Anchorage, AK 99508 (United States); Mallamaci, Carlos C., E-mail: ljz@nao.cas.cn [Observatorio Astronomico Felix Aguilar, Universidad Nacional de San Juan (Argentina)

    2013-05-15

    The selection of young stellar objects (YSOs) based on excess emission in the infrared is easily contaminated by post-main-sequence stars and various types of emission line stars with similar properties. We define in this paper stringent criteria for an efficient selection and classification of stellar sources with infrared excess emission based on combined Two Micron All Sky Survey (2MASS) and AKARI colors. First of all, bright dwarfs and giants with known spectral types were selected from the Hipparcos Catalogue and cross-identified with the 2MASS and AKARI Point Source Catalogues to produce the main-sequence and the post-main-sequence tracks, which appear as expected as tight tracks with very small dispersion. However, several of the main-sequence stars indicate excess emission in the color space. Further investigations based on the SIMBAD data help to clarify their nature as classical Be stars, which are found to be located in a well isolated region on each of the color-color (C-C) diagrams. Several kinds of contaminants were then removed based on their distribution in the C-C diagrams. A test sample of Herbig Ae/Be stars and classical T Tauri stars were cross-identified with the 2MASS and AKARI catalogs to define the loci of YSOs with different masses on the C-C diagrams. Well classified Class I and Class II sources were taken as a second test sample to discriminate between various types of YSOs at possibly different evolutionary stages. This helped to define the loci of different types of YSOs and a set of criteria for selecting YSOs based on their colors in the near- and mid-infrared. Candidate YSOs toward IC 1396 indicating excess emission in the near-infrared were employed to verify the validity of the new source selection criteria defined based on C-C diagrams compiled with the 2MASS and AKARI data. Optical spectroscopy and spectral energy distributions of the IC 1396 sample yield a clear identification of the YSOs and further confirm the criteria defined

  11. EFFICIENT SELECTION AND CLASSIFICATION OF INFRARED EXCESS EMISSION STARS BASED ON AKARI AND 2MASS DATA

    International Nuclear Information System (INIS)

    Huang Yafang; Li Jinzeng; Rector, Travis A.; Mallamaci, Carlos C.

    2013-01-01

    The selection of young stellar objects (YSOs) based on excess emission in the infrared is easily contaminated by post-main-sequence stars and various types of emission line stars with similar properties. We define in this paper stringent criteria for an efficient selection and classification of stellar sources with infrared excess emission based on combined Two Micron All Sky Survey (2MASS) and AKARI colors. First of all, bright dwarfs and giants with known spectral types were selected from the Hipparcos Catalogue and cross-identified with the 2MASS and AKARI Point Source Catalogues to produce the main-sequence and the post-main-sequence tracks, which appear as expected as tight tracks with very small dispersion. However, several of the main-sequence stars indicate excess emission in the color space. Further investigations based on the SIMBAD data help to clarify their nature as classical Be stars, which are found to be located in a well isolated region on each of the color-color (C-C) diagrams. Several kinds of contaminants were then removed based on their distribution in the C-C diagrams. A test sample of Herbig Ae/Be stars and classical T Tauri stars were cross-identified with the 2MASS and AKARI catalogs to define the loci of YSOs with different masses on the C-C diagrams. Well classified Class I and Class II sources were taken as a second test sample to discriminate between various types of YSOs at possibly different evolutionary stages. This helped to define the loci of different types of YSOs and a set of criteria for selecting YSOs based on their colors in the near- and mid-infrared. Candidate YSOs toward IC 1396 indicating excess emission in the near-infrared were employed to verify the validity of the new source selection criteria defined based on C-C diagrams compiled with the 2MASS and AKARI data. Optical spectroscopy and spectral energy distributions of the IC 1396 sample yield a clear identification of the YSOs and further confirm the criteria defined

  12. 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.

  13. ON THE RADII OF BROWN DWARFS MEASURED WITH AKARI NEAR-INFRARED SPECTROSCOPY

    International Nuclear Information System (INIS)

    Sorahana, S.; Yamamura, I.; Murakami, H.

    2013-01-01

    We derive the radii of 16 brown dwarfs observed by AKARI using their parallaxes and the ratios of observed to model fluxes. We find that the brown dwarf radius ranges between 0.64-1.13 R J with an average radius of 0.83 R J . We find a trend in the relation between radii and T eff ; the radius is at a minimum at T eff ∼ 1600 K, which corresponds to the spectral types of mid- to late-L. The result is interpreted by a combination of radius-mass and radius-age relations that are theoretically expected for brown dwarfs older than 10 8 yr.

  14. AKARI INFRARED CAMERA SURVEY OF THE LARGE MAGELLANIC CLOUD. II. THE NEAR-INFRARED SPECTROSCOPIC CATALOG

    International Nuclear Information System (INIS)

    Shimonishi, Takashi; Onaka, Takashi; Kato, Daisuke; Sakon, Itsuki; Ita, Yoshifusa; Kawamura, Akiko; Kaneda, Hidehiro

    2013-01-01

    We performed a near-infrared spectroscopic survey toward an area of ∼10 deg 2 of the Large Magellanic Cloud (LMC) with the infrared satellite AKARI. Observations were carried out as part of the AKARI Large-area Survey of the Large Magellanic Cloud (LSLMC). The slitless multi-object spectroscopic capability of the AKARI/IRC enabled us to obtain low-resolution (R ∼ 20) spectra in 2-5 μm for a large number of point sources in the LMC. As a result of the survey, we extracted about 2000 infrared spectra of point sources. The data are organized as a near-infrared spectroscopic catalog. The catalog includes various infrared objects such as young stellar objects (YSOs), asymptotic giant branch (AGB) stars, supergiants, and so on. It is shown that 97% of the catalog sources have corresponding photometric data in the wavelength range from 1.2 to 11 μm, and 67% of the sources also have photometric data up to 24 μm. The catalog allows us to investigate near-infrared spectral features of sources by comparison with their infrared spectral energy distributions. In addition, it is estimated that about 10% of the catalog sources are observed at more than two different epochs. This enables us to study a spectroscopic variability of sources by using the present catalog. Initial results of source classifications for the LSLMC samples are presented. We classified 659 LSLMC spectra based on their near-infrared spectral features by visual inspection. As a result, it is shown that the present catalog includes 7 YSOs, 160 C-rich AGBs, 8 C-rich AGB candidates, 85 O-rich AGBs, 122 blue and yellow supergiants, 150 red super giants, and 128 unclassified sources. Distributions of the classified sources on the color-color and color-magnitude diagrams are discussed in the text. Continuous wavelength coverage and high spectroscopic sensitivity in 2-5 μm can only be achieved by space observations. This is an unprecedented large-scale spectroscopic survey toward the LMC in the near

  15. 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.

  16. BRIGHTNESS AND FLUCTUATION OF THE MID-INFRARED SKY FROM AKARI OBSERVATIONS TOWARD THE NORTH ECLIPTIC POLE

    International Nuclear Information System (INIS)

    Pyo, Jeonghyun; Jeong, Woong-Seob; Matsumoto, Toshio; Matsuura, Shuji

    2012-01-01

    We present the smoothness of the mid-infrared sky from observations by the Japanese infrared astronomical satellite AKARI. AKARI monitored the north ecliptic pole (NEP) during its cold phase with nine wave bands covering from 2.4 to 24 μm, out of which six mid-infrared bands were used in this study. We applied power-spectrum analysis to the images in order to search for the fluctuation of the sky brightness. Observed fluctuation is explained by fluctuation of photon noise, shot noise of faint sources, and Galactic cirrus. The fluctuations at a few arcminutes scales at short mid-infrared wavelengths (7, 9, and 11 μm) are largely caused by the diffuse Galactic light of the interstellar dust cirrus. At long mid-infrared wavelengths (15, 18, and 24 μm), photon noise is the dominant source of fluctuation over the scale from arcseconds to a few arcminutes. The residual fluctuation amplitude at 200'' after removing these contributions is at most 1.04 ± 0.23 nW m –2 sr –1 or 0.05% of the brightness at 24 μm and at least 0.47 ± 0.14 nW m –2 sr –1 or 0.02% at 18 μm. We conclude that the upper limit of the fluctuation in the zodiacal light toward the NEP is 0.03% of the sky brightness, taking 2σ error into account.

  17. VizieR Online Data Catalog: AKARI IRC asteroid sample diameters & albedos (Ali-Lagoa+, 2018)

    Science.gov (United States)

    Ali-Lagoa, V.; Mueller, T. G.; Usui, F.; Hasegawa, S.

    2017-11-01

    Table 1 contains the best-fitting values of size and beaming parameter and corresponding visible geometric albedos for the full AKARI IRC sample. We fitted the near-Earth asteroid thermal model (NEATM) of Harris (1998Icar..131..291H) to the AKARI IRC thermal infrared data (Murakami et al., 2007PASJ...59S.369M, Onaka et al., 2007PASJ...59S.401O, Ishihara et al., 2010A&A...514A...1I, Cat. II/297, Usui et al., 2011PASJ...63.1117U, Cat. J/PASJ/63/1117, Takita et al., 2012PASJ...64..126T, Hasegawa et al., 2013PASJ...65...34H, Cat. J/PASJ/65/34). The NEATM implementation is described in Ali-Lagoa and Delbo' (2017A&A...603A..55A, cat. J/A+A/603/A55). Minimum relative errors of 10, 15, and 20 percent are given for size, beaming parameter and albedo in those cases where the beaming parameter could be fitted. Otherwise, a default value of the beaming parameter is assumed based on Eq. 1 in the article, and the minimum relative errors in size and albedo increase to 20 and 40 percent (see the discussions in Mainzer et al., 2011ApJ...736..100M, Ali-Lagoa et al., 2016A&A...591A..14A, Cat. J/A+A/591/A14). We also provide the asteroid absolute magnitudes and G12 slope parameters retrieved from Oszkiewicz et al. (2012), the number of observations used in each IRC band (S9W and L18W), plus the heliocentric and geocentric distances and phase angle (r, Delta, alpha) based on the ephemerides taken from the MIRIADE service (http://vo.imcce.fr/webservices/miriade/?ephemph). (1 data file).

  18. 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.

  19. 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.

  20. The AGN fraction of submm-selected galaxies and contributions to the submm/mm-wave extragalactic background light

    Science.gov (United States)

    Serjeant, S.; Negrello, M.; Pearson, C.; Mortier, A.; Austermann, J.; Aretxaga, I.; Clements, D.; Chapman, S.; Dye, S.; Dunlop, J.; Dunne, L.; Farrah, D.; Hughes, D.; Lee, H.-M.; Matsuhara, H.; Ibar, E.; Im, M.; Jeong, W.-S.; Kim, S.; Oyabu, S.; Takagi, T.; Wada, T.; Wilson, G.; Vaccari, M.; Yun, M.

    2010-05-01

    We present a comparison of the SCUBA half degree extragalactic survey (SHADES) at 450 μm, 850 μm and 1100 μm with deep guaranteed time 15 μm AKARI FU-HYU survey data and Spitzer guaranteed time data at 3.6-24 μm in the Lockman hole east. The AKARI data was analysed using bespoke software based in part on the drizzling and minimum-variance matched filtering developed for SHADES, and was cross-calibrated against ISO fluxes. Our stacking analyses find AKARI 15 μm galaxies with ⪆200 μJy contribute >10% of the 450 μm background, but only 0.3.

  1. Llaki and ñakary: idioms of distress and suffering among the highland Quechua in the Peruvian Andes.

    Science.gov (United States)

    Pedersen, Duncan; Kienzler, Hanna; Gamarra, Jeffrey

    2010-06-01

    This article examines some of the long-term health outcomes of extreme adversities and the ways in which social inequalities and idioms of distress are historically and socially produced in the Peruvian context. We describe how the highland Quechua of northern Ayacucho construct and experience expressions of distress and suffering such as pinsamientuwan (worrying thoughts, worries), ñakary (suffering) and llaki (sorrow, sadness), in a context of persistent social inequalities, social exclusion and a recent history of political violence. It is concluded that the multiple expressions of distress and suffering are closely related to past and current events, shaped by beliefs, core values and cultural norms and, in this process, transformed, recreated and invested with new meanings and attributions.

  2. Deep Borehole Field Test Requirements and Controlled Assumptions.

    Energy Technology Data Exchange (ETDEWEB)

    Hardin, Ernest [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-07-01

    This document presents design requirements and controlled assumptions intended for use in the engineering development and testing of: 1) prototype packages for radioactive waste disposal in deep boreholes; 2) a waste package surface handling system; and 3) a subsurface system for emplacing and retrieving packages in deep boreholes. Engineering development and testing is being performed as part of the Deep Borehole Field Test (DBFT; SNL 2014a). This document presents parallel sets of requirements for a waste disposal system and for the DBFT, showing the close relationship. In addition to design, it will also inform planning for drilling, construction, and scientific characterization activities for the DBFT. The information presented here follows typical preparations for engineering design. It includes functional and operating requirements for handling and emplacement/retrieval equipment, waste package design and emplacement requirements, borehole construction requirements, sealing requirements, and performance criteria. Assumptions are included where they could impact engineering design. Design solutions are avoided in the requirements discussion. Deep Borehole Field Test Requirements and Controlled Assumptions July 21, 2015 iv ACKNOWLEDGEMENTS This set of requirements and assumptions has benefited greatly from reviews by Gordon Appel, Geoff Freeze, Kris Kuhlman, Bob MacKinnon, Steve Pye, David Sassani, Dave Sevougian, and Jiann Su.

  3. 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.

  4. A PILOT FOR A VERY LARGE ARRAY H I DEEP FIELD

    International Nuclear Information System (INIS)

    Fernández, Ximena; Van Gorkom, J. H.; Schiminovich, David; Hess, Kelley M.; Pisano, D. J.; Kreckel, Kathryn; Momjian, Emmanuel; Popping, Attila; Oosterloo, Tom; Chomiuk, Laura; Verheijen, M. A. W.; Henning, Patricia A.; Bershady, Matthew A.; Wilcots, Eric M.; Scoville, Nick

    2013-01-01

    High-resolution 21 cm H I deep fields provide spatially and kinematically resolved images of neutral hydrogen at different redshifts, which are key to understanding galaxy evolution across cosmic time and testing predictions of cosmological simulations. Here we present results from a pilot for an H I deep field done with the Karl G. Jansky Very Large Array (VLA). We take advantage of the newly expanded capabilities of the telescope to probe the redshift interval 0 < z < 0.193 in one observation. We observe the COSMOS field for 50 hr, which contains 413 galaxies with optical spectroscopic redshifts in the imaged field of 34' × 34' and the observed redshift interval. We have detected neutral hydrogen gas in 33 galaxies in different environments spanning the probed redshift range, including three without a previously known spectroscopic redshift. The detections have a range of H I and stellar masses, indicating the diversity of galaxies we are probing. We discuss the observations, data reduction, results, and highlight interesting detections. We find that the VLA's B-array is the ideal configuration for H I deep fields since its long spacings mitigate radio frequency interference. This pilot shows that the VLA is ready to carry out such a survey, and serves as a test for future H I deep fields planned with other Square Kilometer Array pathfinders.

  5. A PILOT FOR A VERY LARGE ARRAY H I DEEP FIELD

    Energy Technology Data Exchange (ETDEWEB)

    Fernandez, Ximena; Van Gorkom, J. H.; Schiminovich, David [Department of Astronomy, Columbia University, New York, NY 10027 (United States); Hess, Kelley M. [Department of Astronomy, Astrophysics, Cosmology and Gravity Centre, University of Cape Town, Private Bag X3, Rondebosch 7701 (South Africa); Pisano, D. J. [Department of Physics, West Virginia University, P.O. Box 6315, Morgantown, WV 26506 (United States); Kreckel, Kathryn [Max Planck Institute for Astronomy, Koenigstuhl 17, D-69117 Heidelberg (Germany); Momjian, Emmanuel [National Radio Astronomy Observatory, Socorro, NM 87801 (United States); Popping, Attila [International Centre for Radio Astronomy Research (ICRAR), The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009 (Australia); Oosterloo, Tom [Netherlands Institute for Radio Astronomy (ASTRON), Postbus 2, NL-7990 AA Dwingeloo (Netherlands); Chomiuk, Laura [Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 (United States); Verheijen, M. A. W. [Kapteyn Astronomical Institute, University of Groningen, Postbus 800, NL-9700 AV Groningen (Netherlands); Henning, Patricia A. [Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM 87131 (United States); Bershady, Matthew A.; Wilcots, Eric M. [Department of Astronomy, University of Wisconsin-Madison, Madison, WI 53706 (United States); Scoville, Nick, E-mail: ximena@astro.columbia.edu [Department of Astronomy, California Institute of Technology, Pasadena, CA 91125 (United States)

    2013-06-20

    High-resolution 21 cm H I deep fields provide spatially and kinematically resolved images of neutral hydrogen at different redshifts, which are key to understanding galaxy evolution across cosmic time and testing predictions of cosmological simulations. Here we present results from a pilot for an H I deep field done with the Karl G. Jansky Very Large Array (VLA). We take advantage of the newly expanded capabilities of the telescope to probe the redshift interval 0 < z < 0.193 in one observation. We observe the COSMOS field for 50 hr, which contains 413 galaxies with optical spectroscopic redshifts in the imaged field of 34' Multiplication-Sign 34' and the observed redshift interval. We have detected neutral hydrogen gas in 33 galaxies in different environments spanning the probed redshift range, including three without a previously known spectroscopic redshift. The detections have a range of H I and stellar masses, indicating the diversity of galaxies we are probing. We discuss the observations, data reduction, results, and highlight interesting detections. We find that the VLA's B-array is the ideal configuration for H I deep fields since its long spacings mitigate radio frequency interference. This pilot shows that the VLA is ready to carry out such a survey, and serves as a test for future H I deep fields planned with other Square Kilometer Array pathfinders.

  6. 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.

  7. THE SPITZER DEEP, WIDE-FIELD SURVEY

    International Nuclear Information System (INIS)

    Ashby, M. L. N.; Brodwin, M.; Stern, D.; Griffith, R.; Eisenhardt, P.; Gorjian, V.; Kozlowski, S.; Kochanek, C. S.; Bock, J. J.; Borys, C.; Brand, K.; Grogin, N. A.; Brown, M. J. I.; Cool, R.; Cooray, A.; Croft, S.; Dey, A.; Eisenstein, D.; Gonzalez, A. H.; Ivison, R. J.

    2009-01-01

    The Spitzer Deep, Wide-Field Survey (SDWFS) is a four-epoch infrared survey of 10 deg. 2 in the Booetes field of the NOAO Deep Wide-Field Survey using the IRAC instrument on the Spitzer Space Telescope. SDWFS, a Spitzer Cycle 4 Legacy project, occupies a unique position in the area-depth survey space defined by other Spitzer surveys. The four epochs that make up SDWFS permit-for the first time-the selection of infrared-variable and high proper motion objects over a wide field on timescales of years. Because of its large survey volume, SDWFS is sensitive to galaxies out to z ∼ 3 with relatively little impact from cosmic variance for all but the richest systems. The SDWFS data sets will thus be especially useful for characterizing galaxy evolution beyond z ∼ 1.5. This paper explains the SDWFS observing strategy and data processing, presents the SDWFS mosaics and source catalogs, and discusses some early scientific findings. The publicly released, full-depth catalogs contain 6.78, 5.23, 1.20, and 0.96 x 10 5 distinct sources detected to the average 5σ, 4''-diameter, aperture-corrected limits of 19.77, 18.83, 16.50, and 15.82 Vega mag at 3.6, 4.5, 5.8, and 8.0 μm, respectively. The SDWFS number counts and color-color distribution are consistent with other, earlier Spitzer surveys. At the 6 minute integration time of the SDWFS IRAC imaging, >50% of isolated Faint Images of the Radio Sky at Twenty cm radio sources and >80% of on-axis XBooetes sources are detected out to 8.0 μm. Finally, we present the four highest proper motion IRAC-selected sources identified from the multi-epoch imaging, two of which are likely field brown dwarfs of mid-T spectral class.

  8. The GISMO two-millimeter deep field in GOODS-N

    Energy Technology Data Exchange (ETDEWEB)

    Staguhn, Johannes G. [The Henry A. Rowland Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Kovács, Attila [California Institute of Technology 301-17, 1200 East California Boulevard, Pasadena, CA 91125 (United States); Arendt, Richard G.; Benford, Dominic J.; Dwek, Eli; Fixsen, Dale J.; Jhabvala, Christine A.; Maher, Stephen F.; Miller, Timothy M.; Moseley, S. Harvey; Sharp, Elmer H.; Wollack, Edward J. [Observational Cosmology Lab, Code 665, NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Decarli, Roberto; Walter, Fabian [Max-Planck-Institute für Astronomie, Königstuhl 17, D-69117 Heidelberg (Germany); Hilton, Gene C.; Irwin, Kent D. [NIST Quantum Devices Group, 325 Broadway Mailcode 817.03, Boulder, CO 80305 (United States); Karim, Alexander [Department of Physics, Durham University, South Road, Durham DH1 3LE (United Kingdom); Leclercq, Samuel [Institut de Radio Astronomie Millimétrique, 300 Rue de la Piscine, F-38406 Saint Martin d' Heres (France)

    2014-07-20

    We present deep continuum observations using the GISMO camera at a wavelength of 2 mm centered on the Hubble Deep Field in the GOODS-N field. These are the first deep field observations ever obtained at this wavelength. The 1σ sensitivity in the innermost ∼4' of the 7' diameter map is ∼135 μJy beam{sup –1}, a factor of three higher in flux/beam sensitivity than the deepest available SCUBA 850 μm observations, and almost a factor of four higher in flux/beam sensitivity than the combined MAMBO/AzTEC 1.2 mm observations of this region. Our source extraction algorithm identifies 12 sources directly, and another 3 through correlation with known sources at 1.2 mm and 850 μm. Five of the directly detected GISMO sources have counterparts in the MAMBO/AzTEC catalog, and four of those also have SCUBA counterparts. HDF850.1, one of the first blank-field detected submillimeter galaxies, is now detected at 2 mm. The median redshift of all sources with counterparts of known redshifts is z-tilde =2.91±0.94. Statistically, the detections are most likely real for five of the seven 2 mm sources without shorter wavelength counterparts, while the probability for none of them being real is negligible.

  9. VizieR Online Data Catalog: AGNs in submm-selected Lockman Hole galaxies (Serjeant+, 2010)

    Science.gov (United States)

    Serjeant, S.; Negrello, M.; Pearson, C.; Mortier, A.; Austermann, J.; Aretxaga, I.; Clements, D.; Chapman, S.; Dye, S.; Dunlop, J.; Dunne, L.; Farrah, D.; Hughes, D.; Lee, H. M.; Matsuhara, H.; Ibar, E.; Im, M.; Jeong, W.-S.; Kim, S.; Oyabu, S.; Takagi, T.; Wada, T.; Wilson, G.; Vaccari, M.; Yun, M.

    2013-11-01

    We present a comparison of the SCUBA half degree extragalactic survey (SHADES) at 450μm, 850μm and 1100μm with deep guaranteed time 15μm AKARI FU-HYU survey data and Spitzer guaranteed time data at 3.6-24μm in the Lockman hole east. The AKARI data was analysed using bespoke software based in part on the drizzling and minimum-variance matched filtering developed for SHADES, and was cross-calibrated against ISO fluxes. (2 data files).

  10. THE 2012 HUBBLE ULTRA DEEP FIELD (UDF12): OBSERVATIONAL OVERVIEW

    Energy Technology Data Exchange (ETDEWEB)

    Koekemoer, Anton M. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Ellis, Richard S.; Schenker, Matthew A. [Department of Astrophysics, California Institute of Technology, MS 249-17, Pasadena, CA 91125 (United States); McLure, Ross J.; Dunlop, James S.; Bowler, Rebecca A. A.; Rogers, Alexander B.; Curtis-Lake, Emma; Cirasuolo, Michele; Wild, V.; Targett, T. [Institute for Astronomy, University of Edinburgh, Royal Observatory, Edinburgh EH9 3HJ (United Kingdom); Robertson, Brant E.; Schneider, Evan; Stark, Daniel P. [Department of Astronomy and Steward Observatory, University of Arizona, Tucson, AZ 85721 (United States); Ono, Yoshiaki; Ouchi, Masami [Institute for Cosmic Ray Research, University of Tokyo, Kashiwa City, Chiba 277-8582 (Japan); Charlot, Stephane [UPMC-CNRS, UMR7095, Institut d' Astrophysique de Paris, F-75014, Paris (France); Furlanetto, Steven R. [Department of Physics and Astronomy, University of California, Los Angeles, CA 90095 (United States)

    2013-11-01

    We present the 2012 Hubble Ultra Deep Field campaign (UDF12), a large 128 orbit Cycle 19 Hubble Space Telescope program aimed at extending previous Wide Field Camera 3 (WFC3)/IR observations of the UDF by quadrupling the exposure time in the F105W filter, imaging in an additional F140W filter, and extending the F160W exposure time by 50%, as well as adding an extremely deep parallel field with the Advanced Camera for Surveys (ACS) in the F814W filter with a total exposure time of 128 orbits. The principal scientific goal of this project is to determine whether galaxies reionized the universe; our observations are designed to provide a robust determination of the star formation density at z ∼> 8, improve measurements of the ultraviolet continuum slope at z ∼ 7-8, facilitate the construction of new samples of z ∼ 9-10 candidates, and enable the detection of sources up to z ∼ 12. For this project we committed to combining these and other WFC3/IR imaging observations of the UDF area into a single homogeneous dataset to provide the deepest near-infrared observations of the sky. In this paper we present the observational overview of the project and describe the procedures used in reducing the data as well as the final products that were produced. We present the details of several special procedures that we implemented to correct calibration issues in the data for both the WFC3/IR observations of the main UDF field and our deep 128 orbit ACS/WFC F814W parallel field image, including treatment for persistence, correction for time-variable sky backgrounds, and astrometric alignment to an accuracy of a few milliarcseconds. We release the full, combined mosaics comprising a single, unified set of mosaics of the UDF, providing the deepest near-infrared blank-field view of the universe currently achievable, reaching magnitudes as deep as AB ∼ 30 mag in the near-infrared, and yielding a legacy dataset on this field.

  11. THE 2012 HUBBLE ULTRA DEEP FIELD (UDF12): OBSERVATIONAL OVERVIEW

    International Nuclear Information System (INIS)

    Koekemoer, Anton M.; Ellis, Richard S.; Schenker, Matthew A.; McLure, Ross J.; Dunlop, James S.; Bowler, Rebecca A. A.; Rogers, Alexander B.; Curtis-Lake, Emma; Cirasuolo, Michele; Wild, V.; Targett, T.; Robertson, Brant E.; Schneider, Evan; Stark, Daniel P.; Ono, Yoshiaki; Ouchi, Masami; Charlot, Stephane; Furlanetto, Steven R.

    2013-01-01

    We present the 2012 Hubble Ultra Deep Field campaign (UDF12), a large 128 orbit Cycle 19 Hubble Space Telescope program aimed at extending previous Wide Field Camera 3 (WFC3)/IR observations of the UDF by quadrupling the exposure time in the F105W filter, imaging in an additional F140W filter, and extending the F160W exposure time by 50%, as well as adding an extremely deep parallel field with the Advanced Camera for Surveys (ACS) in the F814W filter with a total exposure time of 128 orbits. The principal scientific goal of this project is to determine whether galaxies reionized the universe; our observations are designed to provide a robust determination of the star formation density at z ∼> 8, improve measurements of the ultraviolet continuum slope at z ∼ 7-8, facilitate the construction of new samples of z ∼ 9-10 candidates, and enable the detection of sources up to z ∼ 12. For this project we committed to combining these and other WFC3/IR imaging observations of the UDF area into a single homogeneous dataset to provide the deepest near-infrared observations of the sky. In this paper we present the observational overview of the project and describe the procedures used in reducing the data as well as the final products that were produced. We present the details of several special procedures that we implemented to correct calibration issues in the data for both the WFC3/IR observations of the main UDF field and our deep 128 orbit ACS/WFC F814W parallel field image, including treatment for persistence, correction for time-variable sky backgrounds, and astrometric alignment to an accuracy of a few milliarcseconds. We release the full, combined mosaics comprising a single, unified set of mosaics of the UDF, providing the deepest near-infrared blank-field view of the universe currently achievable, reaching magnitudes as deep as AB ∼ 30 mag in the near-infrared, and yielding a legacy dataset on this field

  12. 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.

  13. Deep Vadose Zone–Applied Field Research Initiative Fiscal Year 2012 Annual Report

    Energy Technology Data Exchange (ETDEWEB)

    Wellman, Dawn M.; Truex, Michael J.; Johnson, Timothy C.; Bunn, Amoret L.; Golovich, Elizabeth C.

    2013-03-14

    This annual report describes the background of the Deep Vadose Zone-Applied Field Research Initiative, and some of the programmatic approaches and transformational technologies in groundwater and deep vadose zone remediation developed during fiscal year 2012.

  14. Making Data Mobile: The Hubble Deep Field Academy iPad app

    Science.gov (United States)

    Eisenhamer, Bonnie; Cordes, K.; Davis, S.; Eisenhamer, J.

    2013-01-01

    Many school districts are purchasing iPads for educators and students to use as learning tools in the classroom. Educators often prefer these devices to desktop and laptop computers because they offer portability and an intuitive design, while having a larger screen size when compared to smart phones. As a result, we began investigating the potential of adapting online activities for use on Apple’s iPad to enhance the dissemination and usage of these activities in instructional settings while continuing to meet educators’ needs. As a pilot effort, we are developing an iPad app for the “Hubble Deep Field Academy” - an activity that is currently available online and commonly used by middle school educators. The Hubble Deep Field Academy app features the HDF-North image while centering on the theme of how scientists use light to explore and study the universe. It also includes features such as embedded links to vocabulary, images and videos, teacher background materials, and readings about Hubble’s other deep field surveys. It is our goal is to impact students’ engagement in STEM-related activities, while enhancing educators’ usage of NASA data via new and innovative mediums. We also hope to develop and share lessons learned with the E/PO community that can be used to support similar projects. We plan to test the Hubble Deep Field Academy app during the school year to determine if this new activity format is beneficial to the education community.

  15. EMPIRICAL PREDICTIONS FOR (SUB-)MILLIMETER LINE AND CONTINUUM DEEP FIELDS

    Energy Technology Data Exchange (ETDEWEB)

    Da Cunha, Elisabete; Walter, Fabian; Decarli, Roberto; Rix, Hans-Walter [Max-Planck-Institut fuer Astronomie, Koenigstuhl 17, D-69117 Heidelberg (Germany); Bertoldi, Frank [Argelander Institute for Astronomy, University of Bonn, Auf dem Huegel 71, D-53121 Bonn (Germany); Carilli, Chris [National Radio Astronomy Observatory, Pete V. Domenici Array Science Center, P.O. Box O, Socorro, NM 87801 (United States); Daddi, Emanuele; Elbaz, David; Sargent, Mark [Laboratoire AIM, CEA/DSM-CNRS-Universite Paris Diderot, Irfu/Service d' Astrophysique, CEA Saclay, Orme des Merisiers, F-91191 Gif-sur-Yvette Cedex (France); Ivison, Rob [UK Astronomy Technology Centre, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ (United Kingdom); Maiolino, Roberto [Cavendish Laboratory, University of Cambridge, 19 J.J. Thomson Avenue, Cambridge CB3 0HE (United Kingdom); Riechers, Dominik [Astronomy Department, California Institute of Technology, MC 249-17, 1200 East California Boulevard, Pasadena, CA 91125 (United States); Smail, Ian [Institute for Computational Cosmology, Durham University, South Road, Durham DH1 3LE (United Kingdom); Weiss, Axel, E-mail: cunha@mpia.de [Max-Planck-Institut fuer Radioastronomie, Auf dem Huegel 69, D-53121 Bonn (Germany)

    2013-03-01

    Modern (sub-)millimeter/radio interferometers such as ALMA, JVLA, and the PdBI successor NOEMA will enable us to measure the dust and molecular gas emission from galaxies that have luminosities lower than the Milky Way, out to high redshifts and with unprecedented spatial resolution and sensitivity. This will provide new constraints on the star formation properties and gas reservoir in galaxies throughout cosmic times through dedicated deep field campaigns targeting the CO/[C II] lines and dust continuum emission in the (sub-)millimeter regime. In this paper, we present empirical predictions for such line and continuum deep fields. We base these predictions on the deepest available optical/near-infrared Advanced Camera for Surveys and NICMOS data on the Hubble Ultra Deep Field (over an area of about 12 arcmin{sup 2}). Using a physically motivated spectral energy distribution model, we fit the observed optical/near-infrared emission of 13,099 galaxies with redshifts up to z = 5, and obtain median-likelihood estimates of their stellar mass, star formation rate, dust attenuation, and dust luminosity. We combine the attenuated stellar spectra with a library of infrared emission models spanning a wide range of dust temperatures to derive statistical constraints on the dust emission in the infrared and (sub-)millimeter which are consistent with the observed optical/near-infrared emission in terms of energy balance. This allows us to estimate, for each galaxy, the (sub-)millimeter continuum flux densities in several ALMA, PdBI/NOEMA, and JVLA bands. As a consistency check, we verify that the 850 {mu}m number counts and extragalactic background light derived using our predictions are consistent with previous observations. Using empirical relations between the observed CO/[C II] line luminosities and the infrared luminosity of star-forming galaxies, we infer the luminosity of the CO(1-0) and [C II] lines from the estimated infrared luminosity of each galaxy in our sample

  16. Deep Borehole Field Test Conceptual Design Report

    Energy Technology Data Exchange (ETDEWEB)

    Hardin, Ernest L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2016-09-30

    This report documents conceptual design development for the Deep Borehole Field Test (DBFT), including test packages (simulated waste packages, not containing waste) and a system for demonstrating emplacement and retrieval of those packages in the planned Field Test Borehole (FTB). For the DBFT to have demonstration value, it must be based on conceptualization of a deep borehole disposal (DBD) system. This document therefore identifies key options for a DBD system, describes an updated reference DBD concept, and derives a recommended concept for the DBFT demonstration. The objective of the DBFT is to confirm the safety and feasibility of the DBD concept for long-term isolation of radioactive waste. The conceptual design described in this report will demonstrate equipment and operations for safe waste handling and downhole emplacement of test packages, while contributing to an evaluation of the overall safety and practicality of the DBD concept. The DBFT also includes drilling and downhole characterization investigations that are described elsewhere (see Section 1). Importantly, no radioactive waste will be used in the DBFT, nor will the DBFT site be used for disposal of any type of waste. The foremost performance objective for conduct of the DBFT is to demonstrate safe operations in all aspects of the test.

  17. Techniques for Field Operation of Straddle-packer System in Deep Borehole

    International Nuclear Information System (INIS)

    Kim, Kyung Su; Park, Kyung Woo; Kim, Geon Young; Ji, Sung Hoon; Koh, Yong Kwon; Choi, Jong Won

    2010-05-01

    It is necessary to establish an appropriate hydro-testing tool for the qualified characterization of deep geological environments, especially for the hydraulic properties of rock formation. This research project had been initiated for the purpose of establishment of advanced infra-structures in KURT. The straddle packer system was developed for hydraulic characterization of geological formation using deep borehole. This technical report consists of design concept, basic requirements, function of each part, field operation procedures and techniques, detail design drawings, and specifications. The qualified hydro-testing tool, which is suitable for medium to low permeable formation, using large and deep borehole, has been developed. This tool will be applied for the research project on development of HLW disposal technologies and the site characterization activities of LILW disposal project. Prior to field operation using this hydro-testing equipment, every researchers should be well acquainted with this technical report

  18. AKARI OBSERVATION OF THE NORTH ECLIPTIC POLE (NEP) SUPERCLUSTER AT z = 0.087: MID-INFRARED VIEW OF TRANSITION GALAXIES

    International Nuclear Information System (INIS)

    Ko, Jongwan; Im, Myungshin; Lee, Hyung Mok; Lee, Myung Gyoon; Kim, Seong Jin; Jeon, Yiseul; Shim, Hyunjin; Hwang, Ho Seong; Willmer, Christopher N. A.; Weiner, Benjamin J.; Malkan, Matthew A.; Papovich, Casey; Matsuhara, Hideo; Takagi, Toshinobu; Oyabu, Shinki

    2012-01-01

    We present the mid-infrared (MIR) properties of galaxies within a supercluster in the north ecliptic pole region at z ∼ 0.087 observed with the AKARI satellite. We use data from the AKARI NEP-Wide (5.4 deg 2 ) IR survey and the CLusters of galaxies EVoLution studies (CLEVL) mission program. We show that near-IR (3 μm)-mid-IR (11 μm) color can be used as an indicator of the specific star formation rate and the presence of intermediate-age stellar populations. From the MIR observations, we find that red-sequence galaxies consist not only of passively evolving red early-type galaxies, but also of (1) 'weak-SFGs' (disk-dominated star-forming galaxies that have star formation rates lower by ∼4 × than blue-cloud galaxies) and (2) 'intermediate-MXGs' (bulge-dominated galaxies showing stronger MIR dust emission than normal red early-type galaxies). These two populations can be a set of transition galaxies from blue, star-forming, late-type galaxies evolving into red, quiescent, early-type ones. We find that the weak-SFGs are predominant at intermediate masses (10 10 M ☉ * 10.5 M ☉ ) and are typically found in local densities similar to the outskirts of galaxy clusters. As much as 40% of the supercluster member galaxies in this mass range can be classified as weak-SFGs, but their proportion decreases to * > 10 10.5 M ☉ ) at any galaxy density. The fraction of the intermediate-MXG among red-sequence galaxies at 10 10 M ☉ * 11 M ☉ also decreases as the density and mass increase. In particular, ∼42% of the red-sequence galaxies with early-type morphologies are classified as intermediate-MXGs at intermediate densities. These results suggest that the star formation activity is strongly dependent on the stellar mass, but that the morphological transformation is mainly controlled by the environment.

  19. Deep inelastic lepton-nucleus scattering from the light-cone quantum field theory

    International Nuclear Information System (INIS)

    Boqiang Ma; Ji Sun

    1990-01-01

    We show that for deep inelastic lepton-nucleus scattering, the conditions which validate the impulse approximation are hardly satisfied when using ordinary instant form dynamics in the rest frame of the nucleus, whereas they are well satisfied when using instant form dynamics in the infinite-momentum frame, or using light-front form dynamics in an ordinary frame. Therefore a reliable theoretical treatment of deep inelastic lepton-nucleus scattering should be performed in the time-ordered perturbation theory in the infinite-momentum frame, or its equivalent, the light-cone perturbation theory in an ordinary frame. To this end, we extend the light-cone quantum field theory to the baryon-meson field to establish a relativistic composite model of nuclei. We then apply the impulse approximation to deep inelastic lepton-nucleus scattering in this model.(author)

  20. THE AKARI 2.5-5.0 μm SPECTRAL ATLAS OF TYPE-1 ACTIVE GALACTIC NUCLEI: BLACK HOLE MASS ESTIMATOR, LINE RATIO, AND HOT DUST TEMPERATURE

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Dohyeong; Im, Myungshin; Kim, Ji Hoon; Jun, Hyunsung David; Lee, Seong-Kook [Center for the Exploration of the Origin of the Universe (CEOU), Astronomy Program, Department of Physics and Astronomy, Seoul National University, Shillim-Dong, Kwanak-Gu, Seoul 151-742 (Korea, Republic of); Woo, Jong-Hak; Lee, Hyung Mok; Lee, Myung Gyoon [Astronomy Program, Department of Physics and Astronomy, Seoul National University, Shillim-Dong, Kwanak-Gu, Seoul 151-742 (Korea, Republic of); Nakagawa, Takao; Matsuhara, Hideo; Wada, Takehiko; Takagi, Toshinobu [Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, Sagamihara, Kanagawa 252-5210 (Japan); Oyabu, Shinki [Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602 (Japan); Ohyama, Youichi, E-mail: dohyeong@astro.snu.ac.kr, E-mail: mim@astro.snu.ac.kr [Institute of Astronomy and Astrophysics, Academia Sinica, P.O. Box 23-141, Taipei 106, Taiwan (China)

    2015-01-01

    We present 2.5-5.0 μm spectra of 83 nearby (0.002 < z < 0.48) and bright (K < 14 mag) type-1 active galactic nuclei (AGNs) taken with the Infrared Camera on board AKARI. The 2.5-5.0 μm spectral region contains emission lines such as Brβ (2.63 μm), Brα (4.05 μm), and polycyclic aromatic hydrocarbons (3.3 μm), which can be used for studying the black hole (BH) masses and star formation activity in the host galaxies of AGNs. The spectral region also suffers less dust extinction than in the ultra violet (UV) or optical wavelengths, which may provide an unobscured view of dusty AGNs. Our sample is selected from bright quasar surveys of Palomar-Green and SNUQSO, and AGNs with reverberation-mapped BH masses from Peterson et al. Using 11 AGNs with reliable detection of Brackett lines, we derive the Brackett-line-based BH mass estimators. We also find that the observed Brackett line ratios can be explained with the commonly adopted physical conditions of the broad line region. Moreover, we fit the hot and warm dust components of the dust torus by adding photometric data of SDSS, 2MASS, WISE, and ISO to the AKARI spectra, finding hot and warm dust temperatures of ∼1100 K and ∼220 K, respectively, rather than the commonly cited hot dust temperature of 1500 K.

  1. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

    Directory of Open Access Journals (Sweden)

    Sheng Wang

    2015-07-01

    Full Text Available Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields, to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

  2. MOVING OBJECTS IN THE HUBBLE ULTRA DEEP FIELD

    Energy Technology Data Exchange (ETDEWEB)

    Kilic, Mukremin; Gianninas, Alexandros [Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, 440 W. Brooks St., Norman, OK 73019 (United States); Von Hippel, Ted, E-mail: kilic@ou.edu, E-mail: alexg@nhn.ou.edu, E-mail: ted.vonhippel@erau.edu [Embry-Riddle Aeronautical University, 600 S. Clyde Morris Blvd., Daytona Beach, FL 32114 (United States)

    2013-09-01

    We identify proper motion objects in the Hubble Ultra Deep Field (UDF) using the optical data from the original UDF program in 2004 and the near-infrared data from the 128 orbit UDF 2012 campaign. There are 12 sources brighter than I = 27 mag that display >3{sigma} significant proper motions. We do not find any proper motion objects fainter than this magnitude limit. Combining optical and near-infrared photometry, we model the spectral energy distribution of each point-source using stellar templates and state-of-the-art white dwarf models. For I {<=} 27 mag, we identify 23 stars with K0-M6 spectral types and two faint blue objects that are clearly old, thick disk white dwarfs. We measure a thick disk white dwarf space density of 0.1-1.7 Multiplication-Sign 10{sup -3} pc{sup -3} from these two objects. There are no halo white dwarfs in the UDF down to I = 27 mag. Combining the Hubble Deep Field North, South, and the UDF data, we do not see any evidence for dark matter in the form of faint halo white dwarfs, and the observed population of white dwarfs can be explained with the standard Galactic models.

  3. DeepCotton: in-field cotton segmentation using deep fully convolutional network

    Science.gov (United States)

    Li, Yanan; Cao, Zhiguo; Xiao, Yang; Cremers, Armin B.

    2017-09-01

    Automatic ground-based in-field cotton (IFC) segmentation is a challenging task in precision agriculture, which has not been well addressed. Nearly all the existing methods rely on hand-crafted features. Their limited discriminative power results in unsatisfactory performance. To address this, a coarse-to-fine cotton segmentation method termed "DeepCotton" is proposed. It contains two modules, fully convolutional network (FCN) stream and interference region removal stream. First, FCN is employed to predict initially coarse map in an end-to-end manner. The convolutional networks involved in FCN guarantee powerful feature description capability, simultaneously, the regression analysis ability of neural network assures segmentation accuracy. To our knowledge, we are the first to introduce deep learning to IFC segmentation. Second, our proposed "UP" algorithm composed of unary brightness transformation and pairwise region comparison is used for obtaining interference map, which is executed to refine the coarse map. The experiments on constructed IFC dataset demonstrate that our method outperforms other state-of-the-art approaches, either in different common scenarios or single/multiple plants. More remarkable, the "UP" algorithm greatly improves the property of the coarse result, with the average amplifications of 2.6%, 2.4% on accuracy and 8.1%, 5.5% on intersection over union for common scenarios and multiple plants, separately.

  4. Young Stars in the Camelopardalis Dust and Molecular Clouds. VI. YSOs Verified by Spitzer and Akari Infrared Photometry

    Directory of Open Access Journals (Sweden)

    Straižys V.

    2010-06-01

    Full Text Available Using photometric data of infrared surveys, young stellar object (YSO status is verified for 141 objects selected in our previous papers in the Cassiopeia and Camelopardalis segment of the Milky Way bounded by Galactic coordinates (l, b = (132-158°, ±12°. The area includes the known star- forming regions in the emission nebulae W3, W4 and W5 and the massive YSO AFGL490. Spectral energy distribution (SED curves between 700 nm and 160 μm, constructed from the GSC 2, 2MASS, IRAS, MSX, Spitzer and AKARI data, are used to estimate the evolutionary stages of these stars. We confirm the YSO status for most of the objects. If all of the investigated objects were YSOs, 45% of them should belong to Class I, 41% to class II and 14% to Class III. However, SEDs of some of these objects can be affected by nearby extended infrared sources, like compact H II regions, infrared clusters or dusty galaxies.

  5. Topography characterization of a deep grating using near-field imaging

    DEFF Research Database (Denmark)

    Gregersen, Niels; Tromborg, Bjarne; Volkov, Valentyn S.

    2006-01-01

    Using near-field optical microscopy at the wavelength of 633 nm, we image light intensity distributions at several distances above an ~2-mm deep and a 1-mm-period glass grating illuminated from below under the condition of total internal reflection. The intensity distributions are numerically mod...

  6. A MULTIWAVELENGTH STUDY OF TADPOLE GALAXIES IN THE HUBBLE ULTRA DEEP FIELD

    International Nuclear Information System (INIS)

    Straughn, Amber N.; Eufrasio, Rafael T.; Gardner, Jonathan P.; Voyer, Elysse N.; Mello, Duilia de; Soto, Emmaris; Petty, Sara; Kassin, Susan; Ravindranath, Swara

    2015-01-01

    Multiwavelength data are essential in order to provide a complete picture of galaxy evolution and to inform studies of galaxies’ morphological properties across cosmic time. Here we present the results of a multiwavelength investigation of the morphologies of “tadpole” galaxies at intermediate redshift (0.314 < z < 3.175) in the Hubble Ultra Deep Field. These galaxies were previously selected from deep Hubble Space Telescope (HST) F775W data based on their distinct asymmetric knot-plus-tail morphologies. Here we use deep Wide Field Camera 3 near-infrared imaging in addition to the HST optical data in order to study the rest-frame UV/optical morphologies of these galaxies across the redshift range 0.3 < z < 3.2. This study reveals that the majority of these galaxies do retain their general asymmetric morphology in the rest-frame optical over this redshift range, if not the distinct “tadpole” shape. The average stellar mass of tadpole galaxies is lower than that of field galaxies, with the effect being slightly greater at higher redshift within the errors. Estimated from spectral energy distribution fits, the average age of tadpole galaxies is younger than that of field galaxies in the lower-redshift bin, and the average metallicity is lower (whereas the specific star formation rate for tadpoles is roughly the same as field galaxies across the redshift range probed here). These average effects combined support the conclusion that this subset of galaxies is in an active phase of assembly, either late-stage merging or cold gas accretion causing localized clumpy star formation

  7. A MULTIWAVELENGTH STUDY OF TADPOLE GALAXIES IN THE HUBBLE ULTRA DEEP FIELD

    Energy Technology Data Exchange (ETDEWEB)

    Straughn, Amber N.; Eufrasio, Rafael T.; Gardner, Jonathan P. [Astrophysics Science Division, Goddard Space Flight Center, Code 665, Greenbelt, MD 20771 (United States); Voyer, Elysse N. [Randstad at Google, 1129 San Antonio Road, Palo Alto, CA (United States); Mello, Duilia de; Soto, Emmaris [Department of Physics, The Catholic University of America, Washington, DC 20064 (United States); Petty, Sara [Department of Physics, Virginia Tech, Blacksburg, VA 24061 (United States); Kassin, Susan; Ravindranath, Swara [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States)

    2015-12-01

    Multiwavelength data are essential in order to provide a complete picture of galaxy evolution and to inform studies of galaxies’ morphological properties across cosmic time. Here we present the results of a multiwavelength investigation of the morphologies of “tadpole” galaxies at intermediate redshift (0.314 < z < 3.175) in the Hubble Ultra Deep Field. These galaxies were previously selected from deep Hubble Space Telescope (HST) F775W data based on their distinct asymmetric knot-plus-tail morphologies. Here we use deep Wide Field Camera 3 near-infrared imaging in addition to the HST optical data in order to study the rest-frame UV/optical morphologies of these galaxies across the redshift range 0.3 < z < 3.2. This study reveals that the majority of these galaxies do retain their general asymmetric morphology in the rest-frame optical over this redshift range, if not the distinct “tadpole” shape. The average stellar mass of tadpole galaxies is lower than that of field galaxies, with the effect being slightly greater at higher redshift within the errors. Estimated from spectral energy distribution fits, the average age of tadpole galaxies is younger than that of field galaxies in the lower-redshift bin, and the average metallicity is lower (whereas the specific star formation rate for tadpoles is roughly the same as field galaxies across the redshift range probed here). These average effects combined support the conclusion that this subset of galaxies is in an active phase of assembly, either late-stage merging or cold gas accretion causing localized clumpy star formation.

  8. AKARI NEAR-INFRARED SPECTROSCOPIC SURVEY FOR CO2 IN 18 COMETS

    International Nuclear Information System (INIS)

    Ootsubo, Takafumi; Kawakita, Hideyo; Hamada, Saki; Kobayashi, Hitomi; Yamaguchi, Mitsuru; Usui, Fumihiko; Nakagawa, Takao; Ueno, Munetaka; Ishiguro, Masateru; Sekiguchi, Tomohiko; Watanabe, Jun-ichi; Sakon, Itsuki; Shimonishi, Takashi; Onaka, Takashi

    2012-01-01

    We conducted a spectroscopic survey of cometary volatiles with the Infrared Camera on board the Japanese infrared satellite AKARI in the wavelength range from 2.5 to 5 μm. In our survey, 18 comets, including both the Oort cloud comets and the Jupiter-family comets, were observed in the period from 2008 June to 2010 January, most of which were observed at least twice. The prominent emission bands in the observed spectra are the fundamental vibrational bands of water (H 2 O) at 2.7 μm and carbon dioxide (CO 2 ) at 4.3 μm. The fundamental vibrational band of carbon monoxide (CO) around 4.7 μm and the broad emission feature, probably related to carbon-hydrogen-bearing molecules, can also be recognized around the 3.3-3.5-μm region in some of the comets. With respect to H 2 O, gas production rate ratios of CO 2 have been derived in 17 comets, except for the comet 29P/Schwassmann-Wachmann 1. Our data set provides the largest homogeneous database of CO 2 /H 2 O production rate ratios in comets obtained so far. The CO 2 /H 2 O production rate ratios are considered to reflect the composition of cometary ice when a comet is observed at a heliocentric distance within ∼2.5 AU, since H 2 O ice fully sublimates there. The CO 2 /H 2 O ratio in cometary ice spans from several to ∼30% among the comets observed at 2 in the comets seems to be smaller than unity based on our observations, although we only obtain upper limits for CO in most of the comets.

  9. [Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography].

    Science.gov (United States)

    Treder, M; Eter, N

    2018-04-19

    Deep learning is increasingly becoming the focus of various imaging methods in medicine. Due to the large number of different imaging modalities, ophthalmology is particularly suitable for this field of application. This article gives a general overview on the topic of deep learning and its current applications in the field of optical coherence tomography. For the benefit of the reader it focuses on the clinical rather than the technical aspects.

  10. Observations of the Hubble Deep Field with the Infrared Space Observatory .4. Association of sources with Hubble Deep Field galaxies

    DEFF Research Database (Denmark)

    Mann, R.G.; Oliver, S.J.; Serjeant, S.B.G.

    1997-01-01

    We discuss the identification of sources detected by the Infrared Space Observatory (ISO) at 6.7 and 15 mu m in the Hubble Deep Field (HDF) region. We conservatively associate ISO sources with objects in existing optical and near-infrared HDF catalogues using the likelihood ratio method, confirming...... these results (and, in one case, clarifying them) with independent visual searches, We find 15 ISO sources to be reliably associated with bright [I-814(AB) HDF, and one with an I-814(AB)=19.9 star, while a further 11 are associated with objects in the Hubble Flanking Fields (10 galaxies...... and one star), Amongst optically bright HDF galaxies, ISO tends to detect luminous, star-forming galaxies at fairly high redshift and with disturbed morphologies, in preference to nearby ellipticals....

  11. Comparison of the induced fields using different coil configurations during deep transcranial magnetic stimulation.

    Directory of Open Access Journals (Sweden)

    Mai Lu

    Full Text Available Stimulation of deeper brain structures by transcranial magnetic stimulation (TMS plays a role in the study of reward and motivation mechanisms, which may be beneficial in the treatment of several neurological and psychiatric disorders. However, electric field distributions induced in the brain by deep transcranial magnetic stimulation (dTMS are still unknown. In this paper, the double cone coil, H-coil and Halo-circular assembly (HCA coil which have been proposed for dTMS have been numerically designed. The distributions of magnetic flux density, induced electric field in an anatomically based realistic head model by applying the dTMS coils were numerically calculated by the impedance method. Results were compared with that of standard figure-of-eight (Fo8 coil. Simulation results show that double cone, H- and HCA coils have significantly deep field penetration compared to the conventional Fo8 coil, at the expense of induced higher and wider spread electrical fields in superficial cortical regions. Double cone and HCA coils have better ability to stimulate deep brain subregions compared to that of the H-coil. In the mean time, both double cone and HCA coils increase risk for optical nerve excitation. Our results suggest although the dTMS coils offer new tool with potential for both research and clinical applications for psychiatric and neurological disorders associated with dysfunctions of deep brain regions, the selection of the most suitable coil settings for a specific clinical application should be based on a balanced evaluation between stimulation depth and focality.

