Validating and Improving Long-Term Aerosol Data Records from SeaWiFS

NASA Technical Reports Server (NTRS)

Bettenhausen, Corey; Hsu, N. Christina; Sayer, Andrew; Huang, Jinhfeng; Gautam, Ritesh

2011-01-01

Natural and anthropogenic aerosols influence the radiative balance of the Earth through direct and indirect interactions with incoming solar radiation. However, the quantification of these interactions and their ultimate effect on the Earth's climate still have large uncertainties. This is partly due to the limitations of current satellite data records which include short satellite lifetimes, retrieval algorithm uncertainty, or insufficient calibration accuracy. We have taken the first steps in overcoming this hurdle with the production and public release of an aerosol data record using the radiances from the Sea-viewing Wide Field-of-View Sensor (Sea WiFS). Sea WiFS was launched in late 1997 and provided exceptionally well-calibrated top-of-atmosphere radiance data until December 2010, more than 13 years. We have partnered this data with an expanded Deep Blue aerosol retrieval algorithm. In accordance with Deep Blue's original focus, the latest algorithm retrieves aerosol properties not only over bright desert surfaces, but also over oceans and vegetated surfaces. With this combination of a long time series and global algorithm, we can finally identify the changing patterns of regional aerosol loading and provide insight into longterm variability and trends of aerosols on regional and global scales. In this work, we provide an introduction to Sea WiFS, the current algorithms, and our aerosol data records. We have validated the data over land and ocean with ground measurements from the Aerosol Robotic Network (AERONET) and compared them with other satellites such as MODIS and MISR. Looking ahead to the next data release, we will also provide details on the implemented and planned algorithm improvements, and subsequent validation results.

Comparison Between NPP-VIIRS Aerosol Data Products and the MODIS AQUA Deep Blue Collection 6 Dataset Over Land

NASA Technical Reports Server (NTRS)

Sayer, Andrew M.; Hsu, N. C.; Bettenhausen, C.; Lee, J.; Kondragunta, S.

2013-01-01

Aerosols are small particles suspended in the atmosphere and have a variety of natural and man-made sources. Knowledge of aerosol optical depth (AOD), which is a measure of the amount of aerosol in the atmosphere, and its change over time, is important for multiple reasons. These include climate change, air quality (pollution) monitoring, monitoring hazards such as dust storms and volcanic ash, monitoring smoke from biomass burning, determining potential energy yields from solar plants, determining visibility at sea, estimating fertilization of oceans and rainforests by transported mineral dust, understanding changes in weather brought upon by the interaction of aerosols and clouds, and more. The Suomi-NPP satellite was launched late in 2011. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP is being used, among other things, to determine AOD. This study compares the VIIRS dataset to ground-based measurements of AOD, along with a state-of-the-art satellite AOD dataset (the new version of the Moderate Resolution Imaging Spectrometer Deep Blue algorithm) to assess its reliability. The Suomi-NPP satellite was launched late in 2011, carrying several instruments designed to continue the biogeophysical data records of current and previous satellite sensors. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP is being used, among other things, to determine aerosol optical depth (AOD), and related activities since launch have been focused towards validating and understanding this new dataset through comparisons with other satellite and ground-based products. The operational VIIRS AOD product is compared over land with AOD derived from Moderate Resolution Imaging Spectrometer (MODIS) observations using the Deep Blue (DB) algorithm from the forthcoming Collection 6 of MODIS data

Aerosol Retrievals Over Land and Water using Deep Blue Algorithm from SeaWiFS and MODIS during UAE2 Field Campaign

NASA Astrophysics Data System (ADS)

Hsu, N.

2005-12-01

The environment in Southwest Asia exhibits one of the most complex situations for aerosol remote sensing from space. Several air masses with different aerosol characteristics commonly converge in this region. In particular, there are often fine mode pollution particles generated from oil industry activities in the Persian Gulf colliding with coarse mode dust particles lifted from desert sources in the surrounding areas. During the course of the UAE field campaign (August-October, 2004), we provided near-real time information, calculated using the Deep Blue algorithm, of satellite aerosol optical thickness and Angstrom exponent over the Southwest Asia region, including the Arabian Peninsula, Iran, Afghanistan, Pakistan, and part of north Africa. In this paper, we will present results of aerosol characteristics retrieved from SeaWiFS and MODIS over the Arabian Peninsula, Persian Gulf, and the Arabian Sea during the UAE experiment. The spectral surface reflectance data base constructed using satellite reflectance from MODIS and SeaWiFS employed in our algorithm will be discussed. We will also compare the resulting satellite retrieved aerosol optical thickness and Angstrom exponent with those obtained from the ground based sun photometers from AERONET in the region. Finally, we will discuss the changes in shortwave and longwave fluxes at the top of atmosphere in response to changes in aerosol optical thickness (i.e. aerosol forcing).

Cyclometalated Iridium(III) Carbene Phosphors for Highly Efficient Blue Organic Light-Emitting Diodes.

PubMed

Chen, Zhao; Wang, Liqi; Su, Sikai; Zheng, Xingyu; Zhu, Nianyong; Ho, Cheuk-Lam; Chen, Shuming; Wong, Wai-Yeung

2017-11-22

Five deep blue carbene-based iridium(III) phosphors were synthesized and characterized. Interestingly, one of them can be fabricated into deep blue, sky blue and white organic light-emitting diodes (OLEDs) through changing the host materials and exciton blocking layers. These deep and sky blue devices exhibit Commission Internationale de l'Éclairage (CIE) coordinates of (0.145, 0.186) and (0.152, 0.277) with external quantum efficiency (EQE) of 15.2% and 9.6%, respectively. The EQE of the deep blue device can be further improved up to 19.0% by choosing a host with suitable energy level of its lowest unoccupied molecular orbital (LUMO).

Extending 'Deep Blue' aerosol retrieval coverage to cases of absorbing aerosols above clouds: sensitivity analysis and first case studies

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

Sayer, Andrew M.; Hsu, C.; Bettenhausen, Corey

Cases of absorbing aerosols above clouds (AAC), such as smoke or mineral dust, are omitted from most routinely-processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar

Acquiring High-Performance Deep-Blue OLED Emitters through an Unexpected Blueshift Color-Tuning Effect Induced by Electron-Donating -OMe Substituents.

PubMed

Peng, Song; Zhao, Yihuan; Fu, Caixia; Pu, Xuemei; Zhou, Liang; Huang, Yan; Lu, Zhiyun

2018-06-07

A series of blue-emissive 7-(diphenylamino)-4-phenoxycoumarin derivatives bearing -CF 3 , -OMe, or -N(Me) 2 substituents on the phenoxy subunit were synthesized. Although both the -CF 3 and -N(Me) 2 modifications were found to trigger redshifted fluorescence, the -OMe substitution was demonstrated to exert an unexpected blueshift color-tuning effect toward the deep-blue region. The reason is that the moderate electron-donating -OMe group can endow coumarins with unaltered HOMO but elevated LUMO energy levels. Moreover, the -OMe substitution was found to be beneficial to the thermal stability of these coumarins. Therefore, the trimethoxy-substituted objective compound can act as a high-performance deep-blue organic light-emitting diode (OLED) emitter, and OLED based on it emits deep-blue light with CIE coordinates of (0.148, 0.084), maximum luminance of 7800 cd m -2 , and maximum external quantum efficiency of 5.1 %. These results not only shed light on the molecular design strategy for high-performance deep-blue OLED emitters through color-tuning, but also show the perspective of coumarin derivatives as deep-blue OLED emitters. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Combing Visible and Infrared Spectral Tests for Dust Identification

NASA Technical Reports Server (NTRS)

Zhou, Yaping; Levy, Robert; Kleidman, Richard; Remer, Lorraine; Mattoo, Shana

2016-01-01

The MODIS Dark Target aerosol algorithm over Ocean (DT-O) uses spectral reflectance in the visible, near-IR and SWIR wavelengths to determine aerosol optical depth (AOD) and Angstrom Exponent (AE). Even though DT-O does have "dust-like" models to choose from, dust is not identified a priori before inversion. The "dust-like" models are not true "dust models" as they are spherical and do not have enough absorption at short wavelengths, so retrieved AOD and AE for dusty regions tends to be biased. The inference of "dust" is based on postprocessing criteria for AOD and AE by users. Dust aerosol has known spectral signatures in the near-UV (Deep blue), visible, and thermal infrared (TIR) wavelength regions. Multiple dust detection algorithms have been developed over the years with varying detection capabilities. Here, we test a few of these dust detection algorithms, to determine whether they can be useful to help inform the choices made by the DT-O algorithm. We evaluate the following methods: The multichannel imager (MCI) algorithm uses spectral threshold tests in (0.47, 0.64, 0.86, 1.38, 2.26, 3.9, 11.0, 12.0 micrometer) channels and spatial uniformity test [Zhao et al., 2010]. The NOAA dust aerosol index (DAI) uses spectral contrast in the blue channels (412nm and 440nm) [Ciren and Kundragunta, 2014]. The MCI is already included as tests within the "Wisconsin" (MOD35) Cloud mask algorithm.

Satellite Monitoring of Asian Dust Storms from SeaWiFS and MODIS: Source, Pathway, and Interannual Variability

NASA Astrophysics Data System (ADS)

Hsu, N.; Tsay, S.; Jeong, M.; Holben, B.

2006-12-01

Among the many components that contribute to air pollution, airborne mineral dust plays an important role due to its biogeochemical impact on the ecosystem and its radiative-forcing effect on the climate system. In East Asia, dust storms frequently accompany the cold and dry air masses that occur as part of spring-time cold front systems. China's capital, Beijing, and other large cities are on the primary pathway of these dust storm plumes, and their passage over such popu-lation centers causes flight delays, pushes grit through windows and doors, and forces people indoors. Furthermore, during the spring these anthropogenic and natural air pollutants, once generated over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the Pacific into the United States and beyond. In this paper, we will demonstrate the capability of a new satellite algorithm to retrieve aerosol optical thickness and single scattering albedo over bright-reflecting surfaces such as urban areas and deserts. Such retrievals have been dif-ficult to perform using previously available algorithms that use wavelengths from the mid-visible to the near IR because they have trouble separating the aerosol signal from the contribution due to the bright surface reflectance. The new algorithm, called Deep Blue, utilizes blue-wavelength measurements from instruments such as SeaWiFS and MODIS to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. We have validated the satellite retrieved aerosol optical thickness with data from AERONET sunphotometers over desert and semi-desert regions. The compari-sons show reasonable agreements between these two. These new satellite prod-ucts will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from SeaWiFS and MODIS-like instruments. The multiyear satellite measurements since 1998 from SeaWiFS will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with these dust outbreaks in East Asia. The monthly av-eraged aerosol optical thickness during the springtime from SeaWiFS will also be compared with the MODIS Deep Blue products.

Satellite Monitoring of Asian Dust Storms from SeaWiFS and MODIS: Source, pathway and Interannual Variability

NASA Technical Reports Server (NTRS)

Hsu, N. Christina

2007-01-01

Among the many components that contribute to air pollution, airborne mineral dust plays an important role due to its biogeochemical impact on the ecosystem and its radiative-forcing effect on the climate system. In East Asia, dust storms frequently accompany the cold and dry air masses that occur as part of springtime cold front systems. China's capital, Beijing, and other large cities are on the primary pathway of these dust storm plumes, and their passage over such population centers causes flight delays, pushes grit through windows and doors, and forces people indoors. Furthermore, during the spring these anthropogenic and natural air pollutants, once generated over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the Pacific into the United States and beyond. In this paper, we will demonstrate the capability of a new satellite algorithm to retrieve aerosol optical thickness and single scattering albedo over bright-reflecting surfaces such as urban areas and deserts. Such retrievals have been difficult to perform using previously available algorithms that use wavelengths from the mid-visible to the near IR because they have trouble separating the aerosol signal from the contribution due to the bright surface reflectance. The new algorithm, called Deep Blue, utilizes blue-wavelength measurements from instruments such as SeaWiFS and MODIS to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. We have validated the satellite retrieved aerosol optical thickness with data from AERONET sunphotometers over desert and semi-desert regions. The comparisons show reasonable agreements between these two. These new satellite products will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from SeaWiFS and MODIS-like instruments. The multiyear satellite measurements since 1998 from SeaWiFS will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with these dust outbreaks in East Asia. The monthly averaged aerosol optical thickness during the springtime from SeaWiFS will also be compared with the MODIS Deep Blue products.

Satellite Monitoring of Asian Dust Storms from SeaWiFS and MODIS: Source, Pathway, and Interannual Variability

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Tsay, S.-C.; Bettenhausen, C.; Salustro, C.; Jeong, M. J.

2010-01-01

Among the many components that contribute to air pollution, airborne mineral dust plays an important role due to its biogeochernical impact on the ecosystem and its radiative-forcing effect on the climate system. In East Asia, dust storms frequently accompany the cold and dry air masses that occur as part of springtime cold front systems. China's capital, Beijing, and other large cities are on the primary pathway of these dust storm plumes, and their passage over such population centers causes flight delays, pushes grit through windows and doors, and forces people indoors. Furthermore, during the spring these anthropogenic and natural air pollutants, once generated over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the Pacific into the United States and beyond. In this paper, we will demonstrate the capability of a new satellite algorithm to retrieve aerosol optical thickness and single scattering albedo over bright reflecting surfaces such as urban areas and deserts. Such retrievals have been difficult to perform using previously available algorithms that use wavelengths from the mid-visible to the near IR because they have trouble separating the aerosol signal from the contribution due to the bright surface reflectance. The new algorithm, called Deep Blue, utilizes blue-wavelength measurements from instruments such as SeaWiFS and MODIS to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. We have validated the satellite retrieved aerosol optical thickness with data from AERONET sunphotometers over desert and semi-desert regions. The comparisons show reasonable agreements between these two. These new satellite products will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from SeaWiFS and MODIS-like instruments. The multiyear satellite measurements since 1998 from SeaWiFS will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with these dust outbreaks in East Asia. The monthly averaged aerosol optical thickness during the springtime from SeaWiFS will also be compared with the MODIS Deep Blue products.

Satellite Monitoring of Asian Dust Storms from SeaWiFS and MODIS: Source, Pathway, and Interannual Variability

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Tsay, S.-C.; Bettenhausen, C.; Sayer, A.

2011-01-01

Among the many components that contribute to air pollution, airborne mineral dust plays an important role due to its biogeochemical impact on the ecosystem and its radiative-forcing effect on the climate system. In East Asia, dust storms frequently accompany the cold and dry air masses that occur as part of springtime cold front systems. China's capital, Beijing, and other large cities are on the primary pathway of these dust storm plumes, and their passage over such population centers causes flight delays, pushes grit through windows and doors, and forces peop Ie indoors. Furthermore, during the spring these anthropogenic and natural air pollutants, once generated over the source regions, can be tran sported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the Pacific into the United States and beyond. In this paper, we will demonstrate the capability of a new satellite algorithm to retrieve aerosol optical thickness and single scattering albedo over brightreflecting surfaces such as urban areas and deserts. Such retrievals have been difficult to perform using previously available algorithms that use wavelengths from the mid-visible to the near IR because they have trouble separating the aerosol signal from the contribution due to the bright surface reflectance. The new algorithm, called Deep Blue, utilizes blue-wavelength measurements from instruments such as Sea WiFS and MODIS to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. We have validated the satellite retrieved aerosol optical thickness with data from AERONET sunphotometers over desert and semi-desert regions. The comparisons show reasonable agreements between these two. These new satellite products will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from Sea WiFS and MODISlike instruments. The multiyear satellite measurements since 1998 from SeaWiFS will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with these dust outbreaks in East Asia. The monthly averaged aerosol optical thickness during the springtime from SeaWiFS will also be compared with the MODIS Deep Blue products.

Pyrimidine-based twisted donor-acceptor delayed fluorescence molecules: a new universal platform for highly efficient blue electroluminescence.

PubMed

Park, In Seob; Komiyama, Hideaki; Yasuda, Takuma

2017-02-01

Deep-blue emitters that can harvest both singlet and triplet excited states to give high electron-to-photon conversion efficiencies are highly desired for applications in full-color displays and white lighting devices based on organic light-emitting diodes (OLEDs). Thermally activated delayed fluorescence (TADF) molecules based on highly twisted donor-acceptor (D-A) configurations are promising emitting dopants for the construction of efficient deep-blue OLEDs. In this study, a simple and versatile D-A system combining acridan-based donors and pyrimidine-based acceptors has been developed as a new platform for high-efficiency deep-blue TADF emitters. The designed pre-twisted acridan-pyrimidine D-A molecules exhibit small singlet-triplet energy splitting and high photoluminescence quantum yields, functioning as efficient deep-blue TADF emitters. The OLEDs utilizing these TADF emitters display bright blue electroluminescence with external quantum efficiencies of up to 20.4%, maximum current efficiencies of 41.7 cd A -1 , maximum power efficiencies of 37.2 lm W -1 , and color coordinates of (0.16, 0.23). The design strategy featuring such acridan-pyrimidine D-A motifs can offer great prospects for further developing high-performance deep-blue TADF emitters and TADF-OLEDs.

White organic light-emitting diodes utilized by near UV-deep blue emitter and exciplex emission.

PubMed

Park, Young Wook; Kim, Young Min; Choi, Jin Hwan; Park, Tae Hyun; Choi, Hyun Ju; Yu, Hong Jung; Cho, Min Ju; Choi, Dong Hoon; Kim, Sung Hyun; Ju, Byeong Kwon

2011-02-01

Numerous investigations have been made into the development of wide color gamut displays for deep-blue OLEDs, including the RGB sub pixels, and white OLEDs (WOLEDs). One of the well known deep-blue emissive dopants, tris(phenyl-methyl-benzimidazolyl)iridium(III) [Ir(pmb)3], successfully introduced its fascinating color coordinate of Commission Internationale de l'Eclairage (CIE) 1931 (0.17, 0.06), however there have been no reports utilizing its accomplishments as WOLEDs. In this report, using only one phosphorescent dopant, the near UV-deep blue emissive Ir(pmb)3, the WOLEDs having the CIE 1931 coordinate of (0.33, 0.38) at 100 cd/m2 with a color rendering index of 85 are demonstrated. The white emission of the fabricated OLEDs are oriented from the near UV-deep blue emission of Ir(pmb)3 and the successfully controlled exciplex emission, between the Ir(pmb)3-host, and the Ir(pmb)3-interfaced material.

Highly Efficient Deep Blue Organic Light-Emitting Diodes Based on Imidazole: Significantly Enhanced Performance by Effective Energy Transfer with Negligible Efficiency Roll-off.

PubMed

Shan, Tong; Liu, Yulong; Tang, Xiangyang; Bai, Qing; Gao, Yu; Gao, Zhao; Li, Jinyu; Deng, Jian; Yang, Bing; Lu, Ping; Ma, Yuguang

2016-10-26

Great efforts have been devoted to develop efficient deep blue organic light-emitting diodes (OLEDs) materials meeting the standards of European Broadcasting Union (EBU) standard with Commission International de L'Eclairage (CIE) coordinates of (0.15, 0.06) for flat-panel displays and solid-state lightings. However, high-performance deep blue OLEDs are still rare for applications. Herein, two efficient deep blue emitters, PIMNA and PyINA, are designed and synthesized by coupling naphthalene with phenanthreneimidazole and pyreneimidazole, respectively. The balanced ambipolar transporting natures of them are demonstrated by single-carrier devices. Their nondoped OLEDs show deep blue emissions with extremely small CIE y of 0.034 for PIMNA and 0.084 for PyINA, with negligible efficiency roll-off. To take advantage of high photoluminescence quantum efficiency of PIMNA and large fraction of singlet exciton formation of PyINA, doped devices are fabricated by dispersing PyINA into PIMNA. A significantly improved maximum external quantum efficiency (EQE) of 5.05% is obtained through very effective energy transfer with CIE coordinates of (0.156, 0.060), and the EQE remains 4.67% at 1000 cd m -2 , which is among the best of deep blue OLEDs reported matching stringent EBU standard well.

Dust transport model validation using satellite- and ground-based methods in the southwestern United States

NASA Astrophysics Data System (ADS)

Mahler, Anna-Britt; Thome, Kurt; Yin, Dazhong; Sprigg, William A.

2006-08-01

Dust is known to aggravate respiratory diseases. This is an issue in the desert southwestern United States, where windblown dust events are common. The Public Health Applications in Remote Sensing (PHAiRS) project aims to address this problem by using remote-sensing products to assist in public health decision support. As part of PHAiRS, a model for simulating desert dust cycles, the Dust Regional Atmospheric Modeling (DREAM) system is employed to forecast dust events in the southwestern US. Thus far, DREAM has been validated in the southwestern US only in the lower part of the atmosphere by comparison with measurement and analysis products from surface synoptic, surface Meteorological Aerodrome Report (METAR), and upper-air radiosonde. This study examines the validity of the DREAM algorithm dust load prediction in the desert southwestern United States by comparison with satellite-based MODIS level 2 and MODIS Deep Blue aerosol products, and ground-based observations from the AERONET network of sunphotometers. Results indicate that there are difficulties obtaining MODIS L2 aerosol optical thickness (AOT) data in the desert southwest due to low AOT algorithm performance over areas with high surface reflectances. MODIS Deep Blue aerosol products show improvement, but the temporal and vertical resolution of MODIS data limit its utility for DREAM evaluation. AERONET AOT data show low correlation to DREAM dust load predictions. The potential contribution of space- or ground-based lidar to the PHAiRS project is also examined.

Evaluation of AVHRR Aerosol Properties Over Mainland China from Deepblue Algorithm

NASA Astrophysics Data System (ADS)

Xue, Y.; Che, Y.; She, L.

2017-12-01

Advanced Very High Resolution Radiometer (AVHRR) on-board NOAA series satellites is the only operational senor which keeps observing surface of the Earth and cloud over 30 years since 1979. Such long time coverage helps to expand the application of AVHRR to aerosol properties retrieval over both land and ocean successfully. Recently in 2017, the Deep Blue Project has published AVHRR `Deep Blue' dataset version 001 (V001) using `Deep Blue (DB)' algorithm(Sayer et al., 2017). This dataset includes not only aerosol properties over land but also oceanic aerosol product at three periods (NOAA-11: 1989-1990, NOAA-14: 1995-1999, NOAA-18: 2006-2011). We pay much of our attention to DB's performance over mainland China. Therefore, in the presenting paper, we focus on validating AVHRR/DB dataset over different land covers in China in 2007, 2008 and 2010. Both of data from ground-based networks from the Aerosol Robotic NETwork (AERONET) and China Aerosol Remote Sensing Network (CARSNET) are used as reference data. The collocation method is to match data at a time range of of satellite pass-by and at a spatial frame of pixels around ground-based site. Totally, data from 18 AERONET and 25 CARSNET are used as shown in figure, collocating 922 matches with AERONET and 2325 matches with CARSNET. Additionally, we introduced a corrected RMS error as main evaluation metric. As a result, AVHRR/DB underestimates AOD increasingly and more uncertainties and errors will be introduced with the growth of AOD. Otherwise, the performance of AVHRR/DB are better compared with AERONET data than with CARSNET data from RMSbc of 0.35 vs. 0.42. Their Rs (0.757 vs. 0.654) prove this characteristic too. For urban areas, the performances in Beijing are better than that in Xi'an from RMSbc, otherwise RMS in Xi'an (0.324) is lower than others' (0.346 and 0.383) mainly because of small AOD observed range and low R (0.624). For croplands, those performances are at same levels with RMSbc from 0.312 to 0.380 except Huimin with RMSbc = 0.445. For grasslands and sparsely vegetated areas, it lacks AERONET observation sites that only SACOL in central China. Obviously, the algorithm has best performance over Dunhuang site, where the RMSbc = 0.338 and highest R about 0.771. Over the rest of sites, the AVHRR/DB has serious problem in retrieving AOD, high dispersion or poor correlation.

The "Deep Blue" Aerosol Project at NASA GSFC

NASA Technical Reports Server (NTRS)

Sayer, Andrew; Hsu, N. C.; Lee, J.; Bettenhausen, C.; Carletta, N.; Chen, S.; Esmaili, R.

2016-01-01

Atmospheric aerosols such as mineral dust, wildfire smoke, sea spray, and volcanic ash are of interest for a variety of reasons including public health, climate change, hazard avoidance, and more. Deep Blue is a project which uses satellite observations of the Earth from sensors such as SeaWiFS, MODIS, and VIIRS to monitor the global aerosol burden. This talk will cover some basics about aerosols and the principles of aerosol remote sensing, as well as discussing specific results and future directions for the Deep Blue project.

[The Triumph of "Stupidity" : Deep Blue`s Victory over Garri Kasparov. The Controversy about its Impact on Artficial Intelligence Research].

PubMed

Heßler, Martina

2017-03-01

The competition between the chess computer Deep Blue and the former chess world champion Garri Kasparov in 1997 was a spectacle staged for the media. However, the chess game, like other games, was also a test field for artificial intelligence research. On the one hand Deep Blue's victory was called a "milestone" for AI research, on the other hand, a dead end, since the superiority of the chess computer was based on pure computing power and had nothing to do with "real" AI.The article questions the premises of these different interpretations and maps Deep Blue and its way of playing chess into the history of AI. This also requires an analysis of the underlying concepts of thinking. Finally, the essay calls for assuming different "ways of thinking" for man and computer. Instead of fundamental discussions of concepts of thinking, we should ask about the consequences of the human-machine division of labor.