  12. Deep Vadose Zone-Applied Field Research Initiative Fiscal Year 2011 Annual Report

    International Nuclear Information System (INIS)

    Wellman, Dawn M.; Johnson, Timothy C.; Smith, Ronald M.; Truex, Michael J.; Matthews, Hope E.

    2011-01-01

    This annual report describes the background of the Deep Vadose Zone-Applied Field Research Initiative, and some of the programmatic approaches and transformational technologies in groundwater and deep vadose zone remediation developed during fiscal year 2011. The Department of Energy (DOE) Office of Technology Innovation and Development's (OTID) mission is to transform science into viable solutions for environmental cleanup. In 2010, OTID developed the Impact Plan, Science and Technology to Reduce the Life Cycle Cost of Closure to outline the benefits of research and development of the lifecycle cost of cleanup across the DOE complex. This plan outlines OTID's ability to reduce by $50 billion, the $200 billion life-cycle cost in waste processing, groundwater and soil, nuclear materials, and deactivation and decommissioning. The projected life-cycle costs and return on investment are based on actual savings realized from technology innovation, development, and insertion into remedial strategies and schedules at the Fernald, Mound, and Ashtabula sites. To achieve our goals, OTID developed Applied Field Research Initiatives to facilitate and accelerate collaborative development and implementation of new tools and approaches that reduce risk, cost and time for site closure. The primary mission of the Deep Vadose Zone-Applied Field Research Initiative (DVZ-AFRI) is to protect our nation's water resources, keeping them clean and safe for future generations. The DVZ-AFRI was established for the DOE to develop effective, science-based solutions for remediating, characterizing, monitoring, and predicting the behavior and fate of deep vadose zone contamination. Subsurface contaminants include radionuclides, metals, organics, and liquid waste that originated from various sources, including legacy waste from the nation's nuclear weapons complexes. The DVZ-AFRI project team is translating strategy into action by working to solve these complex challenges in a collaborative

  13. Star Formation at z ~ 6: The Hubble Ultra Deep Parallel Fields

    Science.gov (United States)

    Bouwens, R. J.; Illingworth, G. D.; Thompson, R. I.; Blakeslee, J. P.; Dickinson, M. E.; Broadhurst, T. J.; Eisenstein, D. J.; Fan, X.; Franx, M.; Meurer, G.; van Dokkum, P.

    2004-05-01

    We report on the i-dropouts detected in two exceptionally deep Advanced Camera for Surveys fields (B435, V606, i775, and z850 with 10σ limits of 28.8, 29.0, 28.5, and 27.8, respectively) taken in parallel with the Ultra Deep Field Near-Infrared Camera and Multi-Object Spectrometer observations. Using an i-z>1.4 cut, we find 30 i-dropouts over 21 arcmin2 down to z850,AB=28.1, or 1.4 i-dropouts arcmin-2, with significant field-to-field variation (as expected from cosmic variance). This extends i-dropout searches some ~0.9 mag further down the luminosity function than was possible in the Great Observatories Origins Deep Survey (GOODS) fields, yielding a ~7 times increase in surface density. An estimate of the size evolution for UV-bright objects is obtained by comparing the composite radial flux profile of the bright i-dropouts (z850,ABdropouts. The best fit is found with a (1+z)-1.57+0.50-0.53 scaling in size (for fixed luminosity), extending lower redshift (1dropouts from both GOODS fields, we make incompleteness estimates and construct a z~6 luminosity function (LF) in the rest-frame continuum UV (~1350 Å) over a 3.5 mag baseline, finding a shape consistent with that found at lower redshift. To evaluate the evolution in the LF from z~3.8, we make comparisons against different scalings of a lower redshift B-dropout sample. Although a strong degeneracy is found between luminosity and density evolution, our best-fit model scales as (1+z)-2.8 in number and (1+z)0.1 in luminosity, suggesting a rest-frame continuum UV luminosity density at z~6 that is just 0.38+0.09-0.07 times that at z~3.8. Our inclusion of the size evolution makes the present estimate lower than previous z~6 estimates. Based on observations made with the NASA/ESA Hubble Space Telescope, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. These observations are associated with program 9803.

  14. Spontaneous and Widespread Electricity Generation in Natural Deep-Sea Hydrothermal Fields.

    Science.gov (United States)

    Yamamoto, Masahiro; Nakamura, Ryuhei; Kasaya, Takafumi; Kumagai, Hidenori; Suzuki, Katsuhiko; Takai, Ken

    2017-05-15

    Deep-sea hydrothermal vents discharge abundant reductive energy into oxidative seawater. Herein, we demonstrated that in situ measurements of redox potentials on the surfaces of active hydrothermal mineral deposits were more negative than the surrounding seawater potential, driving electrical current generation. We also demonstrated that negative potentials in the surface of minerals were widespread in the hydrothermal fields, regardless of the proximity to hydrothermal fluid discharges. Lab experiments verified that the negative potential of the mineral surface was induced by a distant electron transfer from the hydrothermal fluid through the metallic and catalytic properties of minerals. These results indicate that electric current is spontaneously and widely generated in natural mineral deposits in deep-sea hydrothermal fields. Our discovery provides important insights into the microbial communities that are supported by extracellular electron transfer and the prebiotic chemical and metabolic evolution of the ocean hydrothermal systems. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Deep underground disposal of radioactive wastes: Near field effects

    International Nuclear Information System (INIS)

    1985-01-01

    This report reviews the important near-field effects of the disposal of wastes in deep rock formations. The basic characteristics of waste form, container and package, buffer and backfill materials and potential host-rock types are discussed from the perspective of the performance requirements of the total repository system. Effects of waste emplacement on the separate system components and on the system as a whole are discussed. The effects include interactions between groundwater and brines and the other system components, thermal and thermo-mechanical effects, and chemical and geochemical reactions. Special consideration is given to the radiation field that exists in proximity to the waste containers and also to the coupled effects of different phenomena

  16. AzTEC on ASTE Survey of Submillimeter Galaxies

    Science.gov (United States)

    Kohno, K.; Tamura, Y.; Hatsukade, B.; Nakanishi, K.; Iono, D.; Takata, T.; Wilson, G. W.; Yun, M. S.; Perera, T.; Austermann, J. E.; Scott, K. S.; Hughes, H.; Aretxaga, I.; Tanaka, K.; Oshima, T.; Yamaguchi, N.; Matsuo, H.; Ezawa, H.; Kawabe, R.

    2008-10-01

    We have conducted an unprecedented survey of submillimeter galaxies (SMGs) using the 144 pixel bolometer camera AzTEC mounted on the ASTE 10-m dish in Chile. We have already obtained many (>20) wide (typically 12' × 12' or wider) and deep (1 σ sensitivity of 0.5-1.0 mJy) 1.1 mm continuum images of known blank fields and over-density regions/protoclusters across a wide range of redshifts with a spatial resolution of ˜ 30''. It has resulted in the numerous (˜ a few 100, almost equivalent to the total number of the previously known SMGs) new and secure detections of SMGs. In this paper, we present initial results of two selected fields, SSA 22 and AKARI Deep Field South (ADF-S). A significnat clustering of bright SMGs toward the density peak of LAEs is found in SSA 22. We derived the differential and cumulative number counts from the detected sources in ADF-S, which probe the faintest flux densities (down to ˜1 mJy) among 1-mm blank field surveys to date.

  17. Anomalous Capacitive Sheath with Deep Radio Frequency Electric Field Penetration

    International Nuclear Information System (INIS)

    Kaganovich, Igor D.

    2002-01-01

    A novel nonlinear effect of anomalously deep penetration of an external radio-frequency electric field into a plasma is described. A self-consistent kinetic treatment reveals a transition region between the sheath and the plasma. Because of the electron velocity modulation in the sheath, bunches in the energetic electron density are formed in the transition region adjusted to the sheath. The width of the region is of order V(subscript T)/omega, where V(subscript T) is the electron thermal velocity, and w is frequency of the electric field. The presence of the electric field in the transition region results in a cooling of the energetic electrons and an additional heating of the cold electrons in comparison with the case when the transition region is neglected

  18. XMM-Newton 13H deep field - I. X-ray sources

    Science.gov (United States)

    Loaring, N. S.; Dwelly, T.; Page, M. J.; Mason, K.; McHardy, I.; Gunn, K.; Moss, D.; Seymour, N.; Newsam, A. M.; Takata, T.; Sekguchi, K.; Sasseen, T.; Cordova, F.

    2005-10-01

    We present the results of a deep X-ray survey conducted with XMM-Newton, centred on the UK ROSAT13H deep field area. This region covers 0.18 deg2, and is the first of the two areas covered with XMM-Newton as part of an extensive multiwavelength survey designed to study the nature and evolution of the faint X-ray source population. We have produced detailed Monte Carlo simulations to obtain a quantitative characterization of the source detection procedure and to assess the reliability of the resultant sourcelist. We use the simulations to establish a likelihood threshold, above which we expect less than seven (3 per cent) of our sources to be spurious. We present the final catalogue of 225 sources. Within the central 9 arcmin, 68 per cent of source positions are accurate to 2 arcsec, making optical follow-up relatively straightforward. We construct the N(>S) relation in four energy bands: 0.2-0.5, 0.5-2, 2-5 and 5-10 keV. In all but our highest energy band we find that the source counts can be represented by a double power law with a bright-end slope consistent with the Euclidean case and a break around 10-14yergcm-2s-1. Below this flux, the counts exhibit a flattening. Our source counts reach densities of 700, 1300, 900 and 300 deg-2 at fluxes of 4.1 × 10-16,4.5 × 10-16,1.1 × 10-15 and 5.3 × 10-15ergcm-2s-1 in the 0.2-0.5, 0.5-2, 2-5 and 5-10 keV energy bands, respectively. We have compared our source counts with those in the two Chandra deep fields and Lockman hole, and found our source counts to be amongst the highest of these fields in all energy bands. We resolve >51 per cent (>50 per cent) of the X-ray background emission in the 1-2 keV (2-5 keV) energy bands.

  19. Application of AMT in detecting deep geological structures in Lejia district of Xiangshan uranium ore field

    International Nuclear Information System (INIS)

    Duan Shuxin; Liu Hu

    2014-01-01

    In recent years, exploration in Xiangshan uranium ore field shows that the intersection of faults and the interface of different rock formation and the basement is an important sign of deep ore- prospecting. In order to evaluate deep uranium resource in Lejia district, audio magnetotelluric method (AMT) was undertaken to carry out profile investigation. With that method, we discerned the interface of different rock formation and the basement successfully, and faults in the deep, which provides a good basis for the prediction of deep uranium resource. Drilling results show that AMT method has an obvious advantage in detecting deep geological structures in Xiangshan. (authors)

  20. Deep rooting conferred by DEEPER ROOTING 1 enhances rice yield in paddy fields.

    Science.gov (United States)

    Arai-Sanoh, Yumiko; Takai, Toshiyuki; Yoshinaga, Satoshi; Nakano, Hiroshi; Kojima, Mikiko; Sakakibara, Hitoshi; Kondo, Motohiko; Uga, Yusaku

    2014-07-03

    To clarify the effect of deep rooting on grain yield in rice (Oryza sativa L.) in an irrigated paddy field with or without fertilizer, we used the shallow-rooting IR64 and the deep-rooting Dro1-NIL (a near-isogenic line homozygous for the Kinandang Patong allele of DEEPER ROOTING 1 (DRO1) in the IR64 genetic background). Although total root length was similar in both lines, more roots were distributed within the lower soil layer of the paddy field in Dro1-NIL than in IR64, irrespective of fertilizer treatment. At maturity, Dro1-NIL showed approximately 10% higher grain yield than IR64, irrespective of fertilizer treatment. Higher grain yield of Dro1-NIL was mainly due to the increased 1000-kernel weight and increased percentage of ripened grains, which resulted in a higher harvest index. After heading, the uptake of nitrogen from soil and leaf nitrogen concentration were higher in Dro1-NIL than in IR64. At the mid-grain-filling stage, Dro1-NIL maintained higher cytokinin fluxes from roots to shoots than IR64. These results suggest that deep rooting by DRO1 enhances nitrogen uptake and cytokinin fluxes at late stages, resulting in better grain filling in Dro1-NIL in a paddy field in this study.

  1. Deep Borehole Field Test Laboratory and Borehole Testing Strategy

    Energy Technology Data Exchange (ETDEWEB)

    Kuhlman, Kristopher L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brady, Patrick V. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); MacKinnon, Robert J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Heath, Jason E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Herrick, Courtney G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jensen, Richard P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Gardner, W. Payton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sevougian, S. David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bryan, Charles R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jang, Je-Hun [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stein, Emily R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bauer, Stephen J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Daley, Tom [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Freifeld, Barry M. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Birkholzer, Jens [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Spane, Frank A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2016-09-19

    Deep Borehole Disposal (DBD) of high-level radioactive wastes has been considered an option for geological isolation for many years (Hess et al. 1957). Recent advances in drilling technology have decreased costs and increased reliability for large-diameter (i.e., ≥50 cm [19.7”]) boreholes to depths of several kilometers (Beswick 2008; Beswick et al. 2014). These advances have therefore also increased the feasibility of the DBD concept (Brady et al. 2009; Cornwall 2015), and the current field test design will demonstrate the DBD concept and these advances. The US Department of Energy (DOE) Strategy for the Management and Disposal of Used Nuclear Fuel and High-Level Radioactive Waste (DOE 2013) specifically recommended developing a research and development plan for DBD. DOE sought input or expression of interest from States, local communities, individuals, private groups, academia, or any other stakeholders willing to host a Deep Borehole Field Test (DBFT). The DBFT includes drilling two boreholes nominally 200m [656’] apart to approximately 5 km [16,400’] total depth, in a region where crystalline basement is expected to begin at less than 2 km depth [6,560’]. The characterization borehole (CB) is the smaller-diameter borehole (i.e., 21.6 cm [8.5”] diameter at total depth), and will be drilled first. The geologic, hydrogeologic, geochemical, geomechanical and thermal testing will take place in the CB. The field test borehole (FTB) is the larger-diameter borehole (i.e., 43.2 cm [17”] diameter at total depth). Surface handling and borehole emplacement of test package will be demonstrated using the FTB to evaluate engineering feasibility and safety of disposal operations (SNL 2016).

  2. AKARI NEAR-INFRARED SPECTROSCOPIC SURVEY FOR CO{sub 2} IN 18 COMETS

    Energy Technology Data Exchange (ETDEWEB)

    Ootsubo, Takafumi [Astronomical Institute, Graduate School of Science, Tohoku University, Aramaki, Aoba-ku, Sendai 980-8578 (Japan); Kawakita, Hideyo; Hamada, Saki; Kobayashi, Hitomi; Yamaguchi, Mitsuru [Koyama Astronomical Observatory, Kyoto Sangyo University, Motoyama, Kamigamo, Kita-Ku, Kyoto 603-8555 (Japan); Usui, Fumihiko; Nakagawa, Takao; Ueno, Munetaka [Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, 3-1-1 Yoshinodai, Chuo-ku, Sagamihara, Kanagawa 252-5210 (Japan); Ishiguro, Masateru [Department of Physics and Astronomy, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742 (Korea, Republic of); Sekiguchi, Tomohiko [Department of Teacher Training, Hokkaido University of Education, Asahikawa Campus, Hokumon 9, Asahikawa, Hokkaido 070-8621 (Japan); Watanabe, Jun-ichi [National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588 (Japan); Sakon, Itsuki; Shimonishi, Takashi; Onaka, Takashi, E-mail: ootsubo@astr.tohoku.ac.jp [Department of Astronomy, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan)

    2012-06-10

    We conducted a spectroscopic survey of cometary volatiles with the Infrared Camera on board the Japanese infrared satellite AKARI in the wavelength range from 2.5 to 5 {mu}m. In our survey, 18 comets, including both the Oort cloud comets and the Jupiter-family comets, were observed in the period from 2008 June to 2010 January, most of which were observed at least twice. The prominent emission bands in the observed spectra are the fundamental vibrational bands of water (H{sub 2}O) at 2.7 {mu}m and carbon dioxide (CO{sub 2}) at 4.3 {mu}m. The fundamental vibrational band of carbon monoxide (CO) around 4.7 {mu}m and the broad emission feature, probably related to carbon-hydrogen-bearing molecules, can also be recognized around the 3.3-3.5-{mu}m region in some of the comets. With respect to H{sub 2}O, gas production rate ratios of CO{sub 2} have been derived in 17 comets, except for the comet 29P/Schwassmann-Wachmann 1. Our data set provides the largest homogeneous database of CO{sub 2}/H{sub 2}O production rate ratios in comets obtained so far. The CO{sub 2}/H{sub 2}O production rate ratios are considered to reflect the composition of cometary ice when a comet is observed at a heliocentric distance within {approx}2.5 AU, since H{sub 2}O ice fully sublimates there. The CO{sub 2}/H{sub 2}O ratio in cometary ice spans from several to {approx}30% among the comets observed at <2.5 AU (13 out of the 17 comets). Alternatively, the ratio of CO/CO{sub 2} in the comets seems to be smaller than unity based on our observations, although we only obtain upper limits for CO in most of the comets.

  3. Conceptual Design and Requirements for Characterization and Field Test Boreholes: Deep Borehole Field Test

    Energy Technology Data Exchange (ETDEWEB)

    Kuhlman, Kristopher L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brady, Patrick Vane [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); MacKinnon, Robert J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Heath, Jason E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Herrick, Courtney G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jensen, Richard P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rigali, Mark J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hadgu, Teklu [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sevougian, S. David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Birkholzer, Jens [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Freifeld, Barry M. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Daley, Tom [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-09-24

    Deep Borehole Disposal (DBD) of high-level radioactive wastes has been considered an option for geological isolation for many years (Hess et al. 1957). Recent advances in drilling technology have decreased costs and increased reliability for large-diameter (i.e., ≥50 cm [19.7”]) boreholes to depths of several kilometers (Beswick 2008; Beswick et al. 2014). These advances have therefore also increased the feasibility of the DBD concept (Brady et al. 2009; Cornwall 2015), and the current field test, introduced herein, is a demonstration of the DBD concept and these advances.

  4. Field testing of stiffened deep cement mixing piles under lateral cyclic loading

    Science.gov (United States)

    Raongjant, Werasak; Jing, Meng

    2013-06-01

    Construction of seaside and underground wall bracing often uses stiffened deep cement mixed columns (SDCM). This research investigates methods used to improve the level of bearing capacity of these SDCM when subjected to cyclic lateral loading via various types of stiffer cores. Eight piles, two deep cement mixed piles and six stiffened deep cement mixing piles with three different types of cores, H shape cross section prestressed concrete, steel pipe, and H-beam steel, were embedded though soft clay into medium-hard clay on site in Thailand. Cyclic horizontal loading was gradually applied until pile failure and the hysteresis loops of lateral load vs. lateral deformation were recorded. The lateral carrying capacities of the SDCM piles with an H-beam steel core increased by 3-4 times that of the DCM piles. This field research clearly shows that using H-beam steel as a stiffer core for SDCM piles is the best method to improve its lateral carrying capacity, ductility and energy dissipation capacity.

  5. Dro1, a major QTL involved in deep rooting of rice under upland field conditions.

    Science.gov (United States)

    Uga, Yusaku; Okuno, Kazutoshi; Yano, Masahiro

    2011-05-01

    Developing a deep root system is an important strategy for avoiding drought stress in rice. Using the 'basket' method, the ratio of deep rooting (RDR; the proportion of total roots that elongated through the basket bottom) was calculated to evaluate deep rooting. A new major quantitative trait locus (QTL) controlling RDR was detected on chromosome 9 by using 117 recombinant inbred lines (RILs) derived from a cross between the lowland cultivar IR64, with shallow rooting, and the upland cultivar Kinandang Patong (KP), with deep rooting. This QTL explained 66.6% of the total phenotypic variance in RDR in the RILs. A BC(2)F(3) line homozygous for the KP allele of the QTL had an RDR of 40.4%, compared with 2.6% for the homozygous IR64 allele. Fine mapping of this QTL was undertaken using eight BC(2)F(3) recombinant lines. The RDR QTL Dro1 (Deeper rooting 1) was mapped between the markers RM24393 and RM7424, which delimit a 608.4 kb interval in the reference cultivar Nipponbare. To clarify the influence of Dro1 in an upland field, the root distribution in different soil layers was quantified by means of core sampling. A line homozygous for the KP allele of Dro1 (Dro1-KP) and IR64 did not differ in root dry weight in the shallow soil layers (0-25 cm), but root dry weight of Dro1-KP in deep soil layers (25-50 cm) was significantly greater than that of IR64, suggesting that Dro1 plays a crucial role in increased deep rooting under upland field conditions.

  6. Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation.

    Science.gov (United States)

    Mansoor, Awais; Cerrolaza, Juan J; Perez, Geovanny; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

    2017-02-11

    Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0.927 using only the four highest modes of variation (compared to 0.888 with classical ASM 1 (p-value=0.01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects.

  7. Microsurgical robotic system for the deep surgical field: development of a prototype and feasibility studies in animal and cadaveric models.

    Science.gov (United States)

    Morita, Akio; Sora, Shigeo; Mitsuishi, Mamoru; Warisawa, Shinichi; Suruman, Katopo; Asai, Daisuke; Arata, Junpei; Baba, Shoichi; Takahashi, Hidechika; Mochizuki, Ryo; Kirino, Takaaki

    2005-08-01

    To enhance the surgeon's dexterity and maneuverability in the deep surgical field, the authors developed a master-slave microsurgical robotic system. This concept and the results of preliminary experiments are reported in this paper. The system has a master control unit, which conveys motion commands in six degrees of freedom (X, Y, and Z directions; rotation; tip flexion; and grasping) to two arms. The slave manipulator has a hanging base with an additional six degrees of freedom; it holds a motorized operating unit with two manipulators (5 mm in diameter, 18 cm in length). The accuracy of the prototype in both shallow and deep surgical fields was compared with routine freehand microsurgery. Closure of a partial arteriotomy and complete end-to-end anastomosis of the carotid artery (CA) in the deep operative field were performed in 20 Wistar rats. Three routine surgical procedures were also performed in cadavers. The accuracy of pointing with the nondominant hand in the deep surgical field was significantly improved through the use of robotics. The authors successfully closed the partial arteriotomy and completely anastomosed the rat CAs in the deep surgical field. The time needed for stitching was significantly shortened over the course of the first 10 rat experiments. The robotic instruments also moved satisfactorily in cadavers, but the manipulators still need to be smaller to fit into the narrow intracranial space. Computer-controlled surgical manipulation will be an important tool for neurosurgery, and preliminary experiments involving this robotic system demonstrate its promising maneuverability.

  8. Field development. Concept selection in deep water environment offshore Angola

    Energy Technology Data Exchange (ETDEWEB)

    Guenot, A.; Berger, J.C.; Limet, N. [TotalFinaElf, la Defense 6, Rosa-Lirio Project Group, 92 - Courbevoie (France)

    2002-10-01

    The significant oil discoveries made at the end of the 90's in the deep water environment offshore the coast of Angola, has led to a considerable amount of development activities. The first field in production was the turnkey development of the Kuito field on the Block 14 operated by Chevron. More recently the Girassol field has been put successfully in production on the Block 17, operated by TotalFinaElf. Both developments are making use of sub-sea wells connected to a moored dedicated FPSO. On the western side of the Girassol field, several discoveries have been made. They are known as the Rosa Lirio pole, from the names of two of the main channels. Values for water depth are in the same range than on Girassol (1300- 1400 m). A project group has been established in 1999 to evaluate the development of these discoveries. The purpose of this paper is to present the conceptual work which as been carried out, and in particular to show that even if many different concepts have been evaluated, the final choice has been also to make use of sub-sea trees. (authors)

  9. Deep Ly alpha imaging of two z=2.04 GRB host galaxy fields

    DEFF Research Database (Denmark)

    Fynbo, J.P.U.; Møller, Per; Thomsen, Bente

    2002-01-01

    We report on the results of deep narrow-band Lyalpha and broad-band U and I imaging of the fields of two Gamma-Ray bursts at redshift z = 2.04 (GRB 000301C and GRB 000926). We find that the host galaxy of GRB 000926 is an extended (more than 2 arcsec), strong Lyalpha emitter with a rest-frame equ......We report on the results of deep narrow-band Lyalpha and broad-band U and I imaging of the fields of two Gamma-Ray bursts at redshift z = 2.04 (GRB 000301C and GRB 000926). We find that the host galaxy of GRB 000926 is an extended (more than 2 arcsec), strong Lyalpha emitter with a rest...... - I colour than the eastern component, suggesting the presence of at least some dust. We do not detect the host galaxy of GRB 000301C in neither Lyalpha emission nor in U and I broad-band images. The strongest limit comes from combining the narrow and U-band imaging where we infer a limit of U...

  10. Galaxy Size Evolution at High Redshift and Surface Brightness Selection Effects: Constraints from the Hubble Ultra Deep Field

    Science.gov (United States)

    Bouwens, R. J.; Illingworth, G. D.; Blakeslee, J. P.; Broadhurst, T. J.; Franx, M.

    2004-08-01

    We use the exceptional depth of the Ultra Deep Field (UDF) and UDF-parallel Advanced Camera for Surveys fields to study the sizes of high-redshift (z~2-6) galaxies and address long-standing questions about possible biases in the cosmic star formation rate due to surface brightness dimming. Contrasting B-, V-, and i-dropout samples culled from the deeper data with those obtained from the shallower Great Observatories Origins Deep Survey fields, we demonstrate that the shallower data are essentially complete at bright magnitudes to z~0.4", >~3 kpc) low surface brightness galaxies are rare. A simple comparison of the half-light radii of the Hubble Deep Field-North + Hubble Deep Field-South U-dropouts with B-, V-, and i-dropouts from the UDF shows that the sizes follow a (1+z)-1.05+/-0.21 scaling toward high redshift. A more rigorous measurement compares different scalings of our U-dropout sample with the mean profiles for a set of intermediate-magnitude (26.0dropouts from the UDF. The best fit is found with a (1+z)-0.94+0.19-0.25 size scaling (for fixed luminosity). This result is then verified by repeating this experiment with different size measures, low-redshift samples, and magnitude ranges. Very similar scalings are found for all comparisons. A robust measurement of size evolution is thereby demonstrated for galaxies from z~6 to 2.5 using data from the UDF. Based on observations made with the NASA/ESA Hubble Space Telescope, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555.

  11. UNUSUAL LONG AND LUMINOUS OPTICAL TRANSIENT IN THE SUBARU DEEP FIELD

    International Nuclear Information System (INIS)

    Urata, Yuji; Tsai, Patrick P.; Huang, Kuiyun; Morokuma, Tomoki; Motohara, Kentaro; Yasuda, Naoki; Tanaka, Masaomi; Hayashi, Masao; Kashikawa, Nobunari; Ly, Chun; Malkan, Matthew A.

    2012-01-01

    We present observations of SDF-05M05, an unusual optical transient discovered in the Subaru Deep Field (SDF). The duration of the transient is > ∼ 800 days in the observer frame, and the maximum brightness during observation reached approximately 23 mag in the i' and z' bands. The faint host galaxy is clearly identified in all five optical bands of the deep SDF images. The photometric redshift of the host yields z ∼ 0.6 and the corresponding absolute magnitude at maximum is ∼ – 20. This implies that this event shone with an absolute magnitude brighter than –19 mag for approximately 300 days in the rest frame, which is significantly longer than a typical supernova and ultraluminous supernova. The total radiated energy during our observation was 1 × 10 51 erg. The light curves and color evolution are marginally consistent with some luminous IIn supernovae. We suggest that the transient may be a unique and peculiar supernova at intermediate redshift.

  12. UNUSUAL LONG AND LUMINOUS OPTICAL TRANSIENT IN THE SUBARU DEEP FIELD

    Energy Technology Data Exchange (ETDEWEB)

    Urata, Yuji; Tsai, Patrick P. [Institute of Astronomy, National Central University, Chung-Li 32054, Taiwan (China); Huang, Kuiyun [Academia Sinica Institute of Astronomy and Astrophysics, Taipei 106, Taiwan (China); Morokuma, Tomoki; Motohara, Kentaro [Institute of Astronomy, Graduate School of Science, University of Tokyo, Mitaka, Tokyo 181-0015 (Japan); Yasuda, Naoki [Institute for the Physics and Mathematics of the Universe, University of Tokyo, Kashiwa 277-8568 (Japan); Tanaka, Masaomi; Hayashi, Masao; Kashikawa, Nobunari [Optical and Infrared Astronomy Division, National Astronomical Observatory, Mitaka, Tokyo 181-8588 (Japan); Ly, Chun [Space Telescope Science Institute, Baltimore, MD (United States); Malkan, Matthew A., E-mail: urata@astro.ncu.edu.tw [Department of Physics and Astronomy, UCLA, Box 951547, Los Angeles, CA (United States)

    2012-11-20

    We present observations of SDF-05M05, an unusual optical transient discovered in the Subaru Deep Field (SDF). The duration of the transient is > {approx} 800 days in the observer frame, and the maximum brightness during observation reached approximately 23 mag in the i' and z' bands. The faint host galaxy is clearly identified in all five optical bands of the deep SDF images. The photometric redshift of the host yields z {approx} 0.6 and the corresponding absolute magnitude at maximum is {approx} - 20. This implies that this event shone with an absolute magnitude brighter than -19 mag for approximately 300 days in the rest frame, which is significantly longer than a typical supernova and ultraluminous supernova. The total radiated energy during our observation was 1 Multiplication-Sign 10{sup 51} erg. The light curves and color evolution are marginally consistent with some luminous IIn supernovae. We suggest that the transient may be a unique and peculiar supernova at intermediate redshift.

  13. Data to Support Development of Geologic Framework Models for the Deep Borehole Field Test

    Energy Technology Data Exchange (ETDEWEB)

    Perry, Frank Vinton [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Kelley, Richard E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-14

    This report summarizes work conducted in FY2017 to identify and document publically available data for developing a Geologic Framework Model (GFM) for the Deep Borehole Field Test (DBFT). Data was collected for all four of the sites being considered in 2017 for a DBFT site.

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

    Science.gov (United States)

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

    2017-03-01

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

  15. Deep recurrent conditional random field network for protein secondary prediction

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Sønderby, Søren Kaae; Sønderby, Casper Kaae

    2017-01-01

    Deep learning has become the state-of-the-art method for predicting protein secondary structure from only its amino acid residues and sequence profile. Building upon these results, we propose to combine a bi-directional recurrent neural network (biRNN) with a conditional random field (CRF), which...... of the labels for all time-steps. We condition the CRF on the output of biRNN, which learns a distributed representation based on the entire sequence. The biRNN-CRF is therefore close to ideally suited for the secondary structure task because a high degree of cross-talk between neighboring elements can...

  16. The MUSE Hubble Ultra Deep Field Survey. IX. Evolution of galaxy merger fraction since z ≈ 6

    Science.gov (United States)

    Ventou, E.; Contini, T.; Bouché, N.; Epinat, B.; Brinchmann, J.; Bacon, R.; Inami, H.; Lam, D.; Drake, A.; Garel, T.; Michel-Dansac, L.; Pello, R.; Steinmetz, M.; Weilbacher, P. M.; Wisotzki, L.; Carollo, M.

    2017-11-01

    We provide, for the first time, robust observational constraints on the galaxy major merger fraction up to z ≈ 6 using spectroscopic close pair counts. Deep Multi Unit Spectroscopic Explorer (MUSE) observations in the Hubble Ultra Deep Field (HUDF) and Hubble Deep Field South (HDF-S) are used to identify 113 secure close pairs of galaxies among a parent sample of 1801 galaxies spread over a large redshift range (0.2 separation limit of 109.5 M⊙ or the median value of stellar mass computed in each redshift bin. Overall, the major close pair fraction for low-mass and massive galaxies follows the same trend. These new, homogeneous, and robust estimates of the major merger fraction since z ≈ 6 are in good agreement with recent predictions of cosmological numerical simulations. Based on observations made with ESO telescopes at the La Silla-Paranal Observatory under programmes 094.A-0289(B), 095.A-0010(A), 096.A-0045(A) and 096.A-0045(B).

  17. Catwell and Sherdaps for deep-water production fields

    Energy Technology Data Exchange (ETDEWEB)

    Hopper, H.P.; Rey, R. [Cameron, 34 - Beziers (France)

    2000-07-01

    The names Catwell and SherDaps are derived from: - Catenary Well - Subsea Horizontal Extended Reach Drilling And Production System. Both systems use the technique of being able to drill a well in deep-water either through a platform catenary carrier pipe or a catenary drilling riser. They also offer, in addition, significant advantages when drilling into shallow reservoirs and the ability to enhance production using platform artificial lift systems or easily serviceable pumps either in the well or at the mud-line. Catwell is a platform system with surface wellheads/trees whereas SherDaps uses a group of subsea wellheads/trees/BOP's that are accessible from one permanent catenary drilling riser. Both systems allow drilling/completing and future well intervention from a central location that otherwise would have required several drilling centres (i.e. platforms or subsea) if the conventional approach was followed. It is envisaged that well targets close to a platform will use well conductors possibly with mud-line wellheads, then Catwell to reach the medium range well targets and SherDaps for long range wells. It is considered that this arrangement would allow a single surface drilling/ production centre to have access to well targets giving a foot print range of up to a 20 km diameter. The total Capex savings on a Deep-water Field Development could be in the region of $200 m on a $1 billion development. Opex will be lower with the ability from the drilling center to quickly access any problem well and rectify any faults, minimising lost production. (authors)

  18. UVUDF: Ultraviolet Imaging of the Hubble Ultra Deep Field with Wide-Field Camera 3

    Science.gov (United States)

    Teplitz, Harry I.; Rafelski, Marc; Kurczynski, Peter; Bond, Nicholas A.; Grogin, Norman; Koekemoer, Anton M.; Atek, Hakim; Brown, Thomas M.; Coe, Dan; Colbert, James W.; Ferguson, Henry C.; Finkelstein, Steven L.; Gardner, Jonathan P.; Gawiser, Eric; Giavalisco, Mauro; Gronwall, Caryl; Hanish, Daniel J.; Lee, Kyoung-Soo; de Mello, Duilia F.; Ravindranath, Swara; Ryan, Russell E.; Siana, Brian D.; Scarlata, Claudia; Soto, Emmaris; Voyer, Elysse N.; Wolfe, Arthur M.

    2013-12-01

    We present an overview of a 90 orbit Hubble Space Telescope treasury program to obtain near-ultraviolet imaging of the Hubble Ultra Deep Field using the Wide Field Camera 3 UVIS detector with the F225W, F275W, and F336W filters. This survey is designed to: (1) investigate the episode of peak star formation activity in galaxies at 1 dropouts at redshifts 1.7, 2.1, and 2.7 is largely consistent with the number predicted by published luminosity functions. We also confirm that the image mosaics have sufficient sensitivity and resolution to support the analysis of the evolution of star-forming clumps, reaching 28-29th magnitude depth at 5σ in a 0.''2 radius aperture depending on filter and observing epoch. Based on observations made with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are #12534.

  19. Observations of the Hubble Deep Field with the Infrared Space Observatory .1. Data reduction, maps and sky coverage

    DEFF Research Database (Denmark)

    Serjeant, S.B.G.; Eaton, N.; Oliver, S.J.

    1997-01-01

    We present deep imaging at 6.7 and 15 mu m from the CAM instrument on the Infrared Space Observatory (ISO), centred on the Hubble Deep Field (HDF). These are the deepest integrations published to date at these wavelengths in any region of sky. We discuss the observational strategy and the data...... reduction. The observed source density appears to approach the CAM confusion limit at 15 mu m, and fluctuations in the 6.7-mu m sky background may be identifiable with similar spatial fluctuations in the HDF galaxy counts. ISO appears to be detecting comparable field galaxy populations to the HDF, and our...

  20. Propulsion Utilizing Laser-Driven Ponderomotive Fields for Deep-Space Missions

    International Nuclear Information System (INIS)

    Williams, George J.; Gilland, James H.

    2009-01-01

    The generation of large amplitude electric fields in plasmas by high-power lasers has been studied for several years in the context of high-energy particle acceleration. Fields on the order of GeV/m are generated in the plasma wake of the laser by non-linear ponderomotive forces. The laser fields generate longitudinal and translational electron plasma waves with phase velocities close to the speed of light. These fields and velocities offer the potential to revolutionize spacecraft propulsion, leading to extended deep space robotic probes. Based on these initial calculations, plasma acceleration by means of laser-induced ponderomotive forces appears to offer significant potential for spacecraft propulsion. Relatively high-efficiencies appear possible with proper beam conditioning, resulting in an order of magnitude more thrust than alternative concepts for high I SP (>10 5 s) and elimination of the primary life-limiting erosion phenomena associated with conventional electric propulsion systems. Ponderomotive propulsion readily lends itself to beamed power which might overcome some of the constraints of power-limited propulsion concepts. A preliminary assessment of the impact of these propulsion systems for several promising configurations on mission architectures has been conducted. Emphasizing interstellar and interstellar-precursor applications, performance and technical requirements are identified for a number of missions. The use of in-situ plasma and gas for propellant is evaluated as well.

  1. 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.

  2. 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.

  3. Deep Keck u-Band Imaging of the Hubble Ultra Deep Field: A Catalog of z ~ 3 Lyman Break Galaxies

    Science.gov (United States)

    Rafelski, Marc; Wolfe, Arthur M.; Cooke, Jeff; Chen, Hsiao-Wen; Armandroff, Taft E.; Wirth, Gregory D.

    2009-10-01

    We present a sample of 407 z ~ 3 Lyman break galaxies (LBGs) to a limiting isophotal u-band magnitude of 27.6 mag in the Hubble Ultra Deep Field. The LBGs are selected using a combination of photometric redshifts and the u-band drop-out technique enabled by the introduction of an extremely deep u-band image obtained with the Keck I telescope and the blue channel of the Low Resolution Imaging Spectrometer. The Keck u-band image, totaling 9 hr of integration time, has a 1σ depth of 30.7 mag arcsec-2, making it one of the most sensitive u-band images ever obtained. The u-band image also substantially improves the accuracy of photometric redshift measurements of ~50% of the z ~ 3 LBGs, significantly reducing the traditional degeneracy of colors between z ~ 3 and z ~ 0.2 galaxies. This sample provides the most sensitive, high-resolution multi-filter imaging of reliably identified z ~ 3 LBGs for morphological studies of galaxy formation and evolution and the star formation efficiency of gas at high redshift.

  4. THE HST EXTREME DEEP FIELD (XDF): COMBINING ALL ACS AND WFC3/IR DATA ON THE HUDF REGION INTO THE DEEPEST FIELD EVER

    International Nuclear Information System (INIS)

    Illingworth, G. D.; Magee, D.; Oesch, P. A.; Bouwens, R. J.; Labbé, I.; Franx, M.; Stiavelli, M.; Van Dokkum, P. G.; Trenti, M.; Carollo, C. M.; Gonzalez, V.

    2013-01-01

    The eXtreme Deep Field (XDF) combines data from 10 years of observations with the Hubble Space Telescope Advanced Camera for Surveys (ACS) and the Wide-Field Camera 3 Infra-Red (WFC3/IR) into the deepest image of the sky ever in the optical/near-IR. Since the initial observations of the Hubble Ultra-Deep Field (HUDF) in 2003, numerous surveys and programs, including supernovae follow-up, HUDF09, CANDELS, and HUDF12, have contributed additional imaging data across this region. However, these images have never been combined and made available as one complete ultra-deep image dataset. We combine them now with the XDF program. Our new and improved processing techniques provide higher quality reductions of the total dataset. All WFC3/IR and optical ACS data sets have been fully combined and accurately matched, resulting in the deepest imaging ever taken at these wavelengths, ranging from 29.1 to 30.3 AB mag (5σ in a 0.''35 diameter aperture) in 9 filters. The combined image therefore reaches to 31.2 AB mag 5σ (32.9 at 1σ) for a flat f ν source. The gains in the optical for the four filters done in the original ACS HUDF correspond to a typical improvement of 0.15 mag, with gains of 0.25 mag in the deepest areas. Such gains are equivalent to adding ∼130 to ∼240 orbits of ACS data to the HUDF. Improved processing alone results in a typical gain of ∼0.1 mag. Our 5σ (optical+near-IR) SExtractor catalogs reveal about 14,140 sources in the full field and about 7121 galaxies in the deepest part of the XDF

  5. Photoionization spectroscopy of deep defects responsible for current collapse in nitride-based field effect transistors

    International Nuclear Information System (INIS)

    Klein, P B; Binari, S C

    2003-01-01

    This review is concerned with the characterization and identification of the deep centres that cause current collapse in nitride-based field effect transistors. Photoionization spectroscopy is an optical technique that has been developed to probe the characteristics of these defects. Measured spectral dependences provide information on trap depth, lattice coupling and on the location of the defects in the device structure. The spectrum of an individual trap may also be regarded as a 'fingerprint' of the defect, allowing the trap to be followed in response to the variation of external parameters. The basis for these measurements is derived through a modelling procedure that accounts quantitatively for the light-induced drain current increase in the collapsed device. Applying the model to fit the measured variation of drain current increase with light illumination provides an estimate of the concentrations and photoionization cross-sections of the deep defects. The results of photoionization studies of GaN metal-semiconductor field effect transistors and AlGaN/GaN high electron mobility transistors (HEMTs) grown by metal-organic chemical vapour deposition (MOCVD) are presented and the conclusions regarding the nature of the deep traps responsible are discussed. Finally, recent photoionization studies of current collapse induced by short-term (several hours) bias stress in AlGaN/GaN HEMTs are described and analysed for devices grown by both MOCVD and molecular beam epitaxy. (topical review)

  6. 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

  7. Infrared Faint Radio Sources in the Extended Chandra Deep Field South

    Science.gov (United States)

    Huynh, Minh T.

    2009-01-01

    Infrared-Faint Radio Sources (IFRSs) are a class of radio objects found in the Australia Telescope Large Area Survey (ATLAS) which have no observable counterpart in the Spitzer Wide-area Infrared Extragalactic Survey (SWIRE). The extended Chandra Deep Field South now has even deeper Spitzer imaging (3.6 to 70 micron) from a number of Legacy surveys. We report the detections of two IFRS sources in IRAC images. The non-detection of two other IFRSs allows us to constrain the source type. Detailed modeling of the SED of these objects shows that they are consistent with high redshift AGN (z > 2).

  8. A Deep-Structured Conditional Random Field Model for Object Silhouette Tracking.

    Directory of Open Access Journals (Sweden)

    Mohammad Javad Shafiee

    Full Text Available In this work, we introduce a deep-structured conditional random field (DS-CRF model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering.

  9. The Chandra Deep Field South as a test case for Global Multi Conjugate Adaptive Optics

    Science.gov (United States)

    Portaluri, E.; Viotto, V.; Ragazzoni, R.; Gullieuszik, M.; Bergomi, M.; Greggio, D.; Biondi, F.; Dima, M.; Magrin, D.; Farinato, J.

    2017-04-01

    The era of the next generation of giant telescopes requires not only the advent of new technologies but also the development of novel methods, in order to exploit fully the extraordinary potential they are built for. Global Multi Conjugate Adaptive Optics (GMCAO) pursues this approach, with the goal of achieving good performance over a field of view of a few arcmin and an increase in sky coverage. In this article, we show the gain offered by this technique to an astrophysical application, such as the photometric survey strategy applied to the Chandra Deep Field South as a case study. We simulated a close-to-real observation of a 500 × 500 arcsec2 extragalactic deep field with a 40-m class telescope that implements GMCAO. We analysed mock K-band images of 6000 high-redshift (up to z = 2.75) galaxies therein as if they were real to recover the initial input parameters. We attained 94.5 per cent completeness for source detection with SEXTRACTOR. We also measured the morphological parameters of all the sources with the two-dimensional fitting tools GALFIT. The agreement we found between recovered and intrinsic parameters demonstrates GMCAO as a reliable approach to assist extremely large telescope (ELT) observations of extragalactic interest.

  10. Deep Vadose Zone Applied Field Research Center: Transformational Technology Development For Environmental Remediation

    International Nuclear Information System (INIS)

    Wellman, Dawn M.; Triplett, Mark B.; Freshley, Mark D.; Truex, Michael J.; Gephart, Roy E.; Johnson, Timothy C.; Chronister, Glen B.; Gerdes, Kurt D.; Chamberlain, Skip; Marble, Justin; Ramirez, Rosa

    2011-01-01

    DOE-EM, Office of Groundwater and Soil Remediation and DOE Richland, in collaboration with the Hanford site and Pacific Northwest National Laboratory, have established the Deep Vadose Zone Applied Field Research Center (DVZ-AFRC). The DVZ-AFRC leverages DOE investments in basic science from the Office of Science, applied research from DOE EM Office of Technology Innovation and Development, and site operation (e.g., site contractors [CH2M HILL Plateau Remediation Contractor and Washington River Protection Solutions], DOE-EM RL and ORP) in a collaborative effort to address the complex region of the deep vadose zone. Although the aim, goal, motivation, and contractual obligation of each organization is different, the integration of these activities into the framework of the DVZ-AFRC brings the resources and creativity of many to provide sites with viable alternative remedial strategies to current baseline approaches for persistent contaminants and deep vadose zone contamination. This cooperative strategy removes stove pipes, prevents duplication of efforts, maximizes resources, and facilitates development of the scientific foundation needed to make sound and defensible remedial decisions that will successfully meet the target cleanup goals for one of DOE EM's most intractable problems, in a manner that is acceptable by regulators.

  11. Deep 20-GHz survey of the Chandra Deep Field South and SDSS Stripe 82: source catalogue and spectral properties

    Science.gov (United States)

    Franzen, Thomas M. O.; Sadler, Elaine M.; Chhetri, Rajan; Ekers, Ronald D.; Mahony, Elizabeth K.; Murphy, Tara; Norris, Ray P.; Waldram, Elizabeth M.; Whittam, Imogen H.

    2014-04-01

    We present a source catalogue and first results from a deep, blind radio survey carried out at 20 GHz with the Australia Telescope Compact Array, with follow-up observations at 5.5, 9 and 18 GHz. The Australia Telescope 20 GHz (AT20G) deep pilot survey covers a total area of 5 deg2 in the Chandra Deep Field South and in Stripe 82 of the Sloan Digital Sky Survey. We estimate the survey to be 90 per cent complete above 2.5 mJy. Of the 85 sources detected, 55 per cent have steep spectra (α _{1.4}^{20} law spectra between 1.4 and 18 GHz, while the spectral indices of the flat- or inverted-spectrum sources tend to steepen with frequency. Among the 18 inverted-spectrum (α _{1.4}^{20} ≥ 0.0) sources, 10 have clearly defined peaks in their spectra with α _{1.4}^{5.5} > 0.15 and α 9^{18} Cambridge and Tenth Cambridge surveys: there is a shift towards a steeper-spectrum population when going from ˜1 Jy to ˜5 mJy, which is followed by a shift back towards a flatter-spectrum population below ˜5 mJy. The 5-GHz source-count model by Jackson & Wall, which only includes contributions from FRI and FRII sources, and star-forming galaxies, does not reproduce the observed flattening of the flat-spectrum counts below ˜5 mJy. It is therefore possible that another population of sources is contributing to this effect.

  12. DEEP KECK u-BAND IMAGING OF THE HUBBLE ULTRA DEEP FIELD: A CATALOG OF z ∼ 3 LYMAN BREAK GALAXIES

    International Nuclear Information System (INIS)

    Rafelski, Marc; Wolfe, Arthur M.; Cooke, Jeff; Chen, H.-W.; Armandroff, Taft E.; Wirth, Gregory D.

    2009-01-01

    We present a sample of 407 z ∼ 3 Lyman break galaxies (LBGs) to a limiting isophotal u-band magnitude of 27.6 mag in the Hubble Ultra Deep Field. The LBGs are selected using a combination of photometric redshifts and the u-band drop-out technique enabled by the introduction of an extremely deep u-band image obtained with the Keck I telescope and the blue channel of the Low Resolution Imaging Spectrometer. The Keck u-band image, totaling 9 hr of integration time, has a 1σ depth of 30.7 mag arcsec -2 , making it one of the most sensitive u-band images ever obtained. The u-band image also substantially improves the accuracy of photometric redshift measurements of ∼50% of the z ∼ 3 LBGs, significantly reducing the traditional degeneracy of colors between z ∼ 3 and z ∼ 0.2 galaxies. This sample provides the most sensitive, high-resolution multi-filter imaging of reliably identified z ∼ 3 LBGs for morphological studies of galaxy formation and evolution and the star formation efficiency of gas at high redshift.

  13. Metagenomic Signatures of Microbial Communities in Deep-Sea Hydrothermal Sediments of Azores Vent Fields.

    Science.gov (United States)

    Cerqueira, Teresa; Barroso, Cristina; Froufe, Hugo; Egas, Conceição; Bettencourt, Raul

    2018-01-21

    The organisms inhabiting the deep-seafloor are known to play a crucial role in global biogeochemical cycles. Chemolithoautotrophic prokaryotes, which produce biomass from single carbon molecules, constitute the primary source of nutrition for the higher organisms, being critical for the sustainability of food webs and overall life in the deep-sea hydrothermal ecosystems. The present study investigates the metabolic profiles of chemolithoautotrophs inhabiting the sediments of Menez Gwen and Rainbow deep-sea vent fields, in the Mid-Atlantic Ridge. Differences in the microbial community structure might be reflecting the distinct depth, geology, and distance from vent of the studied sediments. A metagenomic sequencing approach was conducted to characterize the microbiome of the deep-sea hydrothermal sediments and the relevant metabolic pathways used by microbes. Both Menez Gwen and Rainbow metagenomes contained a significant number of genes involved in carbon fixation, revealing the largely autotrophic communities thriving in both sites. Carbon fixation at Menez Gwen site was predicted to occur mainly via the reductive tricarboxylic acid cycle, likely reflecting the dominance of sulfur-oxidizing Epsilonproteobacteria at this site, while different autotrophic pathways were identified at Rainbow site, in particular the Calvin-Benson-Bassham cycle. Chemolithotrophy appeared to be primarily driven by the oxidation of reduced sulfur compounds, whether through the SOX-dependent pathway at Menez Gwen site or through reverse sulfate reduction at Rainbow site. Other energy-yielding processes, such as methane, nitrite, or ammonia oxidation, were also detected but presumably contributing less to chemolithoautotrophy. This work furthers our knowledge of the microbial ecology of deep-sea hydrothermal sediments and represents an important repository of novel genes with potential biotechnological interest.

  14. SYSTEMATIC VARIATIONS IN CO2/H2O ICE ABUNDANCE RATIOS IN NEARBY GALAXIES FOUND WITH AKARI NEAR-INFRARED SPECTROSCOPY

    International Nuclear Information System (INIS)

    Yamagishi, M.; Kaneda, H.; Ishihara, D.; Oyabu, S.; Onaka, T.; Shimonishi, T.; Suzuki, T.

    2015-01-01

    We report CO 2 /H 2 O ice abundance ratios in seven nearby star-forming galaxies based on the AKARI near-infrared (2.5–5.0 μm) spectra. The CO 2 /H 2 O ice abundance ratios show clear variations between 0.05 and 0.2 with the averaged value of 0.14 ± 0.01. The previous study on M82 revealed that the CO 2 /H 2 O ice abundance ratios strongly correlate with the intensity ratios of the hydrogen recombination Brα line to the polycyclic aromatic hydrocarbon (PAH) 3.3 μm feature. In the present study, however, we find no correlation for the seven galaxies as a whole due to systematic differences in the relation between CO 2 /H 2 O ice abundance and Brα/PAH 3.3 μm intensity ratios from galaxy to galaxy. This result suggests that there is another parameter that determines the CO 2 /H 2 O ice abundance ratios in a galaxy in addition to the Brα/PAH 3.3 μm ratios. We find that the CO 2 /H 2 O ice abundance ratios positively correlate with the specific star formation rates of the galaxies. From these results, we conclude that CO 2 /H 2 O ice abundance ratios tend to be high in young star-forming galaxies

  15. SOLAR WAVE-FIELD SIMULATION FOR TESTING PROSPECTS OF HELIOSEISMIC MEASUREMENTS OF DEEP MERIDIONAL FLOWS

    International Nuclear Information System (INIS)

    Hartlep, T.; Zhao, J.; Kosovichev, A. G.; Mansour, N. N.

    2013-01-01

    The meridional flow in the Sun is an axisymmetric flow that is generally directed poleward at the surface, and is presumed to be of fundamental importance in the generation and transport of magnetic fields. Its true shape and strength, however, are debated. We present a numerical simulation of helioseismic wave propagation in the whole solar interior in the presence of a prescribed, stationary, single-cell, deep meridional circulation serving as synthetic data for helioseismic measurement techniques. A deep-focusing time-distance helioseismology technique is applied to the synthetic data, showing that it can in fact be used to measure the effects of the meridional flow very deep in the solar convection zone. It is shown that the ray approximation that is commonly used for interpretation of helioseismology measurements remains a reasonable approximation even for very long distances between 12° and 42° corresponding to depths between 52 and 195 Mm. From the measurement noise, we extrapolate that time-resolved observations on the order of a full solar cycle may be needed to probe the flow all the way to the base of the convection zone.

  16. Geomechanical Considerations for the Deep Borehole Field Test

    Science.gov (United States)

    Park, B. Y.

    2015-12-01

    Deep borehole disposal of high-level radioactive waste is under consideration as a potential alternative to shallower mined repositories. The disposal concept consists of drilling a borehole into crystalline basement rocks to a depth of 5 km, emplacement of canisters containing solid waste in the lower 2 km, and plugging and sealing the upper 3 km of the borehole. Crystalline rocks such as granites are particularly attractive for borehole emplacement because of their low permeability and porosity at depth, and high mechanical strength to resist borehole deformation. In addition, high overburden pressures contribute to sealing of some of the fractures that provide transport pathways. We present geomechanical considerations during construction (e.g., borehole breakouts, disturbed rock zone development, and creep closure), relevant to both the smaller-diameter characterization borehole (8.5") and the larger-diameter field test borehole (17"). Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

  17. Horizontal-Longitudinal Correlations of Acoustic Field in Deep Water

    International Nuclear Information System (INIS)

    Li Jun; Li Zheng-Lin; Ren Yun; Li Wen; Zhang Ren-He

    2015-01-01

    The horizontal-longitudinal correlations of the acoustic field in deep water are investigated based on the experimental data obtained in the South China Sea. It is shown that the horizontal-longitudinal correlation coefficients in the convergence zone are high, and the correlation length is consistent with the convergence zone width, which depends on the receiver depth and range. The horizontal-longitudinal correlation coefficients in the convergence zone also have a division structure for the deeper receiver. The signals from the second part of the convergence zone are still correlated with the reference signal in the first part. The horizontal-longitudinal correlation coefficients in the shadow zone are lower than that in the convergence zone, and the correlation length in the shadow zone is also much shorter than that in the convergence zone. The numerical simulation results by using the normal modes theory are qualitatively consistent with the experimental results. (paper)

  18. Industrial automation in floating production vessels for deep water oil and gas fields

    International Nuclear Information System (INIS)

    de Garcia, A.L.; Ferrante, A.J.