DeepBlue epigenomic data server: programmatic data retrieval and analysis of epigenome region sets

PubMed Central

Albrecht, Felipe; List, Markus; Bock, Christoph; Lengauer, Thomas

2016-01-01

Large amounts of epigenomic data are generated under the umbrella of the International Human Epigenome Consortium, which aims to establish 1000 reference epigenomes within the next few years. These data have the potential to unravel the complexity of epigenomic regulation. However, their effective use is hindered by the lack of flexible and easy-to-use methods for data retrieval. Extracting region sets of interest is a cumbersome task that involves several manual steps: identifying the relevant experiments, downloading the corresponding data files and filtering the region sets of interest. Here we present the DeepBlue Epigenomic Data Server, which streamlines epigenomic data analysis as well as software development. DeepBlue provides a comprehensive programmatic interface for finding, selecting, filtering, summarizing and downloading region sets. It contains data from four major epigenome projects, namely ENCODE, ROADMAP, BLUEPRINT and DEEP. DeepBlue comes with a user manual, examples and a well-documented application programming interface (API). The latter is accessed via the XML-RPC protocol supported by many programming languages. To demonstrate usage of the API and to enable convenient data retrieval for non-programmers, we offer an optional web interface. DeepBlue can be openly accessed at http://deepblue.mpi-inf.mpg.de. PMID:27084938

Extending "Deep Blue" aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies

NASA Astrophysics Data System (ADS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Lee, J.; Redemann, J.; Schmid, B.; Shinozuka, Y.

2016-05-01

Cases of absorbing aerosols above clouds (AACs), such as smoke or mineral dust, are omitted from most routinely processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar sensors, for incorporation into a future version of the "Deep Blue" AOD data product. Detailed retrieval simulations suggest that these sensors should be able to determine AAC AOD with a typical level of uncertainty ˜25-50% (with lower uncertainties for more strongly absorbing aerosol types) and COD with an uncertainty ˜10-20%, if an appropriate aerosol optical model is known beforehand. Errors are larger, particularly if the aerosols are only weakly absorbing, if the aerosol optical properties are not known, and the appropriate model to use must also be retrieved. Actual retrieval errors are also compared to uncertainty envelopes obtained through the optimal estimation (OE) technique; OE-based uncertainties are found to be generally reasonable for COD but larger than actual retrieval errors for AOD, due in part to difficulties in quantifying the degree of spectral correlation of forward model error. The algorithm is also applied to two MODIS scenes (one smoke and one dust) for which near-coincident NASA Ames Airborne Tracking Sun photometer (AATS) data were available to use as a ground truth AOD data source, and found to be in good agreement, demonstrating the validity of the technique with real observations.

AERONET-Based Nonspherical Dust Optical Models and Effects on the VIIRS Deep Blue/SOAR Over Water Aerosol Product

NASA Astrophysics Data System (ADS)

Lee, Jaehwa; Hsu, N. Christina; Sayer, Andrew M.; Bettenhausen, Corey; Yang, Ping

2017-10-01

Aerosol Robotic Network (AERONET)-based nonspherical dust optical models are developed and applied to the Satellite Ocean Aerosol Retrieval (SOAR) algorithm as part of the Version 1 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA "Deep Blue" aerosol data product suite. The optical models are created using Version 2 AERONET inversion data at six distinct sites influenced frequently by dust aerosols from different source regions. The same spheroid shape distribution as used in the AERONET inversion algorithm is assumed to account for the nonspherical characteristics of mineral dust, which ensures the consistency between the bulk scattering properties of the developed optical models and the AERONET-retrieved microphysical and optical properties. For the Version 1 SOAR aerosol product, the dust optical model representative for Capo Verde site is used, considering the strong influence of Saharan dust over the global ocean in terms of amount and spatial coverage. Comparisons of the VIIRS-retrieved aerosol optical properties against AERONET direct-Sun observations at five island/coastal sites suggest that the use of nonspherical dust optical models significantly improves the retrievals of aerosol optical depth (AOD) and Ångström exponent by mitigating the well-known artifact of scattering angle dependence of the variables, which is observed when incorrectly assuming spherical dust. The resulting removal of these artifacts results in a more natural spatial pattern of AOD along the transport path of Saharan dust to the Atlantic Ocean; that is, AOD decreases with increasing distance transported, whereas the spherical assumption leads to a strong wave pattern due to the spurious scattering angle dependence of AOD.

Excellent deep-blue emitting materials based on anthracene derivatives for non-doped organic light-emitting diodes

NASA Astrophysics Data System (ADS)

Wang, Zhiqiang; Liu, Wei; Xu, Chen; Ji, Baoming; Zheng, Caijun; Zhang, Xiaohong

2016-08-01

Two deep-blue emitting materials 2-tert-butyl-9,10-bis(3,5-diphenylphenyl)anthracene (An-1) and 2-tert-butyl-9,10-bis(3,5-diphenylbiphenyl-4‧-yl)anthracene (An-2) were successfully synthesized by the Pd-catalyzed Suzuki coupling reaction. Both of these compounds have high thermal stabilities and show strong deep-blue emission as solid-state film as well as in n-hexane solution. Two non-doped electroluminescent devices employing An-1 and An-2 as emitting layers were fabricated by vacuum vapor deposition. These devices exhibited highly efficient and stable deep-blue emission with high color purity. The CIE coordinate and maximum EQE of An-1 based device are 4.2% and (0.16, 0.06), respectively. Device based on An-2 achieved a maximum EQE of 4.0% and a CIE coordinate of (0.16, 0.10).

Highly efficient deep-blue organic light emitting diode with a carbazole based fluorescent emitter

NASA Astrophysics Data System (ADS)

Sahoo, Snehasis; Dubey, Deepak Kumar; Singh, Meenu; Joseph, Vellaichamy; Thomas, K. R. Justin; Jou, Jwo-Huei

2018-04-01

High efficiency deep-blue emission is essential to realize energy-saving, high-quality display and lighting applications. We demonstrate here a deep-blue organic light emitting diode using a novel carbazole based fluorescent emitter 7-[4-(diphenylamino)phenyl]-9-(2-ethylhexyl)-9H-carbazole-2-carbonitrile (JV234). The solution processed resultant device shows a maximum luminance above 1,750 cd m-2 and CIE coordinates (0.15,0.06) with a 1.3 lm W-1 power efficiency, 2.0 cd A-1 current efficiency, and 4.1% external quantum efficiency at 100 cd m-2. The resulting deep-blue emission enables a greater than 100% color saturation. The high efficiency may be attributed to the effective host-to-guest energy transfer, suitable device architecture facilitating balanced carrier injection and low doping concentration preventing efficiency roll-off caused by concentration quenching.

DeepBlue epigenomic data server: programmatic data retrieval and analysis of epigenome region sets.

PubMed

Albrecht, Felipe; List, Markus; Bock, Christoph; Lengauer, Thomas

2016-07-08

Large amounts of epigenomic data are generated under the umbrella of the International Human Epigenome Consortium, which aims to establish 1000 reference epigenomes within the next few years. These data have the potential to unravel the complexity of epigenomic regulation. However, their effective use is hindered by the lack of flexible and easy-to-use methods for data retrieval. Extracting region sets of interest is a cumbersome task that involves several manual steps: identifying the relevant experiments, downloading the corresponding data files and filtering the region sets of interest. Here we present the DeepBlue Epigenomic Data Server, which streamlines epigenomic data analysis as well as software development. DeepBlue provides a comprehensive programmatic interface for finding, selecting, filtering, summarizing and downloading region sets. It contains data from four major epigenome projects, namely ENCODE, ROADMAP, BLUEPRINT and DEEP. DeepBlue comes with a user manual, examples and a well-documented application programming interface (API). The latter is accessed via the XML-RPC protocol supported by many programming languages. To demonstrate usage of the API and to enable convenient data retrieval for non-programmers, we offer an optional web interface. DeepBlue can be openly accessed at http://deepblue.mpi-inf.mpg.de. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Manipulating the Electronic Excited State Energies of Pyrimidine-Based Thermally Activated Delayed Fluorescence Emitters To Realize Efficient Deep-Blue Emission.

PubMed

Komatsu, Ryutaro; Ohsawa, Tatsuya; Sasabe, Hisahiro; Nakao, Kohei; Hayasaka, Yuya; Kido, Junji

2017-02-08

The development of efficient and robust deep-blue emitters is one of the key issues in organic light-emitting devices (OLEDs) for environmentally friendly, large-area displays or general lighting. As a promising technology that realizes 100% conversion from electrons to photons, thermally activated delayed fluorescence (TADF) emitters have attracted considerable attention. However, only a handful of examples of deep-blue TADF emitters have been reported to date, and the emitters generally show large efficiency roll-off at practical luminance over several hundreds to thousands of cd m -2 , most likely because of the long delayed fluorescent lifetime (τ d ). To overcome this problem, we molecularly manipulated the electronic excited state energies of pyrimidine-based TADF emitters to realize deep-blue emission and reduced τ d . We then systematically investigated the relationships among the chemical structure, properties, and device performances. The resultant novel pyrimidine emitters, called Ac-XMHPMs (X = 1, 2, and 3), contain different numbers of bulky methyl substituents at acceptor moieties, increasing the excited singlet (E S ) and triplet state (E T ) energies. Among them, Ac-3MHPM, with a high E T of 2.95 eV, exhibited a high external quantum efficiency (η ext,max ) of 18% and an η ext of 10% at 100 cd m -2 with Commission Internationale de l'Eclairage chromaticity coordinates of (0.16, 0.15). These efficiencies are among the highest values to date for deep-blue TADF OLEDs. Our molecular design strategy provides fundamental guidance to design novel deep-blue TADF emitters.

Recent Progress on Deep Blue Aerosol Algorithm as Applied TO MODIS, SEA WIFS, and VIIRS, and Their Intercomparisons with Ground Based and Other Satellite Measurements

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Bettenhausen, Corey; Sawyer, Andrew; Tsay, Si-Chee

2012-01-01

The impact of natural and anthropogenic sources of aerosols has gained increasing attention from scientific communities in recent years. Indeed, tropospheric aerosols not only perturb radiative energy balance by interacting with solar and terrestrial radiation, but also by changing cloud properties and lifetime. Furthermore, these anthropogenic and natural air particles, once generated over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across oceans and continents resulting in important biogeochemical impacts on the ecosystem. With the launch of SeaWiFS in 1997, Terra/MODIS in 1999, and Aqua/MODIS in 2002, high quality comprehensive aerosol climatology is becoming feasible for the first time. As a result of these unprecedented data records, studies of the radiative and biogeochemical effects due to tropospheric aerosols are now possible. In this talk, we will demonstrate how this newly available SeaWiFS/MODIS aerosol climatology can provide an important piece of puzzles in reducing the uncertainty of estimated climatic forcing due to aerosols. We will start with the global distribution of aerosol loading and their variabilities over both land and ocean on short- and long-term temporal scales observed over the last decade. The recent progress made in Deep Blue aerosol algorithm on improving accuracy of these Sea WiFS / MODIS aerosol products in particular over land will be discussed. The impacts on quantifying physical and optical processes of aerosols over source regions of adding the Deep Blue products of aerosol properties over bright-reflecting surfaces into Sea WiFS / MODIS as well as VIIRS data suite will also be addressed. We will also show the intercomparison results of SeaWiFS/MODIS retrieved aerosol optical thickness with data from ground based AERONET sunphotometers over land and ocean as well as with other satellite measurements. The trends observed in global aerosol loadings of both natural and anthropogenic sources based upon more than a decade of combined MODIS/SeaWiFS data (1997-2011) will be discussed. We will also address the importance of various key issues such as differences in spatial-temporal sampling rates and observation time between different satellite measurements could potentially impact these intercomparisons results, especially for using the monthly mean data, and thus on estimates of long-term aerosol trends.

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

PubMed

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

2015-03-16

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

'Behind blue eyes'†: the association between eye colour and deep infiltrating endometriosis.

PubMed

Vercellini, Paolo; Buggio, Laura; Somigliana, Edgardo; Dridi, Dhouha; Marchese, Maria Antonietta; Viganò, Paola

2014-10-10

Is the prevalence of blue eye colour higher in women with deep endometriosis? Blue eye colour is more common in women with deep endometriosis when compared with both women with ovarian endometriomas and women without a history of endometriosis. Recent and intriguing evidence suggests that women with deep endometriosis may have particular phenotypic characteristics including a higher prevalence of a light-colour iris. Available epidemiological evidence is however weak. Case-control study performed in a large academic department specializing in the study and treatment of endometriosis. Individual iris colour was evaluated in daylight and categorized in three grades, namely blue-grey (blue), hazel-green (green) and brown. One observer assessed iris colour. In addition, the women themselves were invited to indicate the colour of their eyes according to the same classification system. Cases with discordant eye colour determinations between the observer and the woman were excluded from the final analysis. Two hundred and twenty-three women with deep endometriosis (cases), 247 with ovarian endometriomas and 301 without a history of endometriosis were enrolled. After exclusion of 52 discordant cases, the proportions of brown, blue and green eye colours were, respectively, 61, 30 and 9% in the deep endometriosis group, 74, 16 and 10% in the endometrioma group and 75, 15 and 10% in the non-endometriosis group. Women in the deep endometriosis group had a statistically significant excess of blue eyes and a reduced proportion of brown eyes compared with the two control groups (P = 0.002 and P < 0.001, respectively). The proportion of blue eyes was almost identical in the ovarian endometrioma group and the non-endometriosis group, and that of green eyes was substantially similar in all study groups. The OR (95% CI) of having blue eyes in women with deep endometriosis compared with women with ovarian endometriosis and with those without endometriosis was, respectively, 2.2 (1.4-3.6) and 2.5 (1.6-3.9). We cannot exclude that some women without a previous diagnosis of endometriosis indeed had the disease. However, this would have led to a reduction of the observed difference in proportion of blue eyes, thus to a potential underestimation of the real strength of the association. Moreover, under-ascertainment is possible with regard to peritoneal disease, but unlikely with deep endometriotic lesions and ovarian endometriomas. There are two possible explanations for our findings. Both may have intriguing implications for future research on endometriosis. Firstly, genes involved in the control of iris colour transmission may lie in a region with a strong pattern of linkage disequilibrium with genes involved in the invasiveness of endometriosis. Alternatively, blue eye colour could be considered an indicator of a photo-sensitive phenotype resulting in limited exposure to sunlight and UVB radiation. Limited sunlight exposure is associated with reduced circulating 25-hydroxyvitamin D3, an element that has recently been linked to endometriosis development. © The Author 2014. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An Exciplex Host for Deep-Blue Phosphorescent Organic Light-Emitting Diodes.

PubMed

Lim, Hyoungcheol; Shin, Hyun; Kim, Kwon-Hyeon; Yoo, Seung-Jun; Huh, Jin-Suk; Kim, Jang-Joo

2017-11-01

The use of exciplex hosts is attractive for high-performance phosphorescent organic light-emitting diodes (PhOLEDs) and thermally activated delayed fluorescence OLEDs, which have high external quantum efficiency, low driving voltage, and low efficiency roll-off. However, exciplex hosts for deep-blue OLEDs have not yet been reported because of the difficulties in identifying suitable molecules. Here, we report a deep-blue-emitting exciplex system with an exciplex energy of 3.0 eV. It is composed of a carbazole-based hole-transporting material (mCP) and a phosphine-oxide-based electron-transporting material (BM-A10). The blue PhOLEDs exhibited maximum external quantum efficiency of 24% with CIE coordinates of (0.15, 0.21) and longer lifetime than the single host devices.

Extending "Deep Blue" Aerosol Retrieval Coverage to Cases of Absorbing Aerosols Above Clouds: Sensitivity Analysis and First Case Studies

NASA Technical Reports Server (NTRS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Lee, J.; Redemann, J.; Schmid, B.; Shinozuka, Y.

2016-01-01

Cases of absorbing aerosols above clouds (AACs), such as smoke or mineral dust, are omitted from most routinely processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar sensors, for incorporation into a future version of the "Deep Blue" AOD data product. Detailed retrieval simulations suggest that these sensors should be able to determine AAC AOD with a typical level of uncertainty approximately 25-50 percent (with lower uncertainties for more strongly absorbing aerosol types) and COD with an uncertainty approximately10-20 percent, if an appropriate aerosol optical model is known beforehand. Errors are larger, particularly if the aerosols are only weakly absorbing, if the aerosol optical properties are not known, and the appropriate model to use must also be retrieved. Actual retrieval errors are also compared to uncertainty envelopes obtained through the optimal estimation (OE) technique; OE-based uncertainties are found to be generally reasonable for COD but larger than actual retrieval errors for AOD, due in part to difficulties in quantifying the degree of spectral correlation of forward model error. The algorithm is also applied to two MODIS scenes (one smoke and one dust) for which near-coincident NASA Ames Airborne Tracking Sun photometer (AATS) data were available to use as a ground truth AOD data source, and found to be in good agreement, demonstrating the validity of the technique with real observations.

Effect of MODIS Terra Radiometric Calibration Improvements on Collection 6 Deep Blue Aerosol Products: Validation and Terra/Aqua Consistency

NASA Technical Reports Server (NTRS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Jeong, M.-J.; Meister, G.

2015-01-01

The Deep Blue (DB) algorithm's primary data product is midvisible aerosol optical depth (AOD). DB applied to Moderate Resolution Imaging Spectroradiometer (MODIS) measurements provides a data record since early 2000 for MODIS Terra and mid-2002 for MODIS Aqua. In the previous data version (Collection 5, C5), DB production from Terra was halted in 2007 due to sensor degradation; the new Collection 6 (C6) has both improved science algorithms and sensor radiometric calibration. This includes additional calibration corrections developed by the Ocean Biology Processing Group to address MODIS Terra's gain, polarization sensitivity, and detector response versus scan angle, meaning DB can now be applied to the whole Terra record. Through validation with Aerosol Robotic Network (AERONET) data, it is shown that the C6 DB Terra AOD quality is stable throughout the mission to date. Compared to the C5 calibration, in recent years the RMS error compared to AERONET is smaller by approximately 0.04 over bright (e.g., desert) and approximately 0.01-0.02 over darker (e.g., vegetated) land surfaces, and the fraction of points in agreement with AERONET within expected retrieval uncertainty higher by approximately 10% and approximately 5%, respectively. Comparisons to the Aqua C6 time series reveal a high level of correspondence between the two MODIS DB data records, with a small positive (Terra-Aqua) average AOD offset <0.01. The analysis demonstrates both the efficacy of the new radiometric calibration efforts and that the C6 MODIS Terra DB AOD data remain stable (to better than 0.01 AOD) throughout the mission to date, suitable for quantitative scientific analyses.

Benzobisoxazole cruciforms: A tunable, cross-conjugated platform for the generation of deep blue OLED materials

DOE PAGES

Chavez, III, Ramiro; Cai, Min; Tlach, Brian; ...

2016-01-20

Four new cross-conjugated small molecules based on a central benzo[1,2-d:4,5-d']bisoxazole moiety possessing semi-independently tunable HOMO and LUMO levels were synthesized and the properties of these materials were evaluated experimentally and theoretically. The molecules were thermally stable with 5% weight loss occurring well above 350 °C. The cruciforms all exhibited blue emission in solution ranging from 433–450 nm. Host–guest OLEDs fabricated from various concentrations of these materials using the small molecule host 4,4'-bis(9-carbazolyl)-biphenyl (CBP) exhibited deep blue-emission with Commission Internationale de L'Eclairage (CIE) coordinates of (0.15 ≤ x ≤ 0.17, 0.05 ≤ y ≤ 0.11), and maximum luminance efficiencies as highmore » as ~2 cd A–1. Lastly, these results demonstrate the potential of benzobisoxazole cruciforms as emitters for developing high-performance deep blue OLEDs.« less

21 CFR 184.1261 - Copper sulfate.

Code of Federal Regulations, 2014 CFR

2014-04-01

... is used in the pentahydrate form. This form occurs as large, deep blue or ultramarine, triclinic crystals; as blue granules, or as a light blue powder. The ingredient is prepared by the reaction of...

21 CFR 184.1261 - Copper sulfate.

Code of Federal Regulations, 2013 CFR

2013-04-01

... Reg. No. 7758-99-8) usually is used in the pentahydrate form. This form occurs as large, deep blue or ultramarine, triclinic crystals; as blue granules, or as a light blue powder. The ingredient is prepared by...

21 CFR 184.1261 - Copper sulfate.

Code of Federal Regulations, 2012 CFR

2012-04-01

... Reg. No. 7758-99-8) usually is used in the pentahydrate form. This form occurs as large, deep blue or ultramarine, triclinic crystals; as blue granules, or as a light blue powder. The ingredient is prepared by...

21 CFR 184.1261 - Copper sulfate.

Code of Federal Regulations, 2011 CFR

2011-04-01

... Reg. No. 7758-98-7) usually is used in the pentahydrate form. This form occurs as large, deep blue or ultramarine, triclinic crystals; as blue granules, or as a light blue powder. The ingredient is prepared by...

21 CFR 184.1261 - Copper sulfate.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Reg. No. 7758-98-7) usually is used in the pentahydrate form. This form occurs as large, deep blue or ultramarine, triclinic crystals; as blue granules, or as a light blue powder. The ingredient is prepared by...

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

PubMed

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

2018-03-01

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

Evaluation of Aerosol Pollution Determination From MODIS Satellite Retrievals for Semi-Arid Reno, NV, USA with In-Situ Measurements

NASA Astrophysics Data System (ADS)

Loria-Salazar, S. Marcela

The aim of the present work is to carry out a detailed analysis of ground and columnar aerosol properties obtained by in-situ Photoacoustic and Integrated Nephelometer (PIN), Cimel CE-318 sunphotometer and MODIS instrument onboard Aqua and Terra satellites, for semi-arid Reno, Nevada, USA in the local summer months of 2012. Satellite determination of local aerosol pollution is desirable because of the potential for broad spatial and temporal coverage. However, retrieval of quantitative measures of air pollution such as Aerosol Optical Depth (AOD) from satellite measurements is challenging because of the underlying surface albedo being heterogeneous in space and time. Therefore, comparisons of satellite retrievals with measurements from ground-based sun photometers are crucial for validation, testing, and further development of instruments and retrieval algorithms. Ground-based sunphotometry and in-situ ground observations show that seasonal weather changes and fire plumes have great influence on the atmosphere aerosol optics. The Apparent Optical Height (AOH) follows the shape of the development of the Convective Boundary Layer (CBL) when fire conditions were not present. However, significant fine particle optical depth was inferred beyond the CBL thereby complicating the use of remote sensing measurements for near-ground aerosol pollution measurements. A meteorological analysis was performed to help diagnose the nature of the aerosols above Reno. The calculation of a Zephyr index and back trajectory analysis demonstrated that a local circulation often induces aerosol transport from Northern CA over the Sierra Nevada Mountains that doubles the Aerosol Optical Depth (AOD) at 500 nm. Sunphotometer measurements were used as a `ground truth' for satellite retrievals to evaluate the current state of the science retrievals in this challenging location. Satellite retrieved for AOD showed the presence of wild fires in Northern CA during August. AOD retrieved using the "dark-target algorithm" may be unrealistically high over the Great Basin. Low correlation was found between AERONET AOD and dark-target algorithm AOD retrievals from Aqua and Terra during June and July. During fire conditions the dark-target algorithm AOD values correlated better with AERONET measurements in August. Use of the Deep-blue algorithm for MODIS data to retrieve AOD did not provide enough points to compare with AERONET in June and July. In August, AOD from deep-blue and AERONET retrievals exhibited low correlation. AEE from MODIS products and AERONET exhibited low correlation during every month. Apparently satellite AOD retrievals need much improvement for areas like semi-arid Reno.

Colored lenses suppress blue light-emitting diode light-induced damage in photoreceptor-derived cells.

PubMed

Hiromoto, Kaho; Kuse, Yoshiki; Tsuruma, Kazuhiro; Tadokoro, Nobuyuki; Kaneko, Nobuyuki; Shimazawa, Masamitsu; Hara, Hideaki

2016-03-01

Blue light-emitting diodes (LEDs) in liquid crystal displays emit high levels of blue light, exposure to which is harmful to the retina. Here, we investigated the protective effects of colored lenses in blue LED light-induced damage to 661W photoreceptor-derived cells. We used eight kinds of colored lenses and one lens that reflects blue light. Moreover, we evaluated the relationship between the protective effects of the lens and the transmittance of lens at 464 nm. Lenses of six colors, except for the SY, PN, and reflective coating lenses, strongly decreased the reduction in cell damage induced by blue LED light exposure. The deep yellow lens showed the most protective effect from all the lenses, but the reflective coating lens and pink lens did not show any effects on photoreceptor-derived cell damage. Moreover, these results were correlated with the lens transmittance of blue LED light (464 nm). These results suggest that lenses of various colors, especially deep yellow lenses, may protect retinal photoreceptor cells from blue LED light in proportion to the transmittance for the wavelength of blue LED and the suppression of reactive oxygen species production and cell damage.

Rigid biimidazole ancillary ligands as an avenue to bright deep blue cationic iridium(iii) complexes.

PubMed

Henwood, Adam F; Evariste, Sloane; Slawin, Alexandra M Z; Zysman-Colman, Eli

2014-01-01

Herein we report the synthesis and optoelectronic characterisation of three deep blue-emitting cationic iridium complexes, of the form [Ir(dFppy)(2)(N^N)]PF(6), bearing biimidazole-type N^N ancillary ligands (dFppyH = 2-(2,4-difluorophenyl)pyridine). Complex 1 contains the parent biimidazole, biim, while 2 contains a dimethylated analog, dMebiim, and 3 contains an ortho-xylyl-tethered biimidzole, o-xylbiim. We explore a strategy of tethering the biimidazole in order to rigidify the complex and increase the photoluminescent quantum yield, culminating in deep blue (λ(max): 457 nm in MeOH at 298 K) ligand-centered emission with a very high photoluminescent quantum yield of 68% and microsecond emission lifetime. Density functional theory calculations elucidate the origin of such disparate excited state kinetics across this series, especially in light of virtually identical optoelectronic properties observed for these compounds.

Hydroxynaphthyridine-derived group III metal chelates: wide band gap and deep blue analogues of green Alq3 (tris(8-hydroxyquinolate)aluminum) and their versatile applications for organic light-emitting diodes.

PubMed

Liao, Szu-Hung; Shiu, Jin-Ruei; Liu, Shun-Wei; Yeh, Shi-Jay; Chen, Yu-Hung; Chen, Chin-Ti; Chow, Tahsin J; Wu, Chih-I

2009-01-21

A series of group III metal chelates have been synthesized and characterized for the versatile application of organic light-emitting diodes (OLEDs). These metal chelates are based on 4-hydroxy-1,5-naphthyridine derivates as chelating ligands, and they are the blue version analogues of well-known green fluorophore Alq(3) (tris(8-hydroxyquinolinato)aluminum). These chelating ligands and their metal chelates were easily prepared with an improved synthetic method, and they were facially purified by a sublimation process, which enables the materials to be readily available in bulk quantity and facilitates their usage in OLEDs. Unlike most currently known blue analogues of Alq(3) or other deep blue materials, metal chelates of 4-hydroxy-1,5-naphthyridine exhibit very deep blue fluorescence, wide band gap energy, high charge carrier mobility, and superior thermal stability. Using a vacuum-thermal-deposition process in the fabrication of OLEDs, we have successfully demonstrated that the application of these unusual hydroxynaphthyridine metal chelates can be very versatile and effective. First, we have solved or alleviated the problem of exciplex formation that took place between the hole-transporting layer and hydroxynaphthyridine metal chelates, of which OLED application has been prohibited to date. Second, these deep blue materials can play various roles in OLED application. They can be a highly efficient nondopant deep blue emitter: maximum external quantum efficiency eta(ext) of 4.2%; Commision Internationale de L'Eclairage x, y coordinates, CIE(x,y) = 0.15, 0.07. Compared with Alq(3), Bebq(2) (beryllium bis(benzoquinolin-10-olate)), or TPBI (2,2',2''-(1,3,5-phenylene)tris(1-phenyl-1H-benzimidazole), they are a good electron-transporting material: low HOMO energy level of 6.4-6.5 eV and not so high LUMO energy level of 3.0-3.3 eV. They can be ambipolar and possess a high electron mobility of 10(-4) cm(2)/V s at an electric field of 6.4 x 10(5) V/cm. They are a qualified wide band gap host material for efficient blue perylene (CIE(x,y) = 0.14, 0.17 and maximum eta(ext) 3.8%) or deep blue 9,10-diphenylanthracene (CIE(x,y) = 0.15, 0.06 and maximum eta(ext) 2.8%). For solid state lighting application, they are desirable as a host material for yellow dopant (rubrene) in achieving high efficiency (eta(ext) 4.3% and eta(P) 8.7 lm/W at an electroluminance of 100 cd/m(2) or eta(ext) 3.9% and eta(P) 5.1 lm/W at an electroluminance of 1000 cd/m(2)) white electroluminescence (CIE(x,y) = 0.30, 0.35).

Condition and biochemical profile of blue mussels (Mytilus edulis L.) cultured at different depths in a cold water coastal environment

NASA Astrophysics Data System (ADS)

Gallardi, Daria; Mills, Terry; Donnet, Sebastien; Parrish, Christopher C.; Murray, Harry M.

2017-08-01

The growth and health of cultured blue mussels (Mytilus edulis) are affected by environmental conditions. Typically, culture sites are situated in sheltered areas near shore (i.e., < 1 km distance from land, < 20 m depth); however, land runoff, user conflicts and environmental impact in coastal areas are concerns and interest in developing deep water (> 20 m depth) mussel culture has been growing. This study evaluated the effect of culture depth on blue mussels in a cold water coastal environment (Newfoundland, Canada). Culture depth was examined over two years from September 2012 to September 2014; mussels from three shallow water (5 m) and three deep water (15 m) sites were compared for growth and biochemical composition; culture depths were compared for temperature and chlorophyll a. Differences between the two years examined were noted, possibly due to harsh winter conditions in the second year of the experiment. In both years shallow and deep water mussels presented similar condition; in year 2 deep water mussels had a significantly better biochemical profile. Lipid and glycogen analyses showed seasonal variations, but no significant differences between shallow and deep water were noted. Fatty acid profiles showed a significantly higher content of omega-3 s (20:5ω3; EPA) and lower content of bacterial fatty acids in deep water sites in year 2. Everything considered, deep water appeared to provide a more favorable environment for mussel growth than shallow water under harsher weather conditions.

Deep-levels in gallium arsenide for device applications

NASA Astrophysics Data System (ADS)

McManis, Joseph Edward

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

High efficiency blue and white phosphorescent organic light emitting devices

NASA Astrophysics Data System (ADS)

Eom, Sang-Hyun

Organic light-emitting devices (OLEDs) have important applications in full-color flat-panel displays and as solid-state lighting sources. Achieving high efficiency deep-blue phosphorescent OLEDs (PHOLEDs) is necessary for high performance full-color displays and white light sources with a high color rendering index (CRI); however it is more challenging compared to the longer wavelength light emissions such as green and red due to the higher energy excitations for the deep-blue emitter as well as the weak photopic response of deep-blue emission. This thesis details several effective strategies to enhancing efficiencies of deep-blue PHOLEDs based on iridium(III) bis(4',6'-difluorophenylpyridinato)tetrakis(1-pyrazolyl)borate (FIr6), which are further employed to demonstrate high efficiency white OLEDs by combining the deep-blue emitter with green and red emitters. First, we have employed 1,1-bis-(di-4-tolylaminophenyl) cyclohexane (TAPC) as the hole transporting material to enhance electron and triplet exciton confinement in Fir6-based PHOLEDs, which increased external quantum efficiency up to 18 %. Second, dual-emissive-layer (D-EML) structures consisting of an N,N -dicarbazolyl-3,5-benzene (mCP) layer doped with 4 wt % FIr6 and a p-bis (triphenylsilyly)benzene (UGH2) layer doped with 25 wt % FIr6 was employed to maximize exciton generation in the emissive layer. Combined with the p-i-n device structure, high power efficiencies of (25 +/- 2) lm/W at 100 cd/m2 and (20 +/- 2) lm/W at 1000 cd/m 2 were achieved. Moreover, the peak external quantum efficiency of (20 +/- 1) % was achieved by employing tris[3-(3-pyridyl)mesityl]borane (3TPYMB) as the electron transporting material, which further improves the exciton confinement in the emissive layer. With Cs2CO3 doping in the 3TPYMB layer to greatly increase its electrical conductivity, a peak power efficiency up to (36 +/- 2) lm/W from the deep-blue PHOLED was achieved, which also maintains Commission Internationale de L'Eclairage (CIE) coordinates of (0.16, 0.28). High efficiency white PHOLEDs are also demonstrated by incorporating green and red phosphorescent emitters together with the deep-blue emitter FIr6. Similar to the FIr6-only devices, the D-EML structure with high triplet energy charge transport materials leads to a maximum external quantum efficiency of (19 +/- 1) %. Using the p-i-n device structure, a peak power efficiency of (40 +/- 2) lm/W and (36 +/- 2) lm/W at 100 cd/m2 were achieved, and the white PHOLED possesses a CRI of 79 and CIE coordinates of (0.37, 0.40). The limited light extraction from the planar-type OLEDs is also one of the remaining challenges to the OLED efficiency. Here we have developed a simple soft lithography technique to fabricate a transparent, close-packed hemispherical microlens arrays. The application of such microlens arrays to the glass surface of the large-area fluorescent OLEDs enhanced the light extraction efficiency up to (70 +/- 7)%. It is also shown that the light extraction efficiency of the OLEDs is affected by microlens contact angle, OLEDs size, and detailed layer structure of the OLEDs.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

PubMed

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Cadmium-free quantum dot light emitting devices: energy-transfer realizing pure blue emission.

PubMed

Ji, Wenyu; Jing, Pengtao; Fan, Yi; Zhao, Jialong; Wang, Yunjun; Kong, Xianggui

2013-01-01

In this study, deep blue, pure electroluminescence (EL) at 441.5 nm from a ZnSe/ZnS quantum dot light-emitting device (QD-LED) is obtained by using poly (4-butylphenyl-diphenyl-amine) (poly-TPD) as the hole-transport layer (HTL) to open up the channel for energy transfer from poly-TPD to QDs. The emission originating from HTL is observed in the QD-LED with N,N'-bis (tolyl)-N,N'-diphenyl-1,1'-biphenyl-4,4'-diamine functionalized with two styryl groups (2-TPD) as the HTL due to inefficient energy-transfer from 2-TPD to QDs. The poly-TPD based device exhibits color-saturated blue emission with a narrow spectral bandwidth of full width at half maximum (~17.2 nm). These results explore the operating mechanism of the QD EL and signify a remarkable progress in deep blue QD-LEDs based on environmental-friendly QD materials.

'Blue bubble' technique: an ab interno approach for Descemet separation in deep anterior lamellar keratoplasty using trypan blue stained viscoelastic device.

PubMed

Livny, Eitan; Bahar, Irit; Hammel, Naama; Nahum, Yoav

2018-04-01

In this study, we examined a novel variant of 'big-bubble' deep anterior lamellar keratoplasty using trypan-blue-stained viscoelastic device for the creation of a pre-descemetic bubble. Ten corneoscleral rims were mounted on an artificial anterior chamber (AC). The AC was filled with air through a limbal paracentesis. A Melles' triangulated spatula was inserted through the paracentesis, with its tip penetrating the AC, was then slightly retracted and pushed into the deep stroma above the roof of the paracentesis. A mixture of trypan blue and viscoelastic device (Healon, Abbott Medical Optics, Abbott Park, Illinois) was injected into this intra-stromal pocket using a 27-G cannula to create a pre-descemetic separation bubble. Bubble type and visualization of dyed viscoelastic device were noted. The method was later employed in three cases. In all 10 corneoscleral rims, the technique successfully created a visible pre-descemetic (type 1) bubble that could be expanded up to the predicted diameter of trephination. Subsequent trephination and the removal of corneal stroma were uneventful. In two out of four clinical cases, a type 1 bubble was created, while in two others, visco-dissection failed and dyed viscoelastic was seen in the AC. The presented technique holds promise of being a relatively easy to perform, predictable and well-controlled alternative for achieving a type 1 bubble during deep anterior lamellar keratoplasty surgery. The trypan-blue-stained viscoelastic device facilitates proper visualization and control of the separation bubble and assists in identifying the penetrance to the separation bubble prior to removal of the stromal cap. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

Blue whales respond to simulated mid-frequency military sonar.

PubMed

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

2013-08-22

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

New ultra deep blue emitters based on chrysene chromophores

NASA Astrophysics Data System (ADS)

Shin, Hwangyu; Kang, Seokwoo; Jung, Hyocheol; Lee, Hayoon; Lee, Jaehyun; Kim, Beomjin; Park, Jongwook

2016-09-01

Chrysene, which has a wide band gap, was selected as an emission core to develop and study new materials that emit ultra-deep-blue light with high efficiency. Six compounds introducing various side groups were designed and synthesized: 6, 12-bis(30,50-diphenylphenyl)chrysene (TP-C-TP), 6-(30,50-diphenylphenyl)-12-(3,5-diphenylbiphenyl-400-yl)chrysene (TP-C-TPB) and 6,12-bis(300,500-diphenylbiphenyl-40-yl)chrysene (TPB-C-TPB), which contained bulky aromatic si de groups; and N,N,N0 ,N0-tetraphenyl-chrysene-6,12-diamine (DPA-C-DPA), [12-(4-diphenylamino-phenyl)-chrysene-6-yl]-diphenylamine(DPA-C-TPA) and 6,12-bis[4-(diphenylamino)phenyl]chrysene (TPA-C-TPA), which contained aromatic amine groups, were designed to afford improved hole injection properties. The synthesized materials showed maxi mum absorption wavelengths at 342-402 nm in the film state and exhibited deep-blue photoluminescence (PL) emission s at 417-464 nm. The use of TP-C-TPB in a non-doped organic light emitting diode (OLED) device resulted in ultra-deep-blue emission with an external quantum efficiency (EQE) of 4.02% and Commission Internationale de L'Eclairage coo rdinates (CIE x, y) of (0.154, 0.042) through effective control of the internal conjugation length and suppression of the p -p* stacking. The use of TPA-C-TPA, which includes an aromatic amine side group, afforded an excellent EQE of 4.83 % and excellent color coordinates CIE x, y of (0.147, 0.077).

2-(2-Hydroxyphenyl)imidazole-based four-coordinate organoboron compounds with efficient deep blue photoluminescence and electroluminescence.

PubMed

Zhang, Zhenyu; Zhang, Zuolun; Zhang, Hongyu; Wang, Yue

2017-12-19

Two new four-coordinate organoboron compounds with 2-(2-hydroxyphenyl)imidazole derivatives as the chelating ligands have been synthesized. They possess high thermal stability and are able to form an amorphous glass state. Crystallographic analyses indicate that the differences in ligand structure cause the change of ππ stacking character. The CH 2 Cl 2 solutions and thin films of these compounds display bright blue emission, and these compounds have appropriate HOMO and LUMO energy levels for carrier injection in OLEDs. By utilizing the good thermal and luminescent properties, as well as the proper frontier orbital energy levels, bright non-doped OLEDs with a simple structure have been realized. Notably, these simple devices show deep blue electroluminescence with the Commission Internationale de l'Éclairage (CIE) coordinate of ca. (0.16, 0.08), which is close to the CIE coordinate of (0.14, 0.08) for standard blue defined by the National Television System Committee. In addition, one of the devices exhibits good performance, showing brightness, current efficiency, power efficiency and external quantum efficiency up to 2692 cd m -2 , 2.50 cd A -1 , 1.81 lm W -1 and 3.63%, respectively. This study not only provides good deep-blue emitting OLED materials that are rarely achieved by using four-coordinate organoboron compounds, but also allows a deeper understanding of the structure-property relationship of 2-(2-hydroxyphenyl)imidazole-based boron complexes, which benefits the further structural design of this type of material.

Bahama Banks, Tongue of the Ocean, Bahamas

NASA Image and Video Library

1993-01-19

STS054-152-102 (13-19 Jan. 1993) --- This is a south-looking, wide angle view of the northern Bahamas, featuring the islands (from mid-foreground toward background) of Eleuthera, New Providence, and Andros. The northern shore of Cuba can be seen in the background. The resort city of Nassau occupies much of eastern New Providence. The Bahamas host some very distinctive features -- the deep blue channels and the shallow, light blue platforms, feathery sand bars at the edges of the deep water sounds, and colorful lakes and tidal channels like seen on Andros Island.

Toward Obtaining Reliable Particulate Air Quality Information from Satellites

NASA Astrophysics Data System (ADS)

Strawa, A. W.; Chatfield, R. B.; Legg, M.; Esswein, R.; Justice, E.

2009-12-01

Air quality agencies use ground sites to monitor air quality, providing accurate information at particular points. Using measurements from satellite imagery has the potential to provide air quality information in a timely manner with better spatial resolution and at a lower cost that can also useful for model validation. While previous studies show acceptable correlations between Aerosol Optical Depth (AOD) derived from MODIS and surface Particulate Matter (PM) measurements on the eastern US, the data do not correlate well in the western US (Al-Saadi et al., 2005; Engle-Cox et al., 2004) . This paper seeks to improve the AOD-PM correlations by using advanced statistical analysis techniques. Our study area is the San Joaquin Valley in California because air quality in this region has failed to meet state and federal attainment standards for PM for the past several years. A previous investigation found good correlation of the AOD values between MODIS, MISR and AERONET, but poor correlations (R2 ~ 0.02) between satellite-based AOD and surface PM2.5 measurements. PM2.5 measurements correlated somewhat better (R2 ~ 0.18) with MODIS-derived AOD using the Deep Blue surface reflectance algorithm (Hsu et al., 2006) rather than the standard MODIS algorithm. This level of correlation is not adequate for reliable air quality measurements. Pelletier et al. (2007) used generalized additive models (GAMs) and meteorological data to improve the correlation between PM and AERONET AOD in western Europe. Additive models are more flexible than linear models and the functional relationships can be plotted to give a sense of the relationship between the predictor and the response. In this paper we use GAMs to improve surface PM2.5 to MODIS-AOD correlations. For example, we achieve an R2 ~ 0.44 using a GAM that includes the Deep Blue AOD, and day of year as parameters. Including NOx observations, improves the R2 ~ 0.64. Surprisingly Ångström exponent did not prove to be a significant factor. The relationships between the predictor and the response are discussed. Al-Saadi, J., J. Szykman, R.B. Pierce, C. Kittaka, D. Neil, D.A. Chu, L. Remer, L. Gumley, E. Prins, L. Weinstock, C. MacDonald, R. Wayland, F. Dimmick, and J. Fishman, Imporving national air quality forecasts with satellite aerosol observations, Bull. Amer, Met. Soc. (Sept), 1249-1261, 2005. Engle-Cox, J.A., C.H. Holloman, B.W. Coutant, and R.M. Hoff, Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality, Atmos. En., 38, 2495-2509, 2004. Hsu, N.C., S.-C. Tsay, M.D. King, and J.R. Herman, Deep blue retrievals of Asian Aerosol properties during ACE-Asia, IEEE Trans. on Geosci.a nd Remote Sensing, 44 (11), 3180, 2006. Pelletier, B., R. Santer, and J. Vidot, Retrieving of particulate matter from optical measurements: A semi-parametric approach, J. Geophys. Res., 112 (D06208), 2007.

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

PubMed

Movahedi, Faezeh; Coyle, James L; Sejdic, Ervin

2018-05-01

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.

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

PubMed

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

2017-07-18

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

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

NASA Astrophysics Data System (ADS)

Shanmugam, Palanisamy; He, Xianqiang; Singh, Rakesh Kumar; Varunan, Theenathayalan

2018-05-01

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.

Low threshold Amplified Spontaneous Emission properties in deep blue of poly[(9,9-dioctylfluorene-2,7-dyil)-alt-p-phenylene] thin films

NASA Astrophysics Data System (ADS)

Lattante, Sandro; De Giorgi, Maria Luisa; Pasini, Mariacecilia; Anni, Marco

2017-10-01

Amongst the different optoelectronic applications of conjugated polymers, the development of new active materials for optically pumped organic lasers is still an open question particularly in the blue-near UV spectral range. We investigate the emission properties of poly[(9,9-dioctylfluorene-2,7-dyil)- alt-p-phenylene] (PFP) neat films under nanosecond pump. We demonstrate that thanks to the introduction of a phenylene moiety between two fluorene units it is possible to obtain Amplified Spontaneous Emission (ASE) with a lower threshold and a blue shifted wavelength with respect to poly(9,9-dioctylfluorene) (PFO). We demonstrate efficient ASE with a minimum threshold as low as 23 μJcm-2 and a minimum ASE wavelength of 436 nm. A maximum net optical gain of about 26 cm-1 is measured at an excitation density of 0.23 mJcm-2. These results make the PFP a good active material for optically pumped deep blue organic lasers.

An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database

NASA Astrophysics Data System (ADS)

Zhang, Hai; Kondragunta, Shobha; Laszlo, Istvan; Liu, Hongqing; Remer, Lorraine A.; Huang, Jingfeng; Superczynski, Stephen; Ciren, Pubu

2016-09-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has been retrieving aerosol optical thickness (AOT), operationally and globally, over ocean and land since shortly after S-NPP launch in 2011. However, the current operational VIIRS AOT retrieval algorithm over land has two limitations in its assumptions for land surfaces: (1) it only retrieves AOT over the dark surfaces and (2) it assumes that the global surface reflectance ratios between VIIRS bands are constants. In this work, we develop a surface reflectance ratio database over land with a spatial resolution 0.1° × 0.1° using 2 years of VIIRS top of atmosphere reflectances. We enhance the current operational VIIRS AOT retrieval algorithm by applying the surface reflectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOT over both dark and bright surfaces. Over bright surfaces, the VIIRS AOT retrievals from the enhanced algorithm have a correlation of 0.79, mean bias of -0.008, and standard deviation (STD) of error of 0.139 when compared against the ground-based observations at the global AERONET (Aerosol Robotic Network) sites. Over dark surfaces, the VIIRS AOT retrievals using the surface reflectance ratio database improve the root-mean-square error from 0.150 to 0.123. The use of the surface reflectance ratio database also increases the data coverage of more than 20% over dark surfaces. The AOT retrievals over bright surfaces are comparable to MODIS Deep Blue AOT retrievals.

Daytime identification of summer hailstorm cells from MSG data

NASA Astrophysics Data System (ADS)

Merino, A.; López, L.; Sánchez, J. L.; García-Ortega, E.; Cattani, E.; Levizzani, V.

2014-04-01

Identifying deep convection is of paramount importance, as it may be associated with extreme weather phenomena that have significant impact on the environment, property and populations. A new method, the hail detection tool (HDT), is described for identifying hail-bearing storms using multispectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the convective mask (CM) algorithm devised for detection of deep convection, and the second a hail mask algorithm (HM) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HM are based on logistic regression models trained with multispectral MSG data sets comprised of summer convective events in the middle Ebro Valley (Spain) between 2006 and 2010, and detected by the RGB (red-green-blue) visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HM are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients". Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall probability of detection was 76.9 % and the false alarm ratio 16.7 %.

Enhancing the color gamut of white displays using novel deep-blue organic fluorescent dyes to form color-changed thin films with improved efficiency

NASA Astrophysics Data System (ADS)

Liu, Wei-Ting; Huang, Wen-Yao

2012-10-01

This study used the novel fluorescence based deep-blue-emitting molecule BPVPDA in an organic fluorescent color thin film to exhibit deep blue color with CIE coordinates of (0.13, 0.16). The developed original organic RGB color thin film technology enables the optimization of the distinctive features of an organic light emitting diode (OLED) and thin-film-transistor (TFT) LCD display. The color filter structure maintains the same high resolution to obtain a higher level of brightness in comparison with conventional organic RGB color thin film. The image-processing engine is designed to achieve a sharp text image for a TFT LCD with organic color thin films. The organic color thin films structure uses an organic dye dopant in a limpid photoresist. With this technology, the following characteristics can be obtained: 1. high color reproduction of gamut ratio, and 2. improved luminous efficiency with organic color fluorescent thin film. This performance is among the best results ever reported for a color-filter used on TFT-LCD or OLED.

Enhancing the color gamut of white displays using novel deep-blue organic fluorescent dyes to form color-changed thin films with improved efficiency

NASA Astrophysics Data System (ADS)

Liu, Wei-ting; Huang, Wen-Yao

2012-06-01

This study used novel fluorescence based deep-blue-emitting molecules, namely BPVPDA, an organic fluorescence color thin film using BPVPDA exhibit deep blue fluorine with CIE coordinates of (0.13,0.16). The developed original Organic RGB color thin film technology enables the optimization of the distinctive features of an organic light emitting diode (OLED) and (TFT) LCD display. The color filter structure maintains the same high resolution to obtain a higher level of brightness, in comparison with conventional organic RGB color thin film. The image-processing engine is designed to achieve a sharp text image for a thin-film-transistor (TFT) LCD with organic color thin films. The organic color thin films structure uses organic dye dopent in limpid photo resist. With this technology , the following characteristics can be obtained: (1) high color reproduction of gamut ratio, and (2) improved luminous efficiency with organic color fluorescence thin film. This performance is among the best results ever reported for a color-filter used on TFT-LCD and OLED.

Blue whales respond to simulated mid-frequency military sonar

PubMed Central

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

2013-01-01

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

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements.

PubMed

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K; Cai, Chang; Nagarajan, Srikantan S

2018-06-01

Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements

NASA Astrophysics Data System (ADS)

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K.; Cai, Chang; Nagarajan, Srikantan S.