    1990-01-01

    The process supervision in offshore platforms was performed in the past through the use of local pneumatic instrumentation, based on relays, semi-graphic panels and button operated control panels. Considering the advanced technology used in the new floating production projects for deep water, it became mandatory to develop supervision systems capable of integrating different control panels, increasing the level of monitorization and reducing the number of operators and control rooms. From the point of view of field integration, a standardized architecture makes the communication between different production platforms and the regional headquarters, where all the equipment and support infrastructure for the computerized network is installed, possible. This test paper describes the characteristics of the initial systems, the main problems observed, the studies performed and the results obtained in relation to the design and implementation of computational systems with open architecture for automation of process control in floating production systems for deep water in Brazil

  19. A Comparison between Deep and Shallow Stress Fields in Korea Using Earthquake Focal Mechanism Inversions and Hydraulic Fracturing Stress Measurements

    Science.gov (United States)

    Lee, Rayeon; Chang, Chandong; Hong, Tae-kyung; Lee, Junhyung; Bae, Seong-Ho; Park, Eui-Seob; Park, Chan

    2016-04-01

    We are characterizing stress fields in Korea using two types of stress data: earthquake focal mechanism inversions (FMF) and hydraulic fracturing stress measurements (HF). The earthquake focal mechanism inversion data represent stress conditions at 2-20 km depths, whereas the hydraulic fracturing stress measurements, mostly conducted for geotechnical purposes, have been carried out at depths shallower than 1 km. We classified individual stress data based on the World Stress Map quality ranking scheme. A total of 20 FMF data were classified into A-B quality, possibly representing tectonic stress fields. A total of 83 HF data out of compiled 226 data were classified into B-C quality, which we use for shallow stress field characterization. The tectonic stress, revealed from the FMF data, is characterized by a remarkable consistency in its maximum stress (σ1) directions in and around Korea (N79±2° E), indicating a quite uniform deep stress field throughout. On the other hand, the shallow stress field, represented by HF data, exhibits local variations in σ1 directions, possibly due to effects of topography and geologic structures such as faults. Nonetheless, there is a general similarity in σ1 directions between deep and shallow stress fields. To investigate the shallow stress field statistically, we follow 'the mean orientation and wavelength analysis' suggested by Reiter et al. (2014). After the stress pattern analysis, the resulting stress points distribute sporadically over the country, not covering the entire region evenly. In the western part of Korea, the shallow σ1directions are generally uniform with their search radius reaching 100 km, where the average stress direction agrees well with those of the deep tectonic stress. We note two noticeable differences between shallow and deep stresses in the eastern part of Korea. First, the shallow σ1 orientations are markedly non-uniform in the southeastern part of Korea with their search radius less than 25 km

  20. Sub-mm emission line deep fields: CO and [C II] luminosity functions out to z = 6

    NARCIS (Netherlands)

    Popping, Gergö; van Kampen, Eelco; Decarli, Roberto; Spaans, Marco; Somerville, Rachel S.; Trager, Scott C.

    2016-01-01

    Now that Atacama Large (Sub)Millimeter Array is reaching its full capabilities, observations of sub-mm emission line deep fields become feasible. We couple a semi-analytic model of galaxy formation with a radiative transfer code to make predictions for the luminosity function of CO J =1-0 out to CO

  1. 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...

  2. Deep Brain Stimulation of the H Fields of Forel Alleviates Tics in Tourette Syndrome

    Directory of Open Access Journals (Sweden)

    Clemens Neudorfer

    2017-06-01

    Full Text Available The current rationale for target selection in Tourette syndrome revolves around the notion of cortico-basal ganglia circuit involvement in the pathophysiology of the disease. However, despite extensive research, the ideal target for deep brain stimulation (DBS is still under debate, with many structures being neglected and underexplored. Based on clinical observations and taking into account the prevailing hypotheses of network processing in Tourette syndrome, we chose the fields of Forel, namely field H1, as a target for DBS. The fields of Forel constitute the main link between the striatopallidal system and the thalamocortical network, relaying pallidothalamic projections from core anatomical structures to the thalamic ventral nuclear group. In a retrospective study we investigated two patients suffering from chronic, medically intractable Tourette syndrome who underwent bilateral lead implantation in field H1 of Forel. Clinical scales revealed significant alleviation of tics and comorbid symptoms, namely depression and anxiety, in the postoperative course in both patients.

  3. Bridging the gap between the deep Earth and lithospheric gravity field

    Science.gov (United States)

    Root, B. C.; Ebbing, J.; Martinec, Z.; van der Wal, W.

    2017-12-01

    Global gravity field data obtained by dedicated satellite missions can be used to study the density distribution of the lithosphere. The gravitational signal from the deep Earth is usually removed by high-pass filtering of the data. However, this will also remove any long-wavelength signal of the lithosphere. Furthermore, it is still unclear what value for the truncation limit is best suited. An alternative is to forward model the deep situated mass anomalies and subtract the gravitational signal from the observed data. This requires knowledge of the mantle mass anomalies, dynamic topography, and CMB topography. Global tomography provides the VS distribution in the mantle, which is related to the density distribution in the mantle. There are difficulties in constructing a density model from this data. Tomography relies on regularisation which smoothens the mantle anomalies. Also, the VS anomalies need to be converted to density anomalies with uncertain conversion factors. We study the observed reduction in magnitude of the density anomalies due to the regularisation of the global tomography models. The reduced magnitude of the anomalies cannot be recovered by increasing the conversion factor from VS-to-density transformation. The reduction of the tomographic results seems to resemble the effect of a spatial Gaussian filter. By determining the spectral difference between tomographic and gravimetric models a reverse filter can be constructed to reproduce correct density variations in the complete mantle. The long-wavelengths of the global tomography models are less affected by the regularisation and can fix the value of the conversion factor. However, the low degree gravity signals are also dominated by the D" region. Therefore, different approaches are used to determine the effect of this region on the gravity field. The density anomalies in the mantle, as well as the effect of CMB undulations, are forward modelled into their gravitational potential field, such that

  4. Lyman Break Galaxies in the Hubble Ultra Deep Field through Deep U-Band Imaging

    Science.gov (United States)

    Rafelski, Marc; Wolfe, A. M.; Cooke, J.; Chen, H. W.; Armandroff, T. E.; Wirth, G. D.

    2009-12-01

    We introduce an extremely deep U-band image taken of the Hubble Ultra Deep Field (HUDF), with a one sigma depth of 30.7 mag arcsec-2 and a detection limiting magnitude of 28 mag arcsec-2. The observations were carried out on the Keck I telescope using the LRIS-B detector. The U-band image substantially improves the accuracy of photometric redshift measurements of faint galaxies in the HUDF at z=[2.5,3.5]. The U-band for these galaxies is attenuated by lyman limit absorption, allowing for more reliable selections of candidate Lyman Break Galaxies (LBGs) than from photometric redshifts without U-band. We present a reliable sample of 300 LBGs at z=[2.5,3.5] in the HUDF. Accurate redshifts of faint galaxies at z=[2.5,3.5] are needed to obtain empirical constraints on the star formation efficiency of neutral gas at high redshift. Wolfe & Chen (2006) showed that the star formation rate (SFR) density in damped Ly-alpha absorption systems (DLAs) at z=[2.5,3.5] is significantly lower than predicted by the Kennicutt-Schmidt law for nearby galaxies. One caveat to this result that we wish to test is whether LBGs are embedded in DLAs. If in-situ star formation is occurring in DLAs, we would see it as extended low surface brightness emission around LBGs. We shall use the more accurate photometric redshifts to create a sample of LBGs around which we will look for extended emission in the more sensitive and higher resolution HUDF images. The absence of extended emission would put limits on the SFR density of DLAs associated with LBGs at high redshift. On the other hand, detection of faint emission on scales large compared to the bright LBG cores would indicate the presence of in situ star formation in those DLAs. Such gas would presumably fuel the higher star formation rates present in the LBG cores.

  5. Clusters, groups, and filaments in the Chandra deep field-south up to redshift 1

    International Nuclear Information System (INIS)

    Dehghan, S.; Johnston-Hollitt, M.

    2014-01-01

    We present a comprehensive structure detection analysis of the 0.3 deg 2 area of the MUSYC-ACES field, which covers the Chandra Deep Field-South (CDFS). Using a density-based clustering algorithm on the MUSYC and ACES photometric and spectroscopic catalogs, we find 62 overdense regions up to redshifts of 1, including clusters, groups, and filaments. We also present the detection of a relatively small void of ∼10 Mpc 2 at z ∼ 0.53. All structures are confirmed using the DBSCAN method, including the detection of nine structures previously reported in the literature. We present a catalog of all structures present, including their central position, mean redshift, velocity dispersions, and classification based on their morphological and spectroscopic distributions. In particular, we find 13 galaxy clusters and 6 large groups/small clusters. Comparison of these massive structures with published XMM-Newton imaging (where available) shows that 80% of these structures are associated with diffuse, soft-band (0.4-1 keV) X-ray emission, including 90% of all objects classified as clusters. The presence of soft-band X-ray emission in these massive structures (M 200 ≥ 4.9 × 10 13 M ☉ ) provides a strong independent confirmation of our methodology and classification scheme. In the closest two clusters identified (z < 0.13) high-quality optical imaging from the Deep2c field of the Garching-Bonn Deep Survey reveals the cD galaxies and demonstrates that they sit at the center of the detected X-ray emission. Nearly 60% of the clusters, groups, and filaments are detected in the known enhanced density regions of the CDFS at z ≅ 0.13, 0.52, 0.68, and 0.73. Additionally, all of the clusters, bar the most distant, are found in these overdense redshift regions. Many of the clusters and groups exhibit signs of ongoing formation seen in their velocity distributions, position within the detected cosmic web, and in one case through the presence of tidally disrupted central galaxies

  6. Clusters, groups, and filaments in the Chandra deep field-south up to redshift 1

    Energy Technology Data Exchange (ETDEWEB)

    Dehghan, S.; Johnston-Hollitt, M., E-mail: siamak.dehghan@vuw.ac.nz [School of Chemical and Physical Sciences, Victoria University of Wellington, P.O. Box 600, Wellington 6140 (New Zealand)

    2014-03-01

    We present a comprehensive structure detection analysis of the 0.3 deg{sup 2} area of the MUSYC-ACES field, which covers the Chandra Deep Field-South (CDFS). Using a density-based clustering algorithm on the MUSYC and ACES photometric and spectroscopic catalogs, we find 62 overdense regions up to redshifts of 1, including clusters, groups, and filaments. We also present the detection of a relatively small void of ∼10 Mpc{sup 2} at z ∼ 0.53. All structures are confirmed using the DBSCAN method, including the detection of nine structures previously reported in the literature. We present a catalog of all structures present, including their central position, mean redshift, velocity dispersions, and classification based on their morphological and spectroscopic distributions. In particular, we find 13 galaxy clusters and 6 large groups/small clusters. Comparison of these massive structures with published XMM-Newton imaging (where available) shows that 80% of these structures are associated with diffuse, soft-band (0.4-1 keV) X-ray emission, including 90% of all objects classified as clusters. The presence of soft-band X-ray emission in these massive structures (M {sub 200} ≥ 4.9 × 10{sup 13} M {sub ☉}) provides a strong independent confirmation of our methodology and classification scheme. In the closest two clusters identified (z < 0.13) high-quality optical imaging from the Deep2c field of the Garching-Bonn Deep Survey reveals the cD galaxies and demonstrates that they sit at the center of the detected X-ray emission. Nearly 60% of the clusters, groups, and filaments are detected in the known enhanced density regions of the CDFS at z ≅ 0.13, 0.52, 0.68, and 0.73. Additionally, all of the clusters, bar the most distant, are found in these overdense redshift regions. Many of the clusters and groups exhibit signs of ongoing formation seen in their velocity distributions, position within the detected cosmic web, and in one case through the presence of tidally

  7. Observations of the Hubble Deep Field with the Infrared Space Observatory .2. Source detection and photometry

    DEFF Research Database (Denmark)

    Goldschmidt, P.; Oliver, S.J.; Serjeant, S.B.G.

    1997-01-01

    We present positions and fluxes of point sources found in the Infrared Space Observatory (ISO) images of the Hubble Deep Field (HDF) at 6.7 and 15 mu m. We have constructed algorithmically selected 'complete' flux-limited samples of 19 sources in the 15-mu m image, and seven sources in the 6.7-mu m...

  8. Long Range Effect of The M7.8 April 2015 Nepal Earth Quake on the Deep Groudwater Outflow in a Thousand-Mile-Away Geothermal Field in Southern China's Guangdong

    Science.gov (United States)

    Lu, G.; Yu, S.; Xu, F.; Wang, X.; Yan, K.; Yuen, D. A.

    2015-12-01

    Deep ground waters sustain high temperature and pressure and are susceptible to impact from an earthquake. How an earthquake would have been associated with long-range effect on geological environment of deep groundwater is a question of interest to the scientific community and general public. The massive Richter 8.1 Nepal Earthquake (on April 25, 2015) provided a rare opportunity to test the response of deep groundwater systems. Deep ground waters at elevated temperature would naturally flow to ground surface along preferential flow path such as a deep fault, forming geothermal water flows. Geothermal water flows are susceptible to stress variation and can reflect the physical conditions of supercritical hot water kilometers deep down inside the crust. This paper introduces the monitoring work on the outflow in Xijiang Geothermal Field of Xinyi City, Guangdong Province in southern China. The geothermal field is one of typical geothermal fields with deep faults in Guangdong. The geothermal spring has characteristic daily variation of up to 72% in flow rate, which results from being associated with a north-south run deep fault susceptible to earthquake event. We use year-long monitoring data to illustrate how the Nepal earthquake would have affected the flows at the field site over 2.5 thousand kilometers away. The irregularity of flow is judged by deviation from otherwise good correlation of geothermal spring flow with solid earth tidal waves. This work could potentially provide the basis for further study of deep groundwater systems and insight to earthquake prediction.

  9. 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.

  10. 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.

  11. STAR FORMATION IN THE CHANDRA DEEP FIELD SOUTH: OBSERVATIONS CONFRONT SIMULATIONS

    International Nuclear Information System (INIS)

    Damen, Maaike; Franx, Marijn; Foerster Schreiber, Natascha M.; Labbe, Ivo; Toft, Sune; Van Dokkum, Pieter G.; Wuyts, Stijn

    2009-01-01

    We investigate the star formation history of the universe using FIREWORKS, a multiwavelength survey of the Chandra Deep Field South. We study the evolution of the specific star formation rate (sSFR) with redshift in different mass bins from z = 0 to z ∼ 3. We find that the sSFR increases with redshift for all masses. The logarithmic increase of the sSFR with redshift is nearly independent of mass, but this cannot yet be verified at the lowest-mass bins at z>0.8, due to incompleteness. We convert the sSFRs to a dimensionless growth rate to facilitate a comparison with a semianalytic galaxy formation model that was implemented on the Millennium Simulation. The model predicts that the growth rates and sSFRs increase similarly with redshift for all masses, consistent with the observations. However, we find that for all masses, the inferred observed growth rates increase more rapidly with redshift than the model predictions. We discuss several possible causes for this discrepancy, ranging from field-to-field variance, conversions to SFR, and shape of the initial mass function. We find that none of these can solve the discrepancy completely. We conclude that the models need to be adapted to produce the steep increase in growth rate between redshift z = 0 and z = 1.

  12. 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.

  13. A deep redshift survey of field galaxies. Comments on the reality of the Butcher-Oemler effect

    Science.gov (United States)

    Koo, David C.; Kron, Richard G.

    1987-01-01

    A spectroscopic survey of over 400 field galaxies has been completed in three fields for which we have deep UBVI photographic photometry. The galaxies typically range from B=20 to 22 and possess redshifts z from 0.1 to 0.5 that are often quite spiky in distribution. Little, if any, luminosity evolution is observed up to redshifts z approx 0.5. By such redshifts, however, an unexpectedly large fraction of luminous galaxies has very blue intrinsic colors that suggest extensive star formation; in contrast, the reddest galaxies still have colors that match those of present-day ellipticals.

  14. Ultradeep Near-Infrared ISAAC Observations of the Hubble Deep Field South: Observations, Reduction, Multicolor Catalog, and Photometric Redshifts

    Science.gov (United States)

    Labbé, Ivo; Franx, Marijn; Rudnick, Gregory; Schreiber, Natascha M. Förster; Rix, Hans-Walter; Moorwood, Alan; van Dokkum, Pieter G.; van der Werf, Paul; Röttgering, Huub; van Starkenburg, Lottie; van der Wel, Arjen; Kuijken, Konrad; Daddi, Emanuele

    2003-03-01

    We present deep near-infrared (NIR) Js-, H-, and Ks-band ISAAC imaging of the Wide Field Planetary Camera 2 (WFPC2) field of the Hubble Deep Field South (HDF-S). The 2.5‧×2.5‧ high Galactic latitude field was observed with the Very Large Telescope under the best seeing conditions, with integration times amounting to 33.6 hr in Js, 32.3 hr in H, and 35.6 hr in Ks. We reach total AB magnitudes for point sources of 26.8, 26.2, and 26.2, respectively (3 σ), which make it the deepest ground-based NIR observation to date and the deepest Ks-band data in any field. The effective seeing of the co-added images is ~0.45" in Js, ~0.48" in H, and ~0.46" in Ks. Using published WFPC2 optical data, we constructed a Ks-limited multicolor catalog containing 833 sources down to Ktots,AB2.3 (in Johnson magnitudes). Because they are extremely faint in the observed optical, they would be missed by ultraviolet-optical selection techniques, such as the U-dropout method. Based on service mode observations collected at the European Southern Observatory, Paranal, Chile (ESO Program 164.O-0612). Also based on observations with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under NASA contract NAS 5-26555.

  15. A Study on the Improvement Effect and Field Applicability of the Deep Soft Ground by Ground Heating Method

    Directory of Open Access Journals (Sweden)

    Mincheol Park

    2018-05-01

    Full Text Available The soft ground in coastal areas should be treated when it needs to be used for the sustainably developed of urban or industrial complex constructions. The ground heating method for soft ground improvement was applied in Eastern Europe in the 1960s, but it was not widely used due to economic and environmental problems. The author developed a device for improving soft ground using an electric heating pipe. This paper investigates the improvement effect and field application of deep soft ground by the ground heating method using the electric heating pipe. Ground heating increases the temperature of the deep soft ground and increases the tip resistance of the static electronic piezo-cone penetration test. Additionally, the pressure of the pore water decreases because the pore water is evaporated due to the ground heating. As a result of the experiment, it was verified that there was an improvement in the effect of deep soft ground by the ground heating method. With ground heating for 96 h, the tip resistance was increased by 61% at a point 0.35 m horizontally away from the electric heat pipe, 22% at 0.97 m, and 2% at 1.31 m. As a result of the field test, it was found that there were no problems in the power supply of the diesel generator and the control panel. It was easy to install the electric heating pipes in the deep soft ground. However, due to boring, the ground was disturbed and water vapor was discharged through this gap. To minimize the discharge of water vapor, it is necessary to drive the electric heating pipe.

  16. Irreversible magnetization deep in the vortex-liquid state of a 2D superconductor at high magnetic fields

    International Nuclear Information System (INIS)

    Maniv, T; Zhuravlev, V; Wosnitza, J; Hagel, J

    2004-01-01

    The remarkable phenomenon of weak magnetization hysteresis loops, observed recently deep in the vortex-liquid state of a nearly two-dimensional (2D) superconductor at low temperatures and high magnetic fields, is shown to reflect the existence of an unusual vortex-liquid state, consisting of collectively pinned crystallites of easily sliding vortex chains. (letter to the editor)

  17. The final optical identification content of the Einstein deep x-ray field in Pavo.

    Science.gov (United States)

    Danziger, J. I.; Gilmozzi, R.

    1997-07-01

    The optical identification of all sources revealed in the final analysis of the Einstein deep field observations in Pavo has been completed to the viable limits accessible to spectroscopy. This work combined with previously published data results in the identification of 16 AGN's with the real possibility of 3 further such identifications, while a further 2 probably are spurious. Another AGN is identified in an IPC exposure just outside the boundary of the four HRI exposures. One elliptical galaxy (or cluster) and one dMe star complete the tally. In a log N-log S plot the point represented by these 16-19 AGN's falls precisely on the extension of the line defined by the EMSS data, and somewhat below the line defined by the more recent deep field ROSAT data. It extends to fainter sensitivities than the previously published work from the Einstein observations of the same field. It is consistent with the more recently published data for Pavo obtained with ROSAT even though this latter reaches a slightly fainter sensitivity. This identification work therefore sets a firm lower limit to the AGN content of the X-ray identifications in Pavo. By virtue of having selected in this survey intrinsically fainter-than-average AGN's it has been possible to show, by combination with data for higher luminosity quasars, that a correlation exists between the luminosities and (B-V) colours extending over a luminosity range of 6 magnitudes. This sequence coincides with the sequence obtained by plotting data for all AGN's in the same redshift range taken from the Veron and Veron catalogue. It is argued that the magnitude of this effect cannot be explained by the translation of various strong emission lines through the band-passes of the relevant filters. It may be explained by the influence of host galaxies.

  18. The MUSE Hubble Ultra Deep Field Survey. II. Spectroscopic redshifts and comparisons to color selections of high-redshift galaxies

    Science.gov (United States)

    Inami, H.; Bacon, R.; Brinchmann, J.; Richard, J.; Contini, T.; Conseil, S.; Hamer, S.; Akhlaghi, M.; Bouché, N.; Clément, B.; Desprez, G.; Drake, A. B.; Hashimoto, T.; Leclercq, F.; Maseda, M.; Michel-Dansac, L.; Paalvast, M.; Tresse, L.; Ventou, E.; Kollatschny, W.; Boogaard, L. A.; Finley, H.; Marino, R. A.; Schaye, J.; Wisotzki, L.

    2017-11-01

    We have conducted a two-layered spectroscopic survey (1' × 1' ultra deep and 3' × 3' deep regions) in the Hubble Ultra Deep Field (HUDF) with the Multi Unit Spectroscopic Explorer (MUSE). The combination of a large field of view, high sensitivity, and wide wavelength coverage provides an order of magnitude improvement in spectroscopically confirmed redshifts in the HUDF; i.e., 1206 secure spectroscopic redshifts for Hubble Space Telescope (HST) continuum selected objects, which corresponds to 15% of the total (7904). The redshift distribution extends well beyond z> 3 and to HST/F775W magnitudes as faint as ≈ 30 mag (AB, 1σ). In addition, 132 secure redshifts were obtained for sources with no HST counterparts that were discovered in the MUSE data cubes by a blind search for emission-line features. In total, we present 1338 high quality redshifts, which is a factor of eight increase compared with the previously known spectroscopic redshifts in the same field. We assessed redshifts mainly with the spectral features [O II] at zcolor selection (dropout) diagrams of high-z galaxies. The selection condition for F336W dropouts successfully captures ≈ 80% of the targeted z 2.7 galaxies. However, for higher redshift selections (F435W, F606W, and F775W dropouts), the success rates decrease to ≈ 20-40%. We empirically redefine the selection boundaries to make an attempt to improve them to ≈ 60%. The revised boundaries allow bluer colors that capture Lyα emitters with high Lyα equivalent widths falling in the broadbands used for the color-color selection. Along with this paper, we release the redshift and line flux catalog. Based on observations made with ESO telescopes at the La Silla Paranal Observatory under program IDs 094.A-0289(B), 095.A-0010(A), 096.A-0045(A) and 096.A-0045(B).MUSE Ultra Deep Field redshift catalogs (Full Table A.1) are available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http

  19. Modelling of far-field gas migration from a deep radioactive waste repository

    International Nuclear Information System (INIS)

    Rodwell, W.R.; Nash, P.J.

    1992-01-01

    In assessing the post-closure safety of a deep radioactive waste repository, it is necessary to show that gas generated within the repository can migrate away, through the far-field geology, without affecting repository safety. This paper discusses the contribution of various mechanisms to gas migration through the far field; for example, diffusion of dissolved gas versus gas-phase movement, and bubble flow versus formation of a connected gas stream. It outlines different approaches to modelling gas movement from a repository, with simple semi-analytical models furnishing physical insights into the factors controlling gas migration in the absence of directly applicable experimental data, and more comprehensive numerical computations allowing the exploration of more detailed behaviour when appropriate data is obtained. If gas can induce groundwater movement, this could accelerate the transport of water-borne contaminants. Processes by which this could occur are noted, and the current status of work on possible effects of gas migration on groundwater movement in fractured hard rocks is indicated. 14 refs., 4 figs

  20. 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.

  1. Distinction between the Poole-Frenkel and tunneling models of electric-field-stimulated carrier emission from deep levels in semiconductors

    International Nuclear Information System (INIS)

    Ganichev, S. D.; Ziemann, E.; Prettl, W.; Yassievich, I. N.; Istratov, A. A.; Weber, E. R.

    2000-01-01

    The enhancement of the emission rate of charge carriers from deep-level defects in electric field is routinely used to determine the charge state of the defects. However, only a limited number of defects can be satisfactorily described by the Poole-Frenkel theory. An electric field dependence different from that expected from the Poole-Frenkel theory has been repeatedly reported in the literature, and no unambiguous identification of the charge state of the defect could be made. In this article, the electric field dependencies of emission of carriers from DX centers in Al x Ga 1-x As:Te, Cu pairs in silicon, and Ge:Hg have been studied applying static and terahertz electric fields, and analyzed by using the models of Poole-Frenkel and phonon assisted tunneling. It is shown that phonon assisted tunneling and Poole-Frenkel emission are two competitive mechanisms of enhancement of emission of carriers, and their relative contribution is determined by the charge state of the defect and by the electric-field strength. At high-electric field strengths carrier emission is dominated by tunneling independently of the charge state of the impurity. For neutral impurities, where Poole-Frenkel lowering of the emission barrier does not occur, the phonon assisted tunneling model describes well the experimental data also in the low-field region. For charged impurities the transition from phonon assisted tunneling at high fields to Poole-Frenkel effect at low fields can be traced back. It is suggested that the Poole-Frenkel and tunneling models can be distinguished by plotting logarithm of the emission rate against the square root or against the square of the electric field, respectively. This analysis enables one to unambiguously determine the charge state of a deep-level defect. (c) 2000 The American Physical Society

  2. Quasar Host Galaxies/Neptune Rotation/Galaxy Building Blocks/Hubble Deep Field/Saturn Storm

    Science.gov (United States)

    2001-01-01

    Computerized animations simulate a quasar erupting in the core of a normal spiral galaxy, the collision of two interacting galaxies, and the evolution of the universe. Hubble Space Telescope (HST) images show six quasars' host galaxies (including spirals, ellipticals, and colliding galaxies) and six clumps of galaxies approximately 11 billion light years away. A false color time lapse movie of Neptune displays the planet's 16-hour rotation, and the evolution of a storm on Saturn is seen though a video of the planet's rotation. A zoom sequence starts with a ground-based image of the constellation Ursa major and ends with the Hubble Deep Field through progressively narrower and deeper views.

  3. 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.

  4. 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...

  5. The multiple gas-liquid subsea separation system: development and qualification of a novel solution for deep water field production

    Energy Technology Data Exchange (ETDEWEB)

    Abrand, Stephanie; Butin, Nicolas; Shaiek, Sadia; Hallot, Raymond [Saipem S.p.A., Milano (Italy)

    2012-07-01

    Subsea processing is more and more considered as a viable solution for the development of deep and ultra deep water fields. SAIPEM has developed a deep water gas separation and liquid boosting system, based on its proprietary 'Multi pipe' separator concept, providing a good flexibility in handling a wide range of steady and un-steady multiphase input streams using a relatively simple mechanical arrangement. The Multi pipe Concept features an array of vertical pipes for gas/liquid separation by gravity and adequate liquid hold up volumes. The operating principle is the same as standard gravity vessels. Specific inlet pipe arrangements have been worked out to enhance the separation efficiency and internals can be implemented to further optimize the performances. The limited diameter and wall thickness of the vertical pipes make the Multi pipe Concept particularly suited for deep and ultra-deep water applications and/or high pressure conditions where the selection of a single separator vessel could lead to unpractical wall thicknesses. In most cases, standard API or ASME pipes can be utilized for the Multi pipe Separator, thus enabling conventional fabrication methods, and in turn reducing cost and delivery time and opening opportunities for local content. The qualification testing program has seen two subsequent phases. The first qualification phase aimed at the confirmation of the hydrodynamic behavior of the system. In particular, the homogeneous distribution of the multiphase stream into the pipes and the stability of the liquid levels under un-steady inlet conditions were continuously assessed during the tests. This first qualification phase gave confidence in the viability of the Multi pipe and in its good hydrodynamic behavior under the different inlet conditions that can be encountered during field production. It proved that, having the same liquid level in all the separator pipes, whatever the inlet conditions are, the Multi pipe separator can be

  6. Meeting the flow assurance challenges of deep water developments - from CAPEX development to field start up

    Energy Technology Data Exchange (ETDEWEB)

    Jordan, M.M.; Feasey, N.D. [National Aluminium Company Ltd. (Nalco), Cheshire (United Kingdom); Afonso, M.; Silva, D. [NALCO Brasil Ltda., Sao Paulo, SP (Brazil)

    2008-07-01

    As oil accumulations in easily accessible locations around the world become less available developments in deeper water become a more common target for field development. Deep water projects, particularly sub sea development, present a host of challenges in terms of flow assurance and integrity. In this paper the focus will be on the chemical control of flow assurance challenges in hydrate control, scale control and wax/asphaltene control within deep water (>750 meter) developments. The opportunities for kinetic hydrate control vs. conventional thermodynamic hydrate control will be outlined with examples of where these technologies have been applied and the limitations that still exist. The development of scale control chemical formulations specifically for sub sea application and the challenges of monitoring such control programs will be highlighted with developments in real time and near real time monitoring. Organic deposit control (wax/asphaltene) will focus on the development of new chemicals that have higher activity but lower viscosity than currently used chemicals hence allowing deployment at colder temperatures and over longer distances. The factors that need to be taken into account when selecting chemicals for deep water application will be highlighted. Fluid viscosity, impact of hydrostatic head on injectivity, product stability at low temperature and interaction with other production chemicals will be reviewed as they pertain to effective flow assurance. This paper brings learning from other deep water basins with examples from the Gulf of Mexico, West Africa and Brazil, which will be used to highlight these challenges and some of the solutions currently available along with the technology gaps that exist. (author)

  7. Surface Brightness Profiles of Composite Images of Compact Galaxies at Z approximately equal 4-6 in the Hubble Ultra Deep Field

    National Research Council Canada - National Science Library

    Hathi, N. P; Jansen, R. A; Windhorst, R. A; Cohen, S. H; Keel, W. C; Corbin, M. R; Ryan, Jr, R. E

    2007-01-01

    The Hubble Ultra Deep Field (HUDF) contains a significant number of B-, V-, and iota'-band dropout objects, many of which were recently confirmed to be young star-forming galaxies at Z approximately equal 4-6...

  8. The SCUBA-2 Cosmology Legacy Survey: the EGS deep field - I. Deep number counts and the redshift distribution of the recovered cosmic infrared background at 450 and 850 μ m

    Science.gov (United States)

    Zavala, J. A.; Aretxaga, I.; Geach, J. E.; Hughes, D. H.; Birkinshaw, M.; Chapin, E.; Chapman, S.; Chen, Chian-Chou; Clements, D. L.; Dunlop, J. S.; Farrah, D.; Ivison, R. J.; Jenness, T.; Michałowski, M. J.; Robson, E. I.; Scott, Douglas; Simpson, J.; Spaans, M.; van der Werf, P.

    2017-01-01

    We present deep observations at 450 and 850 μm in the Extended Groth Strip field taken with the SCUBA-2 camera mounted on the James Clerk Maxwell Telescope as part of the deep SCUBA-2 Cosmology Legacy Survey (S2CLS), achieving a central instrumental depth of σ450 = 1.2 mJy beam-1 and σ850 = 0.2 mJy beam-1. We detect 57 sources at 450 μm and 90 at 850 μm with signal-to-noise ratio >3.5 over ˜70 arcmin2. From these detections, we derive the number counts at flux densities S450 > 4.0 mJy and S850 > 0.9 mJy, which represent the deepest number counts at these wavelengths derived using directly extracted sources from only blank-field observations with a single-dish telescope. Our measurements smoothly connect the gap between previous shallower blank-field single-dish observations and deep interferometric ALMA results. We estimate the contribution of our SCUBA-2 detected galaxies to the cosmic infrared background (CIB), as well as the contribution of 24 μm-selected galaxies through a stacking technique, which add a total of 0.26 ± 0.03 and 0.07 ± 0.01 MJy sr-1, at 450 and 850 μm, respectively. These surface brightnesses correspond to 60 ± 20 and 50 ± 20 per cent of the total CIB measurements, where the errors are dominated by those of the total CIB. Using the photometric redshifts of the 24 μm-selected sample and the redshift distributions of the submillimetre galaxies, we find that the redshift distribution of the recovered CIB is different at each wavelength, with a peak at z ˜ 1 for 450 μm and at z ˜ 2 for 850 μm, consistent with previous observations and theoretical models.

  9. 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.

  10. Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.

    Science.gov (United States)

    Luo, Huichun; Huang, Yongzhi; Du, Xueying; Zhang, Yunpeng; Green, Alexander L; Aziz, Tipu Z; Wang, Shouyan

    2018-01-01

    In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep

  11. 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

  12. 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

  13. A tripolar current-steering stimulator ASIC for field shaping in deep brain stimulation.

    Science.gov (United States)

    Valente, Virgilio; Demosthenous, Andreas; Bayford, Richard

    2012-06-01

    A significant problem with clinical deep brain stimulation (DBS) is the high variability of its efficacy and the frequency of side effects, related to the spreading of current beyond the anatomical target area. This is the result of the lack of control that current DBS systems offer on the shaping of the electric potential distribution around the electrode. This paper presents a stimulator ASIC with a tripolar current-steering output stage, aiming at achieving more selectivity and field shaping than current DBS systems. The ASIC was fabricated in a 0.35-μ m CMOS technology occupying a core area of 0.71 mm(2). It consists of three current sourcing/sinking channels. It is capable of generating square and exponential-decay biphasic current pulses with five different time constants up to 28 ms and delivering up to 1.85 mA of cathodic current, in steps of 4 μA, from a 12 V power supply. Field shaping was validated by mapping the potential distribution when injecting current pulses through a multicontact DBS electrode in saline.

  14. Discovery of z ~ 8 Galaxies in the Hubble Ultra Deep Field from Ultra-Deep WFC3/IR Observations

    Science.gov (United States)

    Bouwens, R. J.; Illingworth, G. D.; Oesch, P. A.; Stiavelli, M.; van Dokkum, P.; Trenti, M.; Magee, D.; Labbé, I.; Franx, M.; Carollo, C. M.; Gonzalez, V.

    2010-02-01

    We utilize the newly acquired, ultra-deep WFC3/IR observations over the Hubble Ultra Deep Field (HUDF) to search for star-forming galaxies at z ~ 8-8.5, only 600 million years from recombination, using a Y 105-dropout selection. The new 4.7 arcmin2 WFC3/IR observations reach to ~28.8 AB mag (5σ) in the Y 105 J 125 H 160 bands. These remarkable data reach ~1.5 AB mag deeper than the previous data over the HUDF, and now are an excellent match to the HUDF optical ACS data. For our search criteria, we use a two-color Lyman break selection technique to identify z ~ 8-8.5Y 105-dropouts. We find five likely z ~ 8-8.5 candidates. The sources have H 160-band magnitudes of ~28.3 AB mag and very blue UV-continuum slopes, with a median estimated β of lsim-2.5 (where f λ vprop λβ). This suggests that z ~ 8 galaxies are not only essentially dust free but also may have very young ages or low metallicities. The observed number of Y 105-dropout candidates is smaller than the 20 ± 6 sources expected assuming no evolution from z ~ 6, but is consistent with the five expected extrapolating the Bouwens et al. luminosity function (LF) results to z ~ 8. These results provide evidence that the evolution in the LF seen from z ~ 7 to z ~ 3 continues to z ~ 8. The remarkable improvement in the sensitivity of WFC3/IR has enabled Hubble Space Telescope to cross a threshold, revealing star-forming galaxies at z~ 8-9. Based on observations made with the NASA/ESA Hubble Space Telescope, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with programs 11563, 9797.

  15. 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

  16. Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.

    Science.gov (United States)

    Ohsugi, Hideharu; Tabuchi, Hitoshi; Enno, Hiroki; Ishitobi, Naofumi

    2017-08-25

    Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep learning, a machine-learning technology, to detect RRD using ultra-wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study. The deep learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2-100%] and a high specificity of 96.5% (95% CI, 90.2-100%), and the area under the curve was 0.988 (95% CI, 0.981-0.995). This model can improve medical care in remote areas where eye clinics are not available by using ultra-wide-field fundus ophthalmoscopy for the accurate diagnosis of RRD. Early diagnosis of RRD can prevent blindness.

  17. 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.

  18. Observations of the Hubble Deep Field with the Infrared Space Observatory .3. Source counts and P(D) analysis

    DEFF Research Database (Denmark)

    Oliver, S.J.; Goldschmidt, P.; Franceschini, A.

    1997-01-01

    We present source counts at 6.7 and 15 mu m from our maps of the Hubble Deep Field (HDF) region, reaching 38.6 mu Jy at 6.7 mu m and 255 mu Jy at 15 mu m. These are the first ever extragalactic number counts to be presented at 6.7 mu m, and are three decades fainter than IRAS at 12 mu m. Both...

  19. Effects of applying an external magnetic field during the deep cryogenic heat treatment on the corrosion resistance and wear behavior of 1.2080 tool steel

    International Nuclear Information System (INIS)

    Akhbarizadeh, Amin; Amini, Kamran; Javadpour, Sirus

    2012-01-01

    Highlights: ► Deep cryogenic increases the carbide percentage and make a more homogenous distribution. ► Deep cryogenic improve the wear resistance and corrosion behavior of 1.2080 tool steel. ► Applying the magnetic field weaker the carbide distribution and decreases the carbides percentage. ► Magnetized samples showed weaker corrosion and wear behavior. -- Abstract: This work concerns with the effect of applying an external magnetic field on the corrosion behavior, wear resistance and microstructure of 1.2080 (D2) tool steel during the deep cryogenic heat treatment. These analyses were performed via scanning electron microscope (SEM), optical microscope (OM), transmission electron microscope (TEM) and X-ay diffraction (XRD) to study the microstructure, a pin-on-disk wear testing machine to study the wear behavior, and linear sweep voltammetry to study the corrosion behavior of the samples. It was shown that the deep cryogenic heat treatment eliminates retained austenite and makes a more uniform carbide distribution with higher percentage. It was also observed that the deep cryogenic heat treatment improves the wear behavior and corrosion resistance of 1.2080 tool steel. In comparison between the magnetized and non-magnetized samples, the carbide percentage decreases and the carbide distribution weakened in the magnetized samples; subsequently, the wear behavior and corrosion resistance attenuated compared in the magnetized samples.

  20. Dwarf Galaxies with Gentle Star Formation and the Counts of Galaxies in the Hubble Deep Field

    OpenAIRE

    Campos, Ana

    1997-01-01

    In this paper the counts and colors of the faint galaxies observed in the Hubble Deep Field are fitted by means of simple luminosity evolution models that incorporate a numerous population of fading dwarfs. The observed color distribution of the very faint galaxies now allows us to put constraints on the star formation history in dwarfs. It is shown that the star-forming activity in these small systems has to proceed in a gentle way, i.e., through episodes where each one lasts much longer tha...

  1. THE TAIWAN ECDFS NEAR-INFRARED SURVEY: ULTRA-DEEP J AND K{sub S} IMAGING IN THE EXTENDED CHANDRA DEEP FIELD-SOUTH

    Energy Technology Data Exchange (ETDEWEB)

    Hsieh, Bau-Ching; Wang, Wei-Hao; Hsieh, Chih-Chiang; Lin, Lihwai; Lim, Jeremy; Ho, Paul T. P. [Institute of Astrophysics and Astronomy, Academia Sinica, P.O. Box 23-141, Taipei 106, Taiwan (China); Yan Haojing [Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211 (United States)

    2012-12-15

    We present ultra-deep J and K{sub S} imaging observations covering a 30' Multiplication-Sign 30' area of the Extended Chandra Deep Field-South (ECDFS) carried out by our Taiwan ECDFS Near-Infrared Survey (TENIS). The median 5{sigma} limiting magnitudes for all detected objects in the ECDFS reach 24.5 and 23.9 mag (AB) for J and K{sub S} , respectively. In the inner 400 arcmin{sup 2} region where the sensitivity is more uniform, objects as faint as 25.6 and 25.0 mag are detected at 5{sigma}. Thus, this is by far the deepest J and K{sub S} data sets available for the ECDFS. To combine TENIS with the Spitzer IRAC data for obtaining better spectral energy distributions of high-redshift objects, we developed a novel deconvolution technique (IRACLEAN) to accurately estimate the IRAC fluxes. IRACLEAN can minimize the effect of blending in the IRAC images caused by the large point-spread functions and reduce the confusion noise. We applied IRACLEAN to the images from the Spitzer IRAC/MUSYC Public Legacy in the ECDFS survey (SIMPLE) and generated a J+K{sub S} -selected multi-wavelength catalog including the photometry of both the TENIS near-infrared and the SIMPLE IRAC data. We publicly release the data products derived from this work, including the J and K{sub S} images and the J+K{sub S} -selected multi-wavelength catalog.

  2. The UDF05 Follow-up of the Hubble Ultra Deep Field. III. The Luminosity Function at z ~ 6

    Science.gov (United States)

    Su, Jian; Stiavelli, Massimo; Oesch, Pascal; Trenti, Michele; Bergeron, Eddie; Bradley, Larry; Carollo, Marcella; Dahlen, Tomas; Ferguson, Henry C.; Giavalisco, Mauro; Koekemoer, Anton; Lilly, Simon; Lucas, Ray A.; Mobasher, Bahram; Panagia, Nino; Pavlovsky, Cheryl

    2011-09-01

    In this paper, we present a derivation of the rest-frame 1400 Å luminosity function (LF) at redshift six from a new application of the maximum likelihood method by exploring the five deepest Hubble Space Telescope/Advanced Camera for Surveys (HST/ACS) fields, i.e., the Hubble Ultra Deep Field, two UDF05 fields, and two Great Observatories Origins Deep Survey fields. We work on the latest improved data products, which makes our results more robust than those of previous studies. We use unbinned data and thereby make optimal use of the information contained in the data set. We focus on the analysis to a magnitude limit where the completeness is larger than 50% to avoid possibly large errors in the faint end slope that are difficult to quantify. We also take into account scattering in and out of the dropout sample due to photometric errors by defining for each object a probability that it belongs to the dropout sample. We find the best-fit Schechter parameters to the z ~ 6 LF are α = 1.87 ± 0.14, M * = -20.25 ± 0.23, and phi* = 1.77+0.62 -0.49 × 10-3 Mpc-3. Such a steep slope suggests that galaxies, especially the faint ones, are possibly the main sources of ionizing photons in the universe at redshift six. We also combine results from all studies at z ~ 6 to reach an agreement in the 95% confidence level that -20.45 Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with program 10632 and 11563.

  3. Deep-water northern Gulf of Mexico hydrocarbon plays

    International Nuclear Information System (INIS)

    Peterson, R.H.; Cooke, D.W.

    1995-01-01

    The geologic setting in the deep-water (depths greater than 1,500 feet) Gulf of Mexico is very favorable for the existence of large, commercial hydrocarbon accumulations. These areas have active salt tectonics that create abundant traps, underlying mature Mesozoic source rocks that can be observed expelling oil and gas to the ocean surface, and good quality reservoirs provided by turbidite sand deposits. Despite the limited amount of drilling in the deep-water Gulf of Mexico, 11 deep-water accumulations have been discovered which, when developed, will rank in the top 100 largest fields in the Gulf of Mexico. Proved field discoveries (those with announced development plans) have added over 1 billion barrels of oil equivalent to Gulf of Mexico reserves, and unproved field discoveries may add to additional billion barrels of oil equivalent. The Minerals Management Service, United States Department of the Interior, has completed a gulf-wide review of over 1,086 oil and gas fields and placed every pay sand in each field into a hydrocarbon play (plays are defined by chronostratigraphy, lithostratigraph, structure, and production). Seven productive hydrocarbon plays were identified in the deep-water northern Gulf of Mexico. Regional maps illustrate the productive limits of each play. In addition, field data, dry holes, and wells with sub-economic pay were added to define the facies and structural limits for each play. Areas for exploration potential are identified for each hydrocarbon play. A type field for each play is chosen to demonstrate the play's characteristics

  4. Tic related local field potentials in the thalamus and the effect of deep brain stimulation in Tourette syndrome : Report of three cases

    NARCIS (Netherlands)

    Bour, L. J.; Ackermans, L.; Foncke, E. M. J.; Cath, D.; van der Linden, C.; Vandewalle, V. Visser; Tijssen, M. A.

    Objective: Three patients with intractable Tourette syndrome (TS) underwent thalamic deep brain stimulation (DBS). To investigate the role of thalamic electrical activity in tic generation, local field potentials (LFP), EEG and EMG simultaneously were recorded. Methods: Event related potentials and

  5. 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.)

  6. 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)

  7. DISCOVERY OF z ∼ 8 GALAXIES IN THE HUBBLE ULTRA DEEP FIELD FROM ULTRA-DEEP WFC3/IR OBSERVATIONS

    International Nuclear Information System (INIS)

    Bouwens, R. J.; Illingworth, G. D.; Magee, D.; Gonzalez, V.; Oesch, P. A.; Carollo, C. M.; Stiavelli, M.; Van Dokkum, P.; Trenti, M.; Labbe, I.; Franx, M.

    2010-01-01

    We utilize the newly acquired, ultra-deep WFC3/IR observations over the Hubble Ultra Deep Field (HUDF) to search for star-forming galaxies at z ∼ 8-8.5, only 600 million years from recombination, using a Y 105 -dropout selection. The new 4.7 arcmin 2 WFC3/IR observations reach to ∼28.8 AB mag (5σ) in the Y 105 J 125 H 160 bands. These remarkable data reach ∼1.5 AB mag deeper than the previous data over the HUDF, and now are an excellent match to the HUDF optical ACS data. For our search criteria, we use a two-color Lyman break selection technique to identify z ∼ 8-8.5Y 105 -dropouts. We find five likely z ∼ 8-8.5 candidates. The sources have H 160 -band magnitudes of ∼28.3 AB mag and very blue UV-continuum slopes, with a median estimated β of ∼ λ ∝ λ β ). This suggests that z ∼ 8 galaxies are not only essentially dust free but also may have very young ages or low metallicities. The observed number of Y 105 -dropout candidates is smaller than the 20 ± 6 sources expected assuming no evolution from z ∼ 6, but is consistent with the five expected extrapolating the Bouwens et al. luminosity function (LF) results to z ∼ 8. These results provide evidence that the evolution in the LF seen from z ∼ 7 to z ∼ 3 continues to z ∼ 8. The remarkable improvement in the sensitivity of WFC3/IR has enabled Hubble Space Telescope to cross a threshold, revealing star-forming galaxies at z∼ 8-9.

  8. Low-cost, high-precision micro-lensed optical fiber providing deep-micrometer to deep-nanometer-level light focusing.

    Science.gov (United States)

    Wen, Sy-Bor; Sundaram, Vijay M; McBride, Daniel; Yang, Yu

    2016-04-15

    A new type of micro-lensed optical fiber through stacking appropriate high-refractive microspheres at designed locations with respect to the cleaved end of an optical fiber is numerically and experimentally demonstrated. This new type of micro-lensed optical fiber can be precisely constructed with low cost and high speed. Deep micrometer-scale and submicrometer-scale far-field light spots can be achieved when the optical fibers are multimode and single mode, respectively. By placing an appropriate teardrop dielectric nanoscale scatterer at the far-field spot of this new type of micro-lensed optical fiber, a deep-nanometer near-field spot can also be generated with high intensity and minimum joule heating, which is valuable in high-speed, high-resolution, and high-power nanoscale detection compared with traditional near-field optical fibers containing a significant portion of metallic material.

  9. 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

  10. Deep Extragalactic VIsible Legacy Survey (DEVILS): Motivation, Design and Target Catalogue

    Science.gov (United States)

    Davies, L. J. M.; Robotham, A. S. G.; Driver, S. P.; Lagos, C. P.; Cortese, L.; Mannering, E.; Foster, C.; Lidman, C.; Hashemizadeh, A.; Koushan, S.; O'Toole, S.; Baldry, I. K.; Bilicki, M.; Bland-Hawthorn, J.; Bremer, M. N.; Brown, M. J. I.; Bryant, J. J.; Catinella, B.; Croom, S. M.; Grootes, M. W.; Holwerda, B. W.; Jarvis, M. J.; Maddox, N.; Meyer, M.; Moffett, A. J.; Phillipps, S.; Taylor, E. N.; Windhorst, R. A.; Wolf, C.

    2018-06-01

    The Deep Extragalactic VIsible Legacy Survey (DEVILS) is a large spectroscopic campaign at the Anglo-Australian Telescope (AAT) aimed at bridging the near and distant Universe by producing the highest completeness survey of galaxies and groups at intermediate redshifts (0.3 < z < 1.0). Our sample consists of ˜60,000 galaxies to Y<21.2 mag, over ˜6 deg2 in three well-studied deep extragalactic fields (Cosmic Origins Survey field, COSMOS, Extended Chandra Deep Field South, ECDFS and the X-ray Multi-Mirror Mission Large-Scale Structure region, XMM-LSS - all Large Synoptic Survey Telescope deep-drill fields). This paper presents the broad experimental design of DEVILS. Our target sample has been selected from deep Visible and Infrared Survey Telescope for Astronomy (VISTA) Y-band imaging (VISTA Deep Extragalactic Observations, VIDEO and UltraVISTA), with photometry measured by PROFOUND. Photometric star/galaxy separation is done on the basis of NIR colours, and has been validated by visual inspection. To maximise our observing efficiency for faint targets we employ a redshift feedback strategy, which continually updates our target lists, feeding back the results from the previous night's observations. We also present an overview of the initial spectroscopic observations undertaken in late 2017 and early 2018.

  11. 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

  12. IDENTIFICATIONS AND PHOTOMETRIC REDSHIFTS OF THE 2 Ms CHANDRA DEEP FIELD-SOUTH SOURCES

    International Nuclear Information System (INIS)

    Luo, B.; Brandt, W. N.; Xue, Y. Q.; Rafferty, D. A.; Schneider, D. P.; Brusa, M.; Alexander, D. M.; Lehmer, B. D.; Bauer, F. E.; Comastri, A.; Koekemoer, A.; Mainieri, V.; Silverman, J. D.; Vignali, C.

    2010-01-01

    We present reliable multiwavelength identifications and high-quality photometric redshifts for the 462 X-ray sources in the ∼2 Ms Chandra Deep Field-South (CDF-S) survey. Source identifications are carried out using deep optical-to-radio multiwavelength catalogs, and are then combined to create lists of primary and secondary counterparts for the X-ray sources. We identified reliable counterparts for 442 (95.7%) of the X-ray sources, with an expected false-match probability of ∼ 6.2%; we also selected four additional likely counterparts. The majority of the other 16 X-ray sources appear to be off-nuclear sources, sources associated with galaxy groups and clusters, high-redshift active galactic nuclei (AGNs), or spurious X-ray sources. A likelihood-ratio method is used for source matching, which effectively reduces the false-match probability at faint magnitudes compared to a simple error-circle matching method. We construct a master photometric catalog for the identified X-ray sources including up to 42 bands of UV-to-infrared data, and then calculate their photometric redshifts (photo-z's). High accuracy in the derived photo-z's is accomplished owing to (1) the up-to-date photometric data covering the full spectral energy distributions (SEDs) of the X-ray sources, (2) more accurate photometric data as a result of source deblending for ∼10% of the sources in the infrared bands and a few percent in the optical and near-infrared bands, (3) a set of 265 galaxy, AGN, and galaxy/AGN hybrid templates carefully constructed to best represent all possible SEDs, (4) the Zurich Extragalactic Bayesian Redshift Analyzer used to derive the photo-z's, which corrects the SED templates to best represent the SEDs of real sources at different redshifts and thus improves the photo-z quality. The reliability of the photo-z's is evaluated using the subsample of 220 sources with secure spectroscopic redshifts. We achieve an accuracy of |Δz|/(1 + z) ∼ 1% and an outlier [with |

  13. 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

  14. Deep brain stimulation as a functional scalpel.

    Science.gov (United States)

    Broggi, G; Franzini, A; Tringali, G; Ferroli, P; Marras, C; Romito, L; Maccagnano, E

    2006-01-01

    Since 1995, at the Istituto Nazionale Neurologico "Carlo Besta" in Milan (INNCB,) 401 deep brain electrodes were implanted to treat several drug-resistant neurological syndromes (Fig. 1). More than 200 patients are still available for follow-up and therapeutical considerations. In this paper our experience is reviewed and pioneered fields are highlighted. The reported series of patients extends the use of deep brain stimulation beyond the field of Parkinson's disease to new fields such as cluster headache, disruptive behaviour, SUNCt, epilepsy and tardive dystonia. The low complication rate, the reversibility of the procedure and the available image guided surgery tools will further increase the therapeutic applications of DBS. New therapeutical applications are expected for this functional scalpel.