2018-06-01

Objective. Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. Approach. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Main results. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. Significance. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Global Map of Epithermal Neutrons

NASA Image and Video Library

2002-05-28

Observations by NASA's 2001 Mars Odyssey spacecraft show a global view of Mars in intermediate-energy, or epithermal, neutrons. Soil enriched by hydrogen is indicated by the deep blue colors on the map, which show a low intensity of epithermal neutrons. Progressively smaller amounts of hydrogen are shown in the colors light blue, green, yellow and red. The deep blue areas in the polar regions are believed to contain up to 50 percent water ice in the upper one meter (three feet) of the soil. Hydrogen in the far north is hidden at this time beneath a layer of carbon dioxide frost (dry ice). Light blue regions near the equator contain slightly enhanced near-surface hydrogen, which is most likely chemically or physically bound because water ice is not stable near the equator. The view shown here is a map of measurements made during the first three months of mapping using the neutron spectrometer instrument, part of the gamma ray spectrometer instrument suite. The central meridian in this projection is zero degrees longitude. Topographic features are superimposed on the map for geographic reference. http://photojournal.jpl.nasa.gov/catalog/PIA03800

High-brightness blue organic light emitting diodes with different types of guest-host systems

NASA Astrophysics Data System (ADS)

Wang, Xiao; Zhang, Jing-shuang; Peng, Cui-yun; Guo, Kun-ping; Wei, Bin; Zhang, Hao

2016-03-01

We demonstrate high-brightness blue organic light emitting diodes (OLEDs) using two types of guest-host systems. A series of blue OLEDs were fabricated using three organic emitters of dibenz anthracene (perylene), di(4-fluorophenyl) amino-di (styryl) biphenyl (DSB) and 4,4'-bis[2-(9-ethyl-3-carbazolyl)vinyl]biphenyl (BCzVBi) doped into two hosting materials of 4,4'-bis(9-carbazolyl) biphenyl (CBP) and 2-(4-biphenylyl)-5(4-tert-butyl-phenyl)-1,3,4-oxadiazole (PBD) as blue emitting layers, respectively. We achieve three kinds of devices with colors of deep-blue, pure-blue and sky-blue with the Commission Internationale de L'Eclairage (CIE) coordinates of (0.16, 0.10), (0.15, 0.15) and (0.17, 0.24), respectively, by employing PBD as host material. In addition, we present a microcavity device using the PBD guest-host system and achieve high-purity blue devices with narrowed spectrum.

Blue straggler stars: lessons from open clusters.

NASA Astrophysics Data System (ADS)

Geller, Aaron M.

Open clusters enable a deep dive into blue straggler characteristics. Recent work shows that the binary properties (frequency, orbital elements and companion masses and evolutionary states) of the blue stragglers are the most important diagnostic for determining their origins. To date the multi-epoch radial-velocity observations necessary for characterizing these blue straggler binaries have only been carried out in open clusters. In this paper, I highlight recent results in the open clusters NGC 188, NGC 2682 (M67) and NGC 6819. The characteristics of many of the blue stragglers in these open clusters point directly to origins through mass transfer from an evolved donor star. Additionally, a handful of blue stragglers show clear signatures of past dynamical encounters. These comprehensive, diverse and detailed observations also reveal important challenges for blue straggler formation models (and particularly the mass-transfer channel), which we must overcome to fully understand the origins of blue straggler stars and other mass-transfer products.

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

Chavez, III, Ramiro; Cai, Min; Tlach, Brian

Four new cross-conjugated small molecules based on a central benzo[1,2-d:4,5-d']bisoxazole moiety possessing semi-independently tunable HOMO and LUMO levels were synthesized and the properties of these materials were evaluated experimentally and theoretically. The molecules were thermally stable with 5% weight loss occurring well above 350 °C. The cruciforms all exhibited blue emission in solution ranging from 433–450 nm. Host–guest OLEDs fabricated from various concentrations of these materials using the small molecule host 4,4'-bis(9-carbazolyl)-biphenyl (CBP) exhibited deep blue-emission with Commission Internationale de L'Eclairage (CIE) coordinates of (0.15 ≤ x ≤ 0.17, 0.05 ≤ y ≤ 0.11), and maximum luminance efficiencies as highmore » as ~2 cd A–1. Lastly, these results demonstrate the potential of benzobisoxazole cruciforms as emitters for developing high-performance deep blue OLEDs.« less

Baleen whale infrasonic sounds: Natural variability and function

NASA Astrophysics Data System (ADS)

Clark, Christopher W.

2004-05-01

Blue and fin whales (Balaenoptera musculus and B. physalus) produce very intense, long, patterned sequences of infrasonic sounds. The acoustic characteristics of these sounds suggest strong selection for signals optimized for very long-range propagation in the deep ocean as first hypothesized by Payne and Webb in 1971. This hypothesis has been partially validated by very long-range detections using hydrophone arrays in deep water. Humpback songs recorded in deep water contain units in the 20-l00 Hz range, and these relatively simple song components are detectable out to many hundreds of miles. The mid-winter peak in the occurrence of 20-Hz fin whale sounds led Watkins to hypothesize a reproductive function similar to humpback (Megaptera novaeangliae) song, and by default this function has been extended to blue whale songs. More recent evidence shows that blue and fin whales produce infrasonic calls in high latitudes during the feeding season, and that singing is associated with areas of high productivity where females congregate to feed. Acoustic sampling over broad spatial and temporal scales for baleen species is revealing higher geographic and seasonal variability in the low-frequency vocal behaviors than previously reported, suggesting that present explanations for baleen whale sounds are too simplistic.

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

PubMed

Lee, Jae-Hong; Kim, Do-Hyung; Jeong, Seong-Nyum; Choi, Seong-Ho

2018-04-01

The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

Seasonal and regional differentiation of bio-optical properties within the north polar Atlantic

NASA Astrophysics Data System (ADS)

Stramska, Malgorzata; Stramski, Dariusz; Kaczmarek, SłAwomir; Allison, David B.; Schwarz, Jill

2006-08-01

Using field data from the north polar Atlantic, we examined seasonal variability of the spectral absorption, a(λ), and backscattering, bb(λ), coefficients of surface waters in relation to phytoplankton pigments. For a given chlorophyll a concentration, the concentrations of accessory pigments were lower in spring than in summer. This effect contributed to lower chlorophyll-specific absorption of phytoplankton and total particulate matter in spring. The spring values of the green-to-blue band ratio of a(λ) were higher than the summer ratios. The blue-to-green ratios of bb(λ) were also higher in spring. The higher bb values and lower blue-to-green bb ratios in summer were likely associated with higher concentrations of detrital particles in summer compared to spring. Because the product of these band ratios of a and bb is a proxy for the blue-to-green ratio of remote-sensing reflectance, the performance of ocean color band-ratio algorithms for estimating pigments is significantly affected by seasonal shifts in the relationships between absorption, backscattering, and chlorophyll a. Our results suggest that the algorithm for the spring season would predict chlorophyll a that is higher by as much as a factor of 4-6 compared to that predicted from the summer algorithm. This indicates a need for a seasonal approach in the north polar Atlantic. However, we also found that a fairly good estimate of the particulate beam attenuation coefficient at 660 nm (a proxy for total particulate matter or particulate organic carbon concentration) can be obtained by applying a single blue-to-green band-ratio algorithm regardless of the season.

Performance of the JPEG Estimated Spectrum Adaptive Postfilter (JPEG-ESAP) for Low Bit Rates

NASA Technical Reports Server (NTRS)

Linares, Irving (Inventor)

2016-01-01

Frequency-based, pixel-adaptive filtering using the JPEG-ESAP algorithm for low bit rate JPEG formatted color images may allow for more compressed images while maintaining equivalent quality at a smaller file size or bitrate. For RGB, an image is decomposed into three color bands--red, green, and blue. The JPEG-ESAP algorithm is then applied to each band (e.g., once for red, once for green, and once for blue) and the output of each application of the algorithm is rebuilt as a single color image. The ESAP algorithm may be repeatedly applied to MPEG-2 video frames to reduce their bit rate by a factor of 2 or 3, while maintaining equivalent video quality, both perceptually, and objectively, as recorded in the computed PSNR values.

Spectral matching technology for light-emitting diode-based jaundice photodynamic therapy device

NASA Astrophysics Data System (ADS)

Gan, Ru-ting; Guo, Zhen-ning; Lin, Jie-ben

2015-02-01

The objective of this paper is to obtain the spectrum of light-emitting diode (LED)-based jaundice photodynamic therapy device (JPTD), the bilirubin absorption spectrum in vivo was regarded as target spectrum. According to the spectral constructing theory, a simple genetic algorithm as the spectral matching algorithm was first proposed in this study. The optimal combination ratios of LEDs were obtained, and the required LEDs number was then calculated. Meanwhile, the algorithm was compared with the existing spectral matching algorithms. The results show that this algorithm runs faster with higher efficiency, the switching time consumed is 2.06 s, and the fitting spectrum is very similar to the target spectrum with 98.15% matching degree. Thus, blue LED-based JPTD can replace traditional blue fluorescent tube, the spectral matching technology that has been put forward can be applied to the light source spectral matching for jaundice photodynamic therapy and other medical phototherapy.

An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.

PubMed

Moore, Timothy S; Dowell, Mark D; Bradt, Shane; Verdu, Antonio Ruiz

2014-03-05

Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll- a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll- a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll- a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll- a algorithms-the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands-with RMS error of 0.416 and 0.437 for each in log chlorophyll- a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll- a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll- a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

PubMed

Sun, Wenqing; Zheng, Bin; Qian, Wei

2017-10-01

This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

Ambient temperature deposition of gallium nitride/gallium oxynitride from a deep eutectic electrolyte, under potential control.

PubMed

Sarkar, Sujoy; Sampath, S

2016-05-11

A ternary, ionically conducting, deep eutectic solvent based on acetamide, urea and gallium nitrate is reported for the electrodeposition of gallium nitride/gallium indium nitride under ambient conditions; blue and white light emitting photoluminescent deposits are obtained under potential control.

Eleuthera Island, Bahamas

NASA Technical Reports Server (NTRS)

1981-01-01

Eleuthera Island, (24.5N, 76.0W) Bahamas Island Group, is one of several within the archipelago surrounded by shallow seas, visible here as light blue. Mosaic patterns of sand waves built by sea bottom currents in the shallows stand out in stark contrast to the deep blue of the ocean depths of a thousand feet in the Exuma Sound.

Assessment of 10 Year Record of Aerosol Optical Depth from OMI UV Observations

NASA Technical Reports Server (NTRS)

Ahn, Changwoo; Torres, Omar; Jethva, Hiren

2014-01-01

The Ozone Monitoring Instrument (OMI) onboard the EOS-Aura satellite provides information on aerosol optical properties by making use of the large sensitivity to aerosol absorption in the near-ultraviolet (UV) spectral region. Another important advantage of using near UV observations for aerosol characterization is the low surface albedo of all terrestrial surfaces in this spectral region that reduces retrieval errors associated with land surface reflectance characterization. In spite of the 13 × 24 square kilometers coarse sensor footprint, the OMI near UV aerosol algorithm (OMAERUV) retrieves aerosol optical depth (AOD) and single-scattering albedo under cloud-free conditions from radiance measurements at 354 and 388 nanometers. We present validation results of OMI AOD against space and time collocated Aerosol Robotic Network measured AOD values over multiple stations representing major aerosol episodes and regimes. OMAERUV's performance is also evaluated with respect to those of the Aqua-MODIS Deep Blue and Terra-MISR AOD algorithms over arid and semi-arid regions in Northern Africa. The outcome of the evaluation analysis indicates that in spite of the "row anomaly" problem, affecting the sensor since mid-2007, the long-term aerosol record shows remarkable sensor stability.

Deep blue exciplex organic light-emitting diodes with enhanced efficiency; P-type or E-type triplet conversion to singlet excitons?

PubMed

Jankus, Vygintas; Chiang, Chien-Jung; Dias, Fernando; Monkman, Andrew P

2013-03-13

Simple trilayer, deep blue, fluorescent exciplex organic light-emitting diodes (OLEDs) are reported. These OLEDs emit from an exciplex state formed between the highest occupied molecular orbital (HOMO) of N,N'-bis(1-naphthyl)N,N'-diphenyl-1,1'-biphenyl-4,4'-diamine (NPB) and lowest unoccupied molecular orbital (LUMO) of 1,3,5-tri(1-phenyl-1H-benzo[d]imidazol-2-yl)phenyl (TPBi) and the NPB singlet manifold, yielding 2.7% external quantum efficiency at 450 nm. It is shown that the majority of the delayed emission in electroluminescence arises from P-type triplet fusion at NPB sites not E-type reverse intersystem crossing because of the presence of the NPB triplet state acting as a deep trap. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Physical-Property Measurements on Core samples from Drill-Holes DB-1 and DB-2, Blue Mountain Geothermal Prospect, North-Central Nevada

USGS Publications Warehouse

Ponce, David A.; Watt, Janet T.; Casteel, John; Logsdon, Grant

2009-01-01

From May to June 2008, the U.S. Geological Survey (USGS) collected and measured physical properties on 36 core samples from drill-hole Deep Blue No. 1 (DB-1) and 46 samples from drill-hole Deep Blue No. 2 (DB-2) along the west side of Blue Mountain about 40 km west of Winnemucca, Nev. These data were collected as part of an effort to determine the geophysical setting of the Blue Mountain geothermal prospect as an aid to understanding the geologic framework of geothermal systems throughout the Great Basin. The physical properties of these rocks and other rock types in the area create a distinguishable pattern of gravity and magnetic anomalies that can be used to infer their subsurface geologic structure. Drill-holes DB-1 and DB-2 were spudded in alluvium on the western flank of Blue Mountain in 2002 and 2004, respectively, and are about 1 km apart. Drill-hole DB-1 is at a ground elevation of 1,325 m and was drilled to a depth of 672 m and drill-hole DB-2 is at a ground elevation of 1,392 m and was drilled to a depth of 1522 m. Diameter of the core samples is 6.4 cm. These drill holes penetrate Jurassic and Triassic metasedimentary rocks predominantly consisting of argillite, mudstone, and sandstone; Tertiary diorite and gabbro; and younger Tertiary felsic dikes.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

PubMed

He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-04-17

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

PubMed Central

Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-01-01

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices. PMID:29673171

An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters

PubMed Central

Moore, Timothy S.; Dowell, Mark D.; Bradt, Shane; Verdu, Antonio Ruiz

2014-01-01

Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-a algorithms—the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands—with RMS error of 0.416 and 0.437 for each in log chlorophyll-a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll-a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie. PMID:24839311

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.

PubMed

Kang, Eunhee; Chang, Won; Yoo, Jaejun; Ye, Jong Chul

2018-06-01

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

Predicting Microstegium vimineum invasion in natural plant communities of the southern Blue Ridge Mountains, USA

Treesearch

Dean P. Anderson; Monica G. Turner; Scott M. Pearson; Thomas P. Albright; Robert K. Peet; Ann Wieben

2012-01-01

Shade-tolerant non-native invasive plant species may make deep incursions into natural plant communities, but detecting such species is challenging because occurrences are often sparse. We developed Bayesian models of the distribution of Microstegium vimineum in natural plant communities of the southern Blue Ridge Mountains, USA to address three objectives: (1) to...

Light and vision in the deep-sea benthos: II. Vision in deep-sea crustaceans.

PubMed

Frank, Tamara M; Johnsen, Sönke; Cronin, Thomas W

2012-10-01

Using new collecting techniques with the Johnson-Sea-Link submersible, eight species of deep-sea benthic crustaceans were collected with intact visual systems. Their spectral sensitivities and temporal resolutions were determined shipboard using electroretinography. Useable spectral sensitivity data were obtained from seven species, and in the dark-adapted eyes, the spectral sensitivity peaks were in the blue region of the visible spectrum, ranging from 470 to 497 nm. Under blue chromatic adaptation, a secondary sensitivity peak in the UV portion of the spectrum appeared for two species of anomuran crabs: Eumunida picta (λ(max)363 nm) and Gastroptychus spinifer (λ(max)383 nm). Wavelength-specific differences in response waveforms under blue chromatic adaptation in these two species suggest that two populations of photoreceptor cells are present. Temporal resolution was determined in all eight species using the maximum critical flicker frequency (CFF(max)). The CFF(max) for the isopod Booralana tricarinata of 4 Hz proved to be the lowest ever measured using this technique, and suggests that this species is not able to track even slow-moving prey. Both the putative dual visual pigment system in the crabs and the extremely slow eye of the isopod may be adaptations for seeing bioluminescence in the benthic environment.

Efficient, deep-blue TADF-emitters for OLED display applications (Conference Presentation)

NASA Astrophysics Data System (ADS)

Volz, Daniel; Baumann, Thomas

2016-09-01

Currently, the mobile display market is strongly shifting towards AMOLED technology, in order to enable curved and flexible displays. This leads to a growing demand for highly efficient OLED emitters to reduce the power consumption and increase display resolution at the same time. While highly efficient green and red OLEDs already found their place in commercial OLED-displays, the lack of efficient blue emitters is still an issue. Consequently, the active area for blue is considerably larger than for green and red pixels, to make up for the lower efficiency. We intend to close this efficiency-gap with novel emitters based on thermally activated delayed fluorescence (TADF) technology. Compared to state-of-the-art fluorescent dopants, the efficiency of TADF-emitters is up to four times higher. At the same time, it is possible to design them in a way to maintain deep blue emission, i.e. CIE y < 0.2. These aspects are relevant to produce efficient high resolution AMOLED displays. Apart from these direct customer benefits, our TADF technology does not contain any rare elements, which allows for the fabrication of sustainable OLED technology. In this work, we highlight one of our recently developed blue TADF materials. Basic material properties as well as first device results are discussed. In a bottom-emitting device, a CIEx/CIEy coordinate of (0.16/0.17) was achieved with efficiency values close to 20% EQE.

Coltsfoot as a potential cause of deep vein thrombosis and pulmonary embolism in a patient also consuming kava and blue vervain.

PubMed

Freshour, Jessica E; Odle, Brian; Rikhye, Somi; Stewart, David W

2012-09-01

To report a case of deep vein thrombosis (DVT) with symptomatic pulmonary embolism (PE) possibly associated with the use of coltsfoot, kava, or blue vervain. A 27-year-old white male presented with leg pain and swelling, tachycardia, and pleuritic chest pain. He had no significant medical history. A medication history revealed extensive herbal medication use including: coltsfoot, passionflower, red poppy flower petals, wild lettuce, blue lily flowers, wild dagga flowers, Diviners Three Burning Blend® (comprised of salvia divinorum, blue lily, and wild dagga), kava-kava, St. John's Wort, blue vervain, and Dreamer's Blend® (comprised of Calea zacatechichi, vervain, Entada rheedii, wild lettuce, and Eschscholzia californica). Lower extremity Doppler ultrasound and computed topography (CT) of the chest revealed DVT and PE. A hypercoagulable work-up was negative. The patient was treated with enoxaparin and warfarin and was discharged home. While no distinct agent can be identified as a sole cause of this venous thromboembolic event, coltsfoot could potentially affect coagulation through its effect on vascular endothelial cells as they regulate nitric oxide. Nitric oxide is a known mediator of platelet activity and coagulation, particularly in the pulmonary vasculature. Kava and vervain have estrogenic properties. Of the medications consumed by this self-proclaimed "herbalist," coltsfoot is a potential cause of venous thromboembolic disease (VTE).

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

NASA Astrophysics Data System (ADS)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

Computer aided lung cancer diagnosis with deep learning algorithms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Qian, Wei

2016-03-01

Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

The dissolved yellow substance and the shades of blue in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Morel, A.; Gentili, B.

2009-11-01

When the nominal algorithms commonly in use in Space Agencies are applied to satellite Ocean Color data, the retrieved chlorophyll concentrations in the Mediterranean Sea are recurrently notable overestimates of the field values. Accordingly, several regionally tuned algorithms have been proposed in the past to correct for this deviation. Actually, the blueness of the Mediterranean waters is not as deep as expected from the actual (low) chlorophyll content, and the modified algorithms account for this peculiarity. Among the possible causes for such a deviation, an excessive amount of yellow substance (or of chromophoric dissolved organic matter, CDOM) has been frequently cited. This conjecture is presently tested, by using a new technique simply based on the simultaneous consideration of marine reflectance determined at four spectral bands, namely at 412, 443, 490, and 555 nm, available on the NASA-SeaWiFS sensor (Sea-viewing Wide Field-of-view Sensor). It results from this test that the concentration in yellow colored material (quantified as ay, the absorption coefficient of this material at 443 nm) is about twice that one observed in the nearby Atlantic Ocean at the same latitude. There is a strong seasonal signal, with maximal ay values in late fall and winter, an abrupt decrease beginning in spring, and then a flat minimum during the summer months, which plausibly results from the intense photo-bleaching process favored by the high level of sunshine in these areas. Systematically, the ay values, reproducible from year to year, are higher in the western basin compared with those in the eastern basin (by about 50%). The relative importance of the river discharges into this semi-enclosed sea, as well as the winter deep vertical mixing occurring in the northern parts of the basins may explain the high yellow substance background. The regionally tuned [Chl] algorithms, actually reflect the presence of an excess of CDOM with respect to its standard (Chl-related) values. When corrected for the presence of the actual CDOM content, the [Chl] values as derived via the nominal algorithms are restored to more realistic values, i.e., approximately divided by about two; the strong autumnal increase is smoothed whereas the spring bloom remains as an isolated feature.

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

NASA Technical Reports Server (NTRS)

Goldberg, Mitchell D.; Fleming, Henry E.

1994-01-01

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

An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

PubMed

Park, Seong-Wook; Park, Junyoung; Bong, Kyeongryeol; Shin, Dongjoo; Lee, Jinmook; Choi, Sungpill; Yoo, Hoi-Jun

2015-12-01

Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

Do Doppler color flow algorithms for mapping disturbed flow make sense?

PubMed

Gardin, J M; Lobodzinski, S M

1990-01-01

It has been suggested that a major advantage of Doppler color flow mapping is its ability to visualize areas of disturbed ("turbulent") flow, for example, in valvular stenosis or regurgitation and in shunts. To investigate how various color flow mapping instruments display disturbed flow information, color image processing was used to evaluate the most common velocity-variance color encoding algorithms of seven commercially available ultrasound machines. In six of seven machines, green was reportedly added by the variance display algorithms to map areas of disturbed flow. The amount of green intensity added to each pixel along the red and blue portions of the velocity reference color bar was calculated for each machine. In this study, velocities displayed on the reference color bar ranged from +/- 46 to +/- 64 cm/sec, depending on the Nyquist limit. Of note, changing the Nyquist limits depicted on the color reference bars did not change the distribution of the intensities of red, blue, or green within the contour of the reference map, but merely assigned different velocities to the pixels. Most color flow mapping algorithms in our study added increasing intensities of green to increasing positive (red) or negative (blue) velocities along their color reference bars. Most of these machines also added increasing green to red and blue color intensities horizontally across their reference bars as a marker of increased variance (spectral broadening). However, at any given velocity, marked variations were noted between different color flow mapping instruments in the amount of green added to their color velocity reference bars.(ABSTRACT TRUNCATED AT 250 WORDS)

Midportion achilles tendon microcirculation after intermittent combined cryotherapy and compression compared with cryotherapy alone: a randomized trial.

PubMed

Knobloch, Karsten; Grasemann, Ruth; Spies, Marcus; Vogt, Peter M

2008-11-01

The effect of combined cryotherapy/compression versus cryotherapy alone on the Achilles tendon is undetermined. Standardized combined cryotherapy/compression changes in midportion Achilles tendon microcirculation are superior to those with cryotherapy during intermittent application. Controlled laboratory study. Sixty volunteers were randomized for either combined cryotherapy/compression (Cryo/Cuff, DJO Inc, Vista, California: n = 30; 32 +/- 11 years) or cryotherapy alone (KoldBlue, TLP Industries, Kent, United Kingdom: n = 30; 33 +/- 12 years) with intermittent 3 x 10-minute application. Midportion Achilles tendon microcirculation was determined (O2C, LEA Medizintechnik, Giessen, Germany). Both Cryo/Cuff and KoldBlue significantly reduced superficial and deep capillary tendon blood flow within the first minute of application (43 +/- 46 arbitrary units [AU] vs 10 +/- 19 AU and 42 +/- 46 AU vs 12 +/- 10 AU; P = .0001) without a significant difference throughout all 3 applications. However, during recovery, superficial and deep capillary blood flow was reestablished significantly faster using Cryo/Cuff (P = .023). Tendon oxygen saturation was reduced in both groups significantly (3 minutes Cryo/Cuff: 36% +/- 20% vs 16% +/- 15%; KoldBlue: 42% +/- 19% vs 28% +/- 20%; P < .05) with significantly stronger effects using Cryo/Cuff (P = .014). Cryo/Cuff led to significantly higher tendon oxygenation (Cryo/Cuff: 62% +/- 28% vs baseline 36% +/- 20%; P = .0001) in superficial and deep tissue (Cryo/Cuff: 73% +/- 14% vs baseline 65% +/- 17%; P = .0001) compared with KoldBlue during all recoveries. Postcapillary venous filling pressures were significantly reduced in both groups during application; however, Cryo/Cuff led to significantly, but marginally, lower pressures (Cryo/Cuff: 41 +/- 7 AU vs baseline 51 +/- 13 AU; P = .0001 and KoldBlue: 46 +/- 7 AU vs baseline 56 +/- 11 AU; P = .026 for Cryo/Cuff vs KoldBlue). Increased tendon oxygenation is achieved as tendon preconditioning by combined cryotherapy and compression with significantly increased tendon oxygen saturation during recovery in contrast to cryotherapy alone. Both regimens lead to a significant amelioration of tendinous venous outflow. Combined cryotherapy and compression is superior to cryotherapy alone regarding the Achilles tendon microcirculation. Further studies in tendinopathy and tendon rehabilitation are warranted to elucidate its value regarding functional issues.