  15. 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)

  16. UVUDF: Ultraviolet imaging of the Hubble ultra deep field with wide-field camera 3

    Energy Technology Data Exchange (ETDEWEB)

    Teplitz, Harry I.; Rafelski, Marc; Colbert, James W.; Hanish, Daniel J. [Infrared Processing and Analysis Center, MS 100-22, Caltech, Pasadena, CA 91125 (United States); Kurczynski, Peter; Gawiser, Eric [Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854 (United States); Bond, Nicholas A.; Gardner, Jonathan P.; De Mello, Duilia F. [Laboratory for Observational Cosmology, Astrophysics Science Division, Code 665, Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Grogin, Norman; Koekemoer, Anton M.; Brown, Thomas M.; Coe, Dan; Ferguson, Henry C. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Atek, Hakim [Laboratoire d' Astrophysique, École Polytechnique Fédérale de Lausanne (EPFL), Observatoire, CH-1290 Sauverny (Switzerland); Finkelstein, Steven L. [Department of Astronomy, The University of Texas at Austin, Austin, TX 78712 (United States); Giavalisco, Mauro [Astronomy Department, University of Massachusetts, Amherst, MA 01003 (United States); Gronwall, Caryl [Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802 (United States); Lee, Kyoung-Soo [Department of Physics, Purdue University, 525 Northwestern Avenue, West Lafayette, IN 47907 (United States); Ravindranath, Swara, E-mail: hit@ipac.caltech.edu [Inter-University Centre for Astronomy and Astrophysics, Pune (India); and others

    2013-12-01

    We present an overview of a 90 orbit Hubble Space Telescope treasury program to obtain near-ultraviolet imaging of the Hubble Ultra Deep Field using the Wide Field Camera 3 UVIS detector with the F225W, F275W, and F336W filters. This survey is designed to: (1) investigate the episode of peak star formation activity in galaxies at 1 < z < 2.5; (2) probe the evolution of massive galaxies by resolving sub-galactic units (clumps); (3) examine the escape fraction of ionizing radiation from galaxies at z ∼ 2-3; (4) greatly improve the reliability of photometric redshift estimates; and (5) measure the star formation rate efficiency of neutral atomic-dominated hydrogen gas at z ∼ 1-3. In this overview paper, we describe the survey details and data reduction challenges, including both the necessity of specialized calibrations and the effects of charge transfer inefficiency. We provide a stark demonstration of the effects of charge transfer inefficiency on resultant data products, which when uncorrected, result in uncertain photometry, elongation of morphology in the readout direction, and loss of faint sources far from the readout. We agree with the STScI recommendation that future UVIS observations that require very sensitive measurements use the instrument's capability to add background light through a 'post-flash'. Preliminary results on number counts of UV-selected galaxies and morphology of galaxies at z ∼ 1 are presented. We find that the number density of UV dropouts at redshifts 1.7, 2.1, and 2.7 is largely consistent with the number predicted by published luminosity functions. We also confirm that the image mosaics have sufficient sensitivity and resolution to support the analysis of the evolution of star-forming clumps, reaching 28-29th magnitude depth at 5σ in a 0.''2 radius aperture depending on filter and observing epoch.

  17. Optical colours of AGN in the Extended Chandra Deep Field South: Obscured black holes in early type galaxies

    OpenAIRE

    Rovilos, E.; Georgantopoulos, I.

    2007-01-01

    We investigate the optical colours of X-ray sources from the Extended Chandra Deep Field South (ECDFS) using photometry from the COMBO-17 survey, aiming to explore AGN - galaxy feedback models. The X-ray sources populate both the ``blue'' and the ``red sequence'' on the colour-magnitude diagram. However, sources in the ``red sequence'' appear systematically more obscured. HST imaging from the GEMS survey demonstrates that the nucleus does not affect significantly the observed colours, and the...

  18. Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

    Directory of Open Access Journals (Sweden)

    Mohammad S. Islam

    2017-01-01

    Full Text Available Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs for robust movement decoding of Parkinson’s disease (PD and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value at about 0.729±0.16 for decoding movement from the resting state and about 0.671±0.14 for decoding left and right visually cued movements.

  19. FIRST RESULTS FROM Pan-STARRS1: FAINT, HIGH PROPER MOTION WHITE DWARFS IN THE MEDIUM-DEEP FIELDS

    Energy Technology Data Exchange (ETDEWEB)

    Tonry, J. L.; Flewelling, H. A.; Deacon, N. R.; Burgett, W. S.; Chambers, K. C.; Kaiser, N.; Kudritzki, R.-P.; Hodapp, K. W.; Magnier, E. A.; Morgan, J. S.; Wainscoat, R. J. [Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States); Stubbs, C. W.; Kilic, M.; Chornock, R.; Berger, E. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Price, P. A. [Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States)

    2012-01-20

    The Pan-STARRS1 survey has obtained multi-epoch imaging in five bands (Pan-STARRS1 g{sub P1}, r{sub P1}, i{sub P1}, z{sub P1}, and y{sub P1}) on 12 'Medium-Deep fields', each of which spans a 3.{sup 0}3 circle. For the period between 2009 April and 2011 April these fields were observed 50-200 times. Using a reduced proper motion diagram, we have extracted a list of 47 white dwarf (WD) candidates whose Pan-STARRS1 astrometry indicates a non-zero proper motion at the 6{sigma} level, with a typical 1{sigma} proper motion uncertainty of 10 mas yr{sup -1}. We also used astrometry from the Sloan Digital Sky Survey (when available) and USNO-B to assess our proper motion fits. None of the WD candidates exhibits evidence of statistically significant parallaxes, with a typical 1{sigma} uncertainty of 8 mas. Twelve of these candidates are known WDs, including the high proper motion (1.''7 yr{sup -1}) WD LHS 291. We confirm seven more objects as WDs through optical spectroscopy. Based on the Pan-STARRS1 colors, ten of the stars are likely to be cool WDs with 4170 K Deep Field Survey and the 3{pi} survey, Pan-STARRS1 should find many more high proper motion WDs that are part of the old thick disk and halo.

  20. Journal of Astrophysics and Astronomy | Indian Academy of Sciences

    Indian Academy of Sciences (India)

    cavity—IRIS—AKARI. Abstract. By systematically searching the region of far infrared loops, we found a number of huge cavity-like dust structures at 60 μ m and 100 μ m IRIS maps.By checking these with AKARI maps (90 μ m and 140 μ m), twonew ...

  1. 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.

  2. 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...

  3. Numerical simulation of a TLD pulsed laser-heating scheme for determination of shallow dose and deep dose in low-LET radiation fields

    International Nuclear Information System (INIS)

    Kearfott, K.J.; Han, S.; Wagner, E.C.; Samei, E.; Wang, C.-K.C.

    2000-01-01

    A new method is described to determine the depth-dose distribution in low-LET radiation fields using a thick thermoluminescent dosimeter (TLD) with a pulsed laser-heating scheme to obtain TL glow output. The computational simulation entails heat conduction and glow curve production processes. An iterative algorithm is used to obtain the dose distribution in the detector. The simulation results indicate that the method can predict the shallow and deep dose in various radiation fields with relative errors less than 20%

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

    Science.gov (United States)

    Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

    2018-04-01

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

  5. Coaxial nuclear radiation detector with deep junction and radial field gradient

    International Nuclear Information System (INIS)

    Hall, R.N.

    1979-01-01

    Germanium radiation detectors are manufactured by diffusion lithium into high purity p-type germanium. The diffusion is most readily accomplished from a lithium-lead-bismuth alloy at approximately 430 0 and is monitored by a quartz half cell containing a standard composition of this alloy. Detectors having n-type cores may be constructed by converting high purity p-type germanium to n-type by a lithium diffusion and subsequently diffusing some of the lithium back out through the surface to create a deep p-n junction. Coaxial germanium detectors comprising deep p-n junctions are produced by the lithium diffusion process

  6. Miniature ingestible telemeter devices to measure deep-body temperature

    Science.gov (United States)

    Pope, J. M.; Fryer, T. B. (Inventor)

    1976-01-01

    A telemetry device comprised of a pill-size ingestible transmitter developed to obtain deep body temperature measurements of a human is described. The device has particular utility in the medical field where deep body temperatures provide an indication of general health.

  7. Modeling of Antenna for Deep Target Hydrocarbon Exploration

    Directory of Open Access Journals (Sweden)

    Nadeem Nasir

    2017-11-01

    Full Text Available Nowadays control source electromagnetic method is used for offshore hydrocarbon exploration. Hydrocarbon detection in sea bed logging (SBL is a very challenging task for deep target hydrocarbon reservoir. Response of electromagnetic (EM field from marine environment is very low and it is very difficult to predict deep target reservoir below 2km from the sea floor. This work premise deals with modeling of new antenna for deep water deep target hydrocarbon exploration. Conventional and new EM antennas at 0.125Hz frequency are used in modeling for the detection of deep target hydrocarbon  reservoir.  The  proposed  area  of  the  seabed model   (40km ´ 40km   was   simulated   by using CST (computer simulation technology EM studio based on Finite Integration Method (FIM. Electromagnetic field components were compared at 500m target depth and it was concluded that Ex and Hz components shows better resistivity contrast. Comparison of conventional and new antenna for different target  depths  was  done in  our  proposed  model.  From  the results, it was observed that conventional antenna at 0.125Hz shows 70% ,86% resistivity contrast at target depth of 1000m where   as   new   antenna   showed   329%, 355%   resistivity contrast at the same target depth for Ex and Hz field respectively.  It  was  also  investigated  that  at  frequency of0.125Hz, new antenna gave 46% better delineation of hydrocarbon at 4000m target depth. This is due to focusing of electromagnetic waves by using new antenna. New antenna design gave 125% more extra depth than straight antenna for deep target hydrocarbon detection. Numerical modeling for straight  and  new antenna  was also done to know general equation for electromagnetic field behavior with target depth. From this numerical model it was speculated that this new antenna can detect up to 4.5 km target depth. This new EM antenna may open new frontiers for oil and gas

  8. THE UDF05 FOLLOW-UP OF THE HUBBLE ULTRA DEEP FIELD. III. THE LUMINOSITY FUNCTION AT z ∼ 6

    International Nuclear Information System (INIS)

    Su Jian; Stiavelli, Massimo; Bergeron, Eddie; Bradley, Larry; Dahlen, Tomas; Ferguson, Henry C.; Koekemoer, Anton; Lucas, Ray A.; Panagia, Nino; Pavlovsky, Cheryl; Oesch, Pascal; Carollo, Marcella; Lilly, Simon; Trenti, Michele; Giavalisco, Mauro; Mobasher, Bahram

    2011-01-01

    In this paper, we present a derivation of the rest-frame 1400 A luminosity function (LF) at redshift six from a new application of the maximum likelihood method by exploring the five deepest Hubble Space Telescope/Advanced Camera for Surveys (HST/ACS) fields, i.e., the Hubble Ultra Deep Field, two UDF05 fields, and two Great Observatories Origins Deep Survey fields. We work on the latest improved data products, which makes our results more robust than those of previous studies. We use unbinned data and thereby make optimal use of the information contained in the data set. We focus on the analysis to a magnitude limit where the completeness is larger than 50% to avoid possibly large errors in the faint end slope that are difficult to quantify. We also take into account scattering in and out of the dropout sample due to photometric errors by defining for each object a probability that it belongs to the dropout sample. We find the best-fit Schechter parameters to the z ∼ 6 LF are α = 1.87 ± 0.14, M * = -20.25 ± 0.23, and φ * = 1.77 +0.62 -0.49 x 10 -3 Mpc -3 . Such a steep slope suggests that galaxies, especially the faint ones, are possibly the main sources of ionizing photons in the universe at redshift six. We also combine results from all studies at z ∼ 6 to reach an agreement in the 95% confidence level that -20.45 * < -20.05 and -1.90 < α < -1.55. The luminosity density has been found not to evolve significantly between z ∼ 6 and z ∼ 5, but considerable evolution is detected from z ∼ 6 to z ∼ 3.

  9. Identifications and Photometric Redshifts of the 2 Ms Chandra Deep Field-South Sources

    Science.gov (United States)

    Luo, B.; Brandt, W. N.; Xue, Y. Q.; Brusa, M.; Alexander, D. M.; Bauer, F. E.; Comastri, A.; Koekemoer, A.; Lehmer, B. D.; Mainieri, V.; Rafferty, D. A.; Schneider, D. P.; Silverman, J. D.; Vignali, C.

    2010-04-01

    We present reliable multiwavelength identifications and high-quality photometric redshifts for the 462 X-ray sources in the ≈2 Ms Chandra Deep Field-South (CDF-S) survey. Source identifications are carried out using deep optical-to-radio multiwavelength catalogs, and are then combined to create lists of primary and secondary counterparts for the X-ray sources. We identified reliable counterparts for 442 (95.7%) of the X-ray sources, with an expected false-match probability of ≈ 6.2%; we also selected four additional likely counterparts. The majority of the other 16 X-ray sources appear to be off-nuclear sources, sources associated with galaxy groups and clusters, high-redshift active galactic nuclei (AGNs), or spurious X-ray sources. A likelihood-ratio method is used for source matching, which effectively reduces the false-match probability at faint magnitudes compared to a simple error-circle matching method. We construct a master photometric catalog for the identified X-ray sources including up to 42 bands of UV-to-infrared data, and then calculate their photometric redshifts (photo-z's). High accuracy in the derived photo-z's is accomplished owing to (1) the up-to-date photometric data covering the full spectral energy distributions (SEDs) of the X-ray sources, (2) more accurate photometric data as a result of source deblending for ≈10% of the sources in the infrared bands and a few percent in the optical and near-infrared bands, (3) a set of 265 galaxy, AGN, and galaxy/AGN hybrid templates carefully constructed to best represent all possible SEDs, (4) the Zurich Extragalactic Bayesian Redshift Analyzer used to derive the photo-z's, which corrects the SED templates to best represent the SEDs of real sources at different redshifts and thus improves the photo-z quality. The reliability of the photo-z's is evaluated using the subsample of 220 sources with secure spectroscopic redshifts. We achieve an accuracy of |Δz|/(1 + z) ≈ 1% and an outlier [with |

  10. Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms.

    Science.gov (United States)

    Li, Hui; Giger, Maryellen L; Huynh, Benjamin Q; Antropova, Natalia O

    2017-10-01

    To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases). Deep learning was compared to the use of features from RTA, as well as to a combination of both in the task of distinguishing between high- and low-risk subjects. Similar classification performances were obtained using CNN [area under the curve [Formula: see text]; standard error [Formula: see text

  11. Long-term behaviour of waste-forms in the near-field environment of a deep underground storage site, overview

    International Nuclear Information System (INIS)

    Toulhoat, P.; Lassabatere, Th.; Galle, Ch.; Cranga, M.; Trotignon, L.; Maillard, S.; Iracane, D.

    1997-01-01

    CEA (French Atomic Energy Commission) is responsible for the achievement of high activity and/or long life waste conditioning processes. Various waste-forms are used (glass, bitumen, etc...). ANDRA (French National Agency for Nuclear Waste Management) has to integrate the long-term durability of such waste-forms in the conception of a deep disposal and the assessment of its long-term confinement performances. The influence of near-field and of the boundary conditions imposed by the far-field on the long-term evolution is being more and more documented. Transport properties and reactivity of silica in the near field is one of the best examples of such effects. A coherent framework with relevant successive events (site re-saturation, chemical evolution of the engineered barrier, overpack corrosion) and a thorough analysis of hierarchized couplings are necessary to evaluate the long term durability of waste-form, and finally, to deliver a near-field-integrated source-term of radionuclides versus lime. We present hereafter some preliminary results obtained in the framework of the CEA 'C3P' project - long-term behaviour of waste-forms in their near-field environment. (authors)

  12. The NuSTAR Extragalactic Surveys: Initial Results and Catalog from the Extended Chandra Deep Field South

    DEFF Research Database (Denmark)

    Mullaney, J. R.; Del-Moro, A.; Aird, J.

    2015-01-01

    We present the initial results and the source catalog from the Nuclear Spectroscopic Telescope Array (NuSTAR) survey of the Extended Chandra Deep Field South (hereafter, ECDFS)—currently the deepest contiguous component of the NuSTAR extragalactic survey program. The survey covers the full ≈30......V fluxes) span the range L10 40 keV (0.7 300) 10 erg s» - ´ 43 1 -- ,sampling below the “knee” of the X-ray luminosity function out to z ~ 0.8-1. Finally, we identify oneNuSTAR source that has neither a Chandra nor an XMM-Newton counterpart, but that shows evidence of nuclearactivity at infrared...

  13. A Survey on Deep Learning in Medical Image Analysis

    NARCIS (Netherlands)

    Litjens, G.J.; Kooi, T.; Ehteshami Bejnordi, B.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; Laak, J.A.W.M. van der; Ginneken, B. van; Sanchez, C.I.

    2017-01-01

    Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared

  14. Anomalous enhancement of the lower critical field deep in the superconducting state of LaRu4As12

    Science.gov (United States)

    Juraszek, J.; Bochenek, Ł.; Wawryk, R.; Henkie, Z.; Konczykowski, M.; Cichorek, T.

    2018-05-01

    LaRu4As12 with the critical temperature Tc = 10.4 K displays several features which point at a non-singlet superconducting order parameter, although the bcc crystal structure of the filled skutterudites does not favour the emergence of multiple energy gaps. LaRu4As12 displays an unexpected enhancement of the lower critical field deep in superconducting state which can be attributed to the existence of two superconducting gaps. At T = 0.4 K, the local magnetization measurements were performed utilizing miniaturized Hall sensors.

  15. The Deep Physics Hidden within the Field Expressions of the Radiation Fields of Lightning Return Strokes

    Directory of Open Access Journals (Sweden)

    Vernon Cooray

    2016-01-01

    Full Text Available Based on the electromagnetic fields generated by a current pulse propagating from one point in space to another, a scenario that is frequently used to simulate return strokes in lightning flashes, it is shown that there is a deep physical connection between the electromagnetic energy dissipated by the system, the time over which this energy is dissipated and the charge associated with the current. For a given current pulse, the product of the energy dissipated and the time over which this energy is dissipated, defined as action in this paper, depends on the length of the channel, or the path, through which the current pulse is propagating. As the length of the channel varies, the action plotted against the length of the channel exhibits a maximum value. The location of the maximum value depends on the ratio of the length of the channel to the characteristic length of the current pulse. The latter is defined as the product of the duration of the current pulse and the speed of propagation of the current pulse. The magnitude of this maximum depends on the charge associated with the current pulse. The results show that when the charge associated with the current pulse approaches the electronic charge, the value of this maximum reaches a value close to h/8π where h is the Plank constant. From this result, one can deduce that the time-energy uncertainty principle is the reason for the fact that the smallest charge that can be detected from the electromagnetic radiation is equal to the electronic charge. Since any system that generates electromagnetic radiation can be represented by a current pulse propagating from one point in space to another, the result is deemed valid for electromagnetic radiation fields in general.

  16. Deep Space Gateway Science Opportunities

    Science.gov (United States)

    Quincy, C. D.; Charles, J. B.; Hamill, Doris; Sidney, S. C.

    2018-01-01

    The NASA Life Sciences Research Capabilities Team (LSRCT) has been discussing deep space research needs for the last two years. NASA's programs conducting life sciences studies - the Human Research Program, Space Biology, Astrobiology, and Planetary Protection - see the Deep Space Gateway (DSG) as affording enormous opportunities to investigate biological organisms in a unique environment that cannot be replicated in Earth-based laboratories or on Low Earth Orbit science platforms. These investigations may provide in many cases the definitive answers to risks associated with exploration and living outside Earth's protective magnetic field. Unlike Low Earth Orbit or terrestrial locations, the Gateway location will be subjected to the true deep space spectrum and influence of both galactic cosmic and solar particle radiation and thus presents an opportunity to investigate their long-term exposure effects. The question of how a community of biological organisms change over time within the harsh environment of space flight outside of the magnetic field protection can be investigated. The biological response to the absence of Earth's geomagnetic field can be studied for the first time. Will organisms change in new and unique ways under these new conditions? This may be specifically true on investigations of microbial communities. The Gateway provides a platform for microbiology experiments both inside, to improve understanding of interactions between microbes and human habitats, and outside, to improve understanding of microbe-hardware interactions exposed to the space environment.

  17. Magnetic field fluctuations analysis for the ion trap implementation of the quantum Rabi model in the deep strong coupling regime

    Science.gov (United States)

    Puebla, Ricardo; Casanova, Jorge; Plenio, Martin B.

    2018-03-01

    The dynamics of the quantum Rabi model (QRM) in the deep strong coupling regime is theoretically analyzed in a trapped-ion set-up. Recognizably, the main hallmark of this regime is the emergence of collapses and revivals, whose faithful observation is hindered under realistic magnetic dephasing noise. Here, we discuss how to attain a faithful implementation of the QRM in the deep strong coupling regime which is robust against magnetic field fluctuations and at the same time provides a large tunability of the simulated parameters. This is achieved by combining standing wave laser configuration with continuous dynamical decoupling. In addition, we study the role that amplitude fluctuations play to correctly attain the QRM using the proposed method. In this manner, the present work further supports the suitability of continuous dynamical decoupling techniques in trapped-ion settings to faithfully realize different interacting dynamics.

  18. 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...

  19. Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions.

    Science.gov (United States)

    Choi, Hongyoon

    2018-04-01

    Recent advances in deep learning have impacted various scientific and industrial fields. Due to the rapid application of deep learning in biomedical data, molecular imaging has also started to adopt this technique. In this regard, it is expected that deep learning will potentially affect the roles of molecular imaging experts as well as clinical decision making. This review firstly offers a basic overview of deep learning particularly for image data analysis to give knowledge to nuclear medicine physicians and researchers. Because of the unique characteristics and distinctive aims of various types of molecular imaging, deep learning applications can be different from other fields. In this context, the review deals with current perspectives of deep learning in molecular imaging particularly in terms of development of biomarkers. Finally, future challenges of deep learning application for molecular imaging and future roles of experts in molecular imaging will be discussed.

  20. Subsea innovative boosting technologies on deep water scenarios -- Impacts and demands

    International Nuclear Information System (INIS)

    Caetano, E.F.; Mendonca, J.E.; Pagot, P.R.; Cotrim, M.L.; Camargo, R.M.T.; Assayag, M.I.

    1995-01-01

    This paper presents the importance of deep water scenario for Brazil, the PETROBRAS Deep and Ultra-Deep Water R and D Program (PROCAP-2000) and the candidate fields for the deployment of subsea innovative boosting technologies (ESPS -- electrical submersible pump in subsea wells, SSS -- subsea separation systems and SBMS -- subsea multiphase flow pumping system) as well as the problems associated with the flow assurance in such conditions. The impact of those innovative systems, their technological stage and remaining demands to make them available for deployment in offshore subsea areas, mainly in giant deepwater fields, are discussed and predicted

  1. 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

  2. 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.

  3. 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.

  4. Deep diving odontocetes foraging strategies and their prey field as determined by acoustic techniques

    Science.gov (United States)

    Giorli, Giacomo

    Deep diving odontocetes, like sperm whales, beaked whales, Risso's dolphins, and pilot whales are known to forage at deep depths in the ocean on squid and fish. These marine mammal species are top predators and for this reason are very important for the ecosystems they live in, since they can affect prey populations and control food web dynamics through top-down effects. The studies presented in this thesis investigate deep diving odontocetes. foraging strategies, and the density and size of their potential prey in the deep ocean using passive and active acoustic techniques. Ecological Acoustic Recorders (EAR) were used to monitor the foraging activity of deep diving odontocetes at three locations around the world: the Josephine Seamount High Sea Marine Protected Area (JHSMPA), the Ligurian Sea, and along the Kona coast of the island of Hawaii. In the JHSMPA, sperm whales. and beaked whales. foraging rates do not differ between night-time and day-time. However, in the Ligurian Sea, sperm whales switch to night-time foraging as the winter approaches, while beaked whales alternate between hunting mainly at night, and both at night and at day. Spatial differences were found in deep diving odontocetes. foraging activity in Hawaii where they forage most in areas with higher chlorophyll concentrations. Pilot whales (and false killer whales, clustered together in the category "blackfishes") and Risso's dolphins forage mainly at night at all locations. These two species adjust their foraging activity with the length of the night. The density and size of animals living in deep sea scattering layers was studied using a DIDSON imaging sonar at multiple stations along the Kona coast of Hawaii. The density of animals was affected by location, depth, month, and the time of day. The size of animals was influenced by station and month. The DIDSON proved to be a successful, non-invasive technique to study density and size of animals in the deep sea. Densities were found to be an

  5. Using Deep Learning Techniques to Forecast Environmental Consumption Level

    Directory of Open Access Journals (Sweden)

    Donghyun Lee

    2017-10-01

    Full Text Available Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

  6. Geophysical Properties of Hard Rock for Investigation of Stress Fields in Deep Mines

    Science.gov (United States)

    Tibbo, M.; Young, R. P.; Schmitt, D. R.; Milkereit, B.

    2014-12-01

    A complication in geophysical monitoring of deep mines is the high-stress dependency of the physical properties of hard rocks. In-mine observations show anisotropic variability of the in situ P- and S-wave velocities and resistivity of the hard rocks that are likely related to stress field changes. As part of a comprehensive study in a deep, highly stressed mine located in Sudbury, Ontario, Canada, data from in situ monitoring of the seismicity, conductivity, stress, and stress dependent physical properties has been obtain. In-laboratory experiments are also being performed on borehole cores from the Sudbury mines. These experiments will measure the Norite borehole core's properties including elastic modulus, bulk modulus, P- and S-wave velocities, and density. Hydraulic fracturing has been successfully implemented in industries such as oil and gas and enhanced geothermal systems, and is currently being investigated as a potential method for preconditioning in mining. However, further research is required to quantify how hydraulic fractures propagate through hard, unfractured rock as well as naturally fractured rock typically found in mines. These in laboratory experiments will contribute to a hydraulic fracturing project evaluating the feasibility and effectiveness of hydraulic fracturing as a method of de-stressing hard rock mines. A tri-axial deformation cell equipped with 18 Acoustic Emission (AE) sensors will be used to bring the borehole cores to a tri-axial state of stress. The cores will then be injected with fluid until the the hydraulic fracture has propagated to the edge of the core, while AE waveforms will be digitized continuously at 10 MHz and 12-bit resolution for the duration of each experiment. These laboratory hydraulic fracture experiments will contribute to understanding how parameters including stress ratio, fluid injection rate, and viscosity, affect the fracturing process.

  7. VizieR Online Data Catalog: z~3-6 protoclusters in the CFHTLS deep fields (Toshikawa+, 2016)

    Science.gov (United States)

    Toshikawa, J.; Kashikawa, N.; Overzier, R.; Malkan, M. A.; Furusawa, H.; Ishikawa, S.; Onoue, M.; Ota, K.; Tanaka, M.; Niino, Y.; Uchiyama, H.

    2018-03-01

    We made use of publicly available data from the CFHTLS (T0007: Gwyn 2012AJ....143...38G; Hudelot et al. 2012, Cat. II/317), which was obtained with MegaCam mounted at the prime focus of the CFHT. The Deep Fields of the CFHTLS were used in this study, which consist of four independent fields of about 1 deg2 area each (~4 deg2 area in total) observed in the u*, g', r', i', and z' bands. We selected z~3-6 galaxy candidates using the Lyman-break technique (u-, g-, r-, and i-dropout galaxies). We carried out spectroscopic observations using Subaru/FOCAS (Kashikawa et al. 2002PASJ...54..819K), Keck II/DEIMOS (Faber et al. 2003SPIE.4841.1657F), and Gemini-N/GMOS (Hook et al. 2004PASP..116..425H). In these observations, eight protocluster candidates from z~3 to z~6 were observed in total (two at each redshift). All these observations were conducted with Multi-Object Spectroscopy (MOS) mode. (2 data files).

  8. The Canada-France deep fields survey-II: Lyman-break galaxies and galaxy clustering at z ~ 3

    Science.gov (United States)

    Foucaud, S.; McCracken, H. J.; Le Fèvre, O.; Arnouts, S.; Brodwin, M.; Lilly, S. J.; Crampton, D.; Mellier, Y.

    2003-10-01

    We present a large sample of z ~ 3 U-band dropout galaxies extracted from the Canada-France deep fields survey (CFDF). Our catalogue covers an effective area of ~ 1700 arcmin2 divided between three large, contiguous fields separated widely on the sky. To IAB=24.5, the survey contains 1294 Lyman-break candidates, in agreement with previous measurements by other authors, after appropriate incompleteness corrections have been applied to our data. Based on comparisons with spectroscopic observations and simulations, we estimate that our sample of Lyman-break galaxies is contaminated by stars and interlopers (lower-redshift galaxies) at no more than { ~ } 30%. We find that omega (theta ) is well fitted by a power-law of fixed slope, gamma =1.8, even at small (theta University of Hawaii, and at the Cerro Tololo Inter-American Observatory and Mayall 4-meter Telescopes, divisions of the National Optical Astronomy Observatories, which are operated by the Association of Universities for Research in Astronomy, Inc. under cooperative agreement with the National Science Foundation.

  9. Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

    Science.gov (United States)

    Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

    2016-01-01

    We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

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

    Science.gov (United States)

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

    2018-04-01

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

  11. Research on deep electromagnetic induction methods (Fy 1985)

    Energy Technology Data Exchange (ETDEWEB)

    Murakami, Hiroshi; Uchida, Toshihiro; Tanaka, Shin' ichi

    1987-06-01

    Geological Survey of Japan started from FY 1984 a research of deep electomagnetic induction methods as a part of the research on deep geothermal resources prospecting technology, the Sunshine Project. This article is the report of its second fiscal year. These methods are a generic term of the methods to survey specific resistance structure in the deep part of the earth by utilizing the technique of the electromagnetic induction method and the time domain CSMT method aiming to survey about estimated depth of 5Km as well as the CA method to estimate the general structure of the earth of the depth of 5Km or more are now being developed. This article reports the respective methods separately. Concerning the former, the reception of useful signals were successfully made during the FY 1984 field experiment and based on this, field experiments in a geothermal area were conducted in FY 1985 verifying its effectivenss. With regard to the latter, following FY 1984, CA observations were conducted in the northern part of Tohoku Region and the deep specific resistance structure in a wide area was surveyed. (43 figs, 1 tab, 11 refs)

  12. 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

  13. THE COSMIC INFRARED BACKGROUND EXPERIMENT (CIBER): THE WIDE-FIELD IMAGERS

    Energy Technology Data Exchange (ETDEWEB)

    Bock, J.; Battle, J. [Jet Propulsion Laboratory (JPL), National Aeronautics and Space Administration (NASA), Pasadena, CA 91109 (United States); Sullivan, I. [Department of Physics, University of Washington, Seattle, WA 98195 (United States); Arai, T.; Matsumoto, T.; Matsuura, S.; Tsumura, K. [Department of Space Astronomy and Astrophysics, Institute of Space and Astronautical Science (ISAS), Japan Aerospace Exploration Agency (JAXA), Sagamihara, Kanagawa 252-5210 (Japan); Cooray, A.; Mitchell-Wynne, K.; Smidt, J. [Center for Cosmology, University of California, Irvine, CA 92697 (United States); Hristov, V.; Lam, A. C.; Levenson, L. R.; Mason, P. [Department of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125 (United States); Keating, B.; Renbarger, T. [Department of Physics, University of California, San Diego, San Diego, CA 92093 (United States); Kim, M. G. [Department of Physics and Astronomy, Seoul National University, Seoul 151-742 (Korea, Republic of); Lee, D. H. [Institute of Astronomy and Astrophysics, Academia Sinica, National Taiwan University, Taipei 10617, Taiwan (China); Nam, U. W. [Korea Astronomy and Space Science Institute (KASI), Daejeon 305-348 (Korea, Republic of); Suzuki, K. [Instrument Development Group of Technical Center, Nagoya University, Nagoya, Aichi 464-8602 (Japan); and others

    2013-08-15

    We have developed and characterized an imaging instrument to measure the spatial properties of the diffuse near-infrared extragalactic background light (EBL) in a search for fluctuations from z > 6 galaxies during the epoch of reionization. The instrument is part of the Cosmic Infrared Background Experiment (CIBER), designed to observe the EBL above Earth's atmosphere during a suborbital sounding rocket flight. The imaging instrument incorporates a 2 Degree-Sign Multiplication-Sign 2 Degree-Sign field of view to measure fluctuations over the predicted peak of the spatial power spectrum at 10 arcmin, and 7'' Multiplication-Sign 7'' pixels, to remove lower redshift galaxies to a depth sufficient to reduce the low-redshift galaxy clustering foreground below instrumental sensitivity. The imaging instrument employs two cameras with {Delta}{lambda}/{lambda} {approx} 0.5 bandpasses centered at 1.1 {mu}m and 1.6 {mu}m to spectrally discriminate reionization extragalactic background fluctuations from local foreground fluctuations. CIBER operates at wavelengths where the electromagnetic spectrum of the reionization extragalactic background is thought to peak, and complements fluctuation measurements by AKARI and Spitzer at longer wavelengths. We have characterized the instrument in the laboratory, including measurements of the sensitivity, flat-field response, stray light performance, and noise properties. Several modifications were made to the instrument following a first flight in 2009 February. The instrument performed to specifications in three subsequent flights, and the scientific data are now being analyzed.

  14. 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

  15. Deep inelastic scattering and asymptotic freedom

    International Nuclear Information System (INIS)

    Nachtmann, O.

    1985-01-01

    I recall some facets of the history of the field of deep inelastic scattering. I show how there was a very fruitful interplay between phenomenology on the one side and more abstract field theoretical considerations on the other side, where Kurt Symanzik, whose memory we honour today, made important contributions. Finally I make some remarks on the most recent developments in this field which have to do with the so-called EMC-effect, where EMC stands for European Muon Collaboration. (orig./HSI)

  16. Ground States of Ultracold Spin-1 Atoms in a Deep Double-Well Optical Superlattice in a Weak Magnetic Field

    International Nuclear Information System (INIS)

    Zheng Gong-Ping; Qin Shuai-Feng; Wang Shou-Yang; Jian Wen-Tian

    2013-01-01

    The ground states of the ultracold spin-1 atoms trapped in a deep one-dimensional double-well optical superlattice in a weak magnetic field are obtained. It is shown that the ground-state diagrams of the reduced double-well model are remarkably different for the antiferromagnetic and ferromagnetic condensates. The transition between the singlet state and nematic state is observed for the antiferromagnetic interaction atoms, which can be realized by modulating the tunneling parameter or the quadratic Zeeman energy. An experiment to distinguish the different spin states is suggested. (general)

  17. Deep sea biophysics

    International Nuclear Information System (INIS)

    Yayanos, A.A.

    1982-01-01

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

  18. Model United Nations and Deep Learning: Theoretical and Professional Learning

    Science.gov (United States)

    Engel, Susan; Pallas, Josh; Lambert, Sarah

    2017-01-01

    This article demonstrates that the purposeful subject design, incorporating a Model United Nations (MUN), facilitated deep learning and professional skills attainment in the field of International Relations. Deep learning was promoted in subject design by linking learning objectives to Anderson and Krathwohl's (2001) four levels of knowledge or…

  19. PHOTOMETRY AND PHOTOMETRIC REDSHIFT CATALOGS FOR THE LOCKMAN HOLE DEEP FIELD

    International Nuclear Information System (INIS)

    Fotopoulou, S.; Salvato, M.; Hasinger, G.; Rovilos, E.; Brusa, M.; Lutz, D.; Burwitz, V.; Egami, E.; Henry, J. P.; Huang, J. H.; Rigopoulou, D.; Vaccari, M.

    2012-01-01

    We present broadband photometry and photometric redshifts for 187,611 sources located in ∼0.5 deg 2 in the Lockman Hole area. The catalog includes 388 X-ray-detected sources identified with the very deep XMM-Newton observations available for an area of 0.2 deg 2 . The source detection was performed on the R c -, z'-, and B-band images and the available photometry is spanning from the far-ultraviolet to the mid-infrared, reaching in the best-case scenario 21 bands. Astrometry corrections and photometric cross-calibrations over the entire data set allowed the computation of accurate photometric redshifts. Special treatment is undertaken for the X-ray sources, the majority of which are active galactic nuclei (AGNs). For normal galaxies, comparing the photometric redshifts to the 253 available spectroscopic redshifts, we achieve an accuracy of σ Δz/(1+z) = 0.036, with 12.6% outliers. For the X-ray-detected sources, compared to 115 spectroscopic redshifts, the accuracy is σ Δz/(1+z) = 0.069, with 18.3% outliers, where the outliers are defined as sources with |z phot – z spec | > 0.15 × (1 + z spec ). These results are a significant improvement over the previously available photometric redshifts for normal galaxies in the Lockman Hole, while it is the first time that photometric redshifts are computed and made public for AGNs for this field.

  20. 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.

  1. Natural gas geological characteristics and great discovery of large gas fields in deep-water area of the western South China Sea

    Directory of Open Access Journals (Sweden)

    Zhenfeng Wang

    2015-12-01

    Full Text Available To accelerate the petroleum exploration in deep sea of China, since the period of “the 11th Five-Year Plan”, the sedimentary process, source rock formation and hydrocarbon generation and expulsion process in deep-water area of the Qiongdongnan Basin in the western South China Sea have been studied systematically using the data like large-area 3D seismic survey, logging, drill core (cuttings and geochemical analysis, providing three innovative understandings, i.e. excellent hydrocarbon source conditions, good accumulation conditions, and grouping and zonal distribution of large exploration targets. From the study, the following conclusions are drawn. First, the deep-water area located in the southern and central parts of the Qiongdongnan Basin was formed under the control of such tectonic events as Indosinian–Eurasian Plate collision, Himalayan uplifting and South China Sea expansion, and experienced Paleogene lift and Neogene depression stages. Second, accompanied by lacustrine deposition, faulting activity was violent in Eocene; whereas in Early Oligocene, rift continued to develop under a sedimentary environment of marine–terrestrial transitional facies and littoral-neritic facies. Third, oil generation predominated Eocene lacustrine mudstone and gas generation predominated Lower Oligocene marine–terrestrial transitional facies coal-measure strata compose two sets of major source rocks. Fourth, analysis in respect of thermal evolution level, hydrocarbon generation volume and hydrocarbon generation intensity shows that Ledong, Lingshui, Baodao and Changchang sags belong to potential hydrocarbon-rich kitchens, among which Ledong and Lingshui sags have been proved to have great hydrocarbon generation potential by drilling. Fifth, researches of deep-water sedimentology and hydrocarbon accumulation dynamics reveal that Paleogene and Neogene plays are developed vertically, and favorable hydrocarbon accumulation zones like the Central

  2. 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.

  3. Evaluating lysimeter drainage against soil deep percolation modeled with profile soil moisture, field tracer propagation, and lab measured soil hydraulic properties

    DEFF Research Database (Denmark)

    Vasquez, Vicente; Thomsen, Anton Gårde; Iversen, Bo Vangsø

    them have been reported. To compare among methods, one year of four large-scale lysimeters drainage (D) was evaluated against modeled soil deep percolation using either profile soil moisture, bromide breakthrough curves from suction cups, or measured soil hydraulic properties in the laboratory....... Measured volumetric soil water content (q) was 3-4% higher inside lysimeters than in the field probably due to a zero tension lower boundary condition inside lysimeters. D from soil hydraulic properties measured in the laboratory resulted in a 15% higher evapotranspiration and 12% lower drainage...... predictions than the model calibrated with field measured q. Bromide (Br) breakthrough curves indicated high variability between lysimeters and field suction cups with mean Br velocities at first arrival time of 110 and 33 mm/d, respectively. D was 520 mm/yr with lysimeters, 613 mm/yr with the calibrated...

  4. 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.

  5. Preliminary results from NOAMP deep drifting floats

    International Nuclear Information System (INIS)

    Ollitrault, M.

    1989-01-01

    This paper is a very brief and preliminary outline of first results obtained with deep SOFAR floats in the NOAMP area. The work is now going toward more precise statistical estimations of mean and variable currents, together with better tracking to resolve submesoscales and estimate diffusivities due to mesoscale and smaller scale motions. However the preliminary results confirm that the NOAMP region (and surroundings) has a deep mesoscale eddy field that is considerably more energetic that the mean field (r.m.s. velocities are of order 5 cm s -1 ), although both values are diminished compared to the western basin. A data report containing trajectories and statistics is scheduled to be published by IFREMER in the near future. The project main task is to especially study the dispersion of radioactive substances

  6. 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.

  7. Environmental challenges of deep water activities

    International Nuclear Information System (INIS)

    Sande, Arvid

    1998-01-01

    In this presentation there are discussed the experiences of petroleum industry, and the projects that have been conducted in connection with the planning and drilling of the first deep water wells in Norway. There are also presented views on where to put more effort in the years to come, so as to increase the knowledge of deep water areas. Attention is laid on exploration drilling as this is the only activity with environmental potential that will take place during the next five years or so. The challenges for future field developments in these water depths are briefly discussed. 7 refs

  8. Quantitative phase microscopy using deep neural networks

    Science.gov (United States)

    Li, Shuai; Sinha, Ayan; Lee, Justin; Barbastathis, George

    2018-02-01

    Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved.

  9. Post-drilling changes in seabed landscape and megabenthos in a deep-sea hydrothermal system, the Iheya North field, Okinawa Trough.

    Science.gov (United States)

    Nakajima, Ryota; Yamamoto, Hiroyuki; Kawagucci, Shinsuke; Takaya, Yutaro; Nozaki, Tatsuo; Chen, Chong; Fujikura, Katsunori; Miwa, Tetsuya; Takai, Ken

    2015-01-01

    There has been an increasing interest in seafloor exploitation such as mineral mining in deep-sea hydrothermal fields, but the environmental impact of anthropogenic disturbance to the seafloor is poorly known. In this study, the effect of such anthropogenic disturbance by scientific drilling operations (IODP Expedition 331) on seabed landscape and megafaunal habitation was surveyed for over 3 years using remotely operated vehicle video observation in a deep-sea hydrothermal field, the Iheya North field, in the Okinawa Trough. We focused on observations from a particular drilling site (Site C0014) where the most dynamic change of landscape and megafaunal habitation was observed among the drilling sites of IODP Exp. 331. No visible hydrothermal fluid discharge had been observed at the sedimentary seafloor at Site C0014, where Calyptogena clam colonies were known for more than 10 years, before the drilling event. After drilling commenced, the original Calyptogena colonies were completely buried by the drilling deposits. Several months after the drilling, diffusing high-temperature hydrothermal fluid began to discharge from the sedimentary subseafloor in the area of over 20 m from the drill holes, 'artificially' creating a new hydrothermal vent habitat. Widespread microbial mats developed on the seafloor with the diffusing hydrothermal fluids and the galatheid crab Shinkaia crosnieri endemic to vents dominated the new vent community. The previously soft, sedimentary seafloor was hardened probably due to barite/gypsum mineralization or silicification, becoming rough and undulated with many fissures after the drilling operation. Although the effects of the drilling operation on seabed landscape and megafaunal composition are probably confined to an area of maximally 30 m from the drill holes, the newly established hydrothermal vent ecosystem has already lasted 2 years and is like to continue to exist until the fluid discharge ceases and thus the ecosystem in the area has

  10. 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.

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

    Science.gov (United States)

    2018-01-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

  13. Stability of deep features across CT scanners and field of view using a physical phantom

    Science.gov (United States)

    Paul, Rahul; Shafiq-ul-Hassan, Muhammad; Moros, Eduardo G.; Gillies, Robert J.; Hall, Lawrence O.; Goldgof, Dmitry B.

    2018-02-01

    Radiomics is the process of analyzing radiological images by extracting quantitative features for monitoring and diagnosis of various cancers. Analyzing images acquired from different medical centers is confounded by many choices in acquisition, reconstruction parameters and differences among device manufacturers. Consequently, scanning the same patient or phantom using various acquisition/reconstruction parameters as well as different scanners may result in different feature values. To further evaluate this issue, in this study, CT images from a physical radiomic phantom were used. Recent studies showed that some quantitative features were dependent on voxel size and that this dependency could be reduced or removed by the appropriate normalization factor. Deep features extracted from a convolutional neural network, may also provide additional features for image analysis. Using a transfer learning approach, we obtained deep features from three convolutional neural networks pre-trained on color camera images. An we examination of the dependency of deep features on image pixel size was done. We found that some deep features were pixel size dependent, and to remove this dependency we proposed two effective normalization approaches. For analyzing the effects of normalization, a threshold has been used based on the calculated standard deviation and average distance from a best fit horizontal line among the features' underlying pixel size before and after normalization. The inter and intra scanner dependency of deep features has also been evaluated.

  14. 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

  15. ModelHub: Towards Unified Data and Lifecycle Management for Deep Learning

    OpenAIRE

    Miao, Hui; Li, Ang; Davis, Larry S.; Deshpande, Amol

    2016-01-01

    Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle generates a rich set of data artifacts, such as learned parameters and tr...

  16. A search for planetary eclipses of white dwarfs in the Pan-STARRS1 medium-deep fields

    Energy Technology Data Exchange (ETDEWEB)

    Fulton, B. J.; Tonry, J. L.; Flewelling, H.; Burgett, W. S.; Chambers, K. C.; Hodapp, K. W.; Huber, M. E.; Kaiser, N.; Wainscoat, R. J.; Waters, C. [Institute for Astronomy, University of Hawaii at Manoa, Honolulu, HI 96822 (United States)

    2014-12-01

    We present a search for eclipses of ∼1700 white dwarfs (WDs) in the Pan-STARRS1 medium-deep fields. Candidate eclipse events are selected by identifying low outliers in over 4.3 million light curve measurements. We find no short-duration eclipses consistent with being caused by a planetary size companion. This large data set enables us to place strong constraints on the close-in planet occurrence rates around WDs for planets as small as 2 R {sub ⊕}. Our results indicate that gas giant planets orbiting just outside the Roche limit are rare, occurring around less than 0.5% of WDs. Habitable-zone super-Earths and hot super-Earths are less abundant than similar classes of planets around main-sequence stars. These constraints provide important insight into the ultimate fate of the large population of exoplanets orbiting main-sequence stars.

  17. 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.

  18. Magnetotelluric images of deep crustal structure of the Rehai geothermal field near Tengchong, southern China

    Science.gov (United States)

    Bai, Denghai; Meju, Maxwell A.; Liao, Zhijie

    2001-12-01

    Broadband (0.004-4096s) magnetotelluric (MT) soundings have been applied to the determination of the deep structure across the Rehai geothermal field in a Quaternary volcanic area near the Indo-Eurasian collisional margin. Tensorial analysis of the data show evidence of weak to strong 3-D effects but for approximate 2-D imaging, we obtained dual-mode MT responses for an assumed strike direction coincident with the trend of the regional-scale faults and with the principal impedance azimuth at long periods. The data were subsequently inverted using different approaches. The rapid relaxation inversion models are comparable to the sections constructed from depth-converted invariant impedance phase data. The results from full-domain 2-D conjugate-gradient inversion with different initial models are concordant and evoke a picture of a dome-like structure consisting of a conductive (50-1000 Ωm) cap which is about 5-6km thick in the central part of the known geothermal field and thickens outwards to about 15-20km. The anomalous structure rests on a mid-crustal zone of 20-30 Ωm resistivity extending down to about 25km depth where there appears to be a moderately resistive (>30 Ωm) substratum. The MT images are shown to be in accord with published geological, isotopic and geochemical results that suggested the presence of a magma body underneath the area of study.

  19. 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.

  20. 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).

  1. 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 ...

  2. Deep inelastic scattering in spontaneously broken gauge models

    International Nuclear Information System (INIS)

    Goloskokov, S.V.; Mikhov, S.G.; Morozov, P.T.; Stamenov, D.B.

    1975-01-01

    Deep inelastic lepton hadron scattering in the simplest spontaneously broken symmetry (the Kibble model) is analyzed. A hypothesis that the invariant coupling constant of the quartic selfinteraction for large spacelike momenta tends to a finite asymptotic value without spoiling the asymptotic freedom for the invariant coupling constant of the Yang-Mills field is used. It is shown that Biorken scaling for the moments of the structure functions of the deep inelastic lepton hadron scattering is violated by powers of logarithms

  3. 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.

  4. 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

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

    OpenAIRE

    Sugiarto Indar; Pasila Felix

    2018-01-01

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

  6. Field-reversed bubble in deep plasma channels for high quality electron acceleration

    CERN Document Server

    Pukhov, A; Tueckmantel, T; Thomas, J; Yu, I; Kostyukov, Yu

    2014-01-01

    We study hollow plasma channels with smooth boundaries for laser-driven electron acceleration in the bubble regime. Contrary to the uniform plasma case, the laser forms no optical shock and no etching at the front. This increases the effective bubble phase velocity and energy gain. The longitudinal field has a plateau that allows for mono-energetic acceleration. We observe as low as 10−3 r.m.s. relative witness beam energy uncertainty in each cross-section and 0.3% total energy spread. By varying plasma density profile inside a deep channel, the bubble fields can be adjusted to balance the laser depletion and dephasing lengths. Bubble scaling laws for the deep channel are derived. Ultra-short pancake-like laser pulses lead to the highest energies of accelerated electrons per Joule of laser pulse energy.

  7. Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins.

    Science.gov (United States)

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

    2017-09-05

    In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  8. 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.

  9. Uranium-thorium series radionuclides in brines and reservoir rocks from two deep geothermal boreholes in the Salton Sea geothermal field, southeastern California

    International Nuclear Information System (INIS)

    Zukin, J.G.; Hammond, D.E.; Ku, Tehlung; Elders, W.A.

    1987-01-01

    Naturally occurring U and Th series radionuclides have been analyzed in high temperature brines (∼ 300 degree C, 25 wt% dissolved solids) and associated rocks from two deep geothermal wells located on the northeastern margin of the Salton Sea Geothermal Field (SSGF). These data are part of a study of the SSGF as a natural analog of possible radionuclide behavior near a nuclear waste repository constructed in salt beds, and permit evaluation of some characteristics of water-rock interaction in the SSGF

  10. 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.

  11. [Advantages and Application Prospects of Deep Learning in Image Recognition and Bone Age Assessment].

    Science.gov (United States)

    Hu, T H; Wan, L; Liu, T A; Wang, M W; Chen, T; Wang, Y H

    2017-12-01

    Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment. Copyright© by the Editorial Department of Journal of Forensic Medicine.

  12. 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.

  13. Succeeding in deep water by combining technology qualification and production forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Hussain, A.; Oiungen, B.; Raposo, C. [Det Norske Veritas (DNV), Rio de Janeiro, RJ (Brazil)

    2008-07-01

    All the easy oil and gas is gone, and, as a result the Oil and Gas industry is continuously targeting deeper and more remote fields. The exploration and development of deep water oil and gas fields is associated with enormous costs and multiple uncertainties with regard to equipment reliability and performance. Proper risk management can be used to evaluate the impact of these uncertainties thereby helping to ensure optimal business performance of the future assets, as well as helping the decision maker target investment towards areas where the financial impact will be the greatest. This paper reviews the principles of Technology Qualification and Production Forecasting methodology, both of which are risk management solutions with a proven track record for deep water field developments. (author)

  14. 30 CFR 203.71 - How does MMS allocate a field's suspension volume between my lease and other leases on my field?

    Science.gov (United States)

    2010-07-01

    ... RATES OCS Oil, Gas, and Sulfur General Royalty Relief for Pre-Act Deep Water Leases and for Development... or post-November 2000 deep water lease to your field after we approve your application We will not..., or the minimum suspension volume of the authorized field, whichever is greater (i) You toll the time...

  15. Dalia integrated production bundle (IPB): an innovative riser solution for deep water fields

    Energy Technology Data Exchange (ETDEWEB)

    Reals, Th Boscals de; Gloaguen, M.; Roche, F. [Total E and P (Angola); Marion, A.; Poincheval, A. [Technip, Paris (France)

    2008-07-01

    The Dalia field is located 210 km north west of Luanda (Angola), about 140 km from shore in 1400 meter water-depth. It was the second major discovery out of 15 made in the block 17 operated by Total. The Dalia Umbilical, Flow lines and Risers EPCI Contract was awarded in 2003. The sea-line network to connect and control the 71 wells and 9 manifolds consist of the following: 40 km of insulated pipe in pipe (12 inches into 17 inches) production flow lines; 45 km of 12 inches water and gas injection lines; 6 off 1.7 km flexible water and gas injection risers; 8 off 1.65 km flexible Integrated Production Bundle (IPB) risers; 75 km of control umbilicals. The flow assurance and associated insulation requirement of the production transport system was one of the main challenges of the project. With a crude temperature of 45 deg C at the wellhead and the required minimum temperature of 35 deg C on arrival at the FPSO, this problem was complex. Understanding that, due to the Joule Thompson effect of the riser gas lift, a 'built in' loss of about 5 deg C is induced and together with further losses through the sub sea pipelines, some up to 6 km long, the agreed solution was 'pipe in pipe' for the production flow lines. The innovative flexible IPB riser, incorporating gas lift and heating to keep the fluid temperature above hydrate formation zone, was the selected riser solution. The IPB is new technology for deep water, developed by Technip for Dalia, and consists of a 12 inches nominal central flexible, surrounded by layers of heat tracing cables, small bore gas lift lines, optical fibres and many insulation layers with an Overall Heat Transfer Coefficient of approximately 3,4 W/m{sup 2}K. After an earlier research and development programme, a further extensive qualification programme was conducted during the course of the project, culminating with the deep water testing phase offshore Brazil. The IPB was then approved for fabrication and installation

  16. 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...

  17. A new procedure for deep sea mining tailings disposal

    OpenAIRE

    Ma, W.; Schott, D.L.; Lodewijks, G.

    2017-01-01

    Deep sea mining tailings disposal is a new environmental challenge related to water pollution, mineral crust waste handling, and ocean biology. The objective of this paper is to propose a new tailings disposal procedure for the deep sea mining industry. Through comparisons of the tailings disposal methods which exist in on-land mining and the coastal mining fields, a new tailings disposal procedure, i.e., the submarine–backfill–dam–reuse (SBDR) tailings disposal procedure, is proposed. It com...

  18. DeepBlow - a Lagrangian plume model for deep water blowouts

    International Nuclear Information System (INIS)

    Johansen, Oeistein

    2000-01-01

    This paper presents a sub-sea blowout model designed with special emphasis on deep-water conditions. The model is an integral plume model based on a Lagrangian concept. This concept is applied to multiphase discharges in the formation of water, oil and gas in a stratified water column with variable currents. The gas may be converted to hydrate in combination with seawater, dissolved into the plume water, or leaking out of the plume due to the slip between rising gas bubbles and the plume trajectory. Non-ideal behaviour of the gas is accounted for by the introduction of pressure- and temperature-dependent compressibility z-factor in the equation of state. A number of case studies are presented in the paper. One of the cases (blowout from 100 m depth) is compared with observations from a field experiment conducted in Norwegian waters in June 1996. The model results are found to compare favourably with the field observations when dissolution of gas into seawater is accounted in the model. For discharges at intermediate to shallow depths (100-250 m), the two major processes limiting plume rise will be: (a) dissolution of gas into ambient water, or (b) bubbles rising out of the inclined plume. These processes tend to be self-enforcing, i.e., when a gas is lost by either of these processes, plume rise tends to slow down and more time will be available for dissolution. For discharges in deep waters (700-1500 m depth), hydrate formation is found to be a dominating process in limiting plume rise. (Author)

  19. Albedo Neutron Dosimetry in a Deep Geological Disposal Repository for High-Level Nuclear Waste.

    Science.gov (United States)

    Pang, Bo; Becker, Frank

    2017-04-28

    Albedo neutron dosemeter is the German official personal neutron dosemeter in mixed radiation fields where neutrons contribute to personal dose. In deep geological repositories for high-level nuclear waste, where neutrons can dominate the radiation field, it is of interest to investigate the performance of albedo neutron dosemeter in such facilities. In this study, the deep geological repository is represented by a shielding cask loaded with spent nuclear fuel placed inside a rock salt emplacement drift. Due to the backscattering of neutrons in the drift, issues concerning calibration of the dosemeter arise. Field-specific calibration of the albedo neutron dosemeter was hence performed with Monte Carlo simulations. In order to assess the applicability of the albedo neutron dosemeter in a deep geological repository over a long time scale, spent nuclear fuel with different ages of 50, 100 and 500 years were investigated. It was found out, that the neutron radiation field in a deep geological repository can be assigned to the application area 'N1' of the albedo neutron dosemeter, which is typical in reactors and accelerators with heavy shielding. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  20. A survey on deep learning in medical image analysis.

    Science.gov (United States)

    Litjens, Geert; Kooi, Thijs; Bejnordi, Babak Ehteshami; Setio, Arnaud Arindra Adiyoso; Ciompi, Francesco; Ghafoorian, Mohsen; van der Laak, Jeroen A W M; van Ginneken, Bram; Sánchez, Clara I

    2017-12-01

    Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. The use of interaction matrices for identification, structuring and ranking of FEPs in a repository system. Application on the far-field of a deep geological repository for spent fuel

    Energy Technology Data Exchange (ETDEWEB)

    Skagius, K; Wiborgh, M [Kemakta, Stockholm (Sweden); Stroem, A [Swedish Nuclear Fuel and Waste Management Co., Stockholm (Sweden)

    1995-11-01

    The basic device in the Rock Engineering Systems approach, the interaction matrix, has been used to identify, structure and rank Features, Events, and Processes (FEPs) describing barrier performance and radionuclide behaviour in the far-field of a deep geologic repository for spent fuel. The result is a first version of the Process System (PS), for the far-field of a deep repository, structured in an interaction matrix with supporting documentation. The documentation is compiled in databases, one containing matrix specific information and one containing general FEP descriptions. The study has shown that an interaction matrix is feasible to use both for the structuring of the PS and for visualisation of the PS. The developed documentation system increases the transparency of the system description and makes it possible to trace back the judgements made during the construction of the matrix. This will facilitate review work and future revisions as well as consistent treatment of different issues in the system. This study is a first step in the application of a systematic method to establish a structured description of the PS for a deep repository for spent fuel. The work could be seen as a part of the preparation for the forthcoming performance and safety analysis. The next step would be to develop the PS for the remaining parts of the repository system to the same level as has been done for the far-field system. Before the PS is evaluated for different selected system premises, a scientific review of the contents of the PS for the whole repository system would be beneficial. 5 refs.

  2. The use of interaction matrices for identification, structuring and ranking of FEPs in a repository system. Application on the far-field of a deep geological repository for spent fuel

    International Nuclear Information System (INIS)

    Skagius, K.; Wiborgh, M.; Stroem, A.

    1995-11-01

    The basic device in the Rock Engineering Systems approach, the interaction matrix, has been used to identify, structure and rank Features, Events, and Processes (FEPs) describing barrier performance and radionuclide behaviour in the far-field of a deep geologic repository for spent fuel. The result is a first version of the Process System (PS), for the far-field of a deep repository, structured in an interaction matrix with supporting documentation. The documentation is compiled in databases, one containing matrix specific information and one containing general FEP descriptions. The study has shown that an interaction matrix is feasible to use both for the structuring of the PS and for visualisation of the PS. The developed documentation system increases the transparency of the system description and makes it possible to trace back the judgements made during the construction of the matrix. This will facilitate review work and future revisions as well as consistent treatment of different issues in the system. This study is a first step in the application of a systematic method to establish a structured description of the PS for a deep repository for spent fuel. The work could be seen as a part of the preparation for the forthcoming performance and safety analysis. The next step would be to develop the PS for the remaining parts of the repository system to the same level as has been done for the far-field system. Before the PS is evaluated for different selected system premises, a scientific review of the contents of the PS for the whole repository system would be beneficial. 5 refs

  3. 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.

  4. 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.

  5. Review of excavation methods and their implications for the near-field barrier of a deep underground repository

    International Nuclear Information System (INIS)

    Young, D.K.

    1993-01-01

    The report reviews excavation techniques for use in the construction of deep underground radioactive waste repositories, gives a summary of responses of the host rock to excavation and the means of measuring that response and discusses techniques for predicting that response. The review of excavation techniques included technical developments and current practice. To this end an extensive database was developed reviewing major excavations in rock types relevant to disposal and the techniques employed. Creation of an underground opening alters the properties of the rock mass around it. This study identifies stress, displacement, rock mass deformability and permeability as key parameters and reviews how they may be determined. Finally the report discusses the techniques available for predicting the behaviour of the near-field host rock. This concentrates on methods of numerical analysis since existing empirical or analytical methods are not considered suitable. (author)

  6. 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)

  7. Field-Assisted Splitting of Pure Water Based on Deep-Sub-Debye-Length Nanogap Electrochemical Cells.

    Science.gov (United States)

    Wang, Yifei; Narayanan, S R; Wu, Wei

    2017-08-22

    Owing to the low conductivity of pure water, using an electrolyte is common for achieving efficient water electrolysis. In this paper, we have fundamentally broken through this common sense by using deep-sub-Debye-length nanogap electrochemical cells to achieve efficient electrolysis of pure water (without any added electrolyte) at room temperature. A field-assisted effect resulted from overlapped electrical double layers can greatly enhance water molecules ionization and mass transport, leading to electron-transfer limited reactions. We have named this process "virtual breakdown mechanism" (which is completely different from traditional mechanisms) that couples the two half-reactions together, greatly reducing the energy losses arising from ion transport. This fundamental discovery has been theoretically discussed in this paper and experimentally demonstrated in a group of electrochemical cells with nanogaps between two electrodes down to 37 nm. On the basis of our nanogap electrochemical cells, the electrolysis current density from pure water can be significantly larger than that from 1 mol/L sodium hydroxide solution, indicating the much better performance of pure water splitting as a potential for on-demand clean hydrogen production.

  8. FRONTIER FIELDS CLUSTERS: DEEP CHANDRA OBSERVATIONS OF THE COMPLEX MERGER MACS J1149.6+2223

    Energy Technology Data Exchange (ETDEWEB)

    Ogrean, G. A.; Weeren, R. J. van; Jones, C.; Forman, W.; Andrade-Santos, F.; Murray, S. S.; Nulsen, P.; Bulbul, E.; Kraft, R.; Randall, S. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Dawson, W. A. [Lawrence Livermore National Lab, 7000 East Avenue, Livermore, CA 94550 (United States); Golovich, N. [University of California, One Shields Avenue, Davis, CA 95616 (United States); Roediger, E. [Astronomy and Astrophysics Section, Dublin Institute for Advanced Studies, 31 Fitzwilliam Place, Dublin 2 (Ireland); Zitrin, A.; Sayers, J. [Cahill Center for Astronomy and Astrophysics, California Institute of Technology, MC 249-17, Pasadena, CA 91125 (United States); Goulding, A. [Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States); Umetsu, K. [Institute of Astronomy and Astrophysics, Academia Sinica, P.O. Box 23-141, Taipei 10617, Taiwan (China); Mroczkowski, T. [U.S. Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, DC 20375 (United States); Bonafede, A. [Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, D-21029 Hamburg (Germany); Churazov, E., E-mail: gogrean@cfa.harvard.edu [Max Planck Institute for Astrophysics, Karl-Schwarzschild-Str. 1, D-85741, Garching (Germany); and others

    2016-03-10

    The Hubble Space Telescope Frontier Fields cluster MACS J1149.6+2223 is one of the most complex merging clusters, believed to consist of four dark matter halos. We present results from deep (365 ks) Chandra observations of the cluster, which reveal the most distant cold front (z  =  0.544) discovered to date. In the cluster outskirts, we also detect hints of a surface brightness edge that could be the bow shock preceding the cold front. The substructure analysis of the cluster identified several components with large relative radial velocities, thus indicating that at least some collisions occur almost along the line of sight. The inclination of the mergers with respect to the plane of the sky poses significant observational challenges at X-ray wavelengths. MACS J1149.6+2223 possibly hosts a steep-spectrum radio halo. If the steepness of the radio halo is confirmed, then the radio spectrum, combined with the relatively regular ICM morphology, could indicate that MACS J1149.6+2223 is an old merging cluster.

  9. Solar wind charge exchange emission in the Chandra deep field north

    Energy Technology Data Exchange (ETDEWEB)

    Slavin, Jonathan D.; Wargelin, Bradford J. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Koutroumpa, Dimitra [LATMOS/IPSL, CNRS, Université Versailles Saint Quentin, 11 Boulevard d' Alembert, F-78280, Guyancourt (France)

    2013-12-10

    The diffuse soft X-ray background comes from distant galaxies, from hot Galactic gas, and from within the solar system. The latter emission arises from charge exchange between highly charged solar wind ions and neutral gas. This so-called solar wind charge exchange (SWCX) emission is spatially and temporally variable and interferes with our measurements of more distant cosmic emission while also providing important information on the nature of the solar wind-interstellar medium interaction. We present the results of our analysis of eight Chandra observations of the Chandra Deep Field North (CDFN) with the goal of measuring the cosmic and SWCX contributions to the X-ray background. Our modeling of both geocoronal and heliospheric SWCX emission is the most detailed for any observation to date. After allowing for ∼30% uncertainty in the SWCX emission and subtracting it from the observational data, we estimate that the flux of cosmic background for the CDFN in the O VII Kα, Kβ, and O VIII Lyα lines totals 5.8 ± 1.1 photons s{sup –1} cm{sup –2} sr{sup –1} (or LU). Heliospheric SWCX emission varied for each observation due to differences in solar wind conditions and the line of sight through the solar system, but was typically about half as strong as the cosmic background (i.e., one-third of the total) in those lines. The modeled geocoronal emission was 0.82 LU in one observation but averaged only 0.15 LU in the others. Our measurement of the cosmic background is lower than but marginally consistent with previous estimates based on XMM-Newton data.

  10. Solar wind charge exchange emission in the Chandra deep field north

    International Nuclear Information System (INIS)

    Slavin, Jonathan D.; Wargelin, Bradford J.; Koutroumpa, Dimitra

    2013-01-01

    The diffuse soft X-ray background comes from distant galaxies, from hot Galactic gas, and from within the solar system. The latter emission arises from charge exchange between highly charged solar wind ions and neutral gas. This so-called solar wind charge exchange (SWCX) emission is spatially and temporally variable and interferes with our measurements of more distant cosmic emission while also providing important information on the nature of the solar wind-interstellar medium interaction. We present the results of our analysis of eight Chandra observations of the Chandra Deep Field North (CDFN) with the goal of measuring the cosmic and SWCX contributions to the X-ray background. Our modeling of both geocoronal and heliospheric SWCX emission is the most detailed for any observation to date. After allowing for ∼30% uncertainty in the SWCX emission and subtracting it from the observational data, we estimate that the flux of cosmic background for the CDFN in the O VII Kα, Kβ, and O VIII Lyα lines totals 5.8 ± 1.1 photons s –1 cm –2 sr –1 (or LU). Heliospheric SWCX emission varied for each observation due to differences in solar wind conditions and the line of sight through the solar system, but was typically about half as strong as the cosmic background (i.e., one-third of the total) in those lines. The modeled geocoronal emission was 0.82 LU in one observation but averaged only 0.15 LU in the others. Our measurement of the cosmic background is lower than but marginally consistent with previous estimates based on XMM-Newton data.

  11. Field estimates of groundwater circulation depths in two mountainous watersheds in the western U.S. and the effect of deep circulation on solute concentrations in streamflow

    Science.gov (United States)

    Frisbee, Marty D.; Tolley, Douglas G.; Wilson, John L.

    2017-04-01

    Estimates of groundwater circulation depths based on field data are lacking. These data are critical to inform and refine hydrogeologic models of mountainous watersheds, and to quantify depth and time dependencies of weathering processes in watersheds. Here we test two competing hypotheses on the role of geology and geologic setting in deep groundwater circulation and the role of deep groundwater in the geochemical evolution of streams and springs. We test these hypotheses in two mountainous watersheds that have distinctly different geologic settings (one crystalline, metamorphic bedrock and the other volcanic bedrock). Estimated circulation depths for springs in both watersheds range from 0.6 to 1.6 km and may be as great as 2.5 km. These estimated groundwater circulation depths are much deeper than commonly modeled depths suggesting that we may be forcing groundwater flow paths too shallow in models. In addition, the spatial relationships of groundwater circulation depths are different between the two watersheds. Groundwater circulation depths in the crystalline bedrock watershed increase with decreasing elevation indicative of topography-driven groundwater flow. This relationship is not present in the volcanic bedrock watershed suggesting that both the source of fracturing (tectonic versus volcanic) and increased primary porosity in the volcanic bedrock play a role in deep groundwater circulation. The results from the crystalline bedrock watershed also indicate that relatively deep groundwater circulation can occur at local scales in headwater drainages less than 9.0 km2 and at larger fractions than commonly perceived. Deep groundwater is a primary control on streamflow processes and solute concentrations in both watersheds.

  12. 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.

  13. UV Luminosity Functions at z~4, 5, and 6 from the Hubble Ultra Deep Field and Other Deep Hubble Space Telescope ACS Fields: Evolution and Star Formation History

    Science.gov (United States)

    Bouwens, R. J.; Illingworth, G. D.; Franx, Marijn; Ford, Holland

    2007-12-01

    We use the ACS BViz data from the HUDF and all other deep HST ACS fields (including the GOODS fields) to find large samples of star-forming galaxies at z~4 and ~5 and to extend our previous z~6 sample. These samples contain 4671, 1416, and 627 B-, V-, and i-dropouts, respectively, and reach to extremely low luminosities [(0.01-0.04)L*z=3 or MUV~-16 to -17], allowing us to determine the rest-frame UV LF and faint-end slope α at z~4-6 to high accuracy. We find faint-end slopes α=-1.73+/-0.05, -1.66+/-0.09, and -1.74+/-0.16 at z~4, ~5, and ~6, respectively, suggesting that the faint-end slope is very steep and shows little evolution with cosmic time. We find that M*UV brightens considerably in the 0.7 Gyr from z~6 to ~4 (by ~0.7 mag from M*UV=-20.24+/-0.19 to -20.98+/-0.10). The observed increase in the characteristic luminosity over this range is almost identical to that expected for the halo mass function, suggesting that the observed evolution is likely due to the hierarchical coalescence and merging of galaxies. The evolution in φ* is not significant. The UV luminosity density at z~6 is modestly lower than (0.45+/-0.09 times) that at z~4 (integrated to -17.5 mag) although a larger change is seen in the dust-corrected SFR density. We thoroughly examine published LF results and assess the reasons for their wide dispersion. We argue that the results reported here are the most robust available. The extremely steep faint-end slopes α found here suggest that lower luminosity galaxies play a significant role in reionizing the universe. Finally, recent search results for galaxies at z~7-8 are used to extend our estimates of the evolution of M* from z~7-8 to z~4. Based on observations made with the NASA/ESA Hubble Space Telescope, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with programs 9425, 9575, 9803, 9978, 10189, 10339, 10340, and 10632.

  14. Spatial Vertical Directionality and Correlation of Low-Frequency Ambient Noise in Deep Ocean Direct-Arrival Zones

    Directory of Open Access Journals (Sweden)

    Qiulong Yang

    2018-01-01

    Full Text Available Wind-driven and distant shipping noise sources contribute to the total noise field in the deep ocean direct-arrival zones. Wind-driven and distant shipping noise sources may significantly and simultaneously affect the spatial characteristics of the total noise field to some extent. In this work, a ray approach and parabolic equation solution method were jointly utilized to model the low-frequency ambient noise field in a range-dependent deep ocean environment by considering their calculation accuracy and efficiency in near-field wind-driven and far-field distant shipping noise fields. The reanalysis databases of National Center of Environment Prediction (NCEP and Volunteer Observation System (VOS were used to model the ambient noise source intensity and distribution. Spatial vertical directionality and correlation were analyzed in three scenarios that correspond to three wind speed conditions. The noise field was dominated by distant shipping noise sources when the wind speed was less than 3 m/s, and then the spatial vertical directionality and vertical correlation of the total noise field were nearly consistent with those of distant shipping noise field. The total noise field was completely dominated by near field wind generated noise sources when the wind speed was greater than 12 m/s at 150 Hz, and then the spatial vertical correlation coefficient and directionality pattern of the total noise field was approximately consistent with that of the wind-driven noise field. The spatial characteristics of the total noise field for wind speeds between 3 m/s and 12 m/s were the weighted results of wind-driven and distant shipping noise fields. Furthermore, the spatial characteristics of low-frequency ambient noise field were compared with the classical Cron/Sherman deep water noise field coherence function. Simulation results with the described modeling method showed good agreement with the experimental measurement results based on the vertical line

  15. Spatial Vertical Directionality and Correlation of Low-Frequency Ambient Noise in Deep Ocean Direct-Arrival Zones

    Science.gov (United States)

    Yang, Qiulong; Yang, Kunde; Cao, Ran; Duan, Shunli

    2018-01-01

    Wind-driven and distant shipping noise sources contribute to the total noise field in the deep ocean direct-arrival zones. Wind-driven and distant shipping noise sources may significantly and simultaneously affect the spatial characteristics of the total noise field to some extent. In this work, a ray approach and parabolic equation solution method were jointly utilized to model the low-frequency ambient noise field in a range-dependent deep ocean environment by considering their calculation accuracy and efficiency in near-field wind-driven and far-field distant shipping noise fields. The reanalysis databases of National Center of Environment Prediction (NCEP) and Volunteer Observation System (VOS) were used to model the ambient noise source intensity and distribution. Spatial vertical directionality and correlation were analyzed in three scenarios that correspond to three wind speed conditions. The noise field was dominated by distant shipping noise sources when the wind speed was less than 3 m/s, and then the spatial vertical directionality and vertical correlation of the total noise field were nearly consistent with those of distant shipping noise field. The total noise field was completely dominated by near field wind generated noise sources when the wind speed was greater than 12 m/s at 150 Hz, and then the spatial vertical correlation coefficient and directionality pattern of the total noise field was approximately consistent with that of the wind-driven noise field. The spatial characteristics of the total noise field for wind speeds between 3 m/s and 12 m/s were the weighted results of wind-driven and distant shipping noise fields. Furthermore, the spatial characteristics of low-frequency ambient noise field were compared with the classical Cron/Sherman deep water noise field coherence function. Simulation results with the described modeling method showed good agreement with the experimental measurement results based on the vertical line array deployed near

  16. Time series current meter data from buoys in the North Atlantic as part of the Deep Circulation in the Gulf of Maine Field Program from platforms GYRE and MARY LOUISE between July 25th, 1985 and August 2nd, 1987 (NODC Accession 0053940)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A two-year field study to investigate the deep flow between the major basins in the Gulf of Maine. This deep flow of warm-salty Slope water is an important driving...

  17. Coherence effects in deep inelastic scattering

    International Nuclear Information System (INIS)

    Andersson, B.; Gustafson, G.; Loennblad, L.; Pettersson, U.

    1988-09-01

    We present a framework for deep inelastic scattering, with bound state properties in accordance with a QCD force field acting like a vortex line in a colour superconducting vacuum, which implies some simple coherence effects. Within this scheme one may describe the results of present energies very well, but one obtains an appreciable depletion of gluon radiation in the HERA energy regime. (authors)

  18. Exploring deep potential aquifer in water scarce crystalline rocks

    Indian Academy of Sciences (India)

    out to explore deep groundwater potential zone in a water scarce granitic area. As existing field condi- ... Decision support tool developed in granitic ter- .... cially in terms of fracture system, the aquifer char- acteristics ... Methodologies used.

  19. Deep learning in jet reconstruction at CMS

    CERN Document Server

    Stoye, Markus

    2017-01-01

    Deep learning has led to several breakthroughs outside the field of high energy physics, yet in jet reconstruction for the CMS experiment at the CERN LHC it has not been used so far. This report shows results of applying deep learning strategies to jet reconstruction at the stage of identifying the original parton association of the jet (jet tagging), which is crucial for physics analyses at the LHC experiments. We introduce a custom deep neural network architecture for jet tagging. We compare the performance of this novel method with the other established approaches at CMS and show that the proposed strategy provides a significant improvement. The strategy provides the first multi-class classifier, instead of the few binary classifiers that previously were used, and thus yields more information and in a more convenient way. The performance results obtained with simulation imply a significant improvement for a large number of important physics analysis at the CMS experiment.

  20. Oceanography related to deep sea waste disposal

    International Nuclear Information System (INIS)

    1978-09-01

    In connection with studies on the feasibility of the safe disposal of radioactive waste, from a large scale nuclear power programme, either on the bed of the deep ocean or within the deep ocean bed, preparation of the present document was commissioned by the (United Kingdom) Department of the Environment. It attempts (a) to summarize the present state of knowledge of the deep ocean environment relevant to the disposal options and assess the processes which could aid or hinder dispersal of material released from its container; (b) to identify areas of research in which more work is needed before the safety of disposal on, or beneath, the ocean bed can be assessed; and (c) to indicate which areas of research can or should be undertaken by British scientists. The programmes of international cooperation in this field are discussed. The report is divided into four chapters dealing respectively with geology and geophysics, geochemistry, physical oceanography and marine biology. (U.K.)

  1. 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.

  2. Automated Morphological Classification in Deep Hubble Space Telescope UBVI Fields: Rapidly and Passively Evolving Faint Galaxy Populations

    Science.gov (United States)

    Odewahn, Stephen C.; Windhorst, Rogier A.; Driver, Simon P.; Keel, William C.

    1996-11-01

    We analyze deep Hubble Space Telescope Wide Field Planetary Camera 2 (WFPC2) images in U, B, V, I using artificial neural network (ANN) classifiers, which are based on galaxy surface brightness and light profile (but not on color nor on scale length, rhl). The ANN distinguishes quite well between E/S0, Sabc, and Sd/Irr+M galaxies (M for merging systems) for BJ ~ 24 mag. The faint blue galaxy counts in the B band are dominated by Sd/Irr+M galaxies and can be explained by a moderately steep local luminosity function (LF) undergoing strong luminosity evolution. We suggest that these faint late-type objects (24 mag <~ BJ <~ 28 mag) are a combination of low-luminosity lower redshift dwarf galaxies, plus compact star-forming galaxies and merging systems at z ~= 1--3, possibly the building blocks of the luminous early-type galaxies seen today.

  3. 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...

  4. Research on Daily Objects Detection Based on Deep Neural Network

    Science.gov (United States)

    Ding, Sheng; Zhao, Kun

    2018-03-01

    With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

  5. Deep multi-scale convolutional neural network for hyperspectral image classification

    Science.gov (United States)

    Zhang, Feng-zhe; Yang, Xia

    2018-04-01

    In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

  6. The Atlantic Multidecadal Variability in surface and deep ocean temperature and salinity fields from unperturbed climate simulations

    Science.gov (United States)

    Zanchettin, D.; Jungclaus, J. H.

    2013-12-01

    Large multidecadal fluctuations in basin-average sea-surface temperature (SST) are a known feature of observed, reconstructed and simulated variability in the North Atlantic Ocean. This phenomenon is often referred to as Multidecadal Atlantic Variability or AMV. Historical AMV fluctuations are associated with analog basin-scale changes in sea-surface salinity, so that warming corresponds to salinification and cooling to freshening [Polyakov et al., 2005]. The surface imprint of the AMV further corresponds to same-sign fluctuations in the shallow ocean and with opposite-sign fluctuations in the deep ocean for both temperature and salinity [Polyakov et al., 2005]. This out-of-phase behavior reflects the thermohaline overturning circulation shaping North Atlantic's low-frequency variability. Several processes contribute to the AMV, involving both ocean-atmosphere coupled processes and deep ocean circulation [e.g., Grossmann and Klotzbach, 2009]. In particular, recirculation in the North Atlantic subpolar gyre region of salinity anomalies from Arctic freshwater export may trigger multidecadal variability in the Atlantic meridional overturning circulation, and therefore may be part of the AMV [Jungclaus et al., 2005; Dima and Lohmann, 2007]. With this contribution, we aim to improve the physical interpretation of the AMV by investigating spatial and temporal patterns of temperature and salinity fields in the shallow and deep ocean. We focus on two unperturbed millennial-scale simulations performed with the Max Planck Institute Earth system model in its paleo (MPI-ESM-P) and low-resolution (MPI-ESM-LR) configurations, which provide reference control climates for assessments of pre-industrial and historical climate simulations. The two model configurations only differ for the presence, in MPI-ESM-LR, of an active module for dynamical vegetation. We use spatial-average indices and empirical orthogonal functions/principal components to track the horizontal and vertical

  7. 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.

  8. Deep learning of unsteady laminar flow over a cylinder

    Science.gov (United States)

    Lee, Sangseung; You, Donghyun

    2017-11-01

    Unsteady flow over a circular cylinder is reconstructed using deep learning with a particular emphasis on elucidating the potential of learning the solution of the Navier-Stokes equations. A deep neural network (DNN) is employed for deep learning, while numerical simulations are conducted to produce training database. Instantaneous and mean flow fields which are reconstructed by deep learning are compared with the simulation results. Fourier transform of flow variables has been conducted to validate the ability of DNN to capture both amplitudes and frequencies of flow motions. Basis decomposition of learned flow is performed to understand the underlying mechanisms of learning flow through DNN. The present study suggests that a deep learning technique can be utilized for reconstruction and, potentially, for prediction of fluid flow instead of solving the Navier-Stokes equations. This work was supported by the National Research Foundation of Korea(NRF) Grant funded by the Korea government(Ministry of Science, ICT and Future Planning) (No. 2014R1A2A1A11049599, No. 2015R1A2A1A15056086, No. 2016R1E1A2A01939553).

  9. Deep convolutional neural network based antenna selection in multiple-input multiple-output system

    Science.gov (United States)

    Cai, Jiaxin; Li, Yan; Hu, Ying

    2018-03-01

    Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

  10. Electrodril system field test program. Phase II: Task C-1-deep drilling system demonstration. Final report for Phase II: Task C-1

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, P D

    1981-04-01

    The Electrodril Deep Drilling System field test demonstrations were aborted in July 1979, due to connector problems. Subsequent post test analyses concluded that the field replacable connectors were the probable cause of the problems encountered. The designs for both the male and female connectors, together with their manufacturing processes, were subsequently modified, as was the acceptance test procedures. A total of nine male and nine female connectors were manufactured and delivered during the 2nd Quarter 1980. Exhaustive testing was then conducted on each connector as a precursor to formal qualification testing conducted during the month of October 1980, at the Brown Oil Tool test facility located in Houston, Texas. With this report, requirements under Phase II, Task C-1 are satisfied. The report documents the results of the connector qualification test program which was successfully completed October 28, 1980. In general, it was concluded that connector qualification had been achieved and plans are now in progress to resume the field test demonstration program so that Electrodril System performance predictions and economic viability can be evaluated.

  11. Head pose estimation algorithm based on deep learning

    Science.gov (United States)

    Cao, Yuanming; Liu, Yijun

    2017-05-01

    Head pose estimation has been widely used in the field of artificial intelligence, pattern recognition and intelligent human-computer interaction and so on. Good head pose estimation algorithm should deal with light, noise, identity, shelter and other factors robustly, but so far how to improve the accuracy and robustness of attitude estimation remains a major challenge in the field of computer vision. A method based on deep learning for pose estimation is presented. Deep learning with a strong learning ability, it can extract high-level image features of the input image by through a series of non-linear operation, then classifying the input image using the extracted feature. Such characteristics have greater differences in pose, while they are robust of light, identity, occlusion and other factors. The proposed head pose estimation is evaluated on the CAS-PEAL data set. Experimental results show that this method is effective to improve the accuracy of pose estimation.

  12. A New Calculation Method of Dynamic Kill Fluid Density Variation during Deep Water Drilling

    Directory of Open Access Journals (Sweden)

    Honghai Fan

    2017-01-01

    Full Text Available There are plenty of uncertainties and enormous challenges in deep water drilling due to complicated shallow flow and deep strata of high temperature and pressure. This paper investigates density of dynamic kill fluid and optimum density during the kill operation process in which dynamic kill process can be divided into two stages, that is, dynamic stable stage and static stable stage. The dynamic kill fluid consists of a single liquid phase and different solid phases. In addition, liquid phase is a mixture of water and oil. Therefore, a new method in calculating the temperature and pressure field of deep water wellbore is proposed. The paper calculates the changing trend of kill fluid density under different temperature and pressure by means of superposition method, nonlinear regression, and segment processing technique. By employing the improved model of kill fluid density, deep water kill operation in a well is investigated. By comparison, the calculated density results are in line with the field data. The model proposed in this paper proves to be satisfactory in optimizing dynamic kill operations to ensure the safety in deep water.

  13. The structure of the ISM in the Zone of Avoidance by high-resolution multi-wavelength observations

    Science.gov (United States)

    Tóth, L. V.; Doi, Y.; Pinter, S.; Kovács, T.; Zahorecz, S.; Bagoly, Z.; Balázs, L. G.; Horvath, I.; Racz, I. I.; Onishi, T.

    2018-05-01

    We estimate the column density of the Galactic foreground interstellar medium (GFISM) in the direction of extragalactic sources. All-sky AKARI FIS infrared sky survey data might be used to trace the GFISM with a resolution of 2 arcminutes. The AKARI based GFISM hydrogen column density estimates are compared with similar quantities based on HI 21cm measurements of various resolution and of Planck results. High spatial resolution observations of the GFISM may be important recalculating the physical parameters of gamma-ray burst (GRB) host galaxies using the updated foreground parameters.

  14. Quantum field theory

    CERN Document Server

    Sadovskii, Michael V

    2013-01-01

    This book discusses the main concepts of the Standard Model of elementary particles in a compact and straightforward way. The work illustrates the unity of modern theoretical physics by combining approaches and concepts of the quantum field theory and modern condensed matter theory. The inductive approach allows a deep understanding of ideas and methods used for solving problems in this field.

  15. 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

  16. Computational analysis of transcranial magnetic stimulation in the presence of deep brain stimulation probes

    Science.gov (United States)

    Syeda, F.; Holloway, K.; El-Gendy, A. A.; Hadimani, R. L.

    2017-05-01

    Transcranial Magnetic Stimulation is an emerging non-invasive treatment for depression, Parkinson's disease, and a variety of other neurological disorders. Many Parkinson's patients receive the treatment known as Deep Brain Stimulation, but often require additional therapy for speech and swallowing impairment. Transcranial Magnetic Stimulation has been explored as a possible treatment by stimulating the mouth motor area of the brain. We have calculated induced electric field, magnetic field, and temperature distributions in the brain using finite element analysis and anatomically realistic heterogeneous head models fitted with Deep Brain Stimulation leads. A Figure of 8 coil, current of 5000 A, and frequency of 2.5 kHz are used as simulation parameters. Results suggest that Deep Brain Stimulation leads cause surrounding tissues to experience slightly increased E-field (Δ Emax =30 V/m), but not exceeding the nominal values induced in brain tissue by Transcranial Magnetic Stimulation without leads (215 V/m). The maximum temperature in the brain tissues surrounding leads did not change significantly from the normal human body temperature of 37 °C. Therefore, we ascertain that Transcranial Magnetic Stimulation in the mouth motor area may stimulate brain tissue surrounding Deep Brain Stimulation leads, but will not cause tissue damage.

  17. Basement-involved faults and deep structures in the West Philippine Basin: constrains from gravity field

    Science.gov (United States)

    Wang, Gang; Jiang, Suhua; Li, Sanzhong; Zhang, Huixuan; Lei, Jianping; Gao, Song; Zhao, Feiyu

    2017-06-01

    To reveal the basement-involved faults and deep structures of the West Philippine Basin (WPB), the gravitational responses caused by these faults are observed and analyzed based on the latest spherical gravity model: WGM2012 Model. By mapping the free-air and Bouguer gravity anomalies, several main faults and some other linear structures are located and observed in the WPB. Then, by conducting a 2D discrete multi-scale wavelet decomposition, the Bouguer anomalies are decomposed into the first- to eighth-order detail and approximation fields (the first- to eighth-order Details and Approximations). The first- to third-order Details reflect detailed and localized geological information of the crust at different depths, and of which the higher-order reflects gravity field of the deeper depth. The first- to fourth-order Approximations represent the regional gravity fields at different depths of the crust, respectively. The fourth-order Approximation represents the regional gravity fluctuation caused by the density inhomogeneity of Moho interface. Therefore, taking the fourth-order Approximation as input, and adopting Parker-Oldenburg interactive inversion, We calculated the depth of Moho interface in the WPB. Results show that the Moho interface depth in the WPB ranges approximately from 8 to 12 km, indicating that there is typical oceanic crust in the basin. In the Urdaneta Plateau and the Benham Rise, the Moho interface depths are about 14 and 16 km, respectively, which provides a piece of evidence to support that the Banham Rise could be a transitional crust caused by a large igneous province. The second-order vertical derivative and the horizontal derivatives in direction 0° and 90° are computed based on the data of the third-order Detail, and most of the basement-involved faults and structures in the WPB, such as the Central Basin Fault Zone, the Gagua Ridge, the Luzon-Okinawa Fault Zone, and the Mindanao Fault Zone are interpreted by the gravity derivatives.

  18. Methodology for Radiological Risk Assessment of Deep Borehole Disposal Operations

    Energy Technology Data Exchange (ETDEWEB)

    Hardin, Ernest; Su, Jiann-Cherng; Peretz, Fred(ORNL)

    2017-03-01

    The primary purpose of the preclosure radiological safety assessment (that this document supports) is to identify risk factors for disposal operations, to aid in design for the deep borehole field test (DBFT) engineering demonstration.

  19. The Anisotropy of the Microwave Background to l = 3500: Deep Field Observations with the Cosmic Background Imager

    Science.gov (United States)

    Mason, B. S.; Pearson, T. J.; Readhead, A. C. S.; Shepherd, M. C.; Sievers, J.; Udomprasert, P. S.; Cartwright, J. K.; Farmer, A. J.; Padin, S.; Myers, S. T.; hide

    2002-01-01

    We report measurements of anisotropy in the cosmic microwave background radiation over the multipole range l approximately 200 (right arrow) 3500 with the Cosmic Background Imager based on deep observations of three fields. These results confirm the drop in power with increasing l first reported in earlier measurements with this instrument, and extend the observations of this decline in power out to l approximately 2000. The decline in power is consistent with the predicted damping of primary anisotropies. At larger multipoles, l = 2000-3500, the power is 3.1 sigma greater than standard models for intrinsic microwave background anisotropy in this multipole range, and 3.5 sigma greater than zero. This excess power is not consistent with expected levels of residual radio source contamination but, for sigma 8 is approximately greater than 1, is consistent with predicted levels due to a secondary Sunyaev-Zeldovich anisotropy. Further observations are necessary to confirm the level of this excess and, if confirmed, determine its origin.

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

    International Nuclear Information System (INIS)

    Bork, I.

    1989-01-01

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

  1. 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.

  2. Phenomenology of deep-inelastic processes

    International Nuclear Information System (INIS)

    Moretto, L.G.

    1983-03-01

    The field of heavy-ion deep-inelastic reactions is reviewed with particular attention to the experimental picture. The most important degrees of freedom involved in the process are identified and illustrated with relevant experiments. Energy dissipation and mass transfer are discussed in terms of particles and/or phonons exchanged in the process. The equilibration of the fragment neutron-to-proton ratios is inspected for evidence of giant isovector resonances. The angular momentum effects are observed in the fragment angular distributions and the angular momentum transfer is inferred from the magnitude and alignment of the fragments spins. The possible sources of light particles accompanying the deep-inelastic reactions are discussed. The use of the sequentially emitted particles as angular momentum probes is illustrated. The significance and uses of a thermalized component emitted by the dinucleus is reviewed. The possible presence of Fermi jets in the prompt component is shown to be critical to the justification of the one-body theories

  3. Deep groundwater flow at Palmottu

    International Nuclear Information System (INIS)

    Niini, H.; Vesterinen, M.; Tuokko, T.

    1993-01-01

    Further observations, measurements, and calculations aimed at determining the groundwater flow regimes and periodical variations in flow at deeper levels were carried out in the Lake Palmottu (a natural analogue study site for radioactive waste disposal in southwestern Finland) drainage basin. These water movements affect the migration of radionuclides from the Palmottu U-Th deposit. The deep water flow is essentially restricted to the bedrock fractures which developed under, and are still affected by, the stress state of the bedrock. Determination of the detailed variations was based on fracture-tectonic modelling of the 12 most significant underground water-flow channels that cross the surficial water of the Palmottu area. According to the direction of the hydraulic gradient the deep water flow is mostly outwards from the Palmottu catchment but in the westernmost section it is partly towards the centre. Estimation of the water flow through the U-Th deposit by the water-balance method is still only approximate and needs continued observation series and improved field measurements

  4. Opportunities and Challenges in Deep Mining: A Brief Review

    Directory of Open Access Journals (Sweden)

    Pathegama G. Ranjith

    2017-08-01

    Full Text Available Mineral consumption is increasing rapidly as more consumers enter the market for minerals and as the global standard of living increases. As a result, underground mining continues to progress to deeper levels in order to tackle the mineral supply crisis in the 21st century. However, deep mining occurs in a very technical and challenging environment, in which significant innovative solutions and best practice are required and additional safety standards must be implemented in order to overcome the challenges and reap huge economic gains. These challenges include the catastrophic events that are often met in deep mining engineering: rockbursts, gas outbursts, high in situ and redistributed stresses, large deformation, squeezing and creeping rocks, and high temperature. This review paper presents the current global status of deep mining and highlights some of the newest technological achievements and opportunities associated with rock mechanics and geotechnical engineering in deep mining. Of the various technical achievements, unmanned working-faces and unmanned mines based on fully automated mining and mineral extraction processes have become important fields in the 21st century.

  5. Gas Classification Using Deep Convolutional Neural Networks

    Science.gov (United States)

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

    2018-01-01

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

  6. Gas Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

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

    2018-01-08

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

  7. Opportunities and constraints of deep water projects

    International Nuclear Information System (INIS)

    Anon.

    1995-01-01

    While oil output from deep water areas still is scarce, it however has become a reality in water depths over 300 m. Specific constraints linked to these developments lead to the selection of appropriate concepts for production supports. First deep water developments occurred off Brazil (see other articles in this issue) and the Gulf of Mexico and now expand to other areas worldwide, such as the West of Shetland discoveries, the Northern part of the Norwegian waters and potentially West Africa, the Barents sea and South-East Asia. Fixed platforms and compliant towers have shown their limits (in terms of water depth capacity) and new deep water projects mainly rely on tension leg platforms (TLP) and floaters, either FPSOs or semi-sub based. Research is at work on alternative materials for lighter flexible risers and mooring systems. Operators and manufacturers are eager to develop for the 300 m range systems and equipments that could be used with little modification for oil fields located in deeper waters. (author). 1 fig., 1 tab

  8. 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...

  9. Efficient collective swimming by harnessing vortices through deep reinforcement learning.

    Science.gov (United States)

    Verma, Siddhartha; Novati, Guido; Koumoutsakos, Petros

    2018-06-05

    Fish in schooling formations navigate complex flow fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behavior has been associated with evolutionary advantages including energy savings, yet the underlying physical mechanisms remain unknown. We show that fish can improve their sustained propulsive efficiency by placing themselves in appropriate locations in the wake of other swimmers and intercepting judiciously their shed vortices. This swimming strategy leads to collective energy savings and is revealed through a combination of high-fidelity flow simulations with a deep reinforcement learning (RL) algorithm. The RL algorithm relies on a policy defined by deep, recurrent neural nets, with long-short-term memory cells, that are essential for capturing the unsteadiness of the two-way interactions between the fish and the vortical flow field. Surprisingly, we find that swimming in-line with a leader is not associated with energetic benefits for the follower. Instead, "smart swimmer(s)" place themselves at off-center positions, with respect to the axis of the leader(s) and deform their body to synchronize with the momentum of the oncoming vortices, thus enhancing their swimming efficiency at no cost to the leader(s). The results confirm that fish may harvest energy deposited in vortices and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep RL can produce navigation algorithms for complex unsteady and vortical flow fields, with promising implications for energy savings in autonomous robotic swarms.

  10. Pro deep learning with TensorFlow a mathematical approach to advanced artificial intelligence in Python

    CERN Document Server

    Pattanayak, Santanu

    2017-01-01

    Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community.

  11. The use of plant models in deep learning: an application to leaf counting in rosette plants

    OpenAIRE

    Ubbens, Jordan; Cieslak, Mikolaj; Prusinkiewicz, Przemyslaw; Stavness, Ian

    2018-01-01

    Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the...

  12. Task Order 22 – Engineering and Technical Support, Deep Borehole Field Test. AREVA Summary Review Report

    Energy Technology Data Exchange (ETDEWEB)

    Denton, Mark A. [AREVA Federal Services, Charlotte, NC (United States)

    2016-01-19

    Under Task Order 22 of the industry Advisory and Assistance Services (A&AS) Contract to the Department of Energy (DOE) DE-NE0000291, AREVA has been tasked with providing assistance with engineering, analysis, cost estimating, and design support of a system for disposal of radioactive wastes in deep boreholes (without the use of radioactive waste). As part of this task order, AREVA was requested, through a letter of technical direction, to evaluate Sandia National Laboratory’s (SNL’s) waste package borehole emplacement system concept recommendation using input from DOE and SNL. This summary review report (SRR) documents this evaluation, with its focus on the primary input document titled: “Deep Borehole Field Test Specifications/M2FT-15SN0817091” Rev. 1 [1], hereafter referred to as the “M2 report.” The M2 report focuses on the conceptual design development for the Deep Borehole Field Test (DBFT), mainly the test waste packages (WPs) and the system for demonstrating emplacement and retrieval of those packages in the Field Test Borehole (FTB). This SRR follows the same outline as the M2 report, which allows for easy correlation between AREVA’s review comments, discussion, potential proposed alternatives, and path forward with information established in the M2 report. AREVA’s assessment focused on three primary elements of the M2 report: the conceptual design of the WPs proposed for deep borehole disposal (DBD), the mode of emplacement of the WP into DBD, and the conceptual design of the DBFT. AREVA concurs with the M2 report’s selection of the wireline emplacement mode specifically over the drill-string emplacement mode and generically over alternative emplacement modes. Table 5-1 of this SRR compares the pros and cons of each emplacement mode considered viable for DBD. The primary positive characteristics of the wireline emplacement mode include: (1) considered a mature technology; (2) operations are relatively simple; (3) probability of a

  13. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields.

    Science.gov (United States)

    Grossman, Nir; Bono, David; Dedic, Nina; Kodandaramaiah, Suhasa B; Rudenko, Andrii; Suk, Ho-Jun; Cassara, Antonino M; Neufeld, Esra; Kuster, Niels; Tsai, Li-Huei; Pascual-Leone, Alvaro; Boyden, Edward S

    2017-06-01

    We report a noninvasive strategy for electrically stimulating neurons at depth. By delivering to the brain multiple electric fields at frequencies too high to recruit neural firing, but which differ by a frequency within the dynamic range of neural firing, we can electrically stimulate neurons throughout a region where interference between the multiple fields results in a prominent electric field envelope modulated at the difference frequency. We validated this temporal interference (TI) concept via modeling and physics experiments, and verified that neurons in the living mouse brain could follow the electric field envelope. We demonstrate the utility of TI stimulation by stimulating neurons in the hippocampus of living mice without recruiting neurons of the overlying cortex. Finally, we show that by altering the currents delivered to a set of immobile electrodes, we can steerably evoke different motor patterns in living mice. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. 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...

  15. Stimulation of deep gas wells using HCl/formic acid system : lab studies and field application

    Energy Technology Data Exchange (ETDEWEB)

    Nasr-El-Din, H.A.; Al-Mutairi, S.; Al-Malki, B. [Saudi Aramco (Saudi Arabia); Metcalf, S.; Walters, W. [BJ Services Co USA, Houston, TX (United States)

    2002-06-01

    Well stimulation in the deep carbonate Khuff reservoirs in eastern Saudi Arabia is needed to remove drilling mud filter cakes and to enhance reservoir permeability. A non associated gas is being produced from the reservoirs. This gas is associated with the hydrogen sulfide content that varies from 0 to 10-mol per cent. The average reservoir temperature is 275 degrees F and initial reservoir pressure is 7,000 psi. A special system is needed to stimulate the carbonate reservoir because of this high bottomhole temperature and the corrosive nature of hydrochloric acid (HCl) at high temperature. A rotating disk method was used to determine the reaction rate of an HCl/formic acid system with reservoir rocks. Results from coreflood tests showed that the acid system creates deep wormholes in tight reservoir cores. Corrosion tests showed that the well tubulars could tolerate the acid system. A gelled 15-wt per cent HCl/9-wt per cent formic acid system successfully fractured 3 vertical wells in deep sour gas reservoirs without any operational problems. The treatment resulted in significant increases in gas production and flowing wellhead pressures. In addition, overflush of the treatment successfully eliminated the return of live acid after the treatment. 37 refs., 10 tabs., 17 figs.

  16. Chemical composition of deep hydrothermal fluids in the Ribeira Grande geothermal field (São Miguel, Azores)

    Science.gov (United States)

    Carvalho, M. R.; Forjaz, V. H.; Almeida, C.

    2006-08-01

    The Ribeira Grande geothermal field is a water-dominated geothermal system, located within Água de Pau/Fogo Volcano in the central part of the São Miguel Island. This geothermal system is exploited for energy production by wells sustaining two power plants. The wells produce from a formation of pillow lavas divided into different aquifers, with a fairly isothermal zone from 800 to 1300 m in depth, where reservoir temperature reaches 230 to 245 °C. Below the depth of 1300 m there is a slight temperature reversal. The fluid produced has excess enthalpy and, separated at atmospheric pressure, is characterized by mineralization of sodium-chloride type up to 6-7 g/l, the concentration of dissolved silica varies between 450 and 650 mg/l and the pH ranges between 8 and 8.6. The gas phase is dominantly CO 2, at a concentration of 98% of NCG. The composition of the deep geothermal fluid was obtained by computer simulation, using the WATCH program, and was compared with the composition of the bottom-hole samples. The approximations, in this simulation, were considered the single- and multi-step steam separation. The reference temperatures were based on: (i) the measured temperature in wells; (ii) the Na/K geothermometric temperature and (iii) the enthalpy-saturation temperature. According to both the measured and geothermometric temperatures, the deep fluid of the wells has two phases with a steam fraction up to 0.34, at higher well discharges. The measured enthalpy is always greater than the calculated enthalpy. The calcite equilibrium indicates scaling, since the fluid is flashing, around 2.28 mg/l CaCO 3 at the maximum discharge. The geothermal wells exploit three different aquifers, the lower of which is liquid and slightly colder than the upper ones. The intermediate is a two-phase aquifer with a steam fraction up to 0.081. The upper aquifer is probably of steam phase. The main differences between the aquifers are the temperature and boiling; both enthalpy and

  17. Frontier Fields: Bringing the Distant Universe into View

    Science.gov (United States)

    Eisenhamer, Bonnie; Lawton, Brandon L.; Summers, Frank; Ryer, Holly

    2014-06-01

    The Frontier Fields is a multi-cycle program of six deep-field observations of strong-lensing galaxy clusters that will be taken in parallel with six deep “blank fields.” The three-year long collaborative program centers on observations from NASA’s Great Observatories, who will team up to look deeper into the universe than ever before, and potentially uncover galaxies that are as much as 100 times fainter than what the telescopes can typically see. Because of the unprecedented views of the universe that will be achieved, the Frontier Fields science program is ideal for informing audiences about scientific advances and topics in STEM. For example, the program provides an opportunity to look back on the history of deep field observations and how they changed (and continue to change) astronomy, while exploring the ways astronomers approach big science problems. As a result, the Space Telescope Science Institute’s Office of Public Outreach has initiated an education and public outreach (E/PO) project to follow the progress of the Frontier Fields program - providing a behind-the-scenes perspective of this observing initiative. This poster will highlight the goals of the Frontier Fields E/PO project and the cost-effective approach being used to bring the program’s results to both the public and educational audiences.

  18. Numerical Analysis on Seepage in the deep overburden CFRD

    Science.gov (United States)

    Zeyu, GUO; Junrui, CHAI; Yuan, QIN

    2017-12-01

    There are many problems in the construction of hydraulic structures on deep overburden because of its complex foundation structure and poor geological condition. Seepage failure is one of the main problems. The Combination of the seepage control system of the face rockfill dam and the deep overburden can effectively control the seepage of construction of the concrete face rockfill dam on the deep overburden. Widely used anti-seepage measures are horizontal blanket, waterproof wall, curtain grouting and so on, but the method, technique and its effect of seepage control still have many problems thus need further study. Due to the above considerations, Three-dimensional seepage field numerical analysis based on practical engineering case is conducted to study the seepage prevention effect under different seepage prevention methods, which is of great significance to the development of dam technology and the development of hydropower resources in China.

  19. 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...

  20. Injection grout for deep repositories - Low-pH cementitious grout for larger fractures. Field testing in Finland, Pilot tests

    International Nuclear Information System (INIS)

    Sievaeen, U.; Syrjaenen, P.; Ranta-aho, S.

    2005-10-01

    Posiva, SKB and NUMO have cooperated for developing a low pH injection grout for sealing of the deep repositories for spent nuclear fuel. A project 'Injection grout for deep repositories' was divided into four subprojects. The development of low pH cementitious grout for > 100 μm fractures was carried out in Finland. The development of non-cementitious low pH grout for < 100 μm fractures was carried out in Sweden. This report concerns the cementitious grout. Requirements for pH and penetration ability were set for the grouts to be developed. Besides these, the grouts were desired to fulfil certain targets set for viscosity, bleeding, shear strength, yield value, compressive strength and open time. Also durability, availability of the components and known history in practical engineering were given as requirements. The object of the work presented here was to test if the grout properties developed in laboratory can be met in field conditions. Only the most promising binder material combinations, which have fulfilled the main requirements in laboratory, were tested in field. Evaluations of environmental aspects are included in this report. In the pilot test 1, carried out in a multi-purpose tunnel in Helsinki, Portland cement-cilicasystem and blast furnace slag-based system were chosen to be tested. In field conditions, mixed with ordinary mixer, all grout properties achieved in laboratory, were not verified. Penetration ability was typically good, but fluidity and strength development were not satisfying. The main conclusion was that water to dry material ratio should be diminished. In order to get better rheological properties at the same time, superplastizicer was needed in further development of the mixes. Also accurate dosing and mixing seemed to be very important. Blast furnace slag - system was after this pilot test ruled out due to high leaching of sulphide from the product, not due to the bad technical properties. The development work continued with

  1. 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...

  2. Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Zhibin Yu

    2017-01-01

    Full Text Available Underwater inherent optical properties (IOPs are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

  3. Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network.

    Science.gov (United States)

    Yu, Zhibin; Wang, Yubo; Zheng, Bing; Zheng, Haiyong; Wang, Nan; Gu, Zhaorui

    2017-01-01

    Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

  4. Distinguishing bulk traps and interface states in deep-level transient spectroscopy

    International Nuclear Information System (INIS)

    Coelho, A V P; Adam, M C; Boudinov, H

    2011-01-01

    A new method for the distinction of discrete bulk deep levels and interface states related peaks in deep-level transient spectroscopy spectra is proposed. The measurement of two spectra using different reverse voltages while keeping pulse voltage fixed causes different peak maximum shifts in each case: for a reverse voltage modulus increase, a bulk deep-level related peak maximum will remain unchanged or shift towards lower temperatures while only interface states related peak maximum will be able to shift towards higher temperatures. This method has the advantage of being non-destructive and also works in the case of bulk traps with strong emission rate dependence on the electric field. Silicon MOS capacitors and proton implanted GaAs Schottky diodes were employed to experimentally test the method.

  5. Numerical Simulation and Experimental Study of Deep Bed Corn Drying Based on Water Potential

    Directory of Open Access Journals (Sweden)

    Zhe Liu

    2015-01-01

    Full Text Available The concept and the model of water potential, which were widely used in agricultural field, have been proved to be beneficial in the application of vacuum drying model and have provided a new way to explore the grain drying model since being introduced to grain drying and storage fields. Aiming to overcome the shortcomings of traditional deep bed drying model, for instance, the application range of this method is narrow and such method does not apply to systems of which pressure would be an influential factor such as vacuum drying system in a way combining with water potential drying model. This study established a numerical simulation system of deep bed corn drying process which has been proved to be effective according to the results of numerical simulation and corresponding experimental investigation and has revealed that desorption and adsorption coexist in deep bed drying.