The College English Teaching Reform Based on MOOC

ERIC Educational Resources Information Center

Bing, Wang

2017-01-01

Nowadays the College English course in China is in the deep blue sea which arouses the deep concerns from all walks of life in the society including the students, teaching experts and the English teachers. Based on the MOOC appearing three years ago, the College English class can be more diverse and beneficial by the means of providing the…

77 FR 64144 - Affirmative Decisions on Petitions for Modification Granted in Whole or in Part

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-18

... govern the application, processing, and disposition of petitions for modification. This Federal Register..., West Virginia 25306. Mine: Blue Creek No. 1 Deep Mine, MSHA I.D. No. 46-09297, located in Kanawha...-C. FR Notice: 77 FR 811 (1/6/2012). Petitioner: D & F Deep Mine, 15 Motter Drive, Pine Grove...

Observations of Blue Discharges Associated With Negative Narrow Bipolar Events in Active Deep Convection

NASA Astrophysics Data System (ADS)

Liu, Feifan; Zhu, Baoyou; Lu, Gaopeng; Qin, Zilong; Lei, Jiuhou; Peng, Kang-Ming; Chen, Alfred B.; Huang, Anjing; Cummer, Steven A.; Chen, Mingli; Ma, Ming; Lyu, Fanchao; Zhou, Helin

2018-03-01

On 19 August 2012, the Imager of Sprites and Upper Atmospheric Lightning on board the FORMOSAT-2 satellite captured a sequence of seven blue discharges within 1 min that emanated from a parent thunderstorm over Lake Taihu in East China. The analysis of lightning activity produced in the thunderstorm indicates that at least six of these events occurred in association with negative narrow bipolar events (NBEs) that were concurrent with the blue discharge by less than 1 ms, and negative cloud-to-ground occurred within 6 s before each blue discharge, which is in agreement with the modeling presented by Krehbiel et al. (2008). Therefore, the frequent occurrence of negative cloud-to-ground could provide the favorable condition for the production of blue discharges, and negative NBEs are probably the initial event of blue discharges. The detection of negative NBEs might provide a convenient approach to detect the occurrence of blue discharges as lightning bolt shooting upward from the top of energetic thunderstorms.

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

PubMed

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

2018-02-22

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

Air Quality Monitoring and Forecasting Applications of Suomi NPP VIIRS Aerosol Products

NASA Astrophysics Data System (ADS)

Kondragunta, Shobha

The Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched on October 28, 2011. It provides Aerosol Optical Thickness (AOT) at two different spatial resolutions: a pixel level (~750 m at nadir) product called the Intermediate Product (IP) and an aggregated (~6 km at nadir) product called the Environmental Data Record (EDR), and a Suspended Matter (SM) EDR that provides aerosol type (dust, smoke, sea salt, and volcanic ash) information. An extensive validation of VIIRS best quality aerosol products with ground based L1.5 Aerosol Robotic NETwork (AERONET) data shows that the AOT EDR product has an accuracy/precision of -0.01/0.11 and 0.01/0.08 over land and ocean respectively. Globally, VIIRS mean AOT EDR (0.20) is similar to Aqua MODIS (0.16) with some important regional and seasonal differences. The accuracy of the SM product, however, is found to be very low (20 percent) when compared to Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) and AERONET. Several algorithm updates which include a better approach to retrieve surface reflectance have been developed for AOT retrieval. For dust aerosol type retrieval, a new approach that takes advantage of spectral dependence of Rayleigh scattering, surface reflectance, dust absorption in the deep blue (412 nm), blue (440 nm), and mid-IR (2.2 um) has been developed that detects dust with an accuracy of ~80 percent. For smoke plume identification, a source apportionment algorithm that combines fire hot spots with AOT imagery has been developed that provides smoke plume extent with an accuracy of ~70 percent. The VIIRS aerosol products will provide continuity to the current operational use of aerosol products from Aqua and Terra MODIS. These include aerosol data assimilation in Naval Research Laboratory (NRL) global aerosol model, verification of National Weather Service (NWS) dust and smoke forecasts, exceptional events monitoring by different states, air quality warnings by Environmental Protection Agency (EPA). This talk will provide an overview of VIIRS algorithms, aerosol product validation, and examples of various applications with a discussion on the relevance of product accuracy.

Water Availability Indices – A Literature Review

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

Xu, Hui; Wu, May M.

Fresh water is a critical resource for humanity and the ecosystem. In general, water resources can be partitioned into two major categories: blue water and green water (Falkenmark and Rockström 2006). Precipitation that runs off or percolates into the deep aquifer is defined as blue water, and precipitation that filtrates into soil, which eventually returns to the atmosphere as evaporation, is called green water (Hoekstra et al. 2011). For human purposes, green water is almost exclusively used for agricultural production, but blue water can be used for multiple competing sectors, such as irrigation and municipal water.

Bahama Banks, Tongue of the Ocean, Bahamas

NASA Technical Reports Server (NTRS)

1992-01-01

Most of the Western Bahama Banks, the Tongue of the Ocean and Andros Island (24.0N, 77.0W) as well as north central Cuba with its fringing reefs can be seen in this one view. The green water over the banks is less than 30 ft. deep but the deep blue of the Tongue is 4000 to 6000 ft. deep. All the sediment on the banks, including the material that forms the islands, is calcium carbonate (lime) precipitated from sea water by animals and plants.

Bahama Banks, Tongue of the Ocean, Bahamas

NASA Technical Reports Server (NTRS)

1993-01-01

Most of the Western Bahama Banks, the Tongue of the Ocean and Andros Island (25.0N, 77.0W) as well as north central Cuba with its fringing reefs can be seen in this one view. The green water over the banks is less than 30 ft. deep but the deep blue of the Tongue is 4000 to 6000 ft. deep. All the sediment on the banks, including the material that forms the islands, is calcium carbonate (lime) precipitated from sea water by animals and plants.

Synthesis and photophysical studies of blue phosphorescent Ir(III) complexes with dimethylphenylphospine.

PubMed

Ham, Ho-Wan; Jung, Kyung-Yoon; Kim, Young-Sik

2012-02-01

New blue emitting mixed ligand iridium(III) complexes comprising one cyclometalating, two phosphines trans to each other such as Ir{(CF3)2Meppy}(PPhMe3)2(H)(L) [L = CI, NCMe, CN] [(CF3)2Meppy = 2-(3', 5'-bis-trifluoromethylphenyl)-4-methylpyridine] were synthesized and studied to tune the phosphorescence wavelength to the deep blue region and to enhance the luminescence efficiencies. To achieve deep blue emission, the trifluoromethyl group substituted on the phenyl ring and the methyl group substituted on the pyridyl ring increased HOMO-LUMO gap and achieved the hypsochromic shift. To gain insight into the factors responsible for the emission color change and the different luminescence efficiency, we investigate the electron-withdrawing capabilities of ancillary ligands using the DFT and TD-DFT calculations on the ground and excited states of the complexes. From these results, we discuss how the ancillary ligand influences the emission peak as well as the metal to ligand charge transfer (MLCT) transition efficiency. The maximum emission spectra of Ir{(CF3)2Meppy}(PPhMe3)2(H)(Cl), [Ir{(CF3),Meppy)(PPhMe3),(H)(NCMe)]+ and Ir{(CF3)2Meppy}(PPhMe3)2(H)(CN) were in the ranges of 441, 435, 434 nm, respectively.

Strong ligand field effects of blue phosphorescent Ir(III) complexes with phenylpyrazole and phosphines.

PubMed

Park, Se Won; Ham, Ho Wan; Kim, Young Sik

2012-04-01

In the paper, we describe new Ir complexes for achieving efficient blue phosphorescence. New blue-emitting mixed-ligand Ir complexes comprising one cyclometalating, two phosphines trans to each other such as Ir(dppz)(PPh3)2(H)(L) (Ll= Cl, NCMe+, CN), [dppz = 3,5-Diphenylpyrazole] were synthesized and studied to tune the phosphorescence wavelength to the deep blue region and to enhance the luminescence efficiencies. To gain insight into the factors responsible for the emission color change and the variation of luminescence efficiency, we investigate the electron-withdrawing capabilities of ancillary ligands using DFT and TD-DFT calculations on the ground and excited states of the complexes. To achieve deep blue emission and increase the emission efficiency, (1) we substitute the phenyl group on the 3-position of the pyrazole ring that lowers the triplet energy enough that the quenching channel is not thermally accessible and (2) change the ancillary ligands coordinated to iridium atom to phosphine and cyano groups known as very strong field ligands. Their inclusion in the coordination sphere can increase the HOMO-LUMO gap to achieve the hypsochromic shift in emission color and lower the HOMO and LUMO energy level, which causes a large d-orbital energy splitting and avoids the quenching effect to improve the luminescence efficiency. The maximum emission spectra of Ir(dppz)(PPh3)2(H)(CI) and Ir(dppz)(PPh3)2(H)(CN) were in the ranges of 439, 432 nm, respectively.

Aromatically C6- and C9-Substituted Phenanthro[9,10-d]imidazole Blue Fluorophores: Structure-Property Relationship and Electroluminescent Application.

PubMed

Chen, Wen-Cheng; Yuan, Yi; Xiong, Yuan; Rogach, Andrey L; Tong, Qing-Xiao; Lee, Chun-Sing

2017-08-09

In this study, a series of aromatically substituted phenanthro[9,10-d]imidazole (PI) fluorophores at C6 and C9 (no. 6 and 9 carbon atoms) have been synthesized and systematically characterized by theoretical, thermal, photophysical, electrochemical, and electroluminescent (EL) studies. C6 and C9 modifications have positive influences on the thermal properties of the new materials. Theoretical calculations suggest that the C6 and the C9 positions of PI are electronically different. Theoretical and experimental evidences of intramolecular charge transfer (ICT) between two identical moieties attaching to the C6 and the C9 positions are observed. Photophysical properties of the fluorophores are greatly influenced by size and conjugation extent of the substituents as well as linking steric hindrance. It is found that the C6 and C9 positions afford moderate conjugated extension compared to the C2 modification. Moreover, ICT characteristics of the new fluorophores increase as the size of the substituted aromatic group, and are partially influenced by steric hindrance, with the anthracene and the pyrene derivatives having the strongest ICT excited properties. EL performances of the fluorophores were evaluated as host emitters or dopants in organic light-emitting devices (OLEDs). Most of the devices showed significantly improved efficiencies compared to the OLED using the nonmodified emitter. Among all the devices, a 5 wt % TPI-Py doped device exhibited excellent performances with an external quantum efficiency >5% at 1000 cd/m 2 and a deep-blue color index of (0.155, 0.065), which are comparable to the most advanced deep-blue devices. Our study can give useful information for designing C6/C9-modificated PI fluorophores and provide an efficient approach for constructing high-performance deep-blue OLEDs.

Lake

ERIC Educational Resources Information Center

Wien, Carol Anne

2008-01-01

The lake is blue black and deep. It is a glaciated finger lake, clawed out of rock when ice retracted across Nova Scotia in a northerly direction during the last ice age. The lake is narrow, a little over a mile long, and deep, 90 to 190 feet in places according to local lore, off the charts in others. The author loves to swim there, with a sense…

Scientific Encounters of the Mysterious Sea. Reading Activities That Explore the Mysterious Creatures of the Deep Blue Sea. Grades 4-7.

ERIC Educational Resources Information Center

Embry, Lynn

This activity book presents reading activities for grades 4-7 exploring the mysterious creatures of the deep sea. The creatures include: angel sharks; argonauts; barberfish; comb jelly; croakers; electric rays; flying fish; giganturid; lantern fish; narwhals; northern basket starfish; ocean sunfish; Portuguese man-of-war; sea cucumbers; sea…

Algorithm for removing scalp signals from functional near-infrared spectroscopy signals in real time using multidistance optodes.

PubMed

Kiguchi, Masashi; Funane, Tsukasa

2014-11-01

A real-time algorithm for removing scalp-blood signals from functional near-infrared spectroscopy signals is proposed. Scalp and deep signals have different dependencies on the source-detector distance. These signals were separated using this characteristic. The algorithm was validated through an experiment using a dynamic phantom in which shallow and deep absorptions were independently changed. The algorithm for measurement of oxygenated and deoxygenated hemoglobins using two wavelengths was explicitly obtained. This algorithm is potentially useful for real-time systems, e.g., brain-computer interfaces and neuro-feedback systems.

Missing Black Holes Found!

NASA Image and Video Library

2007-10-25

NASA Spitzer and Chandra space telescopes have uncovered a long-lost population of active supermassive black holes, or quasars located deep in the bellies of distant, massive galaxies circled in blue.

Deep Marginalized Sparse Denoising Auto-Encoder for Image Denoising

NASA Astrophysics Data System (ADS)

Ma, Hongqiang; Ma, Shiping; Xu, Yuelei; Zhu, Mingming

2018-01-01

Stacked Sparse Denoising Auto-Encoder (SSDA) has been successfully applied to image denoising. As a deep network, the SSDA network with powerful data feature learning ability is superior to the traditional image denoising algorithms. However, the algorithm has high computational complexity and slow convergence rate in the training. To address this limitation, we present a method of image denoising based on Deep Marginalized Sparse Denoising Auto-Encoder (DMSDA). The loss function of Sparse Denoising Auto-Encoder is marginalized so that it satisfies both sparseness and marginality. The experimental results show that the proposed algorithm can not only outperform SSDA in the convergence speed and training time, but also has better denoising performance than the current excellent denoising algorithms, including both the subjective and objective evaluation of image denoising.

Expression of deep brain photoreceptors in the Pekin drake: a possible role in the maintenance of testicular function

PubMed Central

Haas, R.; Alenciks, E.; Meddle, S.; Fraley, G. S.

2017-01-01

Abstract Several putative deep brain photoreceptors (DBPs) have been identified, such as melanopsin, opsin 5, and vertebrate ancient opsin. The aim of this study was to elucidate the role of DBPs in gonadal regulation in the Pekin drake. As previously reported, we observed opsin-like immunoreactivity (-ir) in the lateral septum (LS), melanopsin-ir in the premammillary nucleus (PMM), and opsin 5-ir in the periventricular organ. To determine the sensitivity of the DBPs to specific wavelengths of light, drakes were given an acute exposure to red, blue, or white light. Blue light stimulated an increase (P < 0.01) in the immediate early gene fra-2-ir co-expression in melanopsin-ir neurons in the PMM, and red light increased (P < 0.05) fra-2-ir co-expression in opsin-ir neurons, suggesting these neurons are blue- and red-receptive, respectively. To further investigate this photoperiodic response, we exposed drakes to chronic red, long-day white, short-day white, or blue light. Blue light elicited gonadal regression, as testes weight (P < 0.001) and plasma luteinizing hormone (LH) levels (P < 0.001) were lower compared to drakes housed under long-day white light. Photo-regressed drakes experienced complete gonadal recrudescence when housed under long-day red and blue light. qRT-PCR analyses showed that gonadally regressed drakes showed reduced levels (P < 0.01) of gonadotropin releasing hormone (GnRH) mRNA but not photoreceptor or GnIH mRNAs compared to gonadally functional drakes. Our data suggest DBP in the LS may be rhodosin and multiple DBPs are required to fully maintain gonadal function in Pekin drakes. PMID:28339754

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.

PubMed

Van Geit, Werner; Gevaert, Michael; Chindemi, Giuseppe; Rössert, Christian; Courcol, Jean-Denis; Muller, Eilif B; Schürmann, Felix; Segev, Idan; Markram, Henry

2016-01-01

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases.

Earth observations taken by the STS-112 crew

NASA Image and Video Library

2002-10-10

STS112-705-011 (7-18 October 2002) --- The light-blue region in the middle of this view, photographed from the Space Shuttle Atlantis, is the shallow flat platform known as the Great Bahama Bank. The platform is covered by less than 100 feet of water. Andros Island, the biggest island in the Bahamas chain, is the highest part of this platform and appears partly under cloud cover in the center of the view. The edges of the platform are steep, dropping off thousands of feet into the ocean depths, the deepest water indicated by deep blues. The 50-mile-wide Strait of Florida is the deep water along the left and lower sides of the view. The Key Largo part of the Florida Peninsula appears in the extreme lower left.

Bahamas

NASA Image and Video Library

1992-11-01

The light blue shallow water platforms of the Bahamas, (24.0N, 77.0W) which are separated by very deep dark blue channels make for a striking scene. In the foreground is Andros Island and in the background are the Tongue of the Ocean, the Exuma Islands, Exuma Sound and the Atlantic Ocean. The Bahamas are one of the few regions where calcium carbonate precipitates directly out of the water, as the mineral aragonite, to form the coral reef islands.

Marine Mammal Acoustic Monitoring and Habitat Investigation, Southern California Channel Island Region

DTIC Science & Technology

2005-07-01

1998 . Long - range acoustic detection and localization of blue whale calls in the northeast Pacific Ocean. Journal of the Acoustical ...Peninsula. Deep-Sea Research II 51: 2327-2344. Stafford , K.M., C.G. Fox, and D.S. Clark. 1998 . Long - range acoustic detection and localization of blue ...speciation. Phil. Trans. R. Soc. Lond B 357:493-503. Stafford , K. M., Fox, C. G. and Clark, D.S. 1998

Global Neutron View

NASA Image and Video Library

2002-03-01

In this image taken by NASA Mars Odyssey spacecraft during its first week of mapping, soil enriched in hydrogen is indicated by the deep blue colors, which show a low intensity of epithermal neutrons.

Efficient Blue Electroluminescence Using Quantum-Confined Two-Dimensional Perovskites.

PubMed

Kumar, Sudhir; Jagielski, Jakub; Yakunin, Sergii; Rice, Peter; Chiu, Yu-Cheng; Wang, Mingchao; Nedelcu, Georgian; Kim, Yeongin; Lin, Shangchao; Santos, Elton J G; Kovalenko, Maksym V; Shih, Chih-Jen

2016-10-03

Solution-processed hybrid organic-inorganic lead halide perovskites are emerging as one of the most promising candidates for low-cost light-emitting diodes (LEDs). However, due to a small exciton binding energy, it is not yet possible to achieve an efficient electroluminescence within the blue wavelength region at room temperature, as is necessary for full-spectrum light sources. Here, we demonstrate efficient blue LEDs based on the colloidal, quantum-confined 2D perovskites, with precisely controlled stacking down to one-unit-cell thickness (n = 1). A variety of low-k organic host compounds are used to disperse the 2D perovskites, effectively creating a matrix of the dielectric quantum wells, which significantly boosts the exciton binding energy by the dielectric confinement effect. Through the Förster resonance energy transfer, the excitons down-convert and recombine radiatively in the 2D perovskites. We report room-temperature pure green (n = 7-10), sky blue (n = 5), pure blue (n = 3), and deep blue (n = 1) electroluminescence, with record-high external quantum efficiencies in the green-to-blue wavelength region.

A Simple and Universal Aerosol Retrieval Algorithm for Landsat Series Images Over Complex Surfaces

NASA Astrophysics Data System (ADS)

Wei, Jing; Huang, Bo; Sun, Lin; Zhang, Zhaoyang; Wang, Lunche; Bilal, Muhammad

2017-12-01

Operational aerosol optical depth (AOD) products are available at coarse spatial resolutions from several to tens of kilometers. These resolutions limit the application of these products for monitoring atmospheric pollutants at the city level. Therefore, a simple, universal, and high-resolution (30 m) Landsat aerosol retrieval algorithm over complex urban surfaces is developed. The surface reflectance is estimated from a combination of top of atmosphere reflectance at short-wave infrared (2.22 μm) and Landsat 4-7 surface reflectance climate data records over densely vegetated areas and bright areas. The aerosol type is determined using the historical aerosol optical properties derived from the local urban Aerosol Robotic Network (AERONET) site (Beijing). AERONET ground-based sun photometer AOD measurements from five sites located in urban and rural areas are obtained to validate the AOD retrievals. Terra MODerate resolution Imaging Spectrometer Collection (C) 6 AOD products (MOD04) including the dark target (DT), the deep blue (DB), and the combined DT and DB (DT&DB) retrievals at 10 km spatial resolution are obtained for comparison purposes. Validation results show that the Landsat AOD retrievals at a 30 m resolution are well correlated with the AERONET AOD measurements (R2 = 0.932) and that approximately 77.46% of the retrievals fall within the expected error with a low mean absolute error of 0.090 and a root-mean-square error of 0.126. Comparison results show that Landsat AOD retrievals are overall better and less biased than MOD04 AOD products, indicating that the new algorithm is robust and performs well in AOD retrieval over complex surfaces. The new algorithm can provide continuous and detailed spatial distributions of AOD during both low and high aerosol loadings.

Retrieval and Validation of Aerosol Optical Depth by using the GF-1 Remote Sensing Data

NASA Astrophysics Data System (ADS)

Zhang, L.; Xu, S.; Wang, L.; Cai, K.; Ge, Q.

2017-05-01

Based on the characteristics of GF-1 remote sensing data, the method and data processing procedure to retrieve the Aerosol Optical Depth (AOD) are developed in this study. The surface contribution over dense vegetation and urban bright target areas are respectively removed by using the dark target and deep blue algorithms. Our method is applied for the three serious polluted Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions. The retrieved AOD are validated by ground-based AERONET data from Beijing, Hangzhou, Hong Kong sites. Our results show that, 1) the heavy aerosol loadings are usually distributed in high industrial emission and dense populated cities, with the AOD value near 1. 2) There is a good agreement between satellite-retrievals and in-site observations, with the coefficient factors of 0.71 (BTH), 0.55 (YRD) and 0.54(PRD). 3) The GF-1 retrieval uncertainties are mainly from the impact of cloud contamination, high surface reflectance and assumed aerosol model.

Effect of black carbon on dust property retrievals from satellite observations

NASA Astrophysics Data System (ADS)

Lin, Tang-Huang; Yang, Ping; Yi, Bingqi

2013-01-01

The effect of black carbon on the optical properties of polluted mineral dust is studied from a satellite remote-sensing perspective. By including the auxiliary data of surface reflectivity and aerosol mixing weight, the optical properties of mineral dust, or more specifically, the aerosol optical depth (AOD) and single-scattering albedo (SSA), can be retrieved with improved accuracy. Precomputed look-up tables based on the principle of the Deep Blue algorithm are utilized in the retrieval. The mean differences between the retrieved results and the corresponding ground-based measurements are smaller than 1% for both AOD and SSA in the case of pure dust. However, the retrievals can be underestimated by as much as 11.9% for AOD and overestimated by up to 4.1% for SSA in the case of polluted dust with an estimated 10% (in terms of the number-density mixing ratio) of soot aggregates if the black carbon effect on dust aerosols is neglected.

Typhoon Ioke in the Western Pacific

NASA Technical Reports Server (NTRS)

2006-01-01

[figure removed for brevity, see original site] [figure removed for brevity, see original site] Microwave ImageVisible Light Image

These infrared, microwave, and visible images were created with data retrieved by the Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite.

Infrared Image Because infrared radiation does not penetrate through clouds, AIRS infrared images show either the temperature of the cloud tops or the surface of the Earth in cloud-free regions. The lowest temperatures (in purple) are associated with high, cold cloud tops that make up the top of the storm. In cloud-free areas the AIRS instrument will receive the infrared radiation from the surface of the Earth, resulting in the warmest temperatures (orange/red).

Microwave Image In the AIRS microwave imagery, deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. On the other hand, land appears much warmer due to its high radiation emissivity.

In the AIRS microwave imagery, deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. On the other hand, land appears much warmer due to its high radiation emissivity.

Microwave radiation from Earth's surface and lower atmosphere penetrates most clouds to a greater or lesser extent depending upon their water vapor, liquid water and ice content. Precipitation, and ice crystals found at the cloud tops where strong convection is taking place, act as barriers to microwave radiation. Because of this barrier effect, the AIRS microwave sensor detects only the radiation arising at or above their location in the atmospheric column. Where these barriers are not present, the microwave sensor detects radiation arising throughout the air column and down to the surface. Liquid surfaces (oceans, lakes and rivers) have 'low emissivity' (the signal isn't as strong) and their radiation brightness temperature is therefore low. Thus the ocean also appears 'low temperature' in the AIRS microwave images and is assigned the color blue. Therefore deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. Land appears much warmer due to its high radiation emissivity.

Vis/NIR Image The AIRS instrument suite contains a sensor that captures radiation in four bands of the visible/near-infrared portion of the electromagetic spectrum. Data from three of these bands are combined to create 'visible' images similar to a snapshot taken with your camera.

The Atmospheric Infrared Sounder Experiment, with its visible, infrared, and microwave detectors, provides a three-dimensional look at Earth's weather. Working in tandem, the three instruments can make simultaneous observations all the way down to the Earth's surface, even in the presence of heavy clouds. With more than 2,000 channels sensing different regions of the atmosphere, the system creates a global, 3-D map of atmospheric temperature and humidity and provides information on clouds, greenhouse gases, and many other atmospheric phenomena. The AIRS Infrared Sounder Experiment flies onboard NASA's Aqua spacecraft and is managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif., under contract to NASA. JPL is a division of the California Institute of Technology in Pasadena.

Deep, wide-field, multi-band imaging of z approximately equal to 0.4 clusters and their environs

NASA Technical Reports Server (NTRS)

Silva, David R.; Pierce, Michael J.

1993-01-01

The existence of an excess population of blue galaxies in the cores of distant, rich clusters of galaxies, commonly referred to as the 'Butcher-Oemler' effect is now well established. Spectroscopy of clusters at z = 0.2-0.4 has confirmed that the luminous blue populations comprise as much as 20 percent of these clusters. This fraction is much higher that the 2 percent blue fraction found for nearby rich clusters, such as Coma, indicating that rapid galaxy evolution has occurred on a relatively short time scale. Spectroscopy has also shown that the 'blue' galaxies can basically be divided into three classes: 'starburst' galaxies with large (O II) equivalent widths, 'post-starburst' E+A galaxies (i.e. galaxies with strong Balmer lines shortward of 4000A but elliptical-like colors, and normal spiral/irregulars. Unfortunately, it is difficult to obtain enough spectra of individual galaxies in these intermediate redshift clusters to say anything statistically meaningful. Thus, limited information is available about the relative numbers of these three classes of 'blue' galaxies and the associated E/SO population in these intermediate redshift clusters. More statistically meaningful results can be derived from deep imaging of these clusters. However, the best published data to date (e.g. MacLaren et al. 1988; Dressler & Gunn 1992) are limited to the cluster cores and do not sample the galaxy luminosity functions very deeply at the bluest wavelengths. Furthermore, only limited spectro-energy distribution data is available below 4000A in the observed cluster rest frame providing limited sensitivity to 'recent' star formation activity. To improve this situation, we are currently obtaining deep, wide-field UBRI images of all known rich clusters at z approx. equals 0.4. Our main objective is to obtain the necessary color information to distinguish between the E+SO, 'E+A', and spiral/irregular galaxy populations throughout the cluster/supercluster complex. At this redshift, UBRI correspond to rest-frame 2500A/UVR bandpasses. The rest-frame UVR system provides a powerful 'blue' galaxy discriminate given the expected color distribution. Moreover, since 'hot' stars peak near 2500A, that bandpass is a powerful probe of recent star formation activity in all classes of galaxies. In particular, it is sensitive to ellipticals with 'UV excess' populations (MacLaren et al. 1988).

Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection

PubMed Central

Chen, Yaw-Chung

2015-01-01

The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms. PMID:26437335

A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection.

PubMed

Lee, Chun-Liang; Lin, Yi-Shan; Chen, Yaw-Chung

2015-01-01

The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms.

DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation.

PubMed

Kalsi, Shruti; Kaur, Harleen; Chang, Victor

2017-12-05

Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.

High-efficiency emitting materials based on phenylquinoline/carbazole-based compounds for organic light emitting diode applications

NASA Astrophysics Data System (ADS)

Jin, Sung-Ho

2009-08-01

Highly efficient light-emitting materials based on phenylquinoline-carbazole derivative has been synthesized for organic-light emitting diodes (OLEDs). The materials form high quality amorphous thin films by thermal evaporation and the energy levels can be easily adjusted by the introduction of different electron donating and electron withdrawing groups on carbazoylphenylquinoline. Non-doped deep-blue OLEDs using Et-CVz-PhQ as the emitter show bright emission (CIE coordinates, x=0.156, y=0.093) with an external quantum efficiency of 2.45 %. Furthermore, the material works as an excellent host material for BCzVBi to get high-performance OLEDs with excellent deep-blue CIE coordinates (x=0.155, y=0.157), high power efficiency (5.98 lm/W), and high external quantum efficiency (5.22 %). Cyclometalated Ir(III) μ-chloride bridged dimers were synthesized by iridium trichloride hydrate with an excess of our developed deep-blue emitter, Et-CVz-PhQ. The Ir(III) complexes were prepared by the dimers with the corresponding ancillary ligands. The chloride bridged diiridium complexes can be easily converted to mononuclear Ir(III) complexes by replacing the two bridging chlorides with bidentate monoanionic ancillary ligands. Among the various types of ancillary ligands, we firstly used picolinic acid N-oxide, including picolinic acid and acetylacetone as an ancillary ligands for Ir(III) complexes. The PhOLEDs also shows reasonably high brightness and good luminance efficiency of 20,000 cd/m2 and 12 cd/A, respectively.

Photometric Selection of a Massive Galaxy Catalog with z ≥ 0.55

NASA Astrophysics Data System (ADS)

Núñez, Carolina; Spergel, David N.; Ho, Shirley

2017-02-01

We present the development of a photometrically selected massive galaxy catalog, targeting Luminous Red Galaxies (LRGs) and massive blue galaxies at redshifts of z≥slant 0.55. Massive galaxy candidates are selected using infrared/optical color-color cuts, with optical data from the Sloan Digital Sky Survey (SDSS) and infrared data from “unWISE” forced photometry derived from the Wide-field Infrared Survey Explorer (WISE). The selection method is based on previously developed techniques to select LRGs with z> 0.5, and is optimized using receiver operating characteristic curves. The catalog contains 16,191,145 objects, selected over the full SDSS DR10 footprint. The redshift distribution of the resulting catalog is estimated using spectroscopic redshifts from the DEEP2 Galaxy Redshift Survey and photometric redshifts from COSMOS. Restframe U - B colors from DEEP2 are used to estimate LRG selection efficiency. Using DEEP2, the resulting catalog has an average redshift of z = 0.65, with a standard deviation of σ =2.0, and an average restframe of U-B=1.0, with a standard deviation of σ =0.27. Using COSMOS, the resulting catalog has an average redshift of z = 0.60, with a standard deviation of σ =1.8. We estimate 34 % of the catalog to be blue galaxies with z≥slant 0.55. An estimated 9.6 % of selected objects are blue sources with redshift z< 0.55. Stellar contamination is estimated to be 1.8%.

Seasonal And Regional Differentiation Of Bio-Optical Properties Within The North Polar Atlantic

NASA Technical Reports Server (NTRS)

Stramska, Malgorzata; Stramski, Dariusz; Kaczmarek, Slawomir; Allison, David B.; Schwarz, Jill

2005-01-01

Using data collected during spring and summer seasons in the north polar Atlantic we examined the variability of the spectral absorption, a(lambda), and backscattering, b(sub b)(lambda), coefficients of surface waters and its relation to phytoplankton pigment concentration and composition. For a given chlorophyll a concentration (TChla), the concentrations of photosynthetic carotenoids (PSC), photoprotective carotenoids (PPC), and total accessory pigments (AP) were consistently lower in spring than in summer. The chlorophyll-specific absorption coefficients of phytoplankton and total particulate matter were also lower in spring, which can be partly attributed to lower proportions of PPC, PSC, and AP in spring. The spring values of the green-to-blue band ratio of the absorption coefficient were higher than the summer ratios. The blue-to-green ratios of backscattering coefficient were also higher in spring. The higher b(sub b) values and lower blue-to-green b(sub b) ratios in summer were likely associated with higher concentrations of detrital particles in summer compared to spring. Because the product of the green-to-blue absorption ratio and the blue-to-green backscattering ratio is a proxy for the blue-to-green ratio of remote-sensing reflectance, we conclude that the performance of ocean color band-ratio algorithms for estimating pigments in the north polar Atlantic is significantly affected by seasonal shifts in the relationships between absorption and TChla as well as between backscattering and TChla. Intriguingly, however, fairly good estimate of the particulate beam attenuation coefficient at 660 nm (potential measure of total particulate matter or particulate organic carbon concentration) can be obtained by applying a single blue-to-green band-ratio algorithm for both spring and summer seasons.

Student beats the teacher: deep neural networks for lateral ventricles segmentation in brain MR

NASA Astrophysics Data System (ADS)

Ghafoorian, Mohsen; Teuwen, Jonas; Manniesing, Rashindra; Leeuw, Frank-Erik d.; van Ginneken, Bram; Karssemeijer, Nico; Platel, Bram

2018-03-01

Ventricular volume and its progression are known to be linked to several brain diseases such as dementia and schizophrenia. Therefore accurate measurement of ventricle volume is vital for longitudinal studies on these disorders, making automated ventricle segmentation algorithms desirable. In the past few years, deep neural networks have shown to outperform the classical models in many imaging domains. However, the success of deep networks is dependent on manually labeled data sets, which are expensive to acquire especially for higher dimensional data in the medical domain. In this work, we show that deep neural networks can be trained on muchcheaper-to-acquire pseudo-labels (e.g., generated by other automated less accurate methods) and still produce more accurate segmentations compared to the quality of the labels. To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation. Then on a large manually annotated test set, we show that the network significantly outperforms the conventional region growing algorithm which was used to produce the training labels for the network. Our experiments report a Dice Similarity Coefficient (DSC) of 0.874 for the trained network compared to 0.754 for the conventional region growing algorithm (p < 0.001).

Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

PubMed

Abràmoff, Michael David; Lou, Yiyue; Erginay, Ali; Clarida, Warren; Amelon, Ryan; Folk, James C; Niemeijer, Meindert

2016-10-01

To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists. We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR version X2.1. Sensitivity, specificity, negative predictive value, area under the curve (AUC), and their confidence intervals (CIs) were calculated. Sensitivity was 96.8% (95% CI: 93.3%-98.8%), specificity was 87.0% (95% CI: 84.2%-89.4%), with 6/874 false negatives, resulting in a negative predictive value of 99.0% (95% CI: 97.8%-99.6%). No cases of severe NPDR, PDR, or ME were missed. The AUC was 0.980 (95% CI: 0.968-0.992). Sensitivity was not statistically different from published IDP sensitivity, which had a CI of 94.4% to 99.3%, but specificity was significantly better than the published IDP specificity CI of 55.7% to 63.0%. A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Deep learning enhanced algorithms have the potential to improve the efficiency of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.

Two-dimensional joint inversion of Magnetotelluric and local earthquake data: Discussion on the contribution to the solution of deep subsurface structures

NASA Astrophysics Data System (ADS)

Demirci, İsmail; Dikmen, Ünal; Candansayar, M. Emin

2018-02-01

Joint inversion of data sets collected by using several geophysical exploration methods has gained importance and associated algorithms have been developed. To explore the deep subsurface structures, Magnetotelluric and local earthquake tomography algorithms are generally used individually. Due to the usage of natural resources in both methods, it is not possible to increase data quality and resolution of model parameters. For this reason, the solution of the deep structures with the individual usage of the methods cannot be fully attained. In this paper, we firstly focused on the effects of both Magnetotelluric and local earthquake data sets on the solution of deep structures and discussed the results on the basis of the resolving power of the methods. The presence of deep-focus seismic sources increase the resolution of deep structures. Moreover, conductivity distribution of relatively shallow structures can be solved with high resolution by using MT algorithm. Therefore, we developed a new joint inversion algorithm based on the cross gradient function in order to jointly invert Magnetotelluric and local earthquake data sets. In the study, we added a new regularization parameter into the second term of the parameter correction vector of Gallardo and Meju (2003). The new regularization parameter is enhancing the stability of the algorithm and controls the contribution of the cross gradient term in the solution. The results show that even in cases where resistivity and velocity boundaries are different, both methods influence each other positively. In addition, the region of common structural boundaries of the models are clearly mapped compared with original models. Furthermore, deep structures are identified satisfactorily even with using the minimum number of seismic sources. In this paper, in order to understand the future studies, we discussed joint inversion of Magnetotelluric and local earthquake data sets only in two-dimensional space. In the light of these results and by means of the acceleration on the three-dimensional modelling and inversion algorithms, it is thought that it may be easier to identify underground structures with high resolution.

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

PubMed Central

Van Geit, Werner; Gevaert, Michael; Chindemi, Giuseppe; Rössert, Christian; Courcol, Jean-Denis; Muller, Eilif B.; Schürmann, Felix; Segev, Idan; Markram, Henry

2016-01-01

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases. PMID:27375471

SWIM: A Semi-Analytical Ocean Color Inversion Algorithm for Optically Shallow Waters

NASA Technical Reports Server (NTRS)

McKinna, Lachlan I. W.; Werdell, P. Jeremy; Fearns, Peter R. C. S.; Weeks, Scarla J.; Reichstetter, Martina; Franz, Bryan A.; Shea, Donald M.; Feldman, Gene C.

2014-01-01

Ocean color remote sensing provides synoptic-scale, near-daily observations of marine inherent optical properties (IOPs). Whilst contemporary ocean color algorithms are known to perform well in deep oceanic waters, they have difficulty operating in optically clear, shallow marine environments where light reflected from the seafloor contributes to the water-leaving radiance. The effect of benthic reflectance in optically shallow waters is known to adversely affect algorithms developed for optically deep waters [1, 2]. Whilst adapted versions of optically deep ocean color algorithms have been applied to optically shallow regions with reasonable success [3], there is presently no approach that directly corrects for bottom reflectance using existing knowledge of bathymetry and benthic albedo.To address the issue of optically shallow waters, we have developed a semi-analytical ocean color inversion algorithm: the Shallow Water Inversion Model (SWIM). SWIM uses existing bathymetry and a derived benthic albedo map to correct for bottom reflectance using the semi-analytical model of Lee et al [4]. The algorithm was incorporated into the NASA Ocean Biology Processing Groups L2GEN program and tested in optically shallow waters of the Great Barrier Reef, Australia. In-lieu of readily available in situ matchup data, we present a comparison between SWIM and two contemporary ocean color algorithms, the Generalized Inherent Optical Property Algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA).

Algorithms for Processing and Analysis of Ocean Color Satellite Data for Coastal Case 2 Waters. Chapter 16

NASA Technical Reports Server (NTRS)

Stumpf, Richard P.; Arnone, Robert A.; Gould, Richard W., Jr.; Ransibrahmanakul, Varis; Tester, Patricia A.

2003-01-01

SeaWiFS has the ability to enhance our understanding of many oceanographic processes. However, its utility in the coastal zone has been limited by valid bio-optical algorithms and by the determination of accurate water reflectances, particularly in the blue bands (412-490 nm), which have a significant impact on the effectiveness of all bio-optical algorithms. We have made advances in three areas: algorithm development (Table 16.1), field data collection, and data applications.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Deep learning for computational chemistry.

PubMed

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.

Deep learning for computational chemistry

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

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

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. Inmore » 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.« less

Microcracking and healing in granites: new evidence from cathodoluminescence.

PubMed

Sprunt, E S; Nur, A

1979-08-03

Quartz grains in granitic rocks usually have blue cathodoluminescence (CL). Within the blue-luminescing grains, there are often red-luminescing domains which are frequently impossible to detect without CL contrast. This finding suggests that the red-luminescing quartz is sealing preexisting mnicrocracks. The presence of these now-healed microcracks has important implications with respect to the role of pore fluid pressure and fluid transfer in metamorphism, the origih of granites, longperiod crustal deformation, earthquake mechanics, physical properties of rocks, and deep-seated geothermal energy.

Composite Sunrise Butte pluton: Insights into Jurassic–Cretaceous collisional tectonics and magmatism in the Blue Mountains Province, northeastern Oregon

USGS Publications Warehouse

Johnson, Kenneth H.; Schwartz, J.J.; Žák, Jiří; Verner, Krystof; Barnes, Calvin G.; Walton, Clay; Wooden, Joseph L.; Wright, James E.; Kistler, Ronald W.

2015-01-01

The composite Sunrise Butte pluton, in the central part of the Blue Mountains Province, northeastern Oregon, preserves a record of subduction-related magmatism, arc-arc collision, crustal thickening, and deep-crustal anatexis. The earliest phase of the pluton (Desolation Creek unit) was generated in a subduction zone environment, as the oceanic lithosphere between the Wallowa and Olds Ferry island arcs was consumed. Zircons from this unit yielded a 206Pb/238U age of 160.2 ± 2.1 Ma. A magmatic lull ensued during arc-arc collision, after which partial melting at the base of the thickened Wallowa arc crust produced siliceous magma that was emplaced into metasedimentary rocks and serpentinite of the overthrust forearc complex. This magma crystallized to form the bulk of the Sunrise Butte composite pluton (the Sunrise Butte unit; 145.8 ± 2.2 Ma). The heat necessary for crustal anatexis was supplied by coeval mantle-derived magma (the Onion Gulch unit; 147.9 ± 1.8 Ma).The lull in magmatic activity between 160 and 148 Ma encompasses the timing of arc-arc collision (159–154 Ma), and it is similar to those lulls observed in adjacent areas of the Blue Mountains Province related to the same shortening event. Previous researchers have proposed a tectonic link between the Blue Mountains Province and the Klamath Mountains and northern Sierra Nevada Provinces farther to the south; however, timing of Late Jurassic deformation in the Blue Mountains Province predates the timing of the so-called Nevadan orogeny in the Klamath Mountains. In both the Blue Mountains Province and Klamath Mountains, the onset of deep-crustal partial melting initiated at ca. 148 Ma, suggesting a possible geodynamic link. One possibility is that the Late Jurassic shortening event recorded in the Blue Mountains Province may be a northerly extension of the Nevadan orogeny. Differences in the timing of these events in the Blue Mountains Province and the Klamath–Sierra Nevada Provinces suggest that shortening and deformation were diachronous, progressing from north to south. We envision that Late Jurassic deformation may have collapsed a Gulf of California–style oceanic extensional basin that extended from the Klamath Mountains (e.g., Josephine ophiolite) to the central Blue Mountains Province, and possibly as far north as the North Cascades (i.e., the coeval Ingalls ophiolite).

Overpressure, Low Effective Stress, and Slope Failure in the Ursa Region, Deep-Water Gulf of Mexico

NASA Astrophysics Data System (ADS)

Sawyer, D. E.; Flemings, P. B.

2004-12-01

Slope failures are associated with overpressured pore fluids and low effective stresses in the Quaternary strata of the Ursa Region, deep-water Gulf of Mexico. At Ursa, a permeable turbidite sandstone (the Blue Unit) is overlain by a low-permeability mudstone. Overpressure in the mudstone, measured with a pore pressure penetrometer (piezoprobe), begin within a few meters of the seafloor and extend 250-450 meters down to the Blue Unit. The overpressure ratio (λ *=(Pp-Phydrostatic)\\ (Sv-Phydrostatic), where Sv is the overburden stress, Pp is pore pressure, and Phydrostatic is the hydrostatic pressure) ranges from 0.8 where the overburden is thin to 0.4 where the overburden is thick. Detachment surfaces, mapped with high resolution 3D seismic data, are associated with zones where effective stresses are low. Four subsurface slumps were mapped and are oriented generally northwest-southeast. Slump surface areas are less than 250 km2 and maximum scarp-wall height on the largest slide is ˜120 meters. We interpret that asymmetric loading of the Blue Unit by low-permeable mudstone has driven fluids to where overburden is thin, decreased effective stress, and generated slope instability.

THE SIZE DIFFERENCE BETWEEN RED AND BLUE GLOBULAR CLUSTERS IS NOT DUE TO PROJECTION EFFECTS

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

Webb, Jeremy J.; Harris, William E.; Sills, Alison, E-mail: webbjj@mcmaster.ca

Metal-rich (red) globular clusters in massive galaxies are, on average, smaller than metal-poor (blue) globular clusters. One of the possible explanations for this phenomenon is that the two populations of clusters have different spatial distributions. We test this idea by comparing clusters observed in unusually deep, high signal-to-noise images of M87 with a simulated globular cluster population in which the red and blue clusters have different spatial distributions, matching the observations. We compare the overall distribution of cluster effective radii as well as the relationship between effective radius and galactocentric distance for both the observed and simulated red and bluemore » sub-populations. We find that the different spatial distributions does not produce a significant size difference between the red and blue sub-populations as a whole or at a given galactocentric distance. These results suggest that the size difference between red and blue globular clusters is likely due to differences during formation or later evolution.« less

Far red bioluminescence from two deep-sea fishes.

PubMed

Widder, E A; Latz, M I; Herring, P J; Case, J F

1984-08-03

Spectral measurements of red bioluminescence were obtained from the deep-sea stomiatoid fishes Aristostomias scintillans (Gilbert) and Malacosteus niger (Ayres). Red luminescence from suborbital light organs extends to the near infrared, with peak emission at approximately 705 nanometers in the far red. These fishes also have postorbital light organs that emit blue luminescence with maxima between 470 and 480 nanometers. The red bioluminescence may be due to an energy transfer system and wavelength-selective filtering.

Deep learning based syndrome diagnosis of chronic gastritis.

PubMed

Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

2014-01-01

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

PubMed Central

Liu, Guo-Ping; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

2014-01-01

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:24734118

Plant Species Identification by Bi-channel Deep Convolutional Networks

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Spider Bites: First Aid

MedlinePlus

... United States, its range is central and southern states. Signs and symptoms of a brown recluse spider bite vary but may include: At first, a mild pain Redness and intense pain A deep blue or purple area around the bite, which ...

Variation of the external quantum efficiency with temperature and current density in red, blue, and deep ultraviolet light-emitting diodes

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

Park, Jun Hyuk; Lee, Jong Won; Kim, Dong Yeong

The temperature-dependent external quantum efficiencies (EQEs) were investigated for a 620 nm AlGaInP red light-emitting diodes (LEDs), a 450 nm GaInN blue LED, and a 285 nm AlGaN deep-ultraviolet (DUV) LED. We observed distinct differences in the variation of the EQE with temperature and current density for the three types of LEDs. Whereas the EQE of the AlGaInP red LED increases as temperature decreases below room temperature, the EQEs of GaInN blue and AlGaN DUV LEDs decrease for the same change in temperature in a low-current density regime. The free carrier concentration, as determined from the dopant ionization energy, shows a strong material-system-specificmore » dependence, leading to different degrees of asymmetry in carrier concentration for the three types of LEDs. We attribute the EQE variation of the red, blue, and DUV LEDs to the different degrees of asymmetry in carrier concentration, which can be exacerbated at cryogenic temperatures. As for the EQE variation with temperature in a high-current density regime, the efficiency droop for the AlGaInP red and GaInN blue LEDs becomes more apparent as temperature decreases, due to the deterioration of the asymmetry in carrier concentration. However, the EQE of the AlGaN DUV LED initially decreases, then reaches an EQE minimum point, and then increases again due to the field-ionization of acceptors by the Poole-Frenkel effect. The results elucidate that carrier transport phenomena allow for the understanding of the droop phenomenon across different material systems, temperatures, and current densities.« less

Transmission of light in deep sea water at the site of the ANTARES neutrino telescope

NASA Astrophysics Data System (ADS)

ANTARES Collaboration; Aguilar, J. A.; Albert, A.; Amram, P.; Anghinolfi, M.; Anton, G.; Anvar, S.; Ardellier-Desages, F. E.; Aslanides, E.; Aubert, J.-J.; Azoulay, R.; Bailey, D.; Basa, S.; Battaglieri, M.; Becherini, Y.; Bellotti, R.; Beltramelli, J.; Bertin, V.; Billault, M.; Blaes, R.; Blanc, F.; Bland, R. W.; de Botton, N.; Boulesteix, J.; Bouwhuis, M. C.; Brooks, C. B.; Bradbury, S. M.; Bruijn, R.; Brunner, J.; Bugeon, F.; Burgio, G. F.; Cafagna, F.; Calzas, A.; Caponetto, L.; Carmona, E.; Carr, J.; Cartwright, S. L.; Cecchini, S.; Charvis, P.; Circella, M.; Colnard, C.; Compère, C.; Croquette, J.; Cooper, S.; Coyle, P.; Cuneo, S.; Damy, G.; van Dantzig, R.; Deschamps, A.; de Marzo, C.; Destelle, J.-J.; de Vita, R.; Dinkelspiler, B.; Dispau, G.; Drougou, J.-F.; Druillole, F.; Engelen, J.; Favard, S.; Feinstein, F.; Ferry, S.; Festy, D.; Fopma, J.; Fuda, J.-L.; Gallone, J.-M.; Giacomelli, G.; Girard, N.; Goret, P.; Gournay, J.-F.; Hallewell, G.; Hartmann, B.; Heijboer, A.; Hello, Y.; Hernández-Rey, J. J.; Herrouin, G.; Hößl, J.; Hoffmann, C.; Hubbard, J. R.; Jaquet, M.; de Jong, M.; Jouvenot, F.; Kappes, A.; Karg, T.; Karkar, S.; Karolak, M.; Katz, U.; Keller, P.; Kooijman, P.; Korolkova, E. V.; Kouchner, A.; Kretschmer, W.; Kudryavtsev, V. A.; Lafoux, H.; Lagier, P.; Lamare, P.; Languillat, J.-C.; Laubier, L.; Legou, T.; Le Guen, Y.; Le Provost, H.; Le van Suu, A.; Lo Nigro, L.; Lo Presti, D.; Loucatos, S.; Louis, F.; Lyashuk, V.; Magnier, P.; Marcelin, M.; Margiotta, A.; Maron, C.; Massol, A.; Mazéas, F.; Mazeau, B.; Mazure, A.; McMillan, J. E.; Michel, J.-L.; Millot, C.; Milovanovic, A.; Montanet, F.; Montaruli, T.; Morel, J.-P.; Moscoso, L.; Nezri, E.; Niess, V.; Nooren, G. J.; Ogden, P.; Olivetto, C.; Palanque-Delabrouille, N.; Payre, P.; Petta, C.; Pineau, J.-P.; Poinsignon, J.; Popa, V.; Potheau, R.; Pradier, T.; Racca, C.; Randazzo, N.; Real, D.; van Rens, B. A. P.; Réthoré, F.; Ripani, M.; Roca-Blay, V.; Romeyer, A.; Rollin, J.-F.; Romita, M.; Rose, H. J.; Rostovtsev, A.; Ruppi, M.; Russo, G. V.; Sacquin, Y.; Saouter, S.; Schuller, J.-P.; Schuster, W.; Sokalski, I.; Suvorova, O.; Spooner, N. J. C.; Spurio, M.; Stolarczyk, T.; Stubert, D.; Taiuti, M.; Thompson, L. F.; Tilav, S.; Usik, A.; Valdy, P.; Vallage, B.; Vaudaine, G.; Vernin, P.; Virieux, J.; Vladimirsky, E.; de Vries, G.; de Witt Huberts, P.; de Wolf, E.; Zaborov, D.; Zaccone, H.; Zakharov, V.; Zavatarelli, S.; de Zornoza, J. D.; Zúñiga, J.