  6. Theoretical analysis of the local field potential in deep brain stimulation applications.

    Directory of Open Access Journals (Sweden)

    Scott F Lempka

    Full Text Available Deep brain stimulation (DBS is a common therapy for treating movement disorders, such as Parkinson's disease (PD, and provides a unique opportunity to study the neural activity of various subcortical structures in human patients. Local field potential (LFP recordings are often performed with either intraoperative microelectrodes or DBS leads and reflect oscillatory activity within nuclei of the basal ganglia. These LFP recordings have numerous clinical implications and might someday be used to optimize DBS outcomes in closed-loop systems. However, the origin of the recorded LFP is poorly understood. Therefore, the goal of this study was to theoretically analyze LFP recordings within the context of clinical DBS applications. This goal was achieved with a detailed recording model of beta oscillations (∼20 Hz in the subthalamic nucleus. The recording model consisted of finite element models of intraoperative microelectrodes and DBS macroelectrodes implanted in the brain along with multi-compartment cable models of STN projection neurons. Model analysis permitted systematic investigation into a number of variables that can affect the composition of the recorded LFP (e.g. electrode size, electrode impedance, recording configuration, and filtering effects of the brain, electrode-electrolyte interface, and recording electronics. The results of the study suggest that the spatial reach of the LFP can extend several millimeters. Model analysis also showed that variables such as electrode geometry and recording configuration can have a significant effect on LFP amplitude and spatial reach, while the effects of other variables, such as electrode impedance, are often negligible. The results of this study provide insight into the origin of the LFP and identify variables that need to be considered when analyzing LFP recordings in clinical DBS applications.

  7. A SYSTEMATIC SURVEY OF PROTOCLUSTERS AT z ∼ 3–6 IN THE CFHTLS DEEP FIELDS

    Energy Technology Data Exchange (ETDEWEB)

    Toshikawa, Jun; Kashikawa, Nobunari; Furusawa, Hisanori; Tanaka, Masayuki; Niino, Yuu [Optical and Infrared Astronomy Division, National Astronomical Observatory, Mitaka, Tokyo 181-8588 (Japan); Overzier, Roderik [Observatório Nacional, Rua José Cristino, 77. CEP 20921-400, São Cristóvão, Rio de Janeiro-RJ (Brazil); Malkan, Matthew A. [Department of Physics and Astronomy, University of California, Los Angeles, CA 90095-1547 (United States); Ishikawa, Shogo; Onoue, Masafusa; Uchiyama, Hisakazu [Department of Astronomy, School of Science, Graduate University for Advanced Studies, Mitaka, Tokyo 181-8588 (Japan); Ota, Kazuaki, E-mail: jun.toshikawa@nao.ac.jp [Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA (United Kingdom)

    2016-08-01

    We present the discovery of three protoclusters at z ∼ 3–4 with spectroscopic confirmation in the Canada–France–Hawaii Telescope Legacy Survey Deep Fields. In these fields, we investigate the large-scale projected sky distribution of z ∼ 3–6 Lyman-break galaxies and identify 21 protocluster candidates from regions that are overdense at more than 4 σ overdensity significance. Based on cosmological simulations, it is expected that more than 76% of these candidates will evolve into a galaxy cluster of at least a halo mass of 10{sup 14} M {sub ⊙} at z = 0. We perform follow-up spectroscopy for eight of the candidates using Subaru/FOCAS, Keck II/DEIMOS, and Gemini-N/GMOS. In total we target 462 dropout candidates and obtain 138 spectroscopic redshifts. We confirm three real protoclusters at z = 3–4 with more than five members spectroscopically identified and find one to be an incidental overdense region by mere chance alignment. The other four candidate regions at z ∼ 5–6 require more spectroscopic follow-up in order to be conclusive. A z = 3.67 protocluster, which has 11 spectroscopically confirmed members, shows a remarkable core-like structure composed of a central small region (<0.5 physical Mpc) and an outskirts region (∼1.0 physical Mpc). The Ly α equivalent widths of members of the protocluster are significantly smaller than those of field galaxies at the same redshift, while there is no difference in the UV luminosity distributions. These results imply that some environmental effects start operating as early as at z ∼ 4 along with the growth of the protocluster structure. This study provides an important benchmark for our analysis of protoclusters in the upcoming Subaru/HSC imaging survey and its spectroscopic follow-up with the Subaru/PFS that will detect thousands of protoclusters up to z ∼ 6.

  8. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    Science.gov (United States)

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

  9. Development of deep silicon plasma etching for 3D integration technology

    Directory of Open Access Journals (Sweden)

    Golishnikov А. А.

    2014-02-01

    Full Text Available Plasma etch process for thought-silicon via (TSV formation is one of the most important technological operations in the field of metal connections creation between stacked circuits in 3D assemble technology. TSV formation strongly depends on parameters such as Si-wafer thickness, aspect ratio, type of metallization material, etc. The authors investigate deep silicon plasma etch process for formation of TSV with controllable profile. The influence of process parameters on plasma etch rate, silicon etch selectivity to photoresist and the structure profile are researched in this paper. Technology with etch and passivation steps alternation was used as a method of deep silicon plasma etching. Experimental tool «Platrane-100» with high-density plasma reactor based on high-frequency ion source with transformer coupled plasma was used for deep silicon plasma etching. As actuation gases for deep silicon etching were chosen the following gases: SF6 was used for the etch stage and CHF3 was applied on the polymerization stage. As a result of research, the deep plasma etch process has been developed with the following parameters: silicon etch rate 6 µm/min, selectivity to photoresist 60 and structure profile 90±2°. This process provides formation of TSV 370 µm deep and about 120 µm in diameter.

  10. Topics in deep inelastic scattering

    International Nuclear Information System (INIS)

    Wandzura, S.M.

    1977-01-01

    Several topics in deep inelastic lepton--nucleon scattering are discussed, with emphasis on the structure functions appearing in polarized experiments. The major results are: infinite set of new sum rules reducing the number of independent spin dependent structure functions (for electroproduction) from two to one; the application of the techniques of Nachtmann to extract the coefficients appearing in the Wilson operator product expansion; and radiative corrections to the Wilson coefficients of free field theory. Also discussed are the use of dimensional regularization to simplify the calculation of these radiative corrections

  11. Software Graphics Processing Unit (sGPU) for Deep Space Applications

    Science.gov (United States)

    McCabe, Mary; Salazar, George; Steele, Glen

    2015-01-01

    A graphics processing capability will be required for deep space missions and must include a range of applications, from safety-critical vehicle health status to telemedicine for crew health. However, preliminary radiation testing of commercial graphics processing cards suggest they cannot operate in the deep space radiation environment. Investigation into an Software Graphics Processing Unit (sGPU)comprised of commercial-equivalent radiation hardened/tolerant single board computers, field programmable gate arrays, and safety-critical display software shows promising results. Preliminary performance of approximately 30 frames per second (FPS) has been achieved. Use of multi-core processors may provide a significant increase in performance.

  12. 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.

  13. Experimental signature of scaling violation implied by field theories

    International Nuclear Information System (INIS)

    Tung, W.

    1975-01-01

    Renormalizable field theories are found to predict a surprisingly specific pattern of scaling violation in deep inelastic scattering. Comparison with experiments is discussed. The feasibility of distinguishing asymptotically free field theories from conventional field theories is evaluated

  14. My Most Memorable AAS Meeting, or How Stephen Hawking's Chauffeur and Chubby Wise's Fiddle Are Related to the Hubble Deep Field (At Least In My Mind and Experience!)

    Science.gov (United States)

    Lucas, R. A.

    1999-05-01

    Sometimes, in the most extraordinary conditions and times, strange things happen which remind us of just how small a world we really inhabit, and how so many varied things may suddenly be juxtaposed in our lives, and in the lives of others. My most memorable AAS meeting involves not only the meeting but events while getting there. It was January 1996, and we had just finished our observations and initial data reduction of the Hubble Deep Field, the members of the HDF working group doggedly coming in to the STScI by various means over the December holidays and the New Year, in the midst of several blizzards which even closed STScI for a number of days. Not surprisingly, work on the HDF AAS presentations was ongoing until the last minute, until people left snowy Baltimore for sunny San Antonio. My street was plowed for the first time in a week a few hours before my 6AM flight, so after digging out my car, with no time for sleep, between 3AM and 6AM on the morning I left, I soon discovered my own surprising connections between Stephen Hawking's chauffeur, Chubby Wise's fiddle, and the Hubble Deep Field. I'll elaborate in this paper if you're curious!

  15. The phenomenology of deep-inelastic processes

    International Nuclear Information System (INIS)

    Moretto, L.G.

    1983-01-01

    The field of heavy-ion deep-inelastic reactions is reviewed with particular attention to the experimental picture. The most important degrees of freedom involved in the process are identified and illustrated with relevant experiments. Energy dissipation and mass transfer are discussed in terms of particles and/or phonons exchanged in the process. The equilibration of the fragment neutron-to-proton ratios is inspected for evidence of giant isovector resonances. The angular momentum effects are observed in the fragment angular distributions and the angular momentum transfer is inferred from the magnitude and alignment of the fragments spins. The possible sources of light particles accompanying the deep-inelastic reactions are discussed. The use of the sequentially emitted particles as angular momentum probes is illustrated. The significance and uses of a thermalized component emitted by the dinucleus is reviewed. The possible presence of Fermi jets in the prompt component is shown to be critical to the justification of the one-body theories. (orig.)

  16. 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...

  17. LYMAN BREAK GALAXIES AT z ∼ 1.8-2.8: GALEX/NUV IMAGING OF THE SUBARU DEEP FIELD

    International Nuclear Information System (INIS)

    Ly, Chun; Malkan, Matthew A.; Woo, Jong-Hak; Treu, Tommaso; Currie, Thayne; Hayashi, Masao; Shimasaku, Kazuhiro; Yoshida, Makiko; Kashikawa, Nobunari; Motohara, Kentaro

    2009-01-01

    A photometric sample of ∼8000 V C i'z' optical data with deep GALEX/NUV imaging of the Subaru Deep Field. Follow-up spectroscopy confirmed 24 LBGs at 1.5 ∼< z ∼< 2.7. Among the optical spectra, 12 have Lyα emission with rest-frame equivalent widths of ∼5-60 A. The success rate for identifying LBGs as NUV-dropouts at 1.5 < z < 2.7 is 86%. The rest-frame UV (1700 A) luminosity function (LF) is constructed from the photometric sample with corrections for stellar contamination and z < 1.5 interlopers (lower limits). The LF is 1.7 ± 0.1 (1.4 ± 0.1 with a hard upper limit on stellar contamination) times higher than those of z ∼ 2 BXs and z ∼ 3 LBGs. Three explanations were considered, and it is argued that significantly underestimating low-z contamination or effective comoving volume is unlikely: the former would be inconsistent with the spectroscopic sample at 93% confidence, and the second explanation would not resolve the discrepancy. The third scenario is that different photometric selection of the samples yields nonidentical galaxy populations, such that some BX galaxies are LBGs and vice versa. This argument is supported by a higher surface density of LBGs at all magnitudes while the redshift distribution of the two populations is nearly identical. This study, when combined with other star formation rate (SFR) density UV measurements from LBG surveys, indicates that there is a rise in the SFR density: a factor of 3-6 (3-10) increase from z ∼ 5 (z ∼ 6) to z ∼ 2, followed by a decrease to z ∼ 0. This result, along with past sub-mm studies that find a peak at z ∼ 2 in their redshift distribution, suggests that z ∼ 2 is the epoch of peak star formation.

  18. 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.

  19. 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 ...

  20. Photometric redshifts for the next generation of deep radio continuum surveys - I. Template fitting

    Science.gov (United States)

    Duncan, Kenneth J.; Brown, Michael J. I.; Williams, Wendy L.; Best, Philip N.; Buat, Veronique; Burgarella, Denis; Jarvis, Matt J.; Małek, Katarzyna; Oliver, S. J.; Röttgering, Huub J. A.; Smith, Daniel J. B.

    2018-01-01

    We present a study of photometric redshift performance for galaxies and active galactic nuclei detected in deep radio continuum surveys. Using two multiwavelength data sets, over the NOAO Deep Wide Field Survey Boötes and COSMOS fields, we assess photometric redshift (photo-z) performance for a sample of ∼4500 radio continuum sources with spectroscopic redshifts relative to those of ∼63 000 non-radio-detected sources in the same fields. We investigate the performance of three photometric redshift template sets as a function of redshift, radio luminosity and infrared/X-ray properties. We find that no single template library is able to provide the best performance across all subsets of the radio-detected population, with variation in the optimum template set both between subsets and between fields. Through a hierarchical Bayesian combination of the photo-z estimates from all three template sets, we are able to produce a consensus photo-z estimate that equals or improves upon the performance of any individual template set.

  1. 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.

  2. Deep Temporal Models using Identity Skip-Connections for Speech Emotion Recognition

    NARCIS (Netherlands)

    Kim, Jaebok; Englebienne, Gwenn; Truong, Khiet P.; Evers, Vanessa

    2017-01-01

    Deep architectures using identity skip-connections have demonstrated groundbreaking performance in the field of image classification. Recently, empirical studies suggested that identity skip-connections enable ensemble-like behaviour of shallow networks, and that depth is not a solo ingredient for

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

    Directory of Open Access Journals (Sweden)

    Sugiarto Indar

    2018-01-01

    Full Text Available Deep learning (DL has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still sceptical about its true capability: can the intelligence demonstrated by deep learning technique be applied for general tasks? This question motivates the emergence of another research discipline: neuromorphic computing (NC. In NC, researchers try to identify the most fundamental ingredients that construct intelligence behaviour produced by the brain itself. To achieve this, neuromorphic systems are developed to mimic the brain functionality down to cellular level. In this paper, a neuromorphic platform called SpiNNaker is described and evaluated in order to understand its potential use as a platform for a deep learning approach. This paper is a literature review that contains comparative study on algorithms that have been implemented in SpiNNaker.

  4. Magnetic Field

    DEFF Research Database (Denmark)

    Olsen, Nils

    2015-01-01

    he Earth has a large and complicated magnetic field, the major part of which is produced by a self-sustaining dynamo operating in the fluid outer core. Magnetic field observations provide one of the few tools for remote sensing the Earth’s deep interior, especially regarding the dynamics...... of the fluid flow at the top of the core. However, what is measured at or near the surface of the Earth is the superposition of the core field and fields caused by magnetized rocks in the Earth’s crust, by electric currents flowing in the ionosphere, magnetosphere, and oceans, and by currents induced...... in the Earth by time-varying external fields. These sources have their specific characteristics in terms of spatial and temporal variations, and their proper separation, based on magnetic measurements, is a major challenge. Such a separation is a prerequisite for remote sensing by means of magnetic field...

  5. Piranema Field: developing economically small reserves in deep waters; Campo de Piracema: o desafio de desenvolver economicamente pequenas reservas em aguas profundas

    Energy Technology Data Exchange (ETDEWEB)

    Brandao, Renilton M. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil)

    2008-07-01

    Piranema Field is located southeast of the city of Aracaju, in deep waters, sub-basin of Sergipe, about 25 km from the coast, with water depth varying from 200 and 2,000 meters. The biggest challenges for the production of this field, with high quality oil (41 to 44 API), were small reserves, the presence of large submarine canyons separating various geological structures and difficulty installation of pipelines and wax formation in production lines, which could cause its blocking. After several studies, we decided to exploit in two phases, using an FPSO (Floating Production Storage and Offloading), cylindrical, completely innovative, whose cost of construction could make the project economically attractive and gas produced entirely re-injected, which would increase considerably recovery factor. The development will be in two phases, with the first one lasting about 7 years and the second 4 years. It is expected a recovery factor of around 40% over the eleven years of production, with a peak production of around 30,000 bbl/d. The total project cost will be $ 1.1 bi, including investments, operating costs and taxes. (author)

  6. Implications of the Deep Minimum for Slow Solar Wind Origin

    Science.gov (United States)

    Antiochos, S. K.; Mikic, Z.; Lionello, R.; Titov, V. S.; Linker, J. A.

    2009-12-01

    The origin of the slow solar wind has long been one of the most important problems in solar/heliospheric physics. Two observational constraints make this problem especially challenging. First, the slow wind has the composition of the closed-field corona, unlike the fast wind that originates on open field lines. Second, the slow wind has substantial angular extent, of order 30 degrees, which is much larger than the widths observed for streamer stalks or the widths expected theoretically for a dynamic heliospheric current sheet. We propose that the slow wind originates from an intricate network of narrow (possibly singular) open-field corridors that emanate from the polar coronal hole regions. Using topological arguments, we show that these corridors must be ubiquitous in the solar corona. The total solar eclipse in August 2008, near the lowest point of the Deep Minimum, affords an ideal opportunity to test this theory by using the ultra-high resolution Predictive Science's (PSI) eclipse model for the corona and wind. Analysis of the PSI eclipse model demonstrates that the extent and scales of the open-field corridors can account for both the angular width of the slow wind and its closed-field composition. We discuss the implications of our slow wind theory for the structure of the corona and heliosphere at the Deep Minimum and describe further observational and theoretical tests. This work has been supported by the NASA HTP, SR&T, and LWS programs.

  7. 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 ...

  8. The KM3NeT deep-sea neutrino telescope

    Energy Technology Data Exchange (ETDEWEB)

    Margiotta, Annarita

    2014-12-01

    KM3NeT is a deep-sea research infrastructure being constructed in the Mediterranean Sea. It will host the next generation Cherenkov neutrino telescope and nodes for a deep sea multidisciplinary observatory, providing oceanographers, marine biologists, and geophysicists with real time measurements. The neutrino telescope will complement IceCube in its field of view and exceed it substantially in sensitivity. Its main goal is the detection of high energy neutrinos of astrophysical origin. The detector will have a modular structure with six building blocks, each consisting of about 100 Detection Units (DUs). Each DU will be equipped with 18 multi-PMT digital optical modules. The first phase of construction has started and shore and deep-sea infrastructures hosting the future KM3NeT detector are being prepared in offshore Toulon, France and offshore Capo Passero on Sicily, Italy. The technological solutions for the neutrino detector of KM3NeT and the expected performance of the neutrino telescope are presented and discussed. - Highlights: • A deep-sea research infrastructure is being built in the Mediterranean Sea. • It will host a km{sup 3}-size neutrino telescope and a deep-sea multidisciplinary observatory. • The main goal of the neutrino telescope is the search for Galactic neutrino sources. • A major innovation is adopted in the design of the optical module. • 31 3 in. photomultiplier tubes (PMTs) will be hosted in the same glass sphere.

  9. 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 ...

  10. Computer Aided Process Planning for Non-Axisymmetric Deep Drawing Products

    Science.gov (United States)

    Park, Dong Hwan; Yarlagadda, Prasad K. D. V.

    2004-06-01

    In general, deep drawing products have various cross-section shapes such as cylindrical, rectangular and non-axisymmetric shapes. The application of the surface area calculation to non-axisymmetric deep drawing process has not been published yet. In this research, a surface area calculation for non-axisymmetric deep drawing products with elliptical shape was constructed for a design of blank shape of deep drawing products by using an AutoLISP function of AutoCAD software. A computer-aided process planning (CAPP) system for rotationally symmetric deep drawing products has been developed. However, the application of the system to non-axisymmetric components has not been reported yet. Thus, the CAPP system for non-axisymmetric deep drawing products with elliptical shape was constructed by using process sequence design. The system developed in this work consists of four modules. The first is recognition of shape module to recognize non-axisymmetric products. The second is a three-dimensional (3-D) modeling module to calculate the surface area for non-axisymmetric products. The third is a blank design module to create an oval-shaped blank with the identical surface area. The forth is a process planning module based on the production rules that play the best important role in an expert system for manufacturing. The production rules are generated and upgraded by interviewing field engineers. Especially, the drawing coefficient, the punch and die radii for elliptical shape products are considered as main design parameters. The suitability of this system was verified by applying to a real deep drawing product. This CAPP system constructed would be very useful to reduce lead-time for manufacturing and improve an accuracy of products.

  11. Westerbork Ultra-Deep Survey of HI at z=0.2

    NARCIS (Netherlands)

    Verheijen, Marc; Deshev, Boris; van Gorkom, Jacqueline; Poggianti, Bianca; Chung, Aeree; Cybulski, Ryan; Dwarakanath, K. S.; Montero-Castano, Maria; Morrison, Glenn; Schiminovich, David; Szomoru, Arpad; Yun, Min

    2010-01-01

    In this contribution, we present some preliminary observational results from the completed ultra-deep survey of 21cm emission from neutral hydrogen at redshifts z=0.164-0.224 with the Westerbork Synthesis Radio Telescope. In two separate fields, a total of 160 individual galaxies has been detected

  12. A Method for Improving Reliability of Radiation Detection using Deep Learning Framework

    International Nuclear Information System (INIS)

    Chang, Hojong; Kim, Tae-Ho; Han, Byunghun; Kim, Hyunduk; Kim, Ki-duk

    2017-01-01

    Radiation detection is essential technology for overall field of radiation and nuclear engineering. Previously, technology for radiation detection composes of preparation of the table of the input spectrum to output spectrum in advance, which requires simulation of numerous predicted output spectrum with simulation using parameters modeling the spectrum. In this paper, we propose new technique to improve the performance of radiation detector. The software in the radiation detector has been stagnant for a while with possible intrinsic error of simulation. In the proposed method, to predict the input source using output spectrum measured by radiation detector is performed using deep neural network. With highly complex model, we expect that the complex pattern between data and the label can be captured well. Furthermore, the radiation detector should be calibrated regularly and beforehand. We propose a method to calibrate radiation detector using GAN. We hope that the power of deep learning may also reach to radiation detectors and make huge improvement on the field. Using improved radiation detector, the reliability of detection would be confident, and there are many tasks remaining to solve using deep learning in nuclear engineering society.

  13. Geologic Sequestration of CO2 in Deep, Unmineable Coalbeds: An Integrated Researdh and Commercial-Scale Field Demonstration Project

    Energy Technology Data Exchange (ETDEWEB)

    Scott Reeves; George Koperna

    2008-09-30

    The Coal-Seq consortium is a government-industry collaborative consortium with the objective of advancing industry's understanding of complex coalbed methane and gas shale reservoir behavior in the presence of multi-component gases via laboratory experiments, theoretical model development and field validation studies. This will allow primary recovery, enhanced recovery and CO{sub 2} sequestration operations to be commercially enhanced and/or economically deployed. The project was initially launched in 2000 as a U.S. Department of Energy sponsored investigation into CO{sub 2} sequestration in deep, unmineable coalseams. The initial project accomplished a number of important objectives, which mainly revolved around performing baseline experimental studies, documenting and analyzing existing field projects, and establishing a global network for technology exchange. The results from that Phase have been documented in a series of reports which are publicly available. An important outcome of the initial phase was that serious limitations were uncovered in our knowledge of reservoir behavior when CO{sub 2} is injected into coal. To address these limitations, the project was extended in 2005 as a government-industry collaborative consortium. Selected accomplishments from this phase have included the identification and/or development of new models for multi-component sorption and diffusion, laboratory studies of coal geomechanical and permeability behavior with CO{sub 2} injection, additional field validation studies, and continued global technology exchange. Further continuation of the consortium is currently being considered. Some of the topics that have been identified for investigation include further model development/refinement related to multicomponent equations-of-state, sorption and diffusion behavior, geomechanical and permeability studies, technical and economic feasibility studies for major international coal basins, the extension of the work to gas shale

  14. Boosted Jet Tagging with Jet-Images and Deep Neural Networks

    International Nuclear Information System (INIS)

    Kagan, Michael; Oliveira, Luke de; Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel

    2016-01-01

    Building on the jet-image based representation of high energy jets, we develop computer vision based techniques for jet tagging through the use of deep neural networks. Jet-images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing. We show how applying such techniques using deep neural networks can improve the performance to identify highly boosted W bosons with respect to state-of-the-art substructure methods. In addition, we explore new ways to extract and visualize the discriminating features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods

  15. Deep-sea geohazards in the South China Sea

    Science.gov (United States)

    Wu, Shiguo; Wang, Dawei; Völker, David

    2018-02-01

    Various geological processes and features that might inflict hazards identified in the South China Sea by using new technologies and methods. These features include submarine landslides, pockmark fields, shallow free gas, gas hydrates, mud diapirs and earthquake tsunami, which are widely distributed in the continental slope and reefal islands of the South China Sea. Although the study and assessment of geohazards in the South China Sea came into operation only recently, advances in various aspects are evolving at full speed to comply with National Marine Strategy and `the Belt and Road' Policy. The characteristics of geohazards in deep-water seafloor of the South China Sea are summarized based on new scientific advances. This progress is aimed to aid ongoing deep-water drilling activities and decrease geological risks in ocean development.

  16. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

    Science.gov (United States)

    van Ginneken, Bram

    2017-03-01

    Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

  17. MRI-induced heating of deep brain stimulation leads

    International Nuclear Information System (INIS)

    Mohsin, Syed A; Sheikh, Noor M; Saeed, Usman

    2008-01-01

    The radiofrequency (RF) field used in magnetic resonance imaging is scattered by medical implants. The scattered field of a deep brain stimulation lead can be very intense near the electrodes stimulating the brain. The effect is more pronounced if the lead behaves as a resonant antenna. In this paper, we examine the resonant length effect. We also use the finite element method to compute the near field for (i) the lead immersed in inhomogeneous tissue (fat, muscle, and brain tissues) and (ii) the lead connected to an implantable pulse generator. Electric field, specific absorption rate and induced temperature rise distributions have been obtained in the brain tissue surrounding the electrodes. The worst-case scenario has been evaluated by neglecting the effect of blood perfusion. The computed values are in good agreement with in vitro measurements made in the laboratory.

  18. WHATS-3: An improved flow-through multi-bottle fluid sampler for deep-sea geofluid research

    Science.gov (United States)

    Miyazaki, Junichi; Makabe, Akiko; Matsui, Yohei; Ebina, Naoya; Tsutsumi, Saki; Ishibashi, Jun-ichiro; Chen, Chong; Kaneko, Sho; Takai, Ken; Kawagucci, Shinsuke

    2017-06-01

    Deep-sea geofluid systems, such as hydrothermal vents and cold seeps, are key to understanding subseafloor environments of Earth. Fluid chemistry, especially, provides crucial information towards elucidating the physical, chemical and biological processes that occur in these ecosystems. To accurately assess fluid and gas properties of deep-sea geofluids, well-designed pressure-tight fluid samplers are indispensable and as such they are important assets of deep-sea geofluid research. Here, the development of a new flow-through, pressure-tight fluid sampler capable of four independent sampling events (two subsamples for liquid and gas analyses from each) is reported. This new sampler, named WHATS-3, is a new addition to the WHATS-series samplers and a major upgrade from the previous WHATS-2 sampler with improvements in sample number, valve operational time, physical robustness, and ease of maintenance. Routine laboratory-based pressure tests proved that it is suitable for operation up to 35 MPa pressure. Successful field tests of the new sampler were also carried out in five hydrothermal fields, two in Indian Ocean and three in Okinawa Trough (max. depth 3,300 m). Relations of Mg and major ion species demonstrated bimodal mixing trends between a hydrothermal fluid and seawater, confirming the high-quality of fluids sampled. The newly developed WHATS-3 sampler is well-balanced in sampling capability, field usability, and maintenance feasibility, and can serve as one of the best geofluid samplers available at present to conduct efficient research of deep-sea geofluid systems.

  19. Tackling the Challenge of Deep Vadose Zone Remediation at the Hanford Site

    Science.gov (United States)

    Morse, J. G.; Wellman, D. M.; Gephart, R.

    2010-12-01

    The Central Plateau of the Hanford Site in Washington State contains some 800 waste disposal sites where 1.7 trillion liters of contaminated water was once discharged into the subsurface. Most of these sites received liquids from the chemical reprocessing of spent uranium fuel to recover plutonium. In addition, 67 single shell tanks have leaked or are suspected to have leaked 3.8 million liters of high alkali and aluminate rich cesium-contaminated liquids into the sediment. Today, this inventory of subsurface contamination contains an estimated 550,000 curies of radioactivity and 150 million kg (165,000 tons) of metals and hazardous chemicals. Radionuclides range from mobile 99Tc to more immobilized 137Cs, 241Am, uranium, and plutonium. A significant fraction of these contaminants likely remain within the deep vadose zone. Plumes of groundwater containing tritium, nitrate, 129I and other contaminants have migrated through the vadose zone and now extend outward from the Central Plateau to the Columbia River. During most of Hanford Site history, subsurface studies focused on groundwater monitoring and characterization to support waste management decisions. Deep vadose zone studies were not a priority because waste practices relied upon that zone to buffer contaminant releases into the underlying aquifer. Remediation of the deep vadose zone is now central to Hanford Site cleanup because these sediments can provide an ongoing source of contamination to the aquifer and therefore to the Columbia River. However, characterization and remediation of the deep vadose zone pose some unique challenges. These include sediment thickness; contaminant depth; coupled geohydrologic, geochemical, and microbial processes controlling contaminant spread; limited availability and effectiveness of traditional characterization tools and cleanup remedies; and predicting contaminant behavior and remediation performance over long time periods and across molecular to field scales. The U

  20. A 6-cm deep sky survey

    International Nuclear Information System (INIS)

    Fomalont, E.B.; Kellermann, K.I.; Wall, J.V.

    1983-01-01

    In order to extend radio source counts to lower flux density, the authors have used the VLA to survey a small region of sky at 4.885 GHz (6 cm) to a limiting flux density of 50 μJy. Details of this deep survey are given in the paper by kellermann et al. (these proceedings). In addition, they have observed 10 other nearby fields to a limiting flux density of 350 μJy in order to provide better statistics on sources of intermediate flux density. (Auth.)

  1. SPECTROSCOPIC CONFIRMATION OF FAINT LYMAN BREAK GALAXIES NEAR REDSHIFT FIVE IN THE HUBBLE ULTRA DEEP FIELD

    International Nuclear Information System (INIS)

    Rhoads, James E.; Malhotra, Sangeeta; Cohen, Seth; Grogin, Norman; Hathi, Nimish; Ryan, Russell; Straughn, Amber; Windhorst, Rogier A.; Pirzkal, Norbert; Xu Chun; Koekemoer, Anton; Panagia, Nino; Dickinson, Mark; Ferreras, Ignacio; Gronwall, Caryl; Kuemmel, Martin; Walsh, Jeremy; Meurer, Gerhardt; Pasquali, Anna; Yan, H.-J.

    2009-01-01

    We present the faintest spectroscopically confirmed sample of z ∼ 5 Lyman break galaxies (LBGs) to date. The sample is based on slitless grism spectra of the Hubble Ultra Deep Field region from the Grism ACS Program for Extragalactic Science (GRAPES) and Probing Evolution and Reionization Spectroscopically (PEARS) projects, using the G800L grism on the Hubble Space Telescope Advanced Camera for Surveys. We report here confirmations of 39 galaxies, preselected as candidate LBGs using photometric selection criteria. We compare a 'traditional' V-dropout selection, based on the work of Giavalisco et al., to a more liberal one (with V - i > 0.9), and find that the traditional criteria are about 64% complete and 81% reliable. We also study the Lyα emission properties of our sample. We find that Lyα emission is detected in ∼1/4 of the sample, and that the liberal V-dropout color selection includes ∼55% of previously published line-selected Lyα sources. Finally, we examine our stacked two-dimensional spectra. We demonstrate that strong, spatially extended (∼1'') Lyα emission is not a generic property of these LBGs, but that a modest extension of the Lyα photosphere (compared to the starlight) may be present in those galaxies with prominent Lyα emission.

  2. Anticipating Deep Mapping: Tracing the Spatial Practice of Tim Robinson

    Directory of Open Access Journals (Sweden)

    Jos Smith

    2015-07-01

    Full Text Available There has been little academic research published on the work of Tim Robinson despite an illustrious career, first as an artist of the London avant-garde, then as a map-maker in the west of Ireland, and finally as an author of place. In part, this dearth is due to the difficulty of approaching these three diverse strands collectively. However, recent developments in the field of deep mapping encourage us to look back at the continuity of Robinson’s achievements in full and offer a suitable framework for doing so. Socially engaged with living communities and a depth of historical knowledge about place, but at the same time keen to contribute artistically to the ongoing contemporary culture of place, the parameters of deep mapping are broad enough to encompass the range of Robinson’s whole practice and suggest unique ways to illuminate his very unusual career. But Robinson’s achievements also encourage a reflection on the historical context of deep mapping itself, as well as on the nature of its spatial practice (especially where space comes to connote a medium to be worked rather than an area/volume. With this in mind the following article both explores Robinson’s work through deep mapping and deep mapping through the work of this unusual artist.

  3. Stellar Atmospheric Parameterization Based on Deep Learning

    Science.gov (United States)

    Pan, Ru-yang; Li, Xiang-ru

    2017-07-01

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

  4. Increasing the Deep Drawability of Al-1050 Aluminum Sheet using Multi-Point Blank Holder

    Directory of Open Access Journals (Sweden)

    Gavas, M.

    2006-01-01

    Full Text Available Aluminum alloys have been widely used in the fields of automobile and aerospace industries. Due to their bad cold-formability in deep drawing, a lot of forming methods have been implemented to increase the drawing height and the limiting drawing rate (LDR. The conventional deep drawing process is limited to a certain limit drawing ratio beyond which failure will ensue. The purpose of this experimental study is to examine the possibilities of increasing this limitation using the multi-point blank holder. The results from the experiments showed that the multi-point blank holder is effective way to promote deep drawability of Al-1050 sheet.

  5. Non-factorizable contributions to deep inelastic scattering at large x

    International Nuclear Information System (INIS)

    Pecjak, Ben D.

    2005-01-01

    We use soft-collinear effective theory (SCET) to study the factorization properties of deep inelastic scattering in the region of phase space where (1-x) ∼ Λ QCD /Q. By applying a regions analysis to loop diagrams in the Breit frame, we show that the appropriate version of SCET includes anti-hard-collinear, collinear, and soft-collinear fields. We find that the effects of the soft-collinear fields spoil perturbative factorization even at leading order in the 1/Q expansion

  6. 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.

  7. 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

  8. Modeling the effect of deep impurity ionization on GaAs photoconductive switches

    Energy Technology Data Exchange (ETDEWEB)

    Yee, J.H.; Khanaka, G.H.; Druce, R.L.; Pocha, M.D.

    1992-01-01

    The ionization coefficient of deep traps in GaAs is determined from a gas breakdown model together with the recent experimental data obtained at LLNL (Lawrence Livermore National Laboratory) and Boeing. Using this coefficient in our nonlinear device transport code, we have investigated theoretically the nonlinear switching phenomena in GaAs devices. The results obtained from our investigations show that if we take into consideration the effect of the field ionization of the deep traps, we can show how the Lock-On'' phenomena could occur in the device.

  9. Modeling the effect of deep impurity ionization on GaAs photoconductive switches

    Energy Technology Data Exchange (ETDEWEB)

    Yee, J.H.; Khanaka, G.H.; Druce, R.L.; Pocha, M.D.

    1992-01-01

    The ionization coefficient of deep traps in GaAs is determined from a gas breakdown model together with the recent experimental data obtained at LLNL (Lawrence Livermore National Laboratory) and Boeing. Using this coefficient in our nonlinear device transport code, we have investigated theoretically the nonlinear switching phenomena in GaAs devices. The results obtained from our investigations show that if we take into consideration the effect of the field ionization of the deep traps, we can show how the ``Lock-On`` phenomena could occur in the device.

  10. 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.

  11. Economic considerations for deep water Gulf of Mexico development

    International Nuclear Information System (INIS)

    Brown, R.; O'Sullivan, J.; Bayazitoglu, Y.O.

    1994-01-01

    This paper examines the economic drivers behind deep water development in the Gulf of Mexico. Capital costs are also examined versus water depth and required system. Cost categories are compared. The cost analysis was carried out by using the SEAPLAN computer program. The program is an expert system that identifies, conceptually defines, and economically compares technically feasible approaches for developing offshore oil and gas fields. The program's sizing logic and cost data base create physical and cost descriptions of systems representative of developments being planned in the deep water GOM. The examination was done separately for oil and gas developments. The material presented here is for only oil, it serves as a useful framework for viewing development economics and technology trends

  12. 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.

  13. Measurement of neutrino oscillations in atmospheric neutrinos with the IceCube DeepCore detector

    Energy Technology Data Exchange (ETDEWEB)

    Yanez Garza, Juan Pablo

    2014-06-02

    The study of neutrino oscillations is an active field of research. During the last couple of decades many experiments have measured the effects of oscillations, pushing the field from the discovery stage towards an era of precision and deeper understanding of the phenomenon. The IceCube Neutrino Observatory, with its low energy subarray, DeepCore, has the possibility of contributing to this field. IceCube is a 1 km{sup 3} ice Cherenkov neutrino telescope buried deep in the Antarctic glacier. DeepCore, a region of denser instrumentation in the lower center of IceCube, permits the detection of neutrinos with energies as low as 10 GeV. Every year, thousands of atmospheric neutrinos around these energies leave a strong signature in DeepCore. Due to their energy and the distance they travel before being detected, these neutrinos can be used to measure the phenomenon of oscillations. This work starts with a study of the potential of IceCube DeepCore to measure neutrino oscillations in different channels, from which the disappearance of ν{sub μ} is chosen to move forward. It continues by describing a novel method for identifying Cherenkov photons that traveled without being scattered until detected direct photons. These photons are used to reconstruct the incoming zenith angle of muon neutrinos. The total energy of the interacting neutrino is also estimated. In data taken in 343 days during 2011-2012, 1487 neutrino candidates with an energy between 7 GeV and 100 GeV are found inside the DeepCore volume. Compared to the expectation from the atmospheric neutrino flux without oscillations, this corresponds to a deficit of about 500 muon neutrino events. The oscillation parameters that describe the data best are sin{sup 2}(2θ{sub 23})=1(>0.94 at 68 % C.L.) and vertical stroke Δm{sup 2}{sub 32} vertical stroke =2.4{sub -0.4}{sup +0.6}.10{sup -3} eV{sup 2}, which are in agreement with the results reported by other experiments. The simulation follows the data closely

  14. Deep Drilling into a Mantle Plume Volcano: The Hawaii Scientific Drilling Project

    Directory of Open Access Journals (Sweden)

    Donald M. Thomas

    2009-03-01

    Full Text Available Oceanic volcanoes formed by mantle plumes, such as those of Hawaii and Iceland, strongly influence our views about the deep Earth (Morgan, 1971; Sleep, 2006. These volcanoes are the principal geochemical probe into the deep mantle, a testing ground for understanding mantle convection, plate tectonics and volcanism, and an archive of information on Earth’s magnetic field and lithospheredynamics. Study of the petrology, geochemistry, and structure of oceanic volcanoes has contributed immensely to our present understanding of deep Earth processes, but virtually all of this study has been concentrated on rocks available at the surface. In favorable circumstances, surface exposures penetrate to a depth of a few hundred meters, which is a small fraction of the 10- to 15-kilometer height of Hawaiian volcanoes above the depressed seafloor (Moore, 1987; Watts, 2001.

  15. Key technologies for well drilling and completion in ultra-deep sour gas reservoirs, Yuanba Gasfield, Sichuan Basin

    Directory of Open Access Journals (Sweden)

    Jiaxiang Xia

    2016-12-01

    Full Text Available The Yuanba Gasfield is a large gas field discovered by Sinopec in the Sichuan Basin in recent years, and another main exploration area for natural gas reserves and production increase after the Puguang Gasfield. The ultra-deep sour gas reservoir in the Yuanba Gasfield is characterized by complicated geologic structure, deep reservoirs and complex drilled formation, especially in the continental deep strata which are highly abrasive with low ROP (rate of penetration and long drilling period. After many years of drilling practice and technical research, the following six key drilling and completion technologies for this type reservoir are established by introducing new tools and technologies, developing specialized drill bits and optimizing drilling design. They are: casing program optimization technology for ROP increasing and safe well completion; gas drilling technology for shallow continental strata and high-efficiency drilling technology for deep high-abrasion continental strata; drilling fluid support technologies of gas–liquid conversion, ultra-deep highly-deviated wells and horizontal-well lubrication and drag reduction, hole stability control and sour gas contamination prevention; well cementing technologies for gas medium, deep-well long cementing intervals and ultra-high pressure small space; horizontal-well trajectory control technologies for measuring instrument, downhole motor optimization and bottom hole assembly design; and liner completion modes and completion string optimization technologies suitable for this gas reservoir. Field application shows that these key technologies are contributive to ROP increase and efficiency improvement of 7000 m deep horizontal wells and to significant operational cycle shortening.

  16. fields

    Directory of Open Access Journals (Sweden)

    Brad J. Arnold

    2014-07-01

    Full Text Available Surface irrigation, such as flood or furrow, is the predominant form of irrigation in California for agronomic crops. Compared to other irrigation methods, however, it is inefficient in terms of water use; large quantities of water, instead of being used for crop production, are lost to excess deep percolation and tail runoff. In surface-irrigated fields, irrigators commonly cut off the inflow of water when the water advance reaches a familiar or convenient location downfield, but this experience-based strategy has not been very successful in reducing the tail runoff water. Our study compared conventional cutoff practices to a retroactively applied model-based cutoff method in four commercially producing alfalfa fields in Northern California, and evaluated the model using a simple sensor system for practical application in typical alfalfa fields. These field tests illustrated that the model can be used to reduce tail runoff in typical surface-irrigated fields, and using it with a wireless sensor system saves time and labor as well as water.

  17. Detection of Thermal Erosion Gullies from High-Resolution Images Using Deep Learning

    Science.gov (United States)

    Huang, L.; Liu, L.; Jiang, L.; Zhang, T.; Sun, Y.

    2017-12-01

    Thermal erosion gullies, one type of thermokarst landforms, develop due to thawing of ice-rich permafrost. Mapping the location and extent of thermal erosion gullies can help understand the spatial distribution of thermokarst landforms and their temporal evolution. Remote sensing images provide an effective way for mapping thermokarst landforms, especially thermokarst lakes. However, thermal erosion gullies are challenging to map from remote sensing images due to their small sizes and significant variations in geometric/radiometric properties. It is feasible to manually identify these features, as a few previous studies have carried out. However manual methods are labor-intensive, therefore, cannot be used for a large study area. In this work, we conduct automatic mapping of thermal erosion gullies from high-resolution images by using Deep Learning. Our study area is located in Eboling Mountain (Qinghai, China). Within a 6 km2 peatland area underlain by ice-rich permafrost, at least 20 thermal erosional gullies are well developed. The image used is a 15-cm-resolution Digital Orthophoto Map (DOM) generated in July 2016. First, we extracted 14 gully patches and ten non-gully patches as training data. And we performed image augmentation. Next, we fine-tuned the pre-trained model of DeepLab, a deep-learning algorithm for semantic image segmentation based on Deep Convolutional Neural Networks. Then, we performed inference on the whole DOM and obtained intermediate results in forms of polygons for all identified gullies. At last, we removed misidentified polygons based on a few pre-set criteria on the size and shape of each polygon. Our final results include 42 polygons. Validated against field measurements using GPS, most of the gullies are detected correctly. There are 20 false detections due to the small number and low quality of training images. We also found three new gullies that missed in the field observations. This study shows that (1) despite a challenging

  18. Hot-carrier effects on irradiated deep submicron NMOSFET

    International Nuclear Information System (INIS)

    Cui Jiangwei; Zheng Qiwen; Yu Xuefeng; Cong Zhongchao; Zhou Hang; Guo Qi; Wen Lin; Wei Ying; Ren Diyuan

    2014-01-01

    We investigate how γ exposure impacts the hot-carrier degradation in deep submicron NMOSFET with different technologies and device geometries for the first time. The results show that hot-carrier degradations on irradiated devices are greater than those without irradiation, especially for narrow channel device. The reason is attributed to charge traps in STI, which then induce different electric field and impact ionization rates during hot-carrier stress. (semiconductor devices)

  19. The deep, hot biosphere: Twenty-five years of retrospection.

    Science.gov (United States)

    Colman, Daniel R; Poudel, Saroj; Stamps, Blake W; Boyd, Eric S; Spear, John R

    2017-07-03

    Twenty-five years ago this month, Thomas Gold published a seminal manuscript suggesting the presence of a "deep, hot biosphere" in the Earth's crust. Since this publication, a considerable amount of attention has been given to the study of deep biospheres, their role in geochemical cycles, and their potential to inform on the origin of life and its potential outside of Earth. Overwhelming evidence now supports the presence of a deep biosphere ubiquitously distributed on Earth in both terrestrial and marine settings. Furthermore, it has become apparent that much of this life is dependent on lithogenically sourced high-energy compounds to sustain productivity. A vast diversity of uncultivated microorganisms has been detected in subsurface environments, and we show that H 2 , CH 4 , and CO feature prominently in many of their predicted metabolisms. Despite 25 years of intense study, key questions remain on life in the deep subsurface, including whether it is endemic and the extent of its involvement in the anaerobic formation and degradation of hydrocarbons. Emergent data from cultivation and next-generation sequencing approaches continue to provide promising new hints to answer these questions. As Gold suggested, and as has become increasingly evident, to better understand the subsurface is critical to further understanding the Earth, life, the evolution of life, and the potential for life elsewhere. To this end, we suggest the need to develop a robust network of interdisciplinary scientists and accessible field sites for long-term monitoring of the Earth's subsurface in the form of a deep subsurface microbiome initiative.

  20. EVOLUTION OF THE SIZES OF GALAXIES OVER 7 < z < 12 REVEALED BY THE 2012 HUBBLE ULTRA DEEP FIELD CAMPAIGN

    Energy Technology Data Exchange (ETDEWEB)

    Ono, Yoshiaki; Ouchi, Masami [Institute for Cosmic Ray Research, The University of Tokyo, Kashiwa 277-8582 (Japan); Curtis-Lake, Emma; McLure, Ross J.; Dunlop, James S.; Bowler, Rebecca A. A.; Rogers, Alexander B.; Cirasuolo, Michele [Institute for Astronomy, University of Edinburgh, Royal Observatory, Edinburgh EH9 3HJ (United Kingdom); Schenker, Matthew A.; Ellis, Richard S. [Department of Astrophysics, California Institute of Technology, MS 249-17, Pasadena, CA 91125 (United States); Robertson, Brant E.; Schneider, Evan; Stark, Daniel P. [Department of Astronomy and Steward Observatory, University of Arizona, Tucson, AZ 85721 (United States); Koekemoer, Anton M. [Space Telescope Science Institute, Baltimore, MD 21218 (United States); Charlot, Stephane [UPMC-CNRS, UMR7095, Institut d' Astrophysique, F-75014 Paris (France); Shimasaku, Kazuhiro [Department of Astronomy, Graduate School of Science, The University of Tokyo, Tokyo 113-0033 (Japan); Furlanetto, Steven R., E-mail: ono@icrr.u-tokyo.ac.jp [Department of Physics and Astronomy, University of California, Los Angeles, CA 90095 (United States)

    2013-11-10

    We analyze the redshift- and luminosity-dependent sizes of dropout galaxy candidates in the redshift range z ∼ 7-12 using deep images from the 2012 Hubble Ultra Deep Field (UDF12) campaign, which offers two advantages over that used in earlier work. First, we utilize the increased signal-to-noise ratio offered by the UDF12 imaging to provide improved measurements for known galaxies at z ≅ 6.5-8 in the HUDF. Second, because the UDF12 data have allowed the construction of the first robust galaxy sample in the HUDF at z > 8, we have been able to extend the measurement of average galaxy size out to higher redshifts. Restricting our measurements to sources detected at >15σ, we confirm earlier indications that the average half-light radii of z ∼ 7-12 galaxies are extremely small, 0.3-0.4 kpc, comparable to the sizes of giant molecular associations in local star-forming galaxies. We also confirm that there is a clear trend of decreasing half-light radius with increasing redshift, and provide the first evidence that this trend continues beyond z ≅ 8. Modeling the evolution of the average half-light radius as a power law, ∝(1 + z) {sup s}, we obtain a best-fit index of s=-1.30{sup +0.12}{sub -0.14} over z ∼ 4-12. A clear size-luminosity relation is evident in our dropout samples. This relation can be interpreted in terms of a constant surface density of star formation over a range in luminosity of 0.05-1.0 L{sub z=3}. The average star formation surface density in dropout galaxies is 2-3 orders of magnitude lower than that found in extreme starburst galaxies, but is comparable to that seen today in the centers of normal disk galaxies.

  1. Spatial Variability of the Background Diurnal Cycle of Deep Convection around the GoAmazon2014/5 Field Campaign Sites

    Energy Technology Data Exchange (ETDEWEB)

    Burleyson, Casey D.; Feng, Zhe; Hagos, Samson M.; Fast, Jerome; Machado, Luiz A. T.; Martin, Scot T.

    2016-07-01

    The Amazon rainforest is one of a few regions of the world where continental tropical deep convection occurs. The Amazon’s isolation makes it challenging to observe, but also creates a unique natural laboratory to study anthropogenic impacts on clouds and precipitation in an otherwise pristine environment. Extensive measurements were made upwind and downwind of the large city of Manaus, Brazil during the Observations and Modeling of the Green Ocean Amazon 2014-2015 (GoAmazon2014/5) field campaign. In this study, 15 years of high-resolution satellite data are analyzed to examine the spatial and diurnal variability of convection occurring around the GoAmazon2014/5 sites. Interpretation of anthropogenic differences between the upwind (T0) and downwind (T1-T3) sites is complicated by naturally-occurring spatial variability between the sites. During the rainy season, the inland propagation of the previous day’s sea-breeze front happens to be in phase with the background diurnal cycle near Manaus, but is out of phase elsewhere. Enhanced convergence between the river-breezes and the easterly trade winds generates up to 10% more frequent deep convection at the GoAmazon2014/5 sites east of the river (T0a, T0t/k, and T1) compared to the T3 site which was located near the western bank. In general, the annual and diurnal cycles during 2014 were representative of the 2000-2013 distributions. The only exceptions were in March when the monthly mean rainrate was above the 95th percentile and September when both rain frequency and intensity were suppressed. The natural spatial variability must be accounted for before interpreting anthropogenically-induced differences among the GoAmazon2014/5 sites.

  2. EVOLUTION OF THE SIZES OF GALAXIES OVER 7 < z < 12 REVEALED BY THE 2012 HUBBLE ULTRA DEEP FIELD CAMPAIGN

    International Nuclear Information System (INIS)

    Ono, Yoshiaki; Ouchi, Masami; Curtis-Lake, Emma; McLure, Ross J.; Dunlop, James S.; Bowler, Rebecca A. A.; Rogers, Alexander B.; Cirasuolo, Michele; Schenker, Matthew A.; Ellis, Richard S.; Robertson, Brant E.; Schneider, Evan; Stark, Daniel P.; Koekemoer, Anton M.; Charlot, Stephane; Shimasaku, Kazuhiro; Furlanetto, Steven R.

    2013-01-01

    We analyze the redshift- and luminosity-dependent sizes of dropout galaxy candidates in the redshift range z ∼ 7-12 using deep images from the 2012 Hubble Ultra Deep Field (UDF12) campaign, which offers two advantages over that used in earlier work. First, we utilize the increased signal-to-noise ratio offered by the UDF12 imaging to provide improved measurements for known galaxies at z ≅ 6.5-8 in the HUDF. Second, because the UDF12 data have allowed the construction of the first robust galaxy sample in the HUDF at z > 8, we have been able to extend the measurement of average galaxy size out to higher redshifts. Restricting our measurements to sources detected at >15σ, we confirm earlier indications that the average half-light radii of z ∼ 7-12 galaxies are extremely small, 0.3-0.4 kpc, comparable to the sizes of giant molecular associations in local star-forming galaxies. We also confirm that there is a clear trend of decreasing half-light radius with increasing redshift, and provide the first evidence that this trend continues beyond z ≅ 8. Modeling the evolution of the average half-light radius as a power law, ∝(1 + z) s , we obtain a best-fit index of s=-1.30 +0.12 -0.14 over z ∼ 4-12. A clear size-luminosity relation is evident in our dropout samples. This relation can be interpreted in terms of a constant surface density of star formation over a range in luminosity of 0.05-1.0 L z=3 . The average star formation surface density in dropout galaxies is 2-3 orders of magnitude lower than that found in extreme starburst galaxies, but is comparable to that seen today in the centers of normal disk galaxies

  3. Lessons from Suiyo Seamount studies, for understanding extreme (ancient?) microbial ecosystems in the deep-sea hydrothermal fields

    Science.gov (United States)

    Maruyama, A.; Higashi, Y.; Sunamura, M.; Urabe, T.

    2004-12-01

    Deep-sea hydrothermal ecosystems are driven with various geo-thermally modified, mainly reduced, compounds delivered from extremely hot subsurface environments. To date, several unique microbes including thermophilic archaeons have been isolated from/around vent chimneys. However, there is little information about microbes in over-vent and sub-vent fields. Here, we report several new findings on microbial diversity and ecology of the Suiyo Seamount that locates on the Izu-Bonin Arc in the northwest Pacific Ocean, as a result of the Japanese Archaean Park project, with special concern to the sub-vent biosphere. At first, we succeeded to reveal a very unique microbial ecosystem in hydrothermal plume reserved within the outer rim of the seamount crater, that is, it consisted of almost all metabolically active microbes belonged to only two Bacteria phylotypes, probably of sulfur oxidizers. In the center of the caldera seafloor (ca. 1,388-m deep) consisted mainly of whitish sands and pumices, we found many small chimneys (ca. 5-10 cm) and bivalve colonies distributed looking like gray to black patches. These geo/ecological features of the seafloor were supposed to be from a complex mixing of hydrothermal venting and strong water current near the seafloor. Through quantitative FISH analysis for various environmental samples, one of the two representative groups in the plume was assessed to be from some of the bivalve colonies. Using the Benthic Multi-coring System (BMS), total 10 points were drilled and 6 boreholes were maintained with stainless or titanium casing pipes. In the following submersible surveys, newly developed catheter- and column-type in situ growth chambers were deployed in and on the boreholes, respectively, for collecting indigenous sub-vent microbes. Finally, we succeeded to detect several new phylotypes of microbes in these chamber samples, e.g., within epsilon-Proteobacteria, a photosynthetic group of alpha-Proteobacteria, and hyperthermophile

  4. Observations of the Hubble Deep Field with the Infrared Space Observatory .5. Spectral energy distributions, starburst models and star formation history

    DEFF Research Database (Denmark)

    Rowan Robinson, M.; Mann, R.G.; Oliver, S.J.