2005-02-01

The ANTARES neutrino telescope is a large photomultiplier array designed to detect neutrino-induced upward-going muons by their Cherenkov radiation. Understanding the absorption and scattering of light in the deep Mediterranean is fundamental to optimising the design and performance of the detector. This paper presents measurements of blue and UV light transmission at the ANTARES site taken between 1997 and 2000. The derived values for the scattering length and the angular distribution of particulate scattering were found to be highly correlated, and results are therefore presented in terms of an absorption length λabs and an effective scattering length λscteff. The values for blue (UV) light are found to be λabs ≃ 60(26) m, λscteff≃265(122)m, with significant (˜15%) time variability. Finally, the results of ANTARES simulations showing the effect of these water properties on the anticipated performance of the detector are presented.

Visualization of deep ultraviolet photons based on Förster resonance energy transfer and cascade photon reabsorption in diphenylalanine-carbon nitrides composite film

NASA Astrophysics Data System (ADS)

Gan, Zhixing; Zhou, Weiping; Chen, Zhihui; Wang, Huan; Di, Yunsong; Huang, Shisong

2016-11-01

A diphenylalanine (L-Phe-L-Phe, FF)-carbon nitride composite film is designed and fabricated to visualize the deep ultraviolet (DUV, 245-290 nm) photons. The FF film, composed of diphenylalanine molecules, doped with carbon nitrides shows blue emission under excitation of DUV light, which makes the DUV beam observable. Both Förster resonance energy transfer and cascade photon reabsorption contribute to the conversion of photon energy. First, the FF is excited by the DUV photons. On one hand, the energy transfers to the embedded carbon nitrides through nonradiative dipole-dipole couplings. On the other hand, the 284 nm photons emitted from the FF would further excite the carbon nitrides, which will finally convert to blue fluorescence. Herein, the experimental demonstration of a simple device for the visualization of high DUV fluxes is reported.

Towards deep learning with segregated dendrites

PubMed Central

Guerguiev, Jordan; Lillicrap, Timothy P

2017-01-01

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations—the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons. PMID:29205151

Towards deep learning with segregated dendrites.

PubMed

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

2017-12-05

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

Prediction of properties of wheat dough using intelligent deep belief networks

NASA Astrophysics Data System (ADS)

Guha, Paramita; Bhatnagar, Taru; Pal, Ishan; Kamboj, Uma; Mishra, Sunita

2017-11-01

In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented.

SWIM: A Semi-Analytical Ocean Color Inversion Algorithm for Optically Shallow Waters

NASA Technical Reports Server (NTRS)

McKinna, Lachlan I. W.; Werdell, P. Jeremy; Fearns, Peter R. C. S.; Weeks, Scarla J.; Reichstetter, Martina; Franz, Bryan A.; Bailey, Sean W.; Shea, Donald M.; Feldman, Gene C.

2014-01-01

In clear shallow waters, light that is transmitted downward through the water column can reflect off the sea floor and thereby influence the water-leaving radiance signal. This effect can confound contemporary ocean color algorithms designed for deep waters where the seafloor has little or no effect on the water-leaving radiance. Thus, inappropriate use of deep water ocean color algorithms in optically shallow regions can lead to inaccurate retrievals of inherent optical properties (IOPs) and therefore have a detrimental impact on IOP-based estimates of marine parameters, including chlorophyll-a and the diffuse attenuation coefficient. In order to improve IOP retrievals in optically shallow regions, a semi-analytical inversion algorithm, the Shallow Water Inversion Model (SWIM), has been developed. Unlike established ocean color algorithms, SWIM considers both the water column depth and the benthic albedo. A radiative transfer study was conducted that demonstrated how SWIM and two contemporary ocean color algorithms, the Generalized Inherent Optical Properties algorithm (GIOP) and Quasi-Analytical Algorithm (QAA), performed in optically deep and shallow scenarios. The results showed that SWIM performed well, whilst both GIOP and QAA showed distinct positive bias in IOP retrievals in optically shallow waters. The SWIM algorithm was also applied to a test region: the Great Barrier Reef, Australia. Using a single test scene and time series data collected by NASA's MODIS-Aqua sensor (2002-2013), a comparison of IOPs retrieved by SWIM, GIOP and QAA was conducted.

Crustal thickness variations across the Blue Ridge mountains, southern Appalachians: an alternative procedure for migrating wide-angle reflection data

Treesearch

Robert B. Hawman

2008-01-01

Migration of wide-angle reflections generated by quarry blasts suggests that crustal thickness increases from 38 km beneath the Carolina Terrane to 47–51 km along the southeastern flank of the Blue Ridge. The migration algorithm, developed for generating single-fold images from explosions and earthquakes recorded with isolated, short-aperture arrays, uses the localized...

Polar Maps of Thermal and Epithermal Neutrons

NASA Image and Video Library

2002-05-28

Observations by NASA Mars Odyssey spacecraft show views of the polar regions of Mars in thermal neutrons top and epithermal neutrons bottom. In these maps, deep blue indicates a low amount of neutrons and red indicates a high amount.

HST Imaging of the (Almost) Dark ALFALFA Source AGC 229385

NASA Astrophysics Data System (ADS)

Brunker, Samantha; Salzer, John Joseph; McQuinn, Kristen B.; Janowiecki, Steven; Leisman, Luke; Rhode, Katherine L.; Adams, Elizabeth A.; Cannon, John M.; Giovanelli, Riccardo; Haynes, Martha P.

2017-06-01

We present deep HST imaging photometry of the extreme galaxy AGC 229385. This system was first discovered as an HI source in the ALFALFA all-sky HI survey. It was cataloged as an (almost) dark galaxy because it did not exhibit any obvious optical counterpart in the available wide-field survey data (e.g., SDSS). Deep optical imaging with the WIYN 3.5-m telescope revealed an ultra-low surface brightness stellar component located at the center of the HI detection. With a peak central surface brightness of 26.4 mag/sq. arcsec in g and very blue colors (g-r = -0.1), the stellar component to this gas-rich system is quite enigmatic. We have used our HST images to produce a deep CMD of the resolved stellar population present in AGC 229385. We clearly detect a red-giant branch and use it to infer a distance of 5.50 ± 0.23 Mpc. The CMD is dominated by older stars, contrary to expectations given the blue optical colors obtained from our ground-based photometry. Our new distance is substantially lower than earlier estimates, and shows that AGC 229385 is an extreme dwarf galaxy with one of the highest MHI/L ratios known.

Rigidifying fluorescent linkers by metal-organic framework formation for fluorescence blue shift and quantum yield enhancement.

PubMed

Wei, Zhangwen; Gu, Zhi-Yuan; Arvapally, Ravi K; Chen, Ying-Pin; McDougald, Roy N; Ivy, Joshua F; Yakovenko, Andrey A; Feng, Dawei; Omary, Mohammad A; Zhou, Hong-Cai

2014-06-11

We demonstrate that rigidifying the structure of fluorescent linkers by structurally constraining them in metal-organic frameworks (MOFs) to control their conformation effectively tunes the fluorescence energy and enhances the quantum yield. Thus, a new tetraphenylethylene-based zirconium MOF exhibits a deep-blue fluorescent emission at 470 nm with a unity quantum yield (99.9 ± 0.5%) under Ar, representing ca. 3600 cm(-1) blue shift and doubled radiative decay efficiency vs the linker precursor. An anomalous increase in the fluorescence lifetime and relative intensity takes place upon heating the solid MOF from cryogenic to ambient temperatures. The origin of these unusual photoluminescence properties is attributed to twisted linker conformation, intramolecular hindrance, and framework rigidity.

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

PubMed

Ehteshami Bejnordi, Babak; Veta, Mitko; Johannes van Diest, Paul; van Ginneken, Bram; Karssemeijer, Nico; Litjens, Geert; van der Laak, Jeroen A W M; Hermsen, Meyke; Manson, Quirine F; Balkenhol, Maschenka; Geessink, Oscar; Stathonikos, Nikolaos; van Dijk, Marcory Crf; Bult, Peter; Beca, Francisco; Beck, Andrew H; Wang, Dayong; Khosla, Aditya; Gargeya, Rishab; Irshad, Humayun; Zhong, Aoxiao; Dou, Qi; Li, Quanzheng; Chen, Hao; Lin, Huang-Jing; Heng, Pheng-Ann; Haß, Christian; Bruni, Elia; Wong, Quincy; Halici, Ugur; Öner, Mustafa Ümit; Cetin-Atalay, Rengul; Berseth, Matt; Khvatkov, Vitali; Vylegzhanin, Alexei; Kraus, Oren; Shaban, Muhammad; Rajpoot, Nasir; Awan, Ruqayya; Sirinukunwattana, Korsuk; Qaiser, Talha; Tsang, Yee-Wah; Tellez, David; Annuscheit, Jonas; Hufnagl, Peter; Valkonen, Mira; Kartasalo, Kimmo; Latonen, Leena; Ruusuvuori, Pekka; Liimatainen, Kaisa; Albarqouni, Shadi; Mungal, Bharti; George, Ami; Demirci, Stefanie; Navab, Nassir; Watanabe, Seiryo; Seno, Shigeto; Takenaka, Yoichi; Matsuda, Hideo; Ahmady Phoulady, Hady; Kovalev, Vassili; Kalinovsky, Alexander; Liauchuk, Vitali; Bueno, Gloria; Fernandez-Carrobles, M Milagro; Serrano, Ismael; Deniz, Oscar; Racoceanu, Daniel; Venâncio, Rui

2017-12-12

Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

Analysis of L-band Multi-Channel Sea Clutter

DTIC Science & Technology

2010-08-01

Some researchers found that the use of a hybrid algorithm of PS and GA could accelerate the convergence for array beamforming designs (Yeo and Lu...to be shown is array failure correction using the PS algorithm . Assume element 5 of a 32 half-wavelength spacing linear array is in failure. The goal... algorithm . The blue one is the 20 dB Chebyshev pattern and the template in red is the goal pattern to achieve. Two corrected beam patterns are

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

NASA Astrophysics Data System (ADS)

Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

Deep-water fisheries at the Atlantic Frontier

NASA Astrophysics Data System (ADS)

Gordon, J. D. M.

2001-05-01

The deep sea is often thought of as a cold, dark and uniform environment with a low-fish biomass, much of which is highly adapted for life in a food-poor environment. While this might be true of the pelagic fish living in the water column, it is certainly not true of the demersal fish which live on or close to the bottom on the continental slopes around the British Isles (the Atlantic Frontier). These fish are currently being commercially exploited. There is growing evidence to support the view that success of the demersal fish assemblages depends on the pelagic or benthopelagic food sources that impinge both vertically and horizontally onto the slope. There are several quite separate and distinct deep-water fisheries on the Atlantic Frontier. It is a physical barrier, the Wyville-Thomson Ridge, which results in the most significant division of the fisheries. The Ridge, which has a minimum depth of about 500 m, separates the warmer deep Atlantic waters from the much colder Norwegian Sea water and as a result, the deep-water fisheries to the west of the Hebrides and around the offshore banks are quite different from those of the Faroe-Shetland Channel (West of Shetland). The fisheries to the West of the Hebrides can be further divided by the fishing method used into bottom trawl, semipelagic trawl and longline. The bottom-trawl fisheries extend from the shelf-slope break down to about 1700 m and the target species varies with depth. The smallest vessels in the fleet fish on the upper slope, where an important target species is the anglerfish or monkfish ( Lophius spp.). On the mid-slope the main target species are blue ling ( Molva dypterygia) and roundnose grenadier ( Coryphaenoides rupestris), with bycatches of black scabbardfish ( Aphanopus carbo) and deep-water sharks. On the lower slope orange roughy ( Hoplostethus atlanticus) is an important target species. The major semipelagic trawl fishery is a seasonal fishery on spawning aggregations of blue whiting ( Micromesistius poutassou). The other semipelagic fishery is on spawning aggregations of the greater silver smelt or argentine ( Argentina silus). Spanish and UK vessels that target mainly hake ( Merluccius merluccius) and a Norwegian fleet that targets ling ( Molva molva), blue ling and tusk ( Brosme brosme) dominate the upper slope longline fishery. West of Shetland, the fishery on the upper slope has some similarities with that of the Hebridean slope, with anglerfish and blue ling being important target species. A quite different fishery occurs in the transition zone between the Atlantic and Norwegian Sea waters. Here the main target species is Greenland halibut ( Reinhardtius hippoglossoides). Below the transition zone biomass decreases rapidly and there is no fishery. It is generally agreed that many deep-water species have slow growth, a high age at first maturity and a low fecundity, which makes them vulnerable to over-exploitation. Other features of these fishes such as high mortality of discards and escapees will add to the problems. Despite this the only management procedures in place are general limitation of effort measures within the area of jurisdiction of the European Union.

Verification, improvement and application of aerosol optical depths in China Part 1: Inter-comparison of NPP-VIIRS and Aqua-MODIS

NASA Astrophysics Data System (ADS)

Wei, Jing; Sun, Lin; Huang, Bo; Bilal, Muhammad; Zhang, Zhaoyang; Wang, Lunche

2018-02-01

The objective of this study is to evaluate typical aerosol optical depth (AOD) products in China, which experienced seriously increasing atmospheric particulate pollution. For this, the Aqua-MODerate resolution Imaging Spectroradiometer (MODIS) AOD products (MYD04) at 10 km spatial resolution and Visible Infrared Imaging Radiometer Suite (VIIRS) Environmental Data Record (EDR) AOD product at 6 km resolution for different Quality Flags (QF) are obtained for validation against AErosol RObotic NETwork (AERONET) AOD measurements during 2013-2016. Results show that VIIRS EDR similarly Dark Target (DT) and MODIS DT algorithms perform worse with only 45.36% and 45.59% of the retrievals (QF = 3) falling within the Expected Error (EE, ±(0.05 + 15%)) compared to the Deep Blue (DB) algorithm (69.25%, QF ≥ 2). The DT retrievals perform poorly over the Beijing-Tianjin-Hebei (BTH) and Yangtze-River-Delta (YRD) regions, which significantly overestimate the AOD observations, but the performance is better over the Pearl-River-Delta (PRD) region than DB retrievals, which seriously under-estimate the AOD loadings. It is not surprising that the DT algorithm performs better over vegetated areas, while the DB algorithm performs better over bright areas mainly depends on the accuracy of surface reflectance estimation over different land use types. In general, the sensitivity of aerosol to apparent reflectance reduces by about 34% with an increasing surface reflectance by 0.01. Moreover, VIIRS EDR and MODIS DT algorithms perform overall better in the winter as 64.53% and 72.22% of the retrievals are within the EE but with less retrievals. However, the DB algorithm performs worst (57.17%) in summer mainly affected by the vegetation growth but there are overall high accuracies with more than 62% of the collections falling within the EE in other three seasons. Results suggest that the quality assurance process can help improve the overall data quality for MYD04 DB retrievals, but it is not always true for VIIRS EDR and MYD04 DT AOD retrievals.

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

PubMed

Christiansen, Peter; Nielsen, Lars N; Steen, Kim A; Jørgensen, Rasmus N; Karstoft, Henrik

2016-11-11

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

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

PubMed Central

Christiansen, Peter; Nielsen, Lars N.; Steen, Kim A.; Jørgensen, Rasmus N.; Karstoft, Henrik

2016-01-01

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m) than RCNN. RCNN has a similar performance at a short range (0–30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit). PMID:27845717

A Multi-Band Analytical Algorithm for Deriving Absorption and Backscattering Coefficients from Remote-Sensing Reflectance of Optically Deep Waters

NASA Technical Reports Server (NTRS)

Lee, Zhong-Ping; Carder, Kendall L.

2001-01-01

A multi-band analytical (MBA) algorithm is developed to retrieve absorption and backscattering coefficients for optically deep waters, which can be applied to data from past and current satellite sensors, as well as data from hyperspectral sensors. This MBA algorithm applies a remote-sensing reflectance model derived from the Radiative Transfer Equation, and values of absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. There are only limited empirical relationships involved in the algorithm, which implies that this MBA algorithm could be applied to a wide dynamic range of waters. Applying the algorithm to a simulated non-"Case 1" data set, which has no relation to the development of the algorithm, the percentage error for the total absorption coefficient at 440 nm a (sub 440) is approximately 12% for a range of 0.012 - 2.1 per meter (approximately 6% for a (sub 440) less than approximately 0.3 per meter), while a traditional band-ratio approach returns a percentage error of approximately 30%. Applying it to a field data set ranging from 0.025 to 2.0 per meter, the result for a (sub 440) is very close to that using a full spectrum optimization technique (9.6% difference). Compared to the optimization approach, the MBA algorithm cuts the computation time dramatically with only a small sacrifice in accuracy, making it suitable for processing large data sets such as satellite images. Significant improvements over empirical algorithms have also been achieved in retrieving the optical properties of optically deep waters.

Deep spectroscopy of nearby galaxy clusters - II. The Hercules cluster

NASA Astrophysics Data System (ADS)

Agulli, I.; Aguerri, J. A. L.; Diaferio, A.; Dominguez Palmero, L.; Sánchez-Janssen, R.

2017-06-01

We carried out the deep spectroscopic observations of the nearby cluster A 2151 with AF2/WYFFOS@WHT. The caustic technique enables us to identify 360 members brighter than Mr = -16 and within 1.3R200. We separated the members into subsamples according to photometrical and dynamical properties such as colour, local environment and infall time. The completeness of the catalogue and our large sample allow us to analyse the velocity dispersion and the luminosity functions (LFs) of the identified populations. We found evidence of a cluster still in its collapsing phase. The LF of the red population of A 2151 shows a deficit of dwarf red galaxies. Moreover, the normalized LFs of the red and blue populations of A 2151 are comparable to the red and blue LFs of the field, even if the blue galaxies start dominating 1 mag fainter and the red LF is well represented by a single Schechter function rather than a double Schechter function. We discuss how the evolution of cluster galaxies depends on their mass: bright and intermediate galaxies are mainly affected by dynamical friction and internal/mass quenching, while the evolution of dwarfs is driven by environmental processes that need time and a hostile cluster environment to remove the gas reservoirs and halt the star formation.

Investigating the Relationship Between Fin and Blue Whale Locations, Zooplankton Concentrations and Hydrothermal Venting on the Juan de Fuca Ridge

DTIC Science & Technology

2009-09-30

Ridge. Our goal is to understand the influences of globally distributed hydrothermal plumes on the trophic ecology of the deep ocean. OBJECTIVES...to understand the influences of globally distributed hydrothermal plumes on the trophic ecology of the deep ocean. 15. SUBJECT TERMS 16. SECURITY... hydrothermal plume at 1.9 km depth [Burd et al., 1992; Thomson et al., 1991a], leading to the inference that the zooplankton were taking advantage of the

Aerosol optical properties over the Svalbard region of Arctic: ground-based measurements and satellite remote sensing

NASA Astrophysics Data System (ADS)

Gogoi, Mukunda M.; Babu, S. Suresh

2016-05-01

In view of the increasing anthropogenic presence and influence of aerosols in the northern polar regions, long-term continuous measurements of aerosol optical parameters have been investigated over the Svalbard region of Norwegian Arctic (Ny-Ålesund, 79°N, 12°E, 8 m ASL). This study has shown a consistent enhancement in the aerosol scattering and absorption coefficients during spring. The relative dominance of absorbing aerosols is more near the surface (lower single scattering albedo), compared to that at the higher altitude. This is indicative of the presence of local anthropogenic activities. In addition, long-range transported biomass burning aerosols (inferred from the spectral variation of absorption coefficient) also contribute significantly to the higher aerosol absorption in the Arctic spring. Aerosol optical depth (AOD) estimates from ground based Microtop sun-photometer measurements reveals that the columnar abundance of aerosols reaches the peak during spring season. Comparison of AODs between ground based and satellite remote sensing indicates that deep blue algorithm of Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals over Arctic snow surfaces overestimate the columnar AOD.

Assessment of OMI Near-UV Aerosol Optical Depth over Land

NASA Technical Reports Server (NTRS)

Ahn, Changwoo; Torres, Omar; Jethva, Hiren

2014-01-01

This is the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the near-UV observations by the Ozone Monitoring Instrument (OMI) onboard the Aura satellite. The OMI-retrieved AOD by the ultraviolet (UV) aerosol algorithm (OMAERUV version 1.4.2) was evaluated using collocated Aerosol Robotic Network (AERONET) level 2.0 direct Sun AOD measurements over 8 years (2005-2012). A time series analysis of collocated satellite and ground-based AOD observations over 8 years shows no discernible drift in OMI's calibration. A rigorous validation analysis over 4 years (2005-2008) was carried out at 44 globally distributed AERONET land sites. The chosen locations are representative of major aerosol types such as smoke from biomass burning or wildfires, desert mineral dust, and urban/industrial pollutants. Correlation coefficient (p) values of 0.75 or better were obtained at 50 percent of the sites with about 33 percent of the sites in the analysis reporting regression line slope values larger than 0.70 but always less than unity. The combined AERONET-OMAERUV analysis of the 44 sites yielded a p of 0.81, slope of 0.79, Y intercept of 0.10, and 65 percent OMAERUV AOD falling within the expected uncertainty range (largest of 30 percent or 0.1) at 440 nanometers. The most accurate OMAERUV retrievals are reported over northern Africa locations where the predominant aerosol type is desert dust and cloud presence is less frequent. Reliable retrievals were documented at many sites characterized by urban-type aerosols with low to moderate AOD values, concentrated in the boundary layer. These results confirm that the near-ultraviolet observations are sensitive to the entire aerosol column. A simultaneous comparison of OMAERUV, Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue, and Multiangle Imaging Spectroradiometer (MISR) AOD retrievals to AERONET measurements was also carried out to evaluate the OMAERUV accuracy in relation to those of the standard aerosol satellite products. The outcome of the comparison indicates that OMAERUV, MODIS Deep Blue, and MISR retrieval accuracies in arid and semiarid environments are statistically comparable.

Deep learning for healthcare applications based on physiological signals: A review.

PubMed

Faust, Oliver; Hagiwara, Yuki; Hong, Tan Jen; Lih, Oh Shu; Acharya, U Rajendra

2018-07-01

We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

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

PubMed

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

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

Recent Update on MODIS/VIIRS Deep Blue Data Continuity and New Aerosol Products

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Sayer, Andrew M.; Lee, Jaehwa; Bettenhausen, Corey; Carletta, N.; Tsay, Si-Chee

2016-01-01

The MODIS VIIRS 2016 Science Team Meeting was held June 6-10, 2016 at the Sheraton in Silver Spring, MD. The organizers plan to post the presentations and posters here: http:modis.gsfc.nasa.govsci_teammeetings201606.

Leadership Blues.

ERIC Educational Resources Information Center

March, James G.; Weiner, Stephen S.