    1997-01-01

    We have modelled the spectral energy distributions of the 13 Hubble Deep Field (HDF) galaxies reliably detected by the Infrared Space Observatory (ISO). For two galaxies the emission detected by ISO is consistent with being starlight or the infrared 'cirrus' in the galaxies. For the remaining II...... galaxies there is a clear midinfrared excess, which we interpret as emission from dust associated with a strong starburst. 10 of these galaxies are spirals or interacting pairs, while the remaining one is an elliptical with a prominent nucleus and broad emission lines. We give a new discussion of how...... compared with nearby normal galaxies, We discuss the implications of our detections for the history of star and heavy element formation in the Universe, Although uncertainties in the calibration, reliability of source detection, associations and starburst models remain, it is clear that dust plays...

  5. Wireless power transfer to deep-tissue microimplants.

    Science.gov (United States)

    Ho, John S; Yeh, Alexander J; Neofytou, Evgenios; Kim, Sanghoek; Tanabe, Yuji; Patlolla, Bhagat; Beygui, Ramin E; Poon, Ada S Y

    2014-06-03

    The ability to implant electronic systems in the human body has led to many medical advances. Progress in semiconductor technology paved the way for devices at the scale of a millimeter or less ("microimplants"), but the miniaturization of the power source remains challenging. Although wireless powering has been demonstrated, energy transfer beyond superficial depths in tissue has so far been limited by large coils (at least a centimeter in diameter) unsuitable for a microimplant. Here, we show that this limitation can be overcome by a method, termed midfield powering, to create a high-energy density region deep in tissue inside of which the power-harvesting structure can be made extremely small. Unlike conventional near-field (inductively coupled) coils, for which coupling is limited by exponential field decay, a patterned metal plate is used to induce spatially confined and adaptive energy transport through propagating modes in tissue. We use this method to power a microimplant (2 mm, 70 mg) capable of closed-chest wireless control of the heart that is orders of magnitude smaller than conventional pacemakers. With exposure levels below human safety thresholds, milliwatt levels of power can be transferred to a deep-tissue (>5 cm) microimplant for both complex electronic function and physiological stimulation. The approach developed here should enable new generations of implantable systems that can be integrated into the body at minimal cost and risk.

  6. Deep UV LEDs

    Science.gov (United States)

    Han, Jung; Amano, Hiroshi; Schowalter, Leo

    2014-06-01

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

  7. Deep subsurface drip irrigation using coal-bed sodic water: part I. water and solute movement

    Science.gov (United States)

    Bern, Carleton R.; Breit, George N.; Healy, Richard W.; Zupancic, John W.; Hammack, Richard

    2013-01-01

    Water co-produced with coal-bed methane (CBM) in the semi-arid Powder River Basin of Wyoming and Montana commonly has relatively low salinity and high sodium adsorption ratios that can degrade soil permeability where used for irrigation. Nevertheless, a desire to derive beneficial use from the water and a need to dispose of large volumes of it have motivated the design of a deep subsurface drip irrigation (SDI) system capable of utilizing that water. Drip tubing is buried 92 cm deep and irrigates at a relatively constant rate year-round, while evapotranspiration by the alfalfa and grass crops grown is seasonal. We use field data from two sites and computer simulations of unsaturated flow to understand water and solute movements in the SDI fields. Combined irrigation and precipitation exceed potential evapotranspiration by 300-480 mm annually. Initially, excess water contributes to increased storage in the unsaturated zone, and then drainage causes cyclical rises in the water table beneath the fields. Native chloride and nitrate below 200 cm depth are leached by the drainage. Some CBM water moves upward from the drip tubing, drawn by drier conditions above. Chloride from CBM water accumulates there as root uptake removes the water. Year over year accumulations indicated by computer simulations illustrate that infiltration of precipitation water from the surface only partially leaches such accumulations away. Field data show that 7% and 27% of added chloride has accumulated above the drip tubing in an alfalfa and grass field, respectively, following 6 years of irrigation. Maximum chloride concentrations in the alfalfa field are around 45 cm depth but reach the surface in parts of the grass field, illustrating differences driven by crop physiology. Deep SDI offers a means of utilizing marginal quality irrigation waters and managing the accumulation of their associated solutes in the crop rooting zone.

  8. Model of hot-carrier induced degradation in ultra-deep sub-micrometer nMOSFET

    International Nuclear Information System (INIS)

    Lei Xiao-Yi; Liu Hong-Xia; Zhang Yue; Ma Xiao-Hua; Hao Yue

    2014-01-01

    The degradation produced by hot carrier (HC) in ultra-deep sub-micron n-channel metal oxide semiconductor field effect transistor (nMOSFET) has been analyzed in this paper. The generation of negatively charged interface states is the predominant mechanism for the ultra-deep sub-micron nMOSFET. According to our lifetime model of p-channel MOFET (pMOFET) that was reported in a previous publication, a lifetime prediction model for nMOSFET is presented and the parameters in the model are extracted. For the first time, the lifetime models of nMOFET and pMOSFET are unified. In addition, the model can precisely predict the lifetime of the ultra-deep sub-micron nMOSFET and pMOSFET. (condensed matter: electronic structure, electrical, magnetic, and optical properties)

  9. Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

    Science.gov (United States)

    Eskofier, Bjoern M; Lee, Sunghoon I; Daneault, Jean-Francois; Golabchi, Fatemeh N; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo

    2016-08-01

    The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

  10. Cost reduction in deep water production systems

    International Nuclear Information System (INIS)

    Beltrao, R.L.C.

    1995-01-01

    This paper describes a cost reduction program that Petrobras has conceived for its deep water field. Beginning with the Floating Production Unit, a new concept of FPSO was established where a simple system, designed to long term testing, can be upgraded, on the location, to be the definitive production unit. Regarding to the subsea system, the following projects will be considered. (1) Subsea Manifold: There are two 8-well-diverless manifolds designed for 1,000 meters presently under construction and after a value analysis, a new design was achieved for the next generation. Both projects will be discussed and a cost evaluation will also be provided. (2) Subsea Pipelines: Petrobras has just started a large program aiming to reduce cost on this important item. There are several projects such as hybrid (flexible and rigid) pipes for large diameter in deep water, alternatives laying methods, rigid riser on FPS, new material...etc. The authors intend to provide an overview of each project

  11. 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.

  12. 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....

  13. Diagnosis of deep vein thrombosis using autologous indium-III-labelled platelets

    International Nuclear Information System (INIS)

    Fenech, A.; Hussey, J.K.; Smith, F.W.; Dendy, P.P.; Bennett, B.; Douglas, A.S.

    1981-01-01

    Forty-eight patients who had undergone surgical reduction of a fractured neck of femur or in whom deep vein thrombosis was suspected clinically were studied by ascending phlebography and imaging after injection of autologous indium-III-labelled platelets to assess the accuracy and value of the radioisotopic technique in diagnosing deep vein thrombosis. Imaging was performed with a wide-field gammacamera linked with data display facilities. Phlebography showed thrombi in 26 out of 54 limbs examined and a thrombus in the inferior vena cava of one patient; imaging the labelled platelets showed the thrombi in 24 of the 26 limbs and the thrombus in the inferior vena cava. The accumulation of indium-III at sites corresponding to those at which venous thrombi have been shown phlebographically indicates that this radioisotopic technique is a useful addition to methods already available for the detection of deep vein thrombosis. (author)

  14. Diagnosis of deep vein thrombosis using autologous indium-III-labelled platelets

    Energy Technology Data Exchange (ETDEWEB)

    Fenech, A.; Hussey, J.K.; Smith, F.W.; Dendy, P.P.; Bennett, B.; Douglas, A.S. (Aberdeen Univ. (UK))

    1981-03-28

    Forty-eight patients who had undergone surgical reduction of a fractured neck of femur or in whom deep vein thrombosis was suspected clinically were studied by ascending phlebography and imaging after injection of autologous indium-III-labelled platelets to assess the accuracy and value of the radioisotopic technique in diagnosing deep vein thrombosis. Imaging was performed with a wide-field gamma camera linked with data display facilities. Phlebography showed thrombi in 26 out of 54 limbs examined and a thrombus in the inferior vena cava of one patient; imaging the labelled platelets showed the thrombi in 24 of the 26 limbs and the thrombus in the inferior vena cava. The accumulation of indium-III at sites corresponding to those at which venous thrombi have been shown phlebographically indicates that this radioisotopic technique is a useful addition to methods already available for the detection of deep vein thrombosis.

  15. High-speed rupture during the initiation of the 2015 Bonin Islands deep earthquake

    Science.gov (United States)

    Zhan, Z.; Ye, L.; Shearer, P. M.; Lay, T.; Kanamori, H.

    2015-12-01

    Among the long-standing questions on how deep earthquakes rupture, the nucleation phase of large deep events is one of the most puzzling parts. Resolving the rupture properties of the initiation phase is difficult to achieve with far-field data because of the need for accurate corrections for structural effects on the waveforms (e.g., attenuation, scattering, and site effects) and alignment errors. Here, taking the 2015 Mw 7.9 Bonin Islands earthquake (depth = 678 km) as an example, we jointly invert its far-field P waves at multiple stations for the average rupture speed during the first second of the event. We use waveforms from a closely located aftershock as empirical Green's functions, and correct for possible differences in focal mechanisms and waveform misalignments with an iterative approach. We find that the average initial rupture speed is over 5 km/s, significantly higher than the average rupture speed of 3 km/s later in the event. This contrast suggests that rupture speeds of deep earthquakes can be highly variable during individual events and may define different stages of rupture, potentially with different mechanisms.

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

    Science.gov (United States)

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

    2018-04-01

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

  17. Observation of Deep Traps Responsible for Current Collapse in GaN Metal-Semiconductor Field-Effect Transistors

    National Research Council Canada - National Science Library

    Klein, P. B; Freitas, Jr., J. A; Binari, S. C; Wickenden, A. E

    1999-01-01

    ... of current collapse to determine the photoionization spectra of the traps involved. In the n-channel device investigated, the two electron traps observed were found to be very deep and strongly coupled to the lattice...

  18. Galaxy formation in the reionization epoch as hinted by Wide Field Camera 3 observations of the Hubble Ultra Deep Field

    International Nuclear Information System (INIS)

    Yan Haojing; Windhorst, Rogier A.; Cohen, Seth H.; Hathi, Nimish P.; Ryan, Russell E.; O'Connell, Robert W.; McCarthy, Patrick J.

    2010-01-01

    We present a large sample of candidate galaxies at z ∼ 7-10, selected in the Hubble Ultra Deep Field using the new observations of the Wide Field Camera 3 that was recently installed on the Hubble Space Telescope. Our sample is composed of 20 z 850 -dropouts (four new discoveries), 15 Y 105 -dropouts (nine new discoveries) and 20 J 125 -dropouts (all new discoveries). The surface densities of the z 850 -dropouts are close to what was predicted by earlier studies, however, those of the Y 105 - and J 125 -dropouts are quite unexpected. While no Y 105 - or J 125 -dropouts have been found at AB ≤ 28.0 mag, their surface densities seem to increase sharply at fainter levels. While some of these candidates seem to be close to foreground galaxies and thus could possibly be gravitationally lensed, the overall surface densities after excluding such cases are still much higher than what would be expected if the luminosity function does not evolve from z ∼ 7 to 10. Motivated by such steep increases, we tentatively propose a set of Schechter function parameters to describe the luminosity functions at z ∼ 8 and 10. As compared to their counterpart at z ∼ 7, here L * decreases by a factor of ∼ 6.5 and φ * increases by a factor of 17-90. Although such parameters are not yet demanded by the existing observations, they are allowed and seem to agree with the data better than other alternatives. If these luminosity functions are still valid beyond our current detection limit, this would imply a sudden emergence of a large number of low-luminosity galaxies when looking back in time to z ∼ 10, which, while seemingly exotic, would naturally fit in the picture of the cosmic hydrogen reionization. These early galaxies could easily account for the ionizing photon budget required by the reionization, and they would imply that the global star formation rate density might start from a very high value at z ∼ 10, rapidly reach the minimum at z ∼ 7, and start to rise again

  19. Some problems in exploitation of deep-pumping wells in Tatarian oil fields

    Energy Technology Data Exchange (ETDEWEB)

    Ishemguzhin, S B

    1970-01-01

    Difficulty has been experienced in pumping paraffinic oil with rod pumps. The rods have scrapers to remove paraffin from tubing walls, however this method does not work well. In an effort to improve pumping efficiency, gas anchors of various types were tried. Best results were obtained when the pumps, equipped with gas anchors, were placed about 300 m under the dynamic liquid level, and separated gas was steadily removed through the annulus. With this arrangement, more complete filling of the pump was achieved. Experience has shown that with separate production of gas from wells, the useful stroke of the pump plunger is increased as well as productivity of deep-pumping equipment.

  20. WHATS-3: An Improved Flow-Through Multi-bottle Fluid Sampler for Deep-Sea Geofluid Research

    Directory of Open Access Journals (Sweden)

    Junichi Miyazaki

    2017-06-01

    Full Text Available Deep-sea geofluid systems, such as hydrothermal vents and cold seeps, are key to understanding subseafloor environments of Earth. Fluid chemistry, especially, provides crucial information toward elucidating the physical, chemical, and biological processes that occur in these ecosystems. To accurately assess fluid and gas properties of deep-sea geofluids, well-designed pressure-tight fluid samplers are indispensable and as such they are important assets of deep-sea geofluid research. Here, the development of a new flow-through, pressure-tight fluid sampler capable of four independent sampling events (two subsamples for liquid and gas analyses from each is reported. This new sampler, named WHATS-3, is a new addition to the WHATS-series samplers and a major upgrade from the previous WHATS-2 sampler with improvements in sample number, valve operational time, physical robustness, and ease of maintenance. Routine laboratory-based pressure tests proved that it is suitable for operation up to 35 MPa pressure. Successful field tests of the new sampler were also carried out in five hydrothermal fields, two in Indian Ocean, and three in Okinawa Trough (max. depth 3,300 m. Relations of Mg and major ion species demonstrated bimodal mixing trends between a hydrothermal fluid and seawater, confirming the high quality of fluids sampled. The newly developed WHATS-3 sampler is well-balanced in sampling capability, field usability, and maintenance feasibility, and can serve as one of the best geofluid samplers available at present to conduct efficient research of deep-sea geofluid systems.

  1. The deep thermal field of the Upper Rhine Graben

    Science.gov (United States)

    Freymark, Jessica; Sippel, Judith; Scheck-Wenderoth, Magdalena; Bär, Kristian; Stiller, Manfred; Fritsche, Johann-Gerhard; Kracht, Matthias

    2017-01-01

    The Upper Rhine Graben has a significant socioeconomic relevance as it provides a great potential for geothermal energy production. The key for the utilisation of this energy resource is to understand the controlling factors of the thermal field in this area. We have therefore built a data-based lithospheric-scale 3D structural model of the Upper Rhine Graben and its adjacent areas. In addition, 3D gravity modelling was performed to constrain the internal structure of the crystalline crust consistent with seismic information. Based on this lithosphere scale 3D structural model the present-day conductive thermal field was calculated and compared to measured temperatures. Our results show that the regional thermal field is mainly controlled by the configuration of the upper crust, which has different thermal properties characteristic for the Variscan and Alpine domains. Temperature maxima are predicted for the Upper Rhine Graben where thick insulating Cenozoic sediments cause a thermal blanketing effect and where the underlying crustal units are characterised by high radiogenic heat production. The comparison of calculated and measured temperatures overall shows a reasonable fit, while locally occuring model deviations indicate where a larger influence of groundwater flow may be expected.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  3. 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.

  4. Studies on deep electronic levels in silicon and aluminium gallium arsenide alloys

    International Nuclear Information System (INIS)

    Pettersson, H.

    1993-01-01

    This thesis reports on investigations of the electrical and optical properties of deep impurity centers, related to the transition metals (TMs) Ti, Mo, W, V and Ni, in silicon. Emission rates, capture cross sections and photoionization cross sections for these impurities were determined by means of various Junction Space Charge Techniques (JSCTs), such as Deep Level Transient Spectroscopy (DLTS), dark capacitance transient and photo capacitance transient techniques. Changes in Gibbs free energy as a function of temperature were calculated for all levels. From this temperature dependence, the changes in enthalpy and entropy involved in the electron and hole transitions were deduced. The influence of high electric fields on the electronic levels in chalcogen-doped silicon were investigated using the dark capacitance transient technique. The enhancement of the electron emission from the deep centers indicated a more complex field enhancement model than the expected Poole-Frenkel effect for coulombic potentials. The possibility to determine charge states of defects using the Poole-Frenkel effect, as often suggested, is therefore questioned. The observation of a persistent decrease of the dark conductivity due to illumination in simplified AlGaAs/GaAs high Electron Mobility Transistors (HEMTs) over the temperature range 170K< T<300K is reported. A model for this peculiar behavior, based on the recombination of electrons in the two-dimensional electron gas (2DEG) located at the AlGaAs/GaAs interface with holes generated by a two-step excitation process via the deep EL2 center in the GaAs epilayer, is put forward

  5. Multi-anode deep well radiation detector

    International Nuclear Information System (INIS)

    Rogers, A.H.; Sullivan, K.J.; Mansfield, G.R.

    1984-01-01

    An inner cylindrical cathode and outer cylindrical cathode are concentrically positioned about a vertical center axis. Vertical anode electrodes extend parallel to the center axis and are symmetrically arranged around the inter-cylinder space between the cathodes. The ends of the anode wires are supported by a pair of insulator rings and mounted near the top and bottom of the cathode cylinders. A collection voltage applied to each anode wire for establishing an inward radial E field to the inner cathode cylinder and an outward radial E field to the outer cathode cylinder. The anode-cathode assembly is mounted within a housing containing a conversion gas. A radioactive sample is inserted into the inner cathode which functions as a tubular, deep well radiation window between the sample environment and the conversion gas environment. A portion of the gamma radiations passing through the inter-cylinder region interact with the conversion gas to produce free electrons which are accelerated by the E fields and collected on the anode wires. The extremely small diameter of the anode wires intensifies the electric fields proximate each wire causing avalanche multiplication of the free electrons resulting in a detectable charge pulse. (author)

  6. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning

    Directory of Open Access Journals (Sweden)

    Yingfeng Cai

    2016-01-01

    Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.

  7. 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.

  8. Natural deep eutectic solvents as new potential media for green technology

    International Nuclear Information System (INIS)

    Dai, Yuntao; Spronsen, Jaap van; Witkamp, Geert-Jan; Verpoorte, Robert; Choi, Young Hae

    2013-01-01

    Highlights: ► Natural products were used as a source for deep eutectic solvents and ionic liquids. ► We define own chemical and physical properties of natural deep eutectic solvents. ► Interaction between natural deep eutectic solvents and solutes was confirmed by NMR. ► The developed natural deep eutectic solvents were applied as green media. - Abstract: Developing new green solvents is one of the key subjects in Green Chemistry. Ionic liquids (ILs) and deep eutectic solvents, thus, have been paid great attention to replace current harsh organic solvents and have been applied to many chemical processing such as extraction and synthesis. However, current ionic liquids and deep eutectic solvents have still limitations to be applied to a real chemical industry due to toxicity against human and environment and high cost of ILs and solid state of most deep eutectic solvents at room temperature. Recently we discovered that many plant abundant primary metabolites changed their state from solid to liquid when they were mixed in proper ratio. This finding made us hypothesize that natural deep eutectic solvents (NADES) play a role as alternative media to water in living organisms and tested a wide range of natural products, which resulted in discovery of over 100 NADES from nature. In order to prove deep eutectic feature the interaction between the molecules was investigated by nuclear magnetic resonance spectroscopy. All the tested NADES show clear hydrogen bonding between components. As next step physical properties of NADES such as water activity, density, viscosity, polarity and thermal properties were measured as well as the effect of water on the physical properties. In the last stage the novel NADES were applied to the solubilization of wide range of biomolecules such as non-water soluble bioactive natural products, gluten, starch, and DNA. In most cases the solubility of the biomolecules evaluated in this study was greatly higher than water. Based on the

  9. Natural deep eutectic solvents as new potential media for green technology

    Energy Technology Data Exchange (ETDEWEB)

    Dai, Yuntao [Natural Products Laboratory, Institute of Biology, Leiden University, 2300 RA Leiden (Netherlands); Spronsen, Jaap van; Witkamp, Geert-Jan [Laboratory for Process Equipment, Delft University of Technology, Delft (Netherlands); Verpoorte, Robert [Natural Products Laboratory, Institute of Biology, Leiden University, 2300 RA Leiden (Netherlands); Choi, Young Hae, E-mail: y.choi@chem.leidenuniv.nl [Natural Products Laboratory, Institute of Biology, Leiden University, 2300 RA Leiden (Netherlands)

    2013-03-05

    Highlights: ► Natural products were used as a source for deep eutectic solvents and ionic liquids. ► We define own chemical and physical properties of natural deep eutectic solvents. ► Interaction between natural deep eutectic solvents and solutes was confirmed by NMR. ► The developed natural deep eutectic solvents were applied as green media. - Abstract: Developing new green solvents is one of the key subjects in Green Chemistry. Ionic liquids (ILs) and deep eutectic solvents, thus, have been paid great attention to replace current harsh organic solvents and have been applied to many chemical processing such as extraction and synthesis. However, current ionic liquids and deep eutectic solvents have still limitations to be applied to a real chemical industry due to toxicity against human and environment and high cost of ILs and solid state of most deep eutectic solvents at room temperature. Recently we discovered that many plant abundant primary metabolites changed their state from solid to liquid when they were mixed in proper ratio. This finding made us hypothesize that natural deep eutectic solvents (NADES) play a role as alternative media to water in living organisms and tested a wide range of natural products, which resulted in discovery of over 100 NADES from nature. In order to prove deep eutectic feature the interaction between the molecules was investigated by nuclear magnetic resonance spectroscopy. All the tested NADES show clear hydrogen bonding between components. As next step physical properties of NADES such as water activity, density, viscosity, polarity and thermal properties were measured as well as the effect of water on the physical properties. In the last stage the novel NADES were applied to the solubilization of wide range of biomolecules such as non-water soluble bioactive natural products, gluten, starch, and DNA. In most cases the solubility of the biomolecules evaluated in this study was greatly higher than water. Based on the

  10. Magnetic resonance direct thrombus imaging at 3 T field strength in patients with lower limb deep vein thrombosis: a feasibility study

    Energy Technology Data Exchange (ETDEWEB)

    Schmitz, S.A. [Imaging Sciences Department, Imperial College, Hammersmith Hospital, London (United Kingdom); O' Regan, D.P. [Imaging Sciences Department, Imperial College, Hammersmith Hospital, London (United Kingdom)]. E-mail: declan.oregan@imperial.ac.uk; Gibson, D. [Imaging Department, Hammersmith Hospitals NHS Trust, London (United Kingdom); Cunningham, C. [Imaging Department, Hammersmith Hospitals NHS Trust, London (United Kingdom); Fitzpatrick, J. [Imaging Sciences Department, Imperial College, Hammersmith Hospital, London (United Kingdom); Allsop, J. [Imaging Sciences Department, Imperial College, Hammersmith Hospital, London (United Kingdom); Larkman, D.J. [Imaging Sciences Department, Imperial College, Hammersmith Hospital, London (United Kingdom); Hajnal, J.V. [Imaging Sciences Department, Imperial College, Hammersmith Hospital, London (United Kingdom)

    2006-03-15

    AIM: To investigate the feasibility of imaging lower limb deep vein thrombosis using magnetic resonance imaging (MRI) at 3.0 T magnetic field strength with an optimized a T1 magnetization prepared rapid gradient echo technique (MP-RAGE) in patients with normal volunteers as controls. MATERIALS AND METHODS: Patients with deep vein thrombosis (n=4), thrombophlebitis (n=2) and healthy volunteers (n=9) were studied. MRI of the distal thigh and upper calf was performed at 3.0 T with MP-RAGE using two pre-pulses to suppress blood and fat (flip angle 15{sup o}, echo time 5 ms, and repetition time 10 ms). A qualitative analysis was performed for detection of thrombi and image quality. Contrast-to-noise ratios were determined in thrombosed and patent veins. RESULTS: Thrombi were clearly visible as high-signal intensity structures with good suppression of the anatomical background. A blinded reader accurately diagnosed 15 out of 16 cases. The contrast-to-noise ratio measurements showed a positive contrast of thrombus over background muscle 16.9 (SD 4.3, 95% CI: 12.5-21.3) and a negative contrast of the lumen to muscle in patent veins of normal volunteers -7.8 (SD 4.3, 95% CI: -11.1 to -4.5), with p=0.0015. CONCLUSION: Thrombi generate high signal intensity at 3.0 T allowing for their direct visualization if flowing blood, stationary blood and fat are sufficiently suppressed. This preliminary data supports the development of these techniques for other vascular applications.

  11. Clean subglacial access: prospects for future deep hot-water drilling

    Science.gov (United States)

    Pearce, David; Hodgson, Dominic A.; Smith, Andrew M.; Rose, Mike; Ross, Neil; Mowlem, Matt; Parnell, John

    2016-01-01

    Accessing and sampling subglacial environments deep beneath the Antarctic Ice Sheet presents several challenges to existing drilling technologies. With over half of the ice sheet believed to be resting on a wet bed, drilling down to this environment must conform to international agreements on environmental stewardship and protection, making clean hot-water drilling the most viable option. Such a drill, and its water recovery system, must be capable of accessing significantly greater ice depths than previous hot-water drills, and remain fully operational after connecting with the basal hydrological system. The Subglacial Lake Ellsworth (SLE) project developed a comprehensive plan for deep (greater than 3000 m) subglacial lake research, involving the design and development of a clean deep-ice hot-water drill. However, during fieldwork in December 2012 drilling was halted after a succession of equipment issues culminated in a failure to link with a subsurface cavity and abandonment of the access holes. The lessons learned from this experience are presented here. Combining knowledge gained from these lessons with experience from other hot-water drilling programmes, and recent field testing, we describe the most viable technical options and operational procedures for future clean entry into SLE and other deep subglacial access targets. PMID:26667913

  12. Deep features for efficient multi-biometric recognition with face and ear images

    Science.gov (United States)

    Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

    2017-07-01

    Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

  13. 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

  14. DEEP SPACE: High Resolution VR Platform for Multi-user Interactive Narratives

    Science.gov (United States)

    Kuka, Daniela; Elias, Oliver; Martins, Ronald; Lindinger, Christopher; Pramböck, Andreas; Jalsovec, Andreas; Maresch, Pascal; Hörtner, Horst; Brandl, Peter

    DEEP SPACE is a large-scale platform for interactive, stereoscopic and high resolution content. The spatial and the system design of DEEP SPACE are facing constraints of CAVETM-like systems in respect to multi-user interactive storytelling. To be used as research platform and as public exhibition space for many people, DEEP SPACE is capable to process interactive, stereoscopic applications on two projection walls with a size of 16 by 9 meters and a resolution of four times 1080p (4K) each. The processed applications are ranging from Virtual Reality (VR)-environments to 3D-movies to computationally intensive 2D-productions. In this paper, we are describing DEEP SPACE as an experimental VR platform for multi-user interactive storytelling. We are focusing on the system design relevant for the platform, including the integration of the Apple iPod Touch technology as VR control, and a special case study that is demonstrating the research efforts in the field of multi-user interactive storytelling. The described case study, entitled "Papyrate's Island", provides a prototypical scenario of how physical drawings may impact on digital narratives. In this special case, DEEP SPACE helps us to explore the hypothesis that drawing, a primordial human creative skill, gives us access to entirely new creative possibilities in the domain of interactive storytelling.

  15. Deep erosions of the palmar aspect of the navicular bone diagnosed by standing magnetic resonance imaging.

    Science.gov (United States)

    Sherlock, C; Mair, T; Blunden, T

    2008-11-01

    Erosion of the palmar (flexor) aspect of the navicular bone is difficult to diagnose with conventional imaging techniques. To review the clinical, magnetic resonance (MR) and pathological features of deep erosions of the palmar aspect of the navicular bone. Cases of deep erosions of the palmar aspect of the navicular bone, diagnosed by standing low field MR imaging, were selected. Clinical details, results of diagnostic procedures, MR features and pathological findings were reviewed. Deep erosions of the palmar aspect of the navicular bone were diagnosed in 16 mature horses, 6 of which were bilaterally lame. Sudden onset of lameness was recorded in 63%. Radiography prior to MR imaging showed equivocal changes in 7 horses. The MR features consisted of focal areas of intermediate or high signal intensity on T1-, T2*- and T2-weighted images and STIR images affecting the dorsal aspect of the deep digital flexor tendon, the fibrocartilage of the palmar aspect, subchondral compact bone and medulla of the navicular bone. On follow-up, 7/16 horses (44%) had been subjected to euthanasia and only one was being worked at its previous level. Erosions of the palmar aspect of the navicular bone were confirmed post mortem in 2 horses. Histologically, the lesions were characterised by localised degeneration of fibrocartilage with underlying focal osteonecrosis and fibroplasia. The adjacent deep digital flexor tendon showed fibril formation and fibrocartilaginous metaplasia. Deep erosions of the palmar aspect of the navicular bone are more easily diagnosed by standing low field MR imaging than by conventional radiography. The lesions involve degeneration of the palmar fibrocartilage with underlying osteonecrosis and fibroplasia affecting the subchondral compact bone and medulla, and carry a poor prognosis for return to performance. Diagnosis of shallow erosive lesions of the palmar fibrocartilage may allow therapeutic intervention earlier in the disease process, thereby preventing

  16. 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.

  17. 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.

  18. Applications of the observational method in deep foundations

    Directory of Open Access Journals (Sweden)

    Fathi M. Abdrabbo

    2012-12-01

    Full Text Available The observational method was introduced into geotechnical engineering to improve both theories and construction techniques. This method was developed to avoid highly conservative assumptions about soil properties in geotechnical design when faced with unavoidable uncertainties of natural ground conditions. The assumptions involved in soil mechanics theories usually differ to a certain extent from reality. These assumptions can be improved by employing an observational database. Thus, theories of geotechnical engineering can be developed by observations during the construction stage. Moreover, precise management of construction work by close observations is essential to avoid risk and to alter the design if needed to match the real conditions. This paper sheds some light on the importance of the observational concept in deep foundations through three case studies. The first one demonstrates the effect of working hypotheses on design outputs of an open caisson of 22 m internal diameter. The other two case studies present the difference between theory and reality during construction stage of auger cast-in-place piles (ACIP in difficult subsoil conditions. Importance of merging the documented theories with the available observations is discussed. The study shows that working hypotheses and engineering models affect the cost and the time required for construction of deep foundations. Field observations are essential during installation of ACIP at a site. Some precautions should be considered when drilling ACIP through sandstone of inclined top surface. These precautions are mainly dependent upon field observations during construction. ACIP can be used effectively in soil formations that have galleries and caves using a cement–bentonite mixture to fill the holes of the unsuccessful piles. Finally, the paper shares a series of practical guidelines with engineering community all over the world that may assist in design and construction of deep

  19. Using Deep Convolutional Neural Networks to Predict Goal-Scoring Opportunities in Soccer

    NARCIS (Netherlands)

    Wagenaar, Martijn; Okafor, Emmanuel; Frencken, Wouter; Wiering, Marco

    2017-01-01

    Deep learning approaches have successfully been applied to several image recognition tasks, such as face, object, animal and plant classification. However, almost no research has examined on how to use the field of machine learning to predict goal-scoring opportunities in soccer from position data.

  20. 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...

  1. HFF-DeepSpace Photometric Catalogs of the 12 Hubble Frontier Fields, Clusters, and Parallels: Photometry, Photometric Redshifts, and Stellar Masses

    Science.gov (United States)

    Shipley, Heath V.; Lange-Vagle, Daniel; Marchesini, Danilo; Brammer, Gabriel B.; Ferrarese, Laura; Stefanon, Mauro; Kado-Fong, Erin; Whitaker, Katherine E.; Oesch, Pascal A.; Feinstein, Adina D.; Labbé, Ivo; Lundgren, Britt; Martis, Nicholas; Muzzin, Adam; Nedkova, Kalina; Skelton, Rosalind; van der Wel, Arjen

    2018-03-01

    We present Hubble multi-wavelength photometric catalogs, including (up to) 17 filters with the Advanced Camera for Surveys and Wide Field Camera 3 from the ultra-violet to near-infrared for the Hubble Frontier Fields and associated parallels. We have constructed homogeneous photometric catalogs for all six clusters and their parallels. To further expand these data catalogs, we have added ultra-deep K S -band imaging at 2.2 μm from the Very Large Telescope HAWK-I and Keck-I MOSFIRE instruments. We also add post-cryogenic Spitzer imaging at 3.6 and 4.5 μm with the Infrared Array Camera (IRAC), as well as archival IRAC 5.8 and 8.0 μm imaging when available. We introduce the public release of the multi-wavelength (0.2–8 μm) photometric catalogs, and we describe the unique steps applied for the construction of these catalogs. Particular emphasis is given to the source detection band, the contamination of light from the bright cluster galaxies (bCGs), and intra-cluster light (ICL). In addition to the photometric catalogs, we provide catalogs of photometric redshifts and stellar population properties. Furthermore, this includes all the images used in the construction of the catalogs, including the combined models of bCGs and ICL, the residual images, segmentation maps, and more. These catalogs are a robust data set of the Hubble Frontier Fields and will be an important aid in designing future surveys, as well as planning follow-up programs with current and future observatories to answer key questions remaining about first light, reionization, the assembly of galaxies, and many more topics, most notably by identifying high-redshift sources to target.

  2. Modelling interaction of deep groundwaters with bentonite and radionuclide speciation

    International Nuclear Information System (INIS)

    Wanner, H.

    1986-04-01

    In the safety analysis recently reported for a potential Swiss high-level waste repository, radionuclide speciation and solubility limits are calculated for expected granitic groundwater conditions. This report describes a thermodynamic model which is used to estimate the chemical composition of the pore water in compacted sodium bentonite. The model is based on available experimental data and describes the basic reactions between bentonite and groundwater by an ion-exchange model for sodium, potassium, magnesium, and calcium. The model assumes equilibrium with calcite as long as sufficient carbonates remain in the bentonite, as well as quartz saturation. The long-term situation is modelled by the assumption that the near-field of a deep repository behaves like a mixing tank. It is found that sodium bentonite will slowly be converted to calcium bentonite. The modelled composition of the pore water of compacted sodium bentonite is used to estimate radionuclide solubilities in the near-field of a deep repository. The elements considered are: uranium, neptunium, plutonium, thorium, americium, and technetium. The redox potential in the near-field is assumed to be controlled by the corrosion products of the iron canister. Except for uranium and neptunium, radionuclide solubilities turn out to be lower under the modelled near-field conditions than in the groundwater of the surrounding granitic host rock. Uranium and neptunium solubility might be higher by orders of magnitude in the near-field than in the far-field. From the chemical point of view, calcium bentonite seems to be more stable than sodium bentonite in the presence of Swiss Reference Groundwater. The use of calcium bentonite instead of sodium bentonite will improve the reliability in the prediction of source terms for radionuclide transport in the geosphere. (author)

  3. Features of the first great shale gas field in China

    Directory of Open Access Journals (Sweden)

    Ruobing Liu

    2016-04-01

    Full Text Available On the 28th of November 2012, high shale gas flow was confirmed to be 203 × 103 m3 in Longmaxi Formation; this led to the discovery of the Fuling Shale Gas Field. On the 10th of July in 2014, the verified geological reserves of the first shale gas field in China were submitted to the National Reserves Committee. Practices of exploration and development proved that the reservoirs in the Fuling Shale Gas Field had quality shales deposited in the deep-shelf; the deep-shelf had stable distribution, great thickness with no interlayers. The shale gas field was characterized by high well production, high-pressure reservoirs, good gas elements, and satisfactory effects on testing production; it's from the mid-deep depth of the quality natural gas reservoirs that bore high pressure. Comprehensive studies on the regional sedimentary background, lithology, micropore structures, geophysical properties, gas sources, features of gas reservoirs, logging responding features, and producing features of gas wells showed the following: (1 The Longmaxi Formation in the Fuling Shale Gas Field belongs to deep-shelf environment where wells developed due to organic-rich shales. (2 Thermal evolution of shales in Longmaxi Formation was moderate, nanometer-level pores developed as well. (3 The shale gas sources came from kerogens the Longmaxi Formation itself. (4 The shale gas reservoirs of the Fuling Longmaxi Formation were similar to the typical geological features and producing rules in North America. The findings proved that the shale gas produced in the Longmaxi Formation in Fuling was the conventional in-situ detained, self-generated, and self-stored shale gas.

  4. 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

  5. The origin of double peak electric field distribution in heavily irradiated silicon detectors

    CERN Document Server

    Eremin, V; Li, Z

    2002-01-01

    The first observation of double peak (DP) electric field distribution in heavily neutron irradiated (>10 sup 1 sup 4 n/cm sup 2) semiconductor detectors has been published about 6 yr ago. However, this effect was not quantitatively analyzed up to now. The explanation of the DP electric field distribution presented in this paper is based on the properties of radiation induced deep levels in silicon, which act as deep traps, and on the distribution of the thermally generated free carrier concentration in the detector bulk. In the frame of this model, the earlier published considerations on the so-called 'double junction (DJ) effect' are discussed as well. The comparison of the calculated electric field profiles at different temperatures with the experimental ones allows one to determine a set of deep levels. This set of deep levels, and their charge filling status are essential to the value and the distribution of space charge in the space charge region in the range of 305-240 K, which is actual temperature ran...

  6. 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

  7. The effect of external magnetic field changing on the correlated quantum dot dynamics

    Science.gov (United States)

    Mantsevich, V. N.; Maslova, N. S.; Arseyev, P. I.

    2018-06-01

    The non-stationary response of local magnetic moment to abrupt switching "on" and "off" of external magnetic field was studied for a single-level quantum dot (QD) coupled to a reservoir. We found that transient processes look different for the shallow and deep localized energy level. It was demonstrated that for deep energy level the relaxation rates of the local magnetic moment strongly differ in the case of magnetic field switching "on" or "off". Obtained results can be applied in the area of dynamic memory devices stabilization in the presence of magnetic field.

  8. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-06-14

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

  10. 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/ .

  11. SchNet - A deep learning architecture for molecules and materials

    Science.gov (United States)

    Schütt, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Müller, K.-R.

    2018-06-01

    Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

  12. Applications of Deep Learning and Reinforcement Learning to Biological Data.

    Science.gov (United States)

    Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

    2018-06-01

    Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

  13. Deep learning with convolutional neural networks for EEG decoding and visualization.

    Science.gov (United States)

    Schirrmeister, Robin Tibor; Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

    2017-11-01

    Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  14. Human impacts on soil carbon dynamics of deep-rooted Amazonian forests

    Science.gov (United States)

    Nepstad, Daniel C.; Stone, Thomas A.; Davidson, Eric A.

    1994-01-01

    Deforestation and logging degrade more forest in eastern and southern Amazonia than in any other region of the world. This forest alteration affects regional hydrology and the global carbon cycle, but our current understanding of these effects is limited by incomplete knowledge of tropical forest ecosystems. It is widely agreed that roots are concentrated near the soil surface in moist tropical forests, but this generalization incorrectly implies that deep roots are unimportant in water and C budgets. Our results indicate that half of the closed-canopy forests of Brazilian Amazonic occur where rainfall is highly seasonal, and these forests rely on deeply penetrating roots to extract soil water. Pasture vegetation extracts less water from deep soil than the forest it replaces, thus increasing rates of drainage and decreasing rates of evapotranspiration. Deep roots are also a source of modern carbon deep in the soil. The soils of the eastern Amazon contain more carbon below 1 m depth than is present in above-ground biomass. As much as 25 percent of this deep soil C could have annual to decadal turnover times and may be lost to the atmosphere following deforestation. We compared the importance of deep roots in a mature, evergreen forest with an adjacent man-made pasture, the most common type of vegetation on deforested land in Amazonia. The study site is near the town of Paragominas, in the Brazilian state of Para, with a seasonal rainfall pattern and deeply-weathered, kaolinitic soils that are typical for large portions of Amazonia. Root distribution, soil water extraction, and soil carbon dynamics were studied using deep auger holes and shafts in each ecosystem, and the phenology and water status of the leaf canopies were measured. We estimated the geographical distribution of deeply-rooting forests using satellite imagery, rainfall data, and field measurements.

  15. Deep learning with convolutional neural networks for EEG decoding and visualization

    Science.gov (United States)

    Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

    2017-01-01

    Abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc. PMID:28782865

  16. 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.

  17. The Key Factors Analysis of Palisades Temperature in Deep Open-pit Mine

    Science.gov (United States)

    Wang, Yuan; Du, Cuifeng; Jin, Wenbo; Wang, Puyu

    2018-01-01

    In order to study the key factors of palisades temperature field in a deep open-pit mine in the natural environment, the influence of natural factors on the palisades temperature in a deep open-pit mine were analysed based on the principle of heat transfer. Four typical places with different ways of solar radiation were selected to carry out the field test. The results show that solar radiation, atmospheric temperature, and wind speed are three main factors affecting the temperature of palisades and that the direct sunlight plays a leading role. The time period of the sun shining directly on the shady slope of the palisades is short because of blocking effect, whose temperature changes in a smaller scale. At the same time, the sun slope of the palisades suffers from the solar radiation for a long time, whose temperature changes in a larger scale, and the variation is similar to the air temperature.

  18. DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.

    Science.gov (United States)

    Kruthiventi, Srinivas S S; Ayush, Kumar; Babu, R Venkatesh

    2017-09-01

    Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.

  19. 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.

  20. 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/.

  1. High-Resolution Seafloor Mapping at A Deep-Sea Methane Seep Field with an Autonomous Underwater Vehicle

    Science.gov (United States)

    Skarke, A. D.

    2017-12-01

    A growing body of research indicates that points of seafloor gas emission, known as cold-seeps, are a common feature along many continental margins. Results from recent exploration efforts show that benthic environments at cold-seeps are characterized by extensive authigenic carbonate crusts and complex chemosynthetic communities. The seafloor morphology and geophysical properties of these locations are heterogeneous and relatively complex due to the three-dimensional structure created by carbonate buildups and dense bivalve beds. Seeps are often found clustered and the spatial extent of associated seafloor crusts and beds can reach multiple square kilometers. Here, the results of a 1.25 km2 autonomous underwater vehicle (AUV) survey of a deep-sea methane seep field with 13 vents, at a nominal depth of 1400 m, located near Veatch Canyon on the US Atlantic margin are presented. Multibeam sonar, sidescan sonar, and a sub bottom profiler on the AUV were used to make high-resolution observations of seafloor bathymetry (resolution 1m2) as well as water column, seafloor, and subsurface acoustic backscatter intensity. Additionally, a downward oriented camera was used to collect seafloor imagery coincident with acoustic observations at select locations. Acoustic results indicated the location of discrete gas plumes as well as a continuous area of elevated seafloor roughness and backscatter intensity consistent with the presence of large scale authigenic rock outcrops and extensive mussel beds, which were visually confirmed with camera imagery. Additionally, a linear area of particularly elevated seafloor roughness and acoustic backscatter intensity that lies sub-parallel to an adjacent ridge was interpreted to be controlled by underlying geologic processes such as soft sediment faulting. Automated analysis of camera imagery and coincident acoustic backscatter and bathymetry data as well as derivative metrics (e.g. slope and rugosity) was used to segment and classify bed

  2. Near-field effects of asteroid impacts in deep water

    Energy Technology Data Exchange (ETDEWEB)

    Gisler, Galen R [Los Alamos National Laboratory; Weaver, Robert P [Los Alamos National Laboratory; Gittings, Michael L [Los Alamos National Laboratory

    2009-06-11

    Our previous work has shown that ocean impacts of asteroids below 500 m in diameter do not produce devastating long-distance tsunamis. Nevertheless, a significant portion of the ocean lies close enough to land that near-field effects may prove to be the greatest danger from asteroid impacts in the ocean. Crown splashes and central jets that rise up many kilometres into the atmosphere can produce, upon their collapse, highly non-linear breaking waves that could devastate shorelines within a hundred kilometres of the impact site. We present illustrative calculations, in two and three dimensions, of such impacts for a range of asteroid sizes and impact angles. We find that, as for land impacts, the greatest dangers from oceanic impacts are the short-term near-field, and long-term atmospheric effects.

  3. 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.

  4. 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

  5. Observation of deep oscillation usage and its effectiveness on burn scars – case report

    Directory of Open Access Journals (Sweden)

    Justyna A. Pogorzelska

    2017-03-01

    Full Text Available An organism that has undergone tissue damage pursues its immediate recovery. In order to do so, it uses a dynamic and congeneric process of regeneration consisting of several phases. Currently, innovative methods are being sought influencing tissue healing. One such system is deep oscillation, which is based on an intermittent electrostatic field created between the device and the patient’s skin. It causes a unique, deep, and resonant vibration. It is a noninvasive and painless method. The aim of deep oscillation is purposeful interfering in the physiological processes of tissue trophism. In the thesis, the case of 16-month-old girl is presented, who experienced a thermal scald to her left arm and her chest. The aim of the following thesis is observation of deep oscillation use and its effectiveness in the event of newly formed burn scars that undergo remodelling and can lead to curtailment of the healing process.

  6. Emission-Line Galaxies from the PEARS Hubble Ultra Deep Field: A 2-D Detection Method and First Results

    Science.gov (United States)

    Gardner, J. P.; Straughn, Amber N.; Meurer, Gerhardt R.; Pirzkal, Norbert; Cohen, Seth H.; Malhotra, Sangeeta; Rhoads, james; Windhorst, Rogier A.; Gardner, Jonathan P.; Hathi, Nimish P.; hide

    2007-01-01

    The Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) grism PEARS (Probing Evolution And Reionization Spectroscopically) survey provides a large dataset of low-resolution spectra from thousands of galaxies in the GOODS North and South fields. One important subset of objects in these data are emission-line galaxies (ELGs), and we have investigated several different methods aimed at systematically selecting these galaxies. Here we present a new methodology and results of a search for these ELGs in the PEARS observations of the Hubble Ultra Deep Field (HUDF) using a 2D detection method that utilizes the observation that many emission lines originate from clumpy knots within galaxies. This 2D line-finding method proves to be useful in detecting emission lines from compact knots within galaxies that might not otherwise be detected using more traditional 1D line-finding techniques. We find in total 96 emission lines in the HUDF, originating from 81 distinct "knots" within 63 individual galaxies. We find in general that [0 1111 emitters are the most common, comprising 44% of the sample, and on average have high equivalent widths (70% of [0 1111 emitters having rest-frame EW> 100A). There are 12 galaxies with multiple emitting knots; several show evidence of variations in H-alpha flux in the knots, suggesting that the differing star formation properties across a single galaxy can in general be probed at redshifts approximately greater than 0.2 - 0.4. The most prevalent morphologies are large face-on spirals and clumpy interacting systems, many being unique detections owing to the 2D method described here, thus highlighting the strength of this technique.

  7. Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

    Directory of Open Access Journals (Sweden)

    Jie Wang

    2017-03-01

    Full Text Available Deep convolutional neural networks (CNNs have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.

  8. Very deep recurrent convolutional neural network for object recognition

    Science.gov (United States)

    Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

    2017-03-01

    In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

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

    Science.gov (United States)

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

    1997-05-01

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

  10. Smooth Horizonless Geometries Deep Inside the Black-Hole Regime.

    Science.gov (United States)

    Bena, Iosif; Giusto, Stefano; Martinec, Emil J; Russo, Rodolfo; Shigemori, Masaki; Turton, David; Warner, Nicholas P

    2016-11-11

    We construct the first family of horizonless supergravity solutions that have the same mass, charges, and angular momenta as general supersymmetric rotating D1-D5-P black holes in five dimensions. This family includes solutions with arbitrarily small angular momenta, deep within the regime of quantum numbers and couplings for which a large classical black hole exists. These geometries are well approximated by the black-hole solution, and in particular exhibit the same near-horizon throat. Deep in this throat, the black-hole singularity is resolved into a smooth cap. We also identify the holographically dual states in the N=(4,4) D1-D5 orbifold conformal field theory (CFT). Our solutions are among the states counted by the CFT elliptic genus, and provide examples of smooth microstate geometries within the ensemble of supersymmetric black-hole microstates.

  11. 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.

  12. Petroleum geological features and exploration prospect of deep marine carbonate rocks in China onshore: A further discussion

    Directory of Open Access Journals (Sweden)

    Zhao Wenzhi

    2014-10-01

    Full Text Available Deep marine carbonate rocks have become one of the key targets of onshore oil and gas exploration and development for reserves replacement in China. Further geological researches of such rocks may practically facilitate the sustainable, steady and smooth development of the petroleum industry in the country. Therefore, through a deep investigation into the fundamental geological conditions of deep marine carbonate reservoirs, we found higher-than-expected resource potential therein, which may uncover large oil or gas fields. The findings were reflected in four aspects. Firstly, there are two kinds of hydrocarbon kitchens which were respectively formed by conventional source rocks and liquid hydrocarbons cracking that were detained in source rocks, and both of them can provide large-scale hydrocarbons. Secondly, as controlled by the bedding and interstratal karstification, as well as the burial and hydrothermal dolomitization, effective carbonate reservoirs may be extensively developed in the deep and ultra-deep strata. Thirdly, under the coupling action of progressive burial and annealing heating, some marine source rocks could form hydrocarbon accumulations spanning important tectonic phases, and large quantity of liquid hydrocarbons could be kept in late stage, contributing to rich oil and gas in such deep marine strata. Fourthly, large-scale uplifts were formed by the stacking of multi-episodic tectonism and oil and gas could be accumulated in three modes (i.e., stratoid large-area reservoir-forming mode of karst reservoirs in the slope area of uplift, back-flow type large-area reservoir-forming mode of buried hill weathered crust karst reservoirs, and wide-range reservoir-forming mode of reef-shoal reservoirs; groups of stratigraphic and lithologic traps were widely developed in the areas of periclinal structures of paleohighs and continental margins. In conclusion, deep marine carbonate strata in China onshore contain the conditions for

  13. Emerging subspecialties in neurology: deep brain stimulation and electrical neuro-network modulation.

    Science.gov (United States)

    Hassan, Anhar; Okun, Michael S

    2013-01-29

    Deep brain stimulation (DBS) is a surgical therapy that involves the delivery of an electrical current to one or more brain targets. This technology has been rapidly expanding to address movement, neuropsychiatric, and other disorders. The evolution of DBS has created a niche for neurologists, both in the operating room and in the clinic. Since DBS is not always deep, not always brain, and not always simply stimulation, a more accurate term for this field may be electrical neuro-network modulation (ENM). Fellowships will likely in future years evolve their scope to include other technologies, and other nervous system regions beyond typical DBS therapy.

  14. 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.

  15. A SYSTEMATIC SEARCH FOR PERIODICALLY VARYING QUASARS IN PAN-STARRS1: AN EXTENDED BASELINE TEST IN MEDIUM DEEP SURVEY FIELD MD09

    Energy Technology Data Exchange (ETDEWEB)

    Liu, T.; Gezari, S. [Department of Astronomy, University of Maryland, College Park, MD 20742 (United States); Burgett, W. [GMTO Corp, 465 N. Halstead St, Suite 250, Pasadena, CA 91107 (United States); Chambers, K.; Hodapp, K.; Huber, M.; Kudritzki, R.-P.; Magnier, E.; Tonry, J.; Wainscoat, R.; Waters, C. [Institute for Astronomy, University of Hawaii at Manoa, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States); Draper, P.; Metcalfe, N., E-mail: tingting@astro.umd.edu [Department of Physics, University of Durham, South Road, Durham DH1 3LE (United Kingdom)

    2016-12-10

    We present a systematic search for periodically varying quasars and supermassive black hole binary (SMBHB) candidates in the Pan-STARRS1 (PS1) Medium Deep Survey’s MD09 field. From a color-selected sample of 670 quasars extracted from a multi-band deep-stack catalog of point sources, we locally select variable quasars and look for coherent periods with the Lomb–Scargle periodogram. Three candidates from our sample demonstrate strong variability for more than ∼3 cycles, and their PS1 light curves are well fitted to sinusoidal functions. We test the persistence of the candidates’ apparent periodic variations detected during the 4.2 years of the PS1 survey with archival photometric data from the SDSS Stripe 82 survey or new monitoring with the Large Monolithic Imager at the Discovery Channel Telescope. None of the three periodic candidates (including PSO J334.2028+1.4075) remain persistent over the extended baseline of 7–14 years, corresponding to a detection rate of <1 in 670 quasars in a search area of ≈5 deg{sup 2}. Even though SMBHBs should be a common product of the hierarchal growth of galaxies, and periodic variability in SMBHBs has been theoretically predicted, a systematic search for such signatures in a large optical survey is strongly limited by its temporal baseline and the “red noise” associated with normal quasar variability. We show that follow-up long-term monitoring (≳5 cycles) is crucial to our search for these systems.

  16. A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis.

    Directory of Open Access Journals (Sweden)

    Yanping Xue

    Full Text Available Hip Osteoarthritis (OA is a common disease among the middle-aged and elderly people. Conventionally, hip OA is diagnosed by manually assessing X-ray images. This study took the hip joint as the object of observation and explored the diagnostic value of deep learning in hip osteoarthritis. A deep convolutional neural network (CNN was trained and tested on 420 hip X-ray images to automatically diagnose hip OA. This CNN model achieved a balance of high sensitivity of 95.0% and high specificity of 90.7%, as well as an accuracy of 92.8% compared to the chief physicians. The CNN model performance is comparable to an attending physician with 10 years of experience. The results of this study indicate that deep learning has promising potential in the field of intelligent medical image diagnosis practice.

  17. 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.

  18. Relationship between deep structure and oil-gas in the eastern Tarim Basin

    Science.gov (United States)

    Yu, Changqing; Qu, Chen; Han, Jianguang

    2017-04-01

    The Tarim Basin is a large composite superimposed basin which developed in the Presinian continental basement. It is an important area for oil and gas replacement in China. In the eastern part of Tarim Basin, the exploration and research degree is very low and less system, especially in the study of tectonic evolution and physical property change. Basing on the study of geophysics, drilling and regional geological data in this area, analysis of comprehensive geophysical, geological and geophysical analysis comparison are lunched by new methods and new technology of geophysical exploration. Fault, tectonic evolution and change of deep character in the eastern Tarim Basin are analyzed in system. Through in-depth study and understanding of the deep structure and physical changes of the eastern region, we obtain the fault characteristics in the study area and the deep structure and physical change maps to better guide the oil and gas exploration in this area. The east area is located in the eastern Tarim Basin, west from the Garr Man depression, Well Kunan 1 - Well Gucheng 4 line to the East, north to Kuruketage uplift group near Qunke 1 wells, south to Cherchen fault zone, east to Lop Nor depression, an area of about 9 * 104 square kilometres, Including the East of Garr Man sag, Yingjisu depression, Kongquehe slope, Tadong low uplift and the Lop Nor uplift, five two grade tectonic units. The east area of Tarim is belonging to Tarim plate. It changes with the evolution of the Tarim plate. The Tarim plate is closely related to the collision between the Yining - the Junggar plate, the Siberia plate and the southern Qiangtang - the central Kunlun plate. Therefore, it creates a complex tectonic pattern in the eastern Tarim basin. Earth electromagnetic, gravity, deep seismic and other geophysical data are processed by a new generation of geophysical information theory and method, including multi-scale inversion of potential field inversion (Hou and Yang, 2011), 3D

  19. Physical and Numerical Modeling of the Stability of Deep Caverns in Tahe Oil Field in China

    Directory of Open Access Journals (Sweden)

    Chao Wang

    2017-06-01

    Full Text Available Cave collapses emerge during the process of oil reservoir development, seriously affecting oil production. To reveal the collapse and failure mechanism of the carbonate cavern with a buried depth of 5600 m in Tahe Oil Field, using a self-developed ultra-high pressure model test system with the intelligent numerical control function, the model simulation material of carbonate rocks developed to carry out the 3D geo-mechanical model test. The model test and numerical results indicate that: (1 collapse and failure mechanism of the deep-buried caves mainly involve the failure mode of tensile shear. The rupture plane on the side wall is approximately parallel to the direction of maximum principal compressive stress. The V-type tension and split rupture plane then emerges. (2 In the process of forming holes in the model caverns, micro cracks are generated at the foot of the left and right side walls of the caverns, and the roof panels are constantly moving downward. The shorter the distance to the cave wall, the severer the destructiveness of the surrounding rocks will be. (3 The displacement of the top of the model cavern is relatively large and uniform, indicating that the cave roof moves downward as a whole. The area of the cavity suffering damage is 2.3 times as large as the cave span. The research results in this paper lay a solid test basis for revealing the cave collapse and failure mechanism in super depth.

  20. A field strategy to monitor radioactivity associated with investigation derived wastes returned from deep drilling sites

    International Nuclear Information System (INIS)

    Rego, J.H.; Smith, D.K.; Friensehner, A.V.

    1995-01-01

    The U.S. Department of Energy, Nevada Operations Office, Underground Test Area Operable Unit (UGTA) is drilling deep (>1500m) monitoring wells that penetrate both unsaturated (vadose) and saturated zones potentially contaminated by sub-surface nuclear weapons testing at the Nevada Test Site, Nye County, Nevada. Drill site radiological monitoring returns data on drilling effluents to make informed management decisions concerning fluid management. Because of rapid turn-around required for on-site monitoring, a representative sample will be analyzed simultaneously for α, β and γ emitters by instrumentation deployed on-site. For the purposes of field survey, accurate and precise data is returned, in many cases, with minimal sample treatment. A 30% efficient high purity germanium detector and a discriminating liquid scintillation detector are being evaluated for γ and α/β monitoring respectively. Implementation of these detector systems complements a successful on-site tritium monitoring program. Residual radioactivity associated with underground nuclear tests include tritium, activation products, fission products and actinides. Pulse shape discrimination (PSD) is used in α/β liquid scintillation counting and is a function of the time distribution of photon emission. In particular, we hope to measure 241 Am produced from 241 Pu by β decay. Because 241 Pu is depleted in fissile bomb fuels, maximum PSD resolution will be required. The high purity germanium detector employs a multichannel analyzer to count gamma emitting radionuclides; we will designate specific window configurations to selectively monitor diagnostic fission product radionuclides (i.e., 137 Cs)

  1. Uranium facilitated transport by water-dispersible colloids in field and soil columns

    International Nuclear Information System (INIS)

    Crancon, P.; Pili, E.; Charlet, L.

    2010-01-01

    The transport of uranium through a sandy podzolic soil has been investigated in the field and in column experiments. Field monitoring, numerous years after surface contamination by depleted uranium deposits, revealed a 20 cm deep uranium migration in soil. Uranium retention in soil is controlled by the 238 U initially present in the soil column and 233 U brought by input solution are desorbed. The mobilization process observed experimentally after a drop of ionic strength may account for a rapid uranium migration in the field after a rainfall event, and for the significant uranium concentrations found in deep soil horizons and in groundwater, 1 km downstream from the pollution source.

  2. Studies on wide area deep geothermal resources reservoir

    Energy Technology Data Exchange (ETDEWEB)

    None

    1977-10-01

    In order to establish techniques for the exploitation of geothermal reservoirs of large extent and deep location, the Hachimandaira field was chosen as a model. Studies were carried out using the AFMT system, thermographic, remote sensing and geothermometric methods. In the AFMT study the equipment was custom manufactured. It included a five component receiver and a transmitter with an output current of 10 A. Calculations were made for the electromagnetic fields of each transmitting source using both electric and magnetic dipoles. In the thermographic study a thermo-camera was employed to survey springs in Fukushima prefecture as well as the Ofuka springs in Akita prefecture. These studies were made with the intention of deriving correlations between surface heat flow and subterranean structure.

  3. Drag Reduction of an Airfoil Using Deep Learning

    Science.gov (United States)

    Jiang, Chiyu; Sun, Anzhu; Marcus, Philip

    2017-11-01

    We reduced the drag of a 2D airfoil by starting with a NACA-0012 airfoil and used deep learning methods. We created a database which consists of simulations of 2D external flow over randomly generated shapes. We then developed a machine learning framework for external flow field inference given input shapes. Past work which utilized machine learning in Computational Fluid Dynamics focused on estimations of specific flow parameters, but this work is novel in the inference of entire flow fields. We further showed that learned flow patterns are transferable to cases that share certain similarities. This study illustrates the prospects of deeper integration of data-based modeling into current CFD simulation frameworks for faster flow inference and more accurate flow modeling.

  4. String fields, higher spins and number theory

    CERN Document Server

    Polyakov, Dimitri

    2018-01-01

    The book aims to analyze and explore deep and profound relations between string field theory, higher spin gauge theories and holography the disciplines that have been on the cutting edge of theoretical high energy physics and other fields. These intriguing relations and connections involve some profound ideas in number theory, which appear to be part of a unifying language to describe these connections.

  5. 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.

  6. THE SPECTRAL ENERGY DISTRIBUTIONS OF z ∼ 8 GALAXIES FROM THE IRAC ULTRA DEEP FIELDS: EMISSION LINES, STELLAR MASSES, AND SPECIFIC STAR FORMATION RATES AT 650 MYR

    Energy Technology Data Exchange (ETDEWEB)

    Labbé, I.; Bouwens, R. J.; Franx, M. [Leiden Observatory, Leiden University, NL-2300 RA Leiden (Netherlands); Oesch, P. A.; Illingworth, G. D.; Magee, D.; González, V. [UCO/Lick Observatory, University of California, Santa Cruz, CA 95064 (United States); Carollo, C. M. [Institute for Astronomy, ETH Zurich, 8092 Zurich (Switzerland); Trenti, M. [Kavli Institute for Cosmology and Institute of Astronomy, University of Cambridge, Cambridge (United Kingdom); Van Dokkum, P. G. [Department of Astronomy, Yale University, New Haven, CT 06520 (United States); Stiavelli, M. [Space Telescope Science Institute, Baltimore, MD 21218 (United States)

    2013-11-10

    Using new ultradeep Spitzer/InfraRed Array Camera (IRAC) photometry from the IRAC Ultra Deep Field program, we investigate the stellar populations of a sample of 63 Y-dropout galaxy candidates at z ∼ 8, only 650 Myr after the big bang. The sources are selected from HST/ACS+WFC3/IR data over the Hubble Ultra Deep Field (HUDF), two HUDF parallel fields, and wide area data over the CANDELS/GOODS-South. The new Spitzer/IRAC data increase the coverage in [3.6] and [4.5] to ∼120h over the HUDF reaching depths of ∼28 (AB,1σ). The improved depth and inclusion of brighter candidates result in direct ≥3σ InfraRed Array Camera (IRAC) detections of 20/63 sources, of which 11/63 are detected at ≥5σ. The average [3.6]-[4.5] colors of IRAC detected galaxies at z ∼ 8 are markedly redder than those at z ∼ 7, observed only 130 Myr later. The simplest explanation is that we witness strong rest-frame optical emission lines (in particular [O III] λλ4959, 5007 + Hβ) moving through the IRAC bandpasses with redshift. Assuming that the average rest-frame spectrum is the same at both z ∼ 7 and z ∼ 8 we estimate a rest-frame equivalent width of contributing 0.56{sup +0.16}{sub -0.11} mag to the [4.5] filter at z ∼ 8. The corresponding W{sub Hα}=430{sup +160}{sub -110} Å implies an average specific star formation rate of sSFR=11{sub -5}{sup +11} Gyr{sup –1} and a stellar population age of 100{sub -50}{sup +100} Myr. Correcting the spectral energy distribution for the contribution of emission lines lowers the average best-fit stellar masses and mass-to-light ratios by ∼3 ×, decreasing the integrated stellar mass density to ρ{sup *}(z=8,M{sub UV}<-18)=0.6{sup +0.4}{sub -0.3}×10{sup 6} M{sub sun} Mpc{sup –3}.

  7. The Mechanism and Application of Deep-Hole Precracking Blasting on Rockburst Prevention

    Directory of Open Access Journals (Sweden)

    Zhenhua Ouyang

    2015-01-01

    Full Text Available The mechanism of preventing rockburst through deep-hole precracking blasting was studied based on experimental test, numerical simulation, and field testing. The study results indicate that the deep-hole precracking could change the bursting proneness and stress state of coal-rock mass, thereby preventing the occurrence of rockburst. The bursting proneness of the whole composite structure could be weakened by the deep-hole precracking blasting. The change of stress state in the process of precracking blasting is achieved in two ways: (1 artificially break the roof apart, thus weakening the continuity of the roof strata, effectively inducing the roof caving while reducing its impact strength; and (2 the dynamic shattering and air pressure generated by the blasting can structurally change the properties of the coal-rock mass by mitigating the high stress generation and high elastic energy accumulation, thus breaking the conditions of energy transfer and rock burst occurrence.

  8. Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT.

    Science.gov (United States)

    Han, Yoseob; Ye, Jong Chul

    2018-06-01

    X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U-Net variants such as dual frame and tight frame U-Nets satisfy the so-called frame condition which makes them better for effective recovery of high frequency edges in sparse-view CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.

  9. Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

    Science.gov (United States)

    Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

    2017-05-25

    Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

  10. Exploring the Earth Using Deep Learning Techniques

    Science.gov (United States)

    Larraondo, P. R.; Evans, B. J. K.; Antony, J.

    2016-12-01

    Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from

  11. State of knowledge on potential risks, impacts and disturbances related to deep geothermal energy - Study report 10/07/2017

    International Nuclear Information System (INIS)

    Gombert, Philippe; Lahaie, Franz; Cherkaoui, Auxane; Farret, Regis; Franck, Christian; Bigarre, Pascal; Pokryszka, Zbigniew

    2017-01-01

    Deep geothermal is a renewable and non-intermittent source of energy that can contribute to the world transition for a less carbon-intensive and greenhouse gas-emitting energy mix. Only a small part of the worldwide geothermal potential has been exploited so far and many countries, including France, are aiming for a fast growing of this industry in the next decades. Like most industrial activities, deep geothermal energy shows potential local inconveniences and possible risks for the safety of persons and of the environment. Preventing and managing those risks is of utmost importance to ensure that deep geothermal development is fully compatible with the needs and expectations of citizens, especially those of neighboring inhabitants. Indeed, in the past years, concerns have been raised by local populations regarding the development of some deep geothermal projects, especially in the field of high temperature geothermal, based on the risks related to this industry. This report is intended as a scientific and objective contribution to this matter. It aims to present, in a factual and documented way, the state of knowledge on the risks, impacts and potential inconveniences associated with deep geothermal energy. In addition to the scientific literature, it is based on lessons from incidents or accidents in this field of activity. It also makes use of INERIS expertise in the field of risks related to other sectors of activity dealing with underground operations and geo-resources, especially oil wells drilling, to provide a distanced view of deep geothermal technologies. Main lessons learned from this work are provided in the synthesis chapter ending the document. It includes a global and comparative analysis of the risks, impacts or potential inconveniences identified in this sector. Considering the large amount of published works related to this field of this industry both in the research and engineering areas, the authors do not claim to be exhaustive. They tried to

  12. 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.

  13. A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks

    Science.gov (United States)

    Mohan, Arvind; Gaitonde, Datta

    2017-11-01

    Reduced Order Modeling (ROM) can be used as surrogates to prohibitively expensive simulations to model flow behavior for long time periods. ROM is predicated on extracting dominant spatio-temporal features of the flow from CFD or experimental datasets. We explore ROM development with a deep learning approach, which comprises of learning functional relationships between different variables in large datasets for predictive modeling. Although deep learning and related artificial intelligence based predictive modeling techniques have shown varied success in other fields, such approaches are in their initial stages of application to fluid dynamics. Here, we explore the application of the Long Short Term Memory (LSTM) neural network to sequential data, specifically to predict the time coefficients of Proper Orthogonal Decomposition (POD) modes of the flow for future timesteps, by training it on data at previous timesteps. The approach is demonstrated by constructing ROMs of several canonical flows. Additionally, we show that statistical estimates of stationarity in the training data can indicate a priori how amenable a given flow-field is to this approach. Finally, the potential and limitations of deep learning based ROM approaches will be elucidated and further developments discussed.

  14. THE DEEP2 GALAXY REDSHIFT SURVEY: DESIGN, OBSERVATIONS, DATA REDUCTION, AND REDSHIFTS

    International Nuclear Information System (INIS)

    Newman, Jeffrey A.; Cooper, Michael C.; Davis, Marc; Faber, S. M.; Guhathakurta, Puragra; Koo, David C.; Phillips, Andrew C.; Conroy, Charlie; Harker, Justin J.; Lai, Kamson; Coil, Alison L.; Dutton, Aaron A.; Finkbeiner, Douglas P.; Gerke, Brian F.; Rosario, David J.; Weiner, Benjamin J.; Willmer, C. N. A.; Yan Renbin; Kassin, Susan A.; Konidaris, N. P.

    2013-01-01

    We describe the design and data analysis of the DEEP2 Galaxy Redshift Survey, the densest and largest high-precision redshift survey of galaxies at z ∼ 1 completed to date. The survey was designed to conduct a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude M B = –20 at z ∼ 1 via ∼90 nights of observation on the Keck telescope. The survey covers an area of 2.8 deg 2 divided into four separate fields observed to a limiting apparent magnitude of R AB = 24.1. Objects with z ∼ 0.7 to be targeted ∼2.5 times more efficiently than in a purely magnitude-limited sample. Approximately 60% of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets that fail to yield secure redshifts are blue objects that lie beyond z ∼ 1.45, where the [O II] 3727 Å doublet lies in the infrared. The DEIMOS 1200 line mm –1 grating used for the survey delivers high spectral resolution (R ∼ 6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the DEEP2 DEIMOS data reduction pipelines. Extensive details are provided on object selection, mask design, biases in target selection and redshift measurements, the spec2d two-dimensional data-reduction pipeline, the spec1d automated redshift pipeline, and the zspec visual redshift verification process, along with examples of instrumental signatures or other artifacts that in some cases remain after data reduction. Redshift errors and catastrophic failure rates are assessed through more than 2000 objects with duplicate

  15. 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...

  16. Deep learning enhanced mobile-phone microscopy

    KAUST Repository

    Rivenson, Yair

    2017-12-12

    Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy and produce spatial and spectral distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field. After training a convolutional neural network, we successfully imaged various samples, including blood smears, histopathology tissue sections, and parasites, where the recorded images were highly compressed to ease storage and transmission for telemedicine applications. This method is applicable to other low-cost, aberrated imaging systems, and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications.

  17. THE MULTIWAVELENGTH SURVEY BY YALE-CHILE (MUSYC): DEEP MEDIUM-BAND OPTICAL IMAGING AND HIGH-QUALITY 32-BAND PHOTOMETRIC REDSHIFTS IN THE ECDF-S

    International Nuclear Information System (INIS)

    Cardamone, Carolin N.; Van Dokkum, Pieter G.; Urry, C. Megan; Brammer, Gabriel; Taniguchi, Yoshi; Gawiser, Eric; Bond, Nicholas; Taylor, Edward; Damen, Maaike; Treister, Ezequiel; Cobb, Bethany E.; Schawinski, Kevin; Lira, Paulina; Murayama, Takashi; Saito, Tomoki; Sumikawa, Kentaro

    2010-01-01

    We present deep optical 18-medium-band photometry from the Subaru telescope over the ∼30' x 30' Extended Chandra Deep Field-South, as part of the Multiwavelength Survey by Yale-Chile (MUSYC). This field has a wealth of ground- and space-based ancillary data, and contains the GOODS-South field and the Hubble Ultra Deep Field. We combine the Subaru imaging with existing UBVRIzJHK and Spitzer IRAC images to create a uniform catalog. Detecting sources in the MUSYC 'BVR' image we find ∼40,000 galaxies with R AB 3.5. For 0.1 < z < 1.2, we find a 1σ scatter in Δz/(1 + z) of 0.007, similar to results obtained with a similar filter set in the COSMOS field. As a demonstration of the data quality, we show that the red sequence and blue cloud can be cleanly identified in rest-frame color-magnitude diagrams at 0.1 < z < 1.2. We find that ∼20% of the red sequence galaxies show evidence of dust emission at longer rest-frame wavelengths. The reduced images, photometric catalog, and photometric redshifts are provided through the public MUSYC Web site.

  18. DEEP X-RAY OBSERVATIONS OF THE YOUNG HIGH-MAGNETIC-FIELD RADIO PULSAR J1119-6127 AND SUPERNOVA REMNANT G292.2-0.5

    Energy Technology Data Exchange (ETDEWEB)

    Ng, C.-Y.; Kaspi, V. M. [Department of Physics, McGill University, Montreal, QC H3A 2T8 (Canada); Ho, W. C. G. [School of Mathematics, University of Southampton, Southampton SO17 1BJ (United Kingdom); Weltevrede, P. [Jodrell Bank Centre for Astrophysics, University of Manchester, Alan Turing Building, Manchester M13 9PL (United Kingdom); Bogdanov, S. [Columbia Astrophysics Laboratory, Columbia University, 550 West 120th Street, New York, NY 10027 (United States); Shannon, R. [CSIRO Astronomy and Space Sciences, Australia Telescope National Facility, Marsfield, NSW 2210 (Australia); Gonzalez, M. E., E-mail: ncy@physics.mcgill.ca [Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1 (Canada)

    2012-12-10

    High-magnetic-field radio pulsars are important transition objects for understanding the connection between magnetars and conventional radio pulsars. We present a detailed study of the young radio pulsar J1119-6127, which has a characteristic age of 1900 yr and a spin-down-inferred magnetic field of 4.1 Multiplication-Sign 10{sup 13} G, and its associated supernova remnant G292.2-0.5, using deep XMM-Newton and Chandra X-ray Observatory exposures of over 120 ks from each telescope. The pulsar emission shows strong modulation below 2.5 keV with a single-peaked profile and a large pulsed fraction of 0.48 {+-} 0.12. Employing a magnetic, partially ionized hydrogen atmosphere model, we find that the observed pulse profile can be produced by a single hot spot of temperature 0.13 keV covering about one-third of the stellar surface, and we place an upper limit of 0.08 keV for an antipodal hot spot with the same area. The non-uniform surface temperature distribution could be the result of anisotropic heat conduction under a strong magnetic field, and a single-peaked profile seems common among high-B radio pulsars. For the associated remnant G292.2-0.5, its large diameter could be attributed to fast expansion in a low-density wind cavity, likely formed by a Wolf-Rayet progenitor, similar to two other high-B radio pulsars.

  19. 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)

  20. Positron deep-level transient spectroscopy in semi-insulating-GaAs using the positron velocity transient method

    International Nuclear Information System (INIS)

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

    2001-01-01

    Recently a new semiconductor defect spectroscopy, namely positron deep level transient spectroscopy (PDLTS) has been proposed that combines the energy selectivity of deep level transient spectroscopy with the structural sensitivity of positron annihilation spectroscopy. This paper focuses on one variant of PDLTS, namely positron velocity PDLTS, which has no sensitivity towards vacancy defects but nevertheless is useful in studying deep levels in semi-insulators. In the present study the electric field within the depletion region of semi-insulating GaAs is monitored through the measurement of the small Doppler shift in the annihilation radiation that comes from this region as a result of positron drift. The drift is the result of an increasing electric field produced by space charge building up from ionizing deep level defects. Doppler shift transients are measured between 50-300 K. The EL2 level emission transients are clearly seen at temperatures around 300 K that yield E C -0.78±0.08eV for the energy of EL2. The EL2 electron capture rate is found to have an activation energy of 0.61±0.08eV which most probably arises from freeze out of conduction electrons. We find the surprising result that emission and capture transients can be seen at temperatures below 200 K. Possible reasons for these transients are discussed. (orig.)

  1. Using Gaia as an Astrometric Tool for Deep Ground-based Surveys

    Science.gov (United States)

    Casetti-Dinescu, Dana I.; Girard, Terrence M.; Schriefer, Michael

    2018-04-01

    Gaia DR1 positions are used to astrometrically calibrate three epochs' worth of Subaru SuprimeCam images in the fields of globular cluster NGC 2419 and the Sextans dwarf spheroidal galaxy. Distortion-correction ``maps'' are constructed from a combination of offset dithers and reference to Gaia DR1. These are used to derive absolute proper motions in the field of NGC 2419. Notably, we identify the photometrically-detected Monoceros structure in the foreground of NGC 2419 as a kinematically-cold population of stars, distinct from Galactic-field stars. This project demonstrates the feasibility of combining Gaia with deep, ground-based surveys, thus extending high-quality astrometry to magnitudes beyond the limits of Gaia.

  2. 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.

  3. Rhythms and community dynamics of a hydrothermal tubeworm assemblage at main endeavour field - a multidisciplinary deep-sea observatory approach.

    Directory of Open Access Journals (Sweden)

    Daphne Cuvelier

    Full Text Available The NEPTUNE cabled observatory network hosts an ecological module called TEMPO-mini that focuses on hydrothermal vent ecology and time series, granting us real-time access to data originating from the deep sea. In 2011-2012, during TEMPO-mini's first deployment on the NEPTUNE network, the module recorded high-resolution imagery, temperature, iron (Fe and oxygen on a hydrothermal assemblage at 2186 m depth at Main Endeavour Field (North East Pacific. 23 days of continuous imagery were analysed with an hourly frequency. Community dynamics were analysed in detail for Ridgeia piscesae tubeworms, Polynoidae, Pycnogonida and Buccinidae, documenting faunal variations, natural change and biotic interactions in the filmed tubeworm assemblage as well as links with the local environment. Semi-diurnal and diurnal periods were identified both in fauna and environment, revealing the influence of tidal cycles. Species interactions were described and distribution patterns were indicative of possible microhabitat preference. The importance of high-resolution frequencies (<1 h to fully comprehend rhythms in fauna and environment was emphasised, as well as the need for the development of automated or semi-automated imagery analysis tools.

  4. Phylogenetic Paleoecology: Tree-Thinking and Ecology in Deep Time.

    Science.gov (United States)

    Lamsdell, James C; Congreve, Curtis R; Hopkins, Melanie J; Krug, Andrew Z; Patzkowsky, Mark E

    2017-06-01

    The new and emerging field of phylogenetic paleoecology leverages the evolutionary relationships among species to explain temporal and spatial changes in species diversity, abundance, and distribution in deep time. This field is poised for rapid progress as knowledge of the evolutionary relationships among fossil species continues to expand. In particular, this approach will lend new insights to many of the longstanding questions in evolutionary biology, such as: the relationships among character change, ecology, and evolutionary rates; the processes that determine the evolutionary relationships among species within communities and along environmental gradients; and the phylogenetic signal underlying ecological selectivity in background and mass extinctions and in major evolutionary radiations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Searching for the 3.5 keV Line in the Deep Fields with Chandra: The 10 Ms Observations

    Science.gov (United States)

    Cappelluti, Nico; Bulbul, Esra; Foster, Adam; Natarajan, Priyamvada; Urry, Megan C.; Bautz, Mark W.; Civano, Francesca; Miller, Eric; Smith, Randall K.

    2018-02-01

    We report a systematic search for an emission line around 3.5 keV in the spectrum of the cosmic X-ray background using a total of ∼10 Ms Chandra observations toward the COSMOS Legacy and Extended Chandra Deep Field South survey fields. We find marginal evidence of a feature at an energy of ∼3.51 keV with a significance of 2.5–3σ, depending on the choice of statistical treatment. The line intensity is best fit at (8.8 ± 2.9) × 10‑7 ph cm‑2 s‑1 when using a simple Δχ 2 or {10.2}-0.4+0.2× {10}-7 ph cm‑2 s‑1 when Markov chain Monte Carlo is used. Based on our knowledge of Chandra and the reported detection of the line by other instruments, an instrumental origin for the line remains unlikely. We cannot, however, rule out a statistical fluctuation, and in that case our results provide a 3σ upper limit at 1.85 × 10‑6 ph cm‑2 s‑1. We discuss the interpretation of this observed line in terms of the iron line background, S XVI charge exchange, as well as potentially being from sterile neutrino decay. We note that our detection is consistent with previous measurements of this line toward the Galactic center and can be modeled as the result of sterile neutrino decay from the Milky Way for the dark matter distribution modeled as a Navarro–Frenk–White profile. For this case, we estimate a mass m ν ∼ 7.01 keV and a mixing angle sin2(2θ) = (0.83–2.75) × 10‑10. These derived values are in agreement with independent estimates from galaxy clusters, the Galactic center, and M31.

  6. Research on the usage of a deep sea fast reactor

    Energy Technology Data Exchange (ETDEWEB)

    Otsubo, Akira; Kowata, Yasuki [Power Reactor and Nuclear Fuel Development Corp., Oarai, Ibaraki (Japan). Oarai Engineering Center

    1997-09-01

    Many new types of fast reactors have been studied in PNC. A deep sea fast reactor has the highest realization probability of the reactors studied because its development is desired by many specialists of oceanography, meteorology, deep sea bottom oil field, seismology and so on and because the development does not cost big budget and few technical problems remain to be solved. This report explains the outline and the usage of the reactor of 40 kWe and 200 to 400 kWe. The reactor can be used as a power source at an unmanned base for long term climate prediction and the earth science and an oil production base in a deep sea region. On the other hand, it is used for heat and electric power supply to a laboratory in the polar region. In future, it will be used in the space. At the present time, a large FBR development plan does not proceed successfully and a realization goal time of FBR has gone later and later. We think that it is the most important to develop the reactor as fast as possible and to plant a fast reactor technique in our present society. (author)

  7. Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model

    Directory of Open Access Journals (Sweden)

    Bang Liu

    2018-01-01

    Full Text Available In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring.

  8. Dimensioning of lining galleries in deep clays

    International Nuclear Information System (INIS)

    Bernaud, D.; Rousset, G.

    1991-01-01

    The aim of the work presented in this report is to study the mechanical behaviour of lining galleries in deep clays. This text constitutes a part of the researches on the feasibility of a geological disposal of radioactive waste, which the scope is to assure the gallery long term stabilization and also to optimize its dimensioning. In particular, we are interested here in the study of a closure controlled lining, that constitutes a direct application of the convergence-confinement method, especially well fitted to deep clays. The presentation and interpretation of the convergence controlled lining test, which was performed in the experimental gallery of Mol in Belgium, is given in this report. The instrumentation was conceived in order to find out the stress field exerced by the rockmass on the lining, the internal stress field inside the lining and the gallery closure. The analysis of all measurements results, obtained between november 1987 and December 1989, shows that they are all in good agreement and that the lining design was well chosen. Two years after the gallery construction, the average closure is of the order of 2% and the average confinement pressure is about 1.6 MPa (the third of the lithostatic pressure). The time dependent effects of the rockmass are very well modelled by the non linear elasto-viscoplastic law developed at L.M.S. with the laboratory tests. The elastic-plastic model of the lining are shown to be well fitted to simulate the sliding of the ribs. Finally, the numerical results have shown a very good agreement with the measurements results

  9. 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.

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

    DEFF Research Database (Denmark)

    Rocchi, Irene; Gragnano, Carmine Gerardo; Govoni, Laura

    2018-01-01

    In situ measurements of soil suction and water content in deep soil layers still represent an experimental challenge. Mostly developed within agriculture related disciplines, field techniques for the identification of soil retention behaviour have been so far employed in the geotechnical context ...

  11. Avoiding Internal Capsule Stimulation With a New Eight-Channel Steering Deep Brain Stimulation Lead

    NARCIS (Netherlands)

    van Dijk, Kees J.; Verhagen, Rens; Bour, Lo J.; Heida, Ciska; Veltink, Peter H.

    2017-01-01

    Objective: Novel deep brain stimulation (DBS) lead designs are currently entering the market, which are hypothesized to provide a way to steer the stimulation field away from neural populations responsible for side effects and towards populations responsible for beneficial effects. The objective of

  12. CANDELS: The Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey

    Science.gov (United States)

    Grogin, Norman A.; Koekemoer, anton M.; Faber, S. M.; Ferguson, Henry C.; Kocevski, Dale D.; Riess, Adam G.; Acquaviva, Viviana; Alexander, David M.; Almaini, Omar; Ashby, Matthew L. N.; hide

    2011-01-01

    The Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) is designed to document the first third of galactic evolution, from z approx. 8 - 1.5. It will image > 250,000 distant galaxies using three separate cameras on the Hubble Space Tele8cope, from the mid-UV to near-IR, and will find and measure Type Ia supernovae beyond z > 1.5 to test their accuracy as standard candles for cosmology. Five premier multi-wavelength sky regions are selected, each with extensive ancillary data. The use of five widely separated fields mitigates cosmic variance and yields statistically robust and complete samples of galaxies down to a stellar mass of 10(exp 9) solar mass to z approx. 2, reaching the knee of the UV luminosity function of galaxies to z approx. 8. The survey covers approximately 800 square arc minutes and is divided into two parts. The CANDELS/Deep survey (5(sigma) point-source limit H =27.7mag) covers approx. 125 square arcminutes within GOODS-N and GOODS-S. The CANDELS/Wide survey includes GOODS and three additional fields (EGS, COSMOS, and UDS) and covers the full area to a 50(sigma) point-source limit of H ? or approx. = 27.0 mag. Together with the Hubble Ultradeep Fields, the strategy creates a three-tiered "wedding cake" approach that has proven efficient for extragalactic surveys. Data from the survey are non-proprietary and are useful for a wide variety of science investigations. In this paper, we describe the basic motivations for the survey, the CANDELS team science goals and the resulting observational requirements, the field selection and geometry, and the observing design.

  13. Extensive young silicic volcanism produces large deep submarine lava flows in the NE Lau Basin

    Science.gov (United States)

    Embley, Robert W.; Rubin, Kenneth H.

    2018-04-01

    New field observations reveal that extensive (up to 402 km2) aphyric, glassy dacite lavas were erupted at multiple sites in the recent past in the NE Lau basin, located about 200 km southwest of Samoa. This discovery of volumetrically significant and widespread submarine dacite lava flows extends the domain for siliceous effusive volcanism into the deep seafloor. Although several lava flow fields were discovered on the flank of a large silicic seamount, Niuatahi, two of the largest lava fields and several smaller ones ("northern lava flow fields") were found well north of the seamount. The most distal portion of the northernmost of these fields is 60 km north of the center of Niuatahi caldera. We estimate that lava flow lengths from probable eruptive vents to the distal ends of flows range from a few km to more than 10 km. Camera tows on the shallower, near-vent areas show complex lava morphology that includes anastomosing tube-like pillow flows and ropey surfaces, endogenous domes and/or ridges, some with "crease-like" extrusion ridges, and inflated lobes with extrusion structures. A 2 × 1.5 km, 30-m deep depression could be an eruption center for one of the lava flow fields. The Lau lava flow fields appear to have erupted at presumptive high effusion rates and possibly reduced viscosity induced by presumptive high magmatic water content and/or a high eruption temperature, consistent with both erupted composition ( 66% SiO2) and glassy low crystallinity groundmass textures. The large areal extent (236 km2) and relatively small range of compositional variation ( σ = 0.60 for wt% Si02%) within the northern lava flow fields imply the existence of large, eruptible batches of differentiated melt in the upper mantle or lower crust of the NE Lau basin. At this site, the volcanism could be controlled by deep crustal fractures caused by the long-term extension in this rear-arc region. Submarine dacite flows exhibiting similar morphology have been described in ancient

  14. Spitzer IRAC Confirmation of z850-Dropout Galaxies in the Hubble Ultra Deep Field: Stellar Masses and Ages at z ~ 7

    Science.gov (United States)

    Labbé, Ivo; Bouwens, Rychard; Illingworth, G. D.; Franx, M.

    2006-10-01

    Using Spitzer IRAC mid-infrared imaging from the Great Observatories Origins Deep Survey, we study z850-dropout sources in the Hubble Ultra Deep Field. After carefully removing contaminating flux from foreground sources, we clearly detect two z850 dropouts at 3.6 and 4.5 μm, while two others are marginally detected. The mid-infrared fluxes strongly support their interpretation as galaxies at z~7, seen when the universe was only 750 Myr old. The IRAC observations allow us for the first time to constrain the rest-frame optical colors, stellar masses, and ages of the highest redshift galaxies. Fitting stellar population models to the spectral energy distributions, we find photometric redshifts in the range 6.7-7.4, rest-frame colors U-V=0.2-0.4, V-band luminosities LV=(0.6-3)×1010 Lsolar, stellar masses (1-10)×109 Msolar, stellar ages 50-200 Myr, star formation rates up to ~25 Msolar yr-1, and low reddening AV~8, during the era of cosmic reionization, but the star formation rate density derived from their stellar masses and ages is not nearly sufficient to reionize the universe. The simplest explanation for this deficiency is that lower mass galaxies beyond our detection limit reionized the universe. Based on observations with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under NASA contract 1407. Support for this work was provided by NASA through contract 125790 issued by JPL/Caltech. Based on observations with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Based on service mode observations collected at the European Southern Observatory, Paranal, Chile (ESO program 073.A-0764A).

  15. Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

    Directory of Open Access Journals (Sweden)

    Xiu Jin

    2018-03-01

    Full Text Available Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN. After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery.

  16. DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

    Science.gov (United States)

    Kalia, S.; Li, S.; Ganguly, S.; Nemani, R. R.

    2017-12-01

    Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remotesensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud/shadow mask from geostationary satellite data iscritical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds, which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classifycloud/shadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoder-decoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multi-spectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

  17. 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.

  18. Analysis on deep metallogenic trace and simulation experiment in xiangshan large-scale volcanic hydrothermal type uranium deposit

    International Nuclear Information System (INIS)

    Liu Zhengyi; Liu Zhangyue; Wen Zhijian; Du Letian

    2010-01-01

    Based on series experiments on field geologic analysis, and associated with deep metallogenic trace experiment model transformed from establishment of field deep metallogenic trace model, this paper come to the conclusion that distribution coefficients of U and Th first domestic from the magmatic experiment, and then discuss the geochemical behaviors of U, Th, K during magmatic evolution stage. The experiment shows that close relationship between U and Na during the hydrothermal alteration stage; and relationship between U and K during metallogenic stage, which prove that U and K are incompatible and regularity of variation between K and Na. The conclusion of uranium dissolving ability increased accompany with pressure increasing in basement metamorphic rocks and host rocks, is obtained from this experiment, which indicate a good deep metallogenic prospect. Furthermore, Pb, Sr, Nd, He isotopes show that the volcanic rocks and basement rocks are ore source beds; due to the combined functions of volcanic hydrothermal and mantle ichor, uranium undergo multi-migration and enrichment and finally concentrated to large rich deposit. (authors)

  19. Tracing the accretion history of supermassive black holes through X-ray variability: results from the ChandraDeep Field-South

    Science.gov (United States)

    Paolillo, M.; Papadakis, I.; Brandt, W. N.; Luo, B.; Xue, Y. Q.; Tozzi, P.; Shemmer, O.; Allevato, V.; Bauer, F. E.; Comastri, A.; Gilli, R.; Koekemoer, A. M.; Liu, T.; Vignali, C.; Vito, F.; Yang, G.; Wang, J. X.; Zheng, X. C.

    2017-11-01

    We study the X-ray variability properties of distant active galactic nuclei (AGNs) in the ChandraDeep Field-South region over 17 yr, up to z ˜ 4, and compare them with those predicted by models based on local samples. We use the results of Monte Carlo simulations to account for the biases introduced by the discontinuous sampling and the low-count regime. We confirm that variability is a ubiquitous property of AGNs, with no clear dependence on the density of the environment. The variability properties of high-z AGNs, over different temporal time-scales, are most consistent with a power spectral density (PSD) described by a broken (or bending) power law, similar to nearby AGNs. We confirm the presence of an anticorrelation between luminosity and variability, resulting from the dependence of variability on black hole (BH) mass and accretion rate. We explore different models, finding that our acceptable solutions predict that BH mass influences the value of the PSD break frequency, while the Eddington ratio λEdd affects the PSD break frequency and, possibly, the PSD amplitude as well. We derive the evolution of the average λEdd as a function of redshift, finding results in agreement with measurements based on different estimators. The large statistical uncertainties make our results consistent with a constant Eddington ratio, although one of our models suggest a possible increase of λEdd with lookback time up to z ˜ 2-3. We conclude that variability is a viable mean to trace the accretion history of supermassive BHs, whose usefulness will increase with future, wide-field/large effective area X-ray missions.

  20. 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.

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

    Science.gov (United States)

    Wang, Xuefei; Wang, Mingjiang; Zhang, Qiquan

    2017-08-01

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

  2. Avoiding Internal Capsule Stimulation With a New Eight-Channel Steering Deep Brain Stimulation Lead

    NARCIS (Netherlands)

    van Dijk, Kees J.; Verhagen, Rens; Bour, Lo J.; Heida, Ciska; Veltink, Peter H.

    2017-01-01

    Novel deep brain stimulation (DBS) lead designs are currently entering the market, which are hypothesized to provide a way to steer the stimulation field away from neural populations responsible for side effects and towards populations responsible for beneficial effects. The objective of this study

  3. Discriminative deep inelastic tests of strong interaction field theories

    International Nuclear Information System (INIS)

    Glueck, M.; Reya, E.

    1979-02-01

    It is demonstrated that recent measurements of ∫ 0 1 F 2 (x, Q 2 )dx eliminate already all strong interaction field theories except QCD. A detailed study of scaling violations of F 2 (x, Q 2 ) in QCD shows their insensitivity to the gluon content of the hadron at presently measured values of Q 2 . (orig.) [de

  4. Deep-time moles: art and archiving for an uncertain radiological future

    Science.gov (United States)

    Griffiths, Dave; Illingworth, Samuel; Girling, Matt

    2017-04-01

    This paper will present Deep Field [UnclearZine], a 2016 art-science project conducted at Mol and Dessel, two neighbouring rural villages co-existing with sites for planned geological nuclear-waste disposal in eastern Belgium. Dave Griffiths produced a microfiche publication that probes and narrates the scientific testing and politics of decision-making surrounding controversial ONDRAF-NIRAS (Belgian National Agency for Radioactive Waste and Enriched Fissile Materials) projects - at CatA, a tumulus for encasing low-level waste, and HADES, a lab investigating the feasibility and safety-case for deep-time geo-burial of high-level waste in clay strata. Griffiths' field work used qualitative and experiential methods such as ethnographic interviews with state scientists and independent monitoring groups, photographic derive, and sound recording, to sense a wider Anthropogenic narrative of energy production, mineral extraction and terrorist threat. Data were then remixed through narrative responses by scientist-poet Dr Sam Illingworth (Manchester Metropolitan University) and DIY-comix artist Matt Girling. Through experimenting with archaic analogue film technology, Griffiths collaged and miniaturised content to produce an edition of microfiches that have been distributed to zine libraries internationally. This subcultural format attempts to translate the past, present and future history of the repositories as folkloric sites of conflict, complexity and unknowing, for the benefit of a far-future readership. The paper will discuss the contemporary context of epistemological uncertainty around the survival and reception of crucial nuclear-security information in the face of inevitable material, linguistic and political ruination. We suggest that place-markers, as monumental semiotic warnings to the future, along with digital archives, might also be augmented by decentralised analogue fragments that promote ongoing memorialisation of nuclear-heritage sites through

  5. Study on systemizing technology on investigation and analysis of deep underground geological environment. Japanese fiscal year, 2007 (Contract research)

    International Nuclear Information System (INIS)

    Kojima, Keiji; Ohnishi, Yuzo; Aoki, Kenji; Watanabe, Kunio; Nishigaki, Makoto; Tosaka, Hiroyuki; Shimada, Jun; Tochiyama, Osamu; Yoshida, Hidekazu; Ogata, Nobuhisa; Nishio, Kazuhisa

    2009-03-01

    In this year, the following studies were carried out with the aim of systemizing the technology on the investigation and analysis to understand the deep underground geological environment in relation to the radioactive waste disposal. (1) The study on the research and development (R and D) subjects which turned to the practical investigation and analysis of deep underground geological environment. (2) The study on the advanced technical basis for the investigation and analysis of deep underground geological environment. The results obtained from the studies are as follows: Regarding (1), the specific investigations, measurements and numerical and chemical analyses were performed particularly for research subjects: 1) engineering technology and 2) geological environment. Based on the results on (1), 3) tasks of collaboration research on intermediate area between the research fields, including the safety assessment field, were selected. Also redefinition of the NFC (Near Field Concept) were discussed. Regarding (2), based on the extracted tasks of JAEA (Japan Atomic Energy Agency) research project, the study was implemented considering previous R and D results and detailed research at the research field was carried out. This study contributed to the R and D development for its practical application. Concurrently, information exchange and discussion on the 2nd phase (the Construction Phase) of the MIU (Mizunami Underground Research Laboratory) research program were often held. (author)

  6. Electric field estimation of deep transcranial magnetic stimulation clinically used for the treatment of neuropsychiatric disorders in anatomical head models.

    Science.gov (United States)

    Parazzini, Marta; Fiocchi, Serena; Chiaramello, Emma; Roth, Yiftach; Zangen, Abraham; Ravazzani, Paolo

    2017-05-01

    Literature studies showed the ability to treat neuropsychiatric disorders using H1 coil, developed for the deep Transcranial Magnetic Stimulation (dTMS). Despite the positive results of the clinical studies, the electric field (E) distributions inside the brain induced by this coil when it is positioned on the scalp according to the clinical studies themselves are not yet precisely estimated. This study aims to characterize the E distributions due to the H1 coil in the brain of two realistic human models by computational electromagnetic techniques and to compare them with the ones due to the figure-of-8 coil, traditionally used in TMS and positioned as such to simulate the clinical experiments. Despite inter-individual differences, our results show that the dorsolateral prefrontal cortex is the region preferentially stimulated by both H1 and figure-of-8 coil when they are placed in the position on the scalp according to the clinical studies, with a more broad and non-focal distribution in the case of H1 coil. Moreover, the H1 coil spreads more than the figure-of-8 coil both in the prefrontal cortex and medial prefrontal cortex and towards some deeper brain structures and it is characterized by a higher penetration depth in the frontal lobe. This work highlights the importance of the knowledge of the electric field distribution in the brain tissues to interpret the outcomes of the experimental studies and to optimize the treatments. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

  7. Developing a Fault Management Guidebook for Nasa's Deep Space Robotic Missions

    Science.gov (United States)

    Fesq, Lorraine M.; Jacome, Raquel Weitl

    2015-01-01

    NASA designs and builds systems that achieve incredibly ambitious goals, as evidenced by the Curiosity rover traversing on Mars, the highly complex International Space Station orbiting our Earth, and the compelling plans for capturing, retrieving and redirecting an asteroid into a lunar orbit to create a nearby a target to be investigated by astronauts. In order to accomplish these feats, the missions must be imbued with sufficient knowledge and capability not only to realize the goals, but also to identify and respond to off-nominal conditions. Fault Management (FM) is the discipline of establishing how a system will respond to preserve its ability to function even in the presence of faults. In 2012, NASA released a draft FM Handbook in an attempt to coalesce the field by establishing a unified terminology and a common process for designing FM mechanisms. However, FM approaches are very diverse across NASA, especially between the different mission types such as Earth orbiters, launch vehicles, deep space robotic vehicles and human spaceflight missions, and the authors were challenged to capture and represent all of these views. The authors recognized that a necessary precursor step is for each sub-community to codify its FM policies, practices and approaches in individual, focused guidebooks. Then, the sub-communities can look across NASA to better understand the different ways off-nominal conditions are addressed, and to seek commonality or at least an understanding of the multitude of FM approaches. This paper describes the development of the "Deep Space Robotic Fault Management Guidebook," which is intended to be the first of NASA's FM guidebooks. Its purpose is to be a field-guide for FM practitioners working on deep space robotic missions, as well as a planning tool for project managers. Publication of this Deep Space Robotic FM Guidebook is expected in early 2015. The guidebook will be posted on NASA's Engineering Network on the FM Community of Practice

  8. Hot carrier degradation and a new lifetime prediction model in ultra-deep sub-micron pMOSFET

    International Nuclear Information System (INIS)

    Lei Xiao-Yi; Liu Hong-Xia; Zhang Kai; Zhang Yue; Zheng Xue-Feng; Ma Xiao-Hua; Hao Yue

    2013-01-01

    The hot carrier effect (HCE) of an ultra-deep sub-micron p-channel metal—oxide semiconductor field-effect transistor (pMOSFET) is investigated in this paper. Experiments indicate that the generation of positively charged interface states is the predominant mechanism in the case of the ultra-deep sub-micron pMOSFET. The relation of the pMOSFET hot carrier degradation to stress time (t), channel width (W), channel length (L), and stress voltage (V d ) is then discussed. Based on the relation, a lifetime prediction model is proposed, which can predict the lifetime of the ultra-deep sub-micron pMOSFET accurately and reflect the influence of the factors on hot carrier degradation directly. (condensed matter: electronic structure, electrical, magnetic, and optical properties)

  9. 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.

  10. Comment on 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study'.

    Science.gov (United States)

    Valdes, Gilmer; Interian, Yannet

    2018-03-15

    The application of machine learning (ML) presents tremendous opportunities for the field of oncology, thus we read 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study' with great interest. In this article, the authors used state of the art techniques: a pre-trained convolutional neural network (VGG-16 CNN), transfer learning, data augmentation, drop out and early stopping, all of which are directly responsible for the success and the excitement that these algorithms have created in other fields. We believe that the use of these techniques can offer tremendous opportunities in the field of Medical Physics and as such we would like to praise the authors for their pioneering application to the field of Radiation Oncology. That being said, given that the field of Medical Physics has unique characteristics that differentiate us from those fields where these techniques have been applied successfully, we would like to raise some points for future discussion and follow up studies that could help the community understand the limitations and nuances of deep learning techniques.

  11. The generalized Fenyes-Nelson model for free scalar field theory

    International Nuclear Information System (INIS)

    Davidson, M.

    1980-01-01

    The generalized Fenyes-Nelson model of quantum mechanics is applied to the free scalar field. The resulting Markov field is equivalent to the Euclidean Markov field with the times scaled by a common factor which depends on the diffusion parameter. This result is consistent with Guerra's earlier work on stochastic quantization of scalar fields. It suggests a deep connection between Euclidean field theory and the stochastic interpretation of quantum mechanics. The question of Lorentz covariance is also discussed. (orig.)

  12. 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.

  13. Time-dependent electric field in Al/CdTe/Pt detectors

    Energy Technology Data Exchange (ETDEWEB)

    Turturici, A.A. [Dipartimento di Fisica e Chimica,Università di Palermo,Viale delle Scienze, Edificio 18, Palermo 90128 (Italy); Abbene, L., E-mail: leonardo.abbene@unipa.it [Dipartimento di Fisica e Chimica,Università di Palermo,Viale delle Scienze, Edificio 18, Palermo 90128 (Italy); Franc, J.; Grill, R.; Dědič, V. [Institute of Physics of Charles University, MFF, Ke Karlovu 5, Prague 2 (Czech Republic); Principato, F. [Dipartimento di Fisica e Chimica,Università di Palermo,Viale delle Scienze, Edificio 18, Palermo 90128 (Italy)

    2015-09-21

    Al/CdTe/Pt detectors are very attractive devices for high-resolution X-ray spectroscopy, even though they suffer from bias-induced time instability (polarization). Polarization phenomena cause a progressive time-degradation of the spectroscopic performance of the detectors, due to hole trapping and detrapping from deep acceptor levels that directly control the electric field distribution. In this work we present experimental investigations on the electric field profile of planar Al/CdTe/Pt detectors by means of Pockels effect measurements. The time/temperature dependence of the electric field was investigated in a long time window (up to 10 h) and the correlation with the reverse current transients was also studied. Two energy levels (0.62 eV and 1.16 eV) of the deep hole traps were measured, in agreement with our previous results obtained through electrical and spectroscopic approaches.

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

    Science.gov (United States)

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

    2017-09-01

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

  15. Reed-Solomon Codes and the Deep Hole Problem

    Science.gov (United States)

    Keti, Matt

    In many types of modern communication, a message is transmitted over a noisy medium. When this is done, there is a chance that the message will be corrupted. An error-correcting code adds redundant information to the message which allows the receiver to detect and correct errors accrued during the transmission. We will study the famous Reed-Solomon code (found in QR codes, compact discs, deep space probes,ldots) and investigate the limits of its error-correcting capacity. It can be shown that understanding this is related to understanding the "deep hole" problem, which is a question of determining when a received message has, in a sense, incurred the worst possible corruption. We partially resolve this in its traditional context, when the code is based on the finite field F q or Fq*, as well as new contexts, when it is based on a subgroup of F q* or the image of a Dickson polynomial. This is a new and important problem that could give insight on the true error-correcting potential of the Reed-Solomon code.

  16. The Newberry Deep Drilling Project (NDDP)

    Science.gov (United States)

    Bonneville, A.; Cladouhos, T. T.; Petty, S.; Schultz, A.; Sorle, C.; Asanuma, H.; Friðleifsson, G. Ó.; Jaupart, C. P.; Moran, S. C.; de Natale, G.

    2017-12-01

    We present the arguments to drill a deep well to the ductile/brittle transition zone (T>400°C) at Newberry Volcano, central Oregon state, U.S.A. The main research goals are related to heat and mass transfer in the crust from the point of view of natural hazards and geothermal energy: enhanced geothermal system (EGS supercritical and beyond-brittle), volcanic hazards, mechanisms of magmatic intrusions, geomechanics close to a magmatic system, calibration of geophysical imaging techniques and drilling in a high temperature environment. Drilling at Newberry will bring additional information to a very promising field of research initiated by ICDP in the Deep Drilling project in Iceland with IDDP-1 on Krafla in 2009, followed by IDDP-2 on the Reykjanes ridge in 2016, and the future Japan Beyond-Brittle project and Krafla Magma Testbed. Newberry Volcano contains one of the largest geothermal heat reservoirs in the western United States, extensively studied for the last 40 years. All the knowledge and experience collected make this an excellent choice for drilling a well that will reach high temperatures at relatively shallow depths (< 5000 m). The large conductive thermal anomaly (320°C at 3000 m depth), has already been well-characterized by extensive drilling and geophysical surveys. This will extend current knowledge from the existing 3000 m deep boreholes at the sites into and through the brittle-ductile transition approaching regions of partial melt like lateral dykes. The important scientific questions that will form the basis of a full drilling proposal, have been addressed during an International Continental Drilling Program (ICDP) workshop held in Bend, Oregon in September 2017. They will be presented and discussed as well as the strategic plan to address them.

  17. Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data

    Directory of Open Access Journals (Sweden)

    Shanshan Han

    2018-04-01

    Full Text Available Over the past decade, big data, including Global Positioning System (GPS data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

  18. Permeability in fractured rocks from deep geothermal boreholes in the Upper Rhine Graben

    Science.gov (United States)

    Vidal, Jeanne; Whitechurch, Hubert; Genter, Albert; Schmittbuhl, Jean; Baujard, Clément

    2015-04-01

    Permeability in fractured rocks from deep geothermal boreholes in the Upper Rhine Graben Vidal J.1, Whitechurch H.1, Genter A.2, Schmittbuhl J.1, Baujard C.2 1 EOST, Université de Strasbourg 2 ES-Géothermie, Strasbourg The thermal regime of the Upper Rhine Graben (URG) is characterized by a series of geothermal anomalies on its French part near Soultz-sous-Forêts, Rittershoffen and in the surrounding area of Strasbourg. Sedimentary formations of these areas host oil field widely exploited in the past which exhibit exceptionally high temperature gradients. Thus, geothermal anomalies are superimposed to the oil fields which are interpreted as natural brine advection occurring inside a nearly vertical multi-scale fracture system cross-cutting both deep-seated Triassic sediments and Paleozoic crystalline basement. The sediments-basement interface is therefore very challenging for geothermal industry because most of the geothermal resource is trapped there within natural fractures. Several deep geothermal projects exploit local geothermal energy to use the heat or produce electricity and thus target permeable fractured rocks at this interface. In 1980, a geothermal exploration well was drilled close to Strasbourg down to the Permian sediments at 3220 m depth. Bottom hole temperature was estimated to 148°C but the natural flow rate was too low for an economic profitability (geothermal site by drilling five boreholes, three of which extend to 5 km depth. They identified a temperature of 200° C at 5 km depth in the granitic basement but with a variable flow rate. Hydraulic and chemical stimulation operations were applied in order to increase the initial low permeability by reactivating and dissolving sealed fractures in basement. The productivity was considerably improved and allows geothermal exploitation at 165° C and 20 L/s. Recent studies revealed the occurrences of permeable fractures in the limestones of Muschelkalk and the sandstones of Buntsandstein also. For

  19. 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.

  20. 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)