2003-01-01

Discusses the complex nature of college leadership especially in terms of community colleges. Claims that the central feature of leadership problems is a deep mismatch between the conceptions of individual leaders and key features of the organizations they lead. Concludes that civilization will not survive without civil leaders. (JS)

A MEGACAM SURVEY OF OUTER HALO SATELLITES. II. BLUE STRAGGLERS IN THE LOWEST STELLAR DENSITY SYSTEMS

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

Santana, Felipe A.; Munoz, Ricardo R.; Geha, Marla

2013-09-10

We present a homogeneous study of blue straggler stars across 10 outer halo globular clusters, 3 classical dwarf spheroidal galaxies, and 9 ultra-faint galaxies based on deep and wide-field photometric data taken with MegaCam on the Canada-France-Hawaii Telescope. We find blue straggler stars to be ubiquitous among these Milky Way satellites. Based on these data, we can test the importance of primordial binaries or multiple systems on blue straggler star formation in low-density environments. For the outer halo globular clusters, we find an anti-correlation between the specific frequency of blue stragglers and absolute magnitude, similar to that previously observed formore » inner halo clusters. When plotted against density and encounter rate, the frequency of blue stragglers is well fit by a single trend with a smooth transition between dwarf galaxies and globular clusters; this result points to a common origin for these satellites' blue stragglers. The fraction of blue stragglers stays constant and high in the low encounter rate regime spanned by our dwarf galaxies, and decreases with density and encounter rate in the range spanned by our globular clusters. We find that young stars can mimic blue stragglers in dwarf galaxies only if their ages are 2.5 {+-} 0.5 Gyr and they represent {approx}1%-7% of the total number of stars, which we deem highly unlikely. These results point to mass-transfer or mergers of primordial binaries or multiple systems as the dominant blue straggler formation mechanism in low-density systems.« less

The design of red-blue 3D video fusion system based on DM642

NASA Astrophysics Data System (ADS)

Fu, Rongguo; Luo, Hao; Lv, Jin; Feng, Shu; Wei, Yifang; Zhang, Hao

2016-10-01

Aiming at the uncertainty of traditional 3D video capturing including camera focal lengths, distance and angle parameters between two cameras, a red-blue 3D video fusion system based on DM642 hardware processing platform is designed with the parallel optical axis. In view of the brightness reduction of traditional 3D video, the brightness enhancement algorithm based on human visual characteristics is proposed and the luminance component processing method based on YCbCr color space is also proposed. The BIOS real-time operating system is used to improve the real-time performance. The video processing circuit with the core of DM642 enhances the brightness of the images, then converts the video signals of YCbCr to RGB and extracts the R component from one camera, so does the other video and G, B component are extracted synchronously, outputs 3D fusion images finally. The real-time adjustments such as translation and scaling of the two color components are realized through the serial communication between the VC software and BIOS. The system with the method of adding red-blue components reduces the lost of the chrominance components and makes the picture color saturation reduce to more than 95% of the original. Enhancement algorithm after optimization to reduce the amount of data fusion in the processing of video is used to reduce the fusion time and watching effect is improved. Experimental results show that the system can capture images in near distance, output red-blue 3D video and presents the nice experiences to the audience wearing red-blue glasses.

Deep greedy learning under thermal variability in full diurnal cycles

NASA Astrophysics Data System (ADS)

Rauss, Patrick; Rosario, Dalton

2017-08-01

We study the generalization and scalability behavior of a deep belief network (DBN) applied to a challenging long-wave infrared hyperspectral dataset, consisting of radiance from several manmade and natural materials within a fixed site located 500 m from an observation tower. The collections cover multiple full diurnal cycles and include different atmospheric conditions. Using complementary priors, a DBN uses a greedy algorithm that can learn deep, directed belief networks one layer at a time and has two layers form to provide undirected associative memory. The greedy algorithm initializes a slower learning procedure, which fine-tunes the weights, using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of spectral data and their labels, despite significant data variability between and within classes due to environmental and temperature variation occurring within and between full diurnal cycles. We argue, however, that more questions than answers are raised regarding the generalization capacity of these deep nets through experiments aimed at investigating their training and augmented learning behavior.

Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

PubMed

Li, Zhixi; He, Yifan; Keel, Stuart; Meng, Wei; Chang, Robert T; He, Mingguang

2018-03-02

To assess the performance of a deep learning algorithm for detecting referable glaucomatous optic neuropathy (GON) based on color fundus photographs. A deep learning system for the classification of GON was developed for automated classification of GON on color fundus photographs. We retrospectively included 48 116 fundus photographs for the development and validation of a deep learning algorithm. This study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON. The reference standard was made until 3 graders achieved agreement. A separate validation dataset of 8000 fully gradable fundus photographs was used to assess the performance of this algorithm. The area under receiver operator characteristic curve (AUC) with sensitivity and specificity was applied to evaluate the efficacy of the deep learning algorithm detecting referable GON. In the validation dataset, this deep learning system achieved an AUC of 0.986 with sensitivity of 95.6% and specificity of 92.0%. The most common reasons for false-negative grading (n = 87) were GON with coexisting eye conditions (n = 44 [50.6%]), including pathologic or high myopia (n = 37 [42.6%]), diabetic retinopathy (n = 4 [4.6%]), and age-related macular degeneration (n = 3 [3.4%]). The leading reason for false-positive results (n = 480) was having other eye conditions (n = 458 [95.4%]), mainly including physiologic cupping (n = 267 [55.6%]). Misclassification as false-positive results amidst a normal-appearing fundus occurred in only 22 eyes (4.6%). A deep learning system can detect referable GON with high sensitivity and specificity. Coexistence of high or pathologic myopia is the most common cause resulting in false-negative results. Physiologic cupping and pathologic myopia were the most common reasons for false-positive results. Copyright © 2018 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Comparison of ground based indices (API and AQI) with satellite based aerosol products.

PubMed

Zheng, Sheng; Cao, Chun-Xiang; Singh, Ramesh P

2014-08-01

Air quality in mega cities is one of the major concerns due to serious health issues and its indirect impact to the climate. Among mega cities, Beijing city is considered as one of the densely populated cities with extremely poor air quality. The meteorological parameters (wind, surface temperature, air temperature and relative humidity) control the dynamics and dispersion of air pollution. China National Environmental Monitoring Centre (CNEMC) started air pollution index (API) as of 2000 to evaluate air quality, but over the years, it was felt that the air quality is not well represented by API. Recently, the Ministry of Environmental Protection (MEP) of the People's Republic of China (PRC) started using a new index "air quality index (AQI)" from January 2013. We have compared API and AQI with three different MODIS (MODIS - Moderate Resolution Imaging SpectroRadiometer, onboard the Terra/Aqua satellites) AOD (aerosol optical depth) products for ten months, January-October, 2013. The correlation between AQI and Aqua Deep Blue AOD was found to be reasonably good as compared with API, mainly due to inclusion of PM2.5 in the calculation of AQI. In addition, for every month, the correlation coefficient between AQI and Aqua Deep Blue AOD was found to be relatively higher in the month of February to May. According to the monthly average distribution of precipitation, temperature, and PM10, the air quality in the months of June-September was better as compared to those in the months of February-May. AQI and Aqua Deep Blue AOD show highly polluted days associated with dust event, representing true air quality of Beijing. Copyright © 2013 Elsevier B.V. All rights reserved.

The Structure and Kinematics of Little Blue Spheroid Galaxies

NASA Astrophysics Data System (ADS)

Moffett, Amanda J.; Phillipps, Steven; Robotham, Aaron; Driver, Simon; Bremer, Malcolm; GAMA survey team, SAMI survey team

2018-01-01

A population of blue, morphologically early-type galaxies, dubbed "Little Blue Spheroids" (LBSs), has been identified as a significant contributor to the low redshift galaxy population in the GAMA survey. Using deep, high-resolution optical imaging from KiDS and the new Bayesian, two-dimensional galaxy profile modelling code PROFIT, we examine the detailed structural characteristics of LBSs, including low surface brightness components not detected in previous SDSS imaging. We find that these LBS galaxies combine features typical of early-type and late-type populations, with structural properties similar to other low-mass early types and star formation rates similar to low-mass late types. We further consider the environments and SAMI-derived IFU kinematics of LBSs in order to investigate the conditions of their formation and the current state of their dynamical evolution.

Scaling Deep Learning on GPU and Knights Landing clusters

DOE PAGES

You, Yang; Buluc, Aydin; Demmel, James

2017-09-26

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learningmore » systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \\textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.« less

Scaling Deep Learning on GPU and Knights Landing clusters

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

You, Yang; Buluc, Aydin; Demmel, James

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learningmore » systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \\textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.« less

Adaptive Neuron Apoptosis for Accelerating Deep Learning on Large Scale Systems

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

Siegel, Charles M.; Daily, Jeffrey A.; Vishnu, Abhinav

Machine Learning and Data Mining (MLDM) algorithms are becoming ubiquitous in {\\em model learning} from the large volume of data generated using simulations, experiments and handheld devices. Deep Learning algorithms -- a class of MLDM algorithms -- are applied for automatic feature extraction, and learning non-linear models for unsupervised and supervised algorithms. Naturally, several libraries which support large scale Deep Learning -- such as TensorFlow and Caffe -- have become popular. In this paper, we present novel techniques to accelerate the convergence of Deep Learning algorithms by conducting low overhead removal of redundant neurons -- {\\em apoptosis} of neurons --more » which do not contribute to model learning, during the training phase itself. We provide in-depth theoretical underpinnings of our heuristics (bounding accuracy loss and handling apoptosis of several neuron types), and present the methods to conduct adaptive neuron apoptosis. We implement our proposed heuristics with the recently introduced TensorFlow and using its recently proposed extension with MPI. Our performance evaluation on two difference clusters -- one connected with Intel Haswell multi-core systems, and other with nVIDIA GPUs -- using InfiniBand, indicates the efficacy of the proposed heuristics and implementations. Specifically, we are able to improve the training time for several datasets by 2-3x, while reducing the number of parameters by 30x (4-5x on average) on datasets such as ImageNet classification. For the Higgs Boson dataset, our implementation improves the accuracy (measured by Area Under Curve (AUC)) for classification from 0.88/1 to 0.94/1, while reducing the number of parameters by 3x in comparison to existing literature, while achieving a 2.44x speedup in comparison to the default (no apoptosis) algorithm.« less

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

PubMed Central

Veta, Mitko; Johannes van Diest, Paul; van Ginneken, Bram; Karssemeijer, Nico; Litjens, Geert; van der Laak, Jeroen A. W. M.; Hermsen, Meyke; Manson, Quirine F; Balkenhol, Maschenka; Geessink, Oscar; Stathonikos, Nikolaos; van Dijk, Marcory CRF; Bult, Peter; Beca, Francisco; Beck, Andrew H; Wang, Dayong; Khosla, Aditya; Gargeya, Rishab; Irshad, Humayun; Zhong, Aoxiao; Dou, Qi; Li, Quanzheng; Chen, Hao; Lin, Huang-Jing; Heng, Pheng-Ann; Haß, Christian; Bruni, Elia; Wong, Quincy; Halici, Ugur; Öner, Mustafa Ümit; Cetin-Atalay, Rengul; Berseth, Matt; Khvatkov, Vitali; Vylegzhanin, Alexei; Kraus, Oren; Shaban, Muhammad; Rajpoot, Nasir; Awan, Ruqayya; Sirinukunwattana, Korsuk; Qaiser, Talha; Tsang, Yee-Wah; Tellez, David; Annuscheit, Jonas; Hufnagl, Peter; Valkonen, Mira; Kartasalo, Kimmo; Latonen, Leena; Ruusuvuori, Pekka; Liimatainen, Kaisa; Albarqouni, Shadi; Mungal, Bharti; George, Ami; Demirci, Stefanie; Navab, Nassir; Watanabe, Seiryo; Seno, Shigeto; Takenaka, Yoichi; Matsuda, Hideo; Ahmady Phoulady, Hady; Kovalev, Vassili; Kalinovsky, Alexander; Liauchuk, Vitali; Bueno, Gloria; Fernandez-Carrobles, M. Milagro; Serrano, Ismael; Deniz, Oscar; Racoceanu, Daniel; Venâncio, Rui

2017-01-01

Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting. PMID:29234806

Determination of performance characteristics of scientific applications on IBM Blue Gene/Q

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

Evangelinos, C.; Walkup, R. E.; Sachdeva, V.

The IBM Blue Gene®/Q platform presents scientists and engineers with a rich set of hardware features such as 16 cores per chip sharing a Level 2 cache, a wide SIMD (single-instruction, multiple-data) unit, a five-dimensional torus network, and hardware support for collective operations. Especially important is the feature related to cores that have four “hardware threads,” which makes it possible to hide latencies and obtain a high fraction of the peak issue rate from each core. All of these hardware resources present unique performance-tuning opportunities on Blue Gene/Q. We provide an overview of several important applications and solvers and studymore » them on Blue Gene/Q using performance counters and Message Passing Interface profiles. We also discuss how Blue Gene/Q tools help us understand the interaction of the application with the hardware and software layers and provide guidance for optimization. Furthermore, on the basis of our analysis, we discuss code improvement strategies targeting Blue Gene/Q. Information about how these algorithms map to the Blue Gene® architecture is expected to have an impact on future system design as we move to the exascale era.« less

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

PubMed

Gulshan, Varun; Peng, Lily; Coram, Marc; Stumpe, Martin C; Wu, Derek; Narayanaswamy, Arunachalam; Venugopalan, Subhashini; Widner, Kasumi; Madams, Tom; Cuadros, Jorge; Kim, Ramasamy; Raman, Rajiv; Nelson, Philip C; Mega, Jessica L; Webster, Dale R

2016-12-13

Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. Deep learning-trained algorithm. The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%. In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.

MASA's Ultra-Long Duration Balloon Project - Teaching an Old Dog New Tricks

NASA Technical Reports Server (NTRS)

Smith, I.; Cutts, J.

1999-01-01

The leviathan silently slides through the upper atmosphere of the blue planet, its eye steadily staring into the cold, dark recesses of deep space. Periodically the eye looks at different points in the blackness while processing the information it sees.

Intense deep blue exciplex electroluminescence from NPB/TPBi:PPh3O-based OLEDs and their intrinsic degradation mechanisms (Conference Presentation)

NASA Astrophysics Data System (ADS)

Shinar, Joseph; Hippola, Chamika; Danilovic, Dusan; Bhattacharjee, Ujjal; Petrich, Jacob W.; Shinar, Ruth

2016-09-01

We describe intense and efficient deep blue (430 - 440 nm) exciplex emission from NPB/TPBi:PPh3O OLEDs where the luminous efficiency approaches 4 Cd/A and the maximal brightness exceeds 22,000 Cd/m2. Time resolved PL measurements confirm the exciplex emission from NPB:TPBi, as studied earlier by Monkman and coworkers [Adv. Mater. 25, 1455 (2013)]. However, the inclusion of PPh3O improves the OLED performance significantly. The effect of PPh3O on the EL and PL will be discussed. The NPB/TPBi:PPh3O-based OLEDs were also studied by optically and electrically detected magnetic resonance (ODMR and EDMR, respectively). In particular, the amplitude of the negative (EL- and current-quenching) spin 1/2 resonance, previously attributed to enhanced formation of strongly EL-quenching positive bipolarons, increases as the OLEDs degrade in a dry nitrogen atmosphere. This degradation mechanism is discussed in relation to degradation induced by hot polarons that are energized by exciton annihilation.

Impacts of Cross-Platform Vicarious Calibration on the Deep Blue Aerosol Retrievals for Moderate Resolution Imaging Spectroradiometer Aboard Terra

NASA Technical Reports Server (NTRS)

Jeong, Myeong-Jae; Hsu, N. Christina; Kwiatkowska, Ewa J.; Franz, Bryan A.; Meister, Gerhard; Salustro, Clare E.

2012-01-01

The retrieval of aerosol properties from spaceborne sensors requires highly accurate and precise radiometric measurements, thus placing stringent requirements on sensor calibration and characterization. For the Terra/Moderate Resolution Imaging Spedroradiometer (MODIS), the characteristics of the detectors of certain bands, particularly band 8 [(B8); 412 nm], have changed significantly over time, leading to increased calibration uncertainty. In this paper, we explore a possibility of utilizing a cross-calibration method developed for characterizing the Terral MODIS detectors in the ocean bands by the National Aeronautics and Space Administration Ocean Biology Processing Group to improve aerosol retrieval over bright land surfaces. We found that the Terra/MODIS B8 reflectance corrected using the cross calibration method resulted in significant improvements for the retrieved aerosol optical thickness when compared with that from the Multi-angle Imaging Spectroradiometer, Aqua/MODIS, and the Aerosol Robotic Network. The method reported in this paper is implemented for the operational processing of the Terra/MODIS Deep Blue aerosol products.

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity.

PubMed

Onton, Julie A; Kang, Dae Y; Coleman, Todd P

2016-01-01

Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and other indices of sleep quality. By visualizing spectral data down to 0.1 Hz, a differentiation emerged between slow-wave sleep with dominant frequency between 0.1-1 Hz or 1-3 Hz, but rarely both. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. We developed an algorithm to classify five stages (Awake, Light, Hi Deep, Lo Deep and rapid eye movement (REM)) using a Hidden Markov Model (HMM), model fitting with the expectation-maximization (EM) algorithm, and estimation of the most likely sleep state sequence by the Viterbi algorithm. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. In conclusion, this study demonstrates the feasibility of in-home sleep EEG collection, a rapid and informative sleep report format, and novel deep sleep designations accounting for spectral and physiological differences.

Predicting complications of percutaneous coronary intervention using a novel support vector method.

PubMed

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases). The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Predicting complications of percutaneous coronary intervention using a novel support vector method

PubMed Central

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

Objective To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Materials and methods Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. Results The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer–Lemeshow χ2 value (seven cases) and the mean cross-entropy error (eight cases). Conclusions The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains. PMID:23599229

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

Succinate dehydrogenase activity and soma size of motoneurons innervating different portions of the rat tibialis anterior

NASA Technical Reports Server (NTRS)

Ishihara, A.; Roy, R. R.; Edgerton, V. R.

1995-01-01

The spatial distribution, soma size and oxidative enzyme activity of gamma and alpha motoneurons innervating muscle fibres in the deep (away from the surface of the muscle) and superficial (close to the surface of the muscle) portions of the tibialis anterior in normal rats were determined. The deep portion had a higher percentage of high oxidative fibres than the superficial portion of the muscle. Motoneurons were labelled by retrograde neuronal transport of fluorescent tracers: Fast Blue and Nuclear Yellow were injected into the deep portion and Nuclear Yellow into the superficial portion of the muscle. Therefore, motoneurons innervating the deep portion were identified by both a blue fluorescent cytoplasm and a golden-yellow fluorescent nucleus, while motoneurons innervating the superficial portion were identified by only a golden-yellow fluorescent nucleus. After staining for succinate dehydrogenase activity on the same section used for the identification of the motoneurons, soma size and succinate dehydrogenase activity of the motoneurons were measured. The gamma and alpha motoneurons innervating both the deep and superficial portions were located primarily at L4 and were intermingled within the same region of the dorsolateral portion of the ventral horn in the spinal cord. Mean soma size was similar for either gamma or alpha motoneurons in the two portions of the muscle. The alpha motoneurons innervating the superficial portion had a lower mean succinate dehydrogenase activity than those innervating the deep portion of the muscle. An inverse relationship between soma size and succinate dehydrogenase activity of alpha, but not gamma, motoneurons innervating both the deep and superficial portions was observed. Based on three-dimensional reconstructions within the spinal cord, there were no apparent differences in the spatial distribution of the motoneurons, either gamma or alpha, associated with the deep and superficial compartments of the muscle. The data provide evidence for an interdependence in the oxidative capacity between a motoneuron and its target muscle fibres in two subpopulations of motoneurons from the same motor pool, i.e. the same muscle.

Eleuthera Island, Bahamas seen from STS-66

NASA Image and Video Library

1994-11-14

The striking views provided by the Bahama Islands lend insights into the important problems of limestone (CaCO3) production and transport. This photograph includes the southern part of Eleuthera Island in the northern Bahamas. The hook-shaped island encloses a relatively shallow platform (light blue) which is surrounded by deep water (dark blue). The feathery pattern along the western edge of Eleuthera's platform are sand bars and sand channels created by tidal currents sweeping on and off the platform. The channels serve to funnel large amounts of CaCO3 off the platform and into the deeper water.

The Rich Color Variations of Pluto

NASA Image and Video Library

2015-09-24

NASA's New Horizons spacecraft captured this high-resolution enhanced color view of Pluto on July 14, 2015. The image combines blue, red and infrared images taken by the Ralph/Multispectral Visual Imaging Camera (MVIC). Pluto's surface sports a remarkable range of subtle colors, enhanced in this view to a rainbow of pale blues, yellows, oranges, and deep reds. Many landforms have their own distinct colors, telling a complex geological and climatological story that scientists have only just begun to decode. The image resolves details and colors on scales as small as 0.8 miles (1.3 kilometers). http://photojournal.jpl.nasa.gov/catalog/PIA19952

Deep Imaging of Eridanus II and Its Lone Star Cluster

NASA Astrophysics Data System (ADS)

Crnojević, D.; Sand, D. J.; Zaritsky, D.; Spekkens, K.; Willman, B.; Hargis, J. R.

2016-06-01

We present deep imaging of the most distant dwarf discovered by the Dark Energy Survey, Eridanus II (Eri II). Our Magellan/Megacam stellar photometry reaches ˜3 mag deeper than previous work and allows us to confirm the presence of a stellar cluster whose position is consistent with Eri II’s center. This makes Eri II, at {M}V=-7.1, the least luminous galaxy known to host a (possibly central) cluster. The cluster is partially resolved, and at {M}V=-3.5 it accounts for ˜4% of Eri II’s luminosity. We derive updated structural parameters for Eri II, which has a half-light radius of ˜280 pc and is elongated (ɛ ˜ 0.48) at a measured distance of D ˜ 370 kpc. The color-magnitude diagram displays a blue, extended horizontal branch, as well as a less populated red horizontal branch. A central concentration of stars brighter than the old main-sequence turnoff hints at a possible intermediate-age (˜3 Gyr) population; alternatively, these sources could be blue straggler stars. A deep Green Bank Telescope observation of Eri II reveals no associated atomic gas. This paper includes data gathered with the 6.5 m Magellan Telescopes at Las Campanas Observatory, Chile.

Can we do better than the grid survey: Optimal synoptic surveys in presence of variable uncertainty and decorrelation scales

NASA Astrophysics Data System (ADS)

Frolov, Sergey; Garau, Bartolame; Bellingham, James

2014-08-01

Regular grid ("lawnmower") survey is a classical strategy for synoptic sampling of the ocean. Is it possible to achieve a more effective use of available resources if one takes into account a priori knowledge about variability in magnitudes of uncertainty and decorrelation scales? In this article, we develop and compare the performance of several path-planning algorithms: optimized "lawnmower," a graph-search algorithm (A*), and a fully nonlinear genetic algorithm. We use the machinery of the best linear unbiased estimator (BLUE) to quantify the ability of a vehicle fleet to synoptically map distribution of phytoplankton off the central California coast. We used satellite and in situ data to specify covariance information required by the BLUE estimator. Computational experiments showed that two types of sampling strategies are possible: a suboptimal space-filling design (produced by the "lawnmower" and the A* algorithms) and an optimal uncertainty-aware design (produced by the genetic algorithm). Unlike the space-filling designs that attempted to cover the entire survey area, the optimal design focused on revisiting areas of high uncertainty. Results of the multivehicle experiments showed that fleet performance predictors, such as cumulative speed or the weight of the fleet, predicted the performance of a homogeneous fleet well; however, these were poor predictors for comparing the performance of different platforms.

Low vacuum scanning electron microscopy for paraffin sections utilizing the differential stainability of cells and tissues with platinum blue.

PubMed

Inaga, Sumire; Hirashima, Sayuri; Tanaka, Keiichi; Katsumoto, Tetsuo; Kameie, Toshio; Nakane, Hironobu; Naguro, Tomonori

2009-07-01

The present study introduces a novel method for the direct observation of histological paraffin sections by low vacuum scanning electron microscopy (LVSEM) with platinum blue (Pt-blue) treatment. Pt-blue was applied not only as a backscattered electron (BSE) signal enhancer but also as a histologically specific stain. In this method, paraffin sections of the rat tongue prepared for conventional light microscopy (LM) were stained on glass slides with a Pt-blue staining solution (pH 9) and observed in a LVSEM using BSE detector. Under LVSEM, overviews of whole sections as well as three-dimensional detailed observations of individual cells and tissues could be easily made at magnifications from x40 to x10,000. Each kind of cell and tissue observed in the section could be clearly distinguished due to the different yields of BSE signals, which depended on the surface structures and different affinities to Pt-blue. Thus, we roughly classified cellular and tissue components into three groups according to the staining intensity of Pt-blue observed by LM and LVSEM: 1) a strongly stained (deep blue by LM and brightest by LVSEM) group which included epithelial tissue, endothelium and mast cells; 2) a moderately stained (light blue and bright) group which included muscular tissue and nervous tissue; 3) an unstained or weakly stained (colorless and dark) group which included elastic fibers and collagen fibers. We expect that this method will prove useful for the three-dimensional direct observation of histological paraffin sections of various tissues by LVSEM with higher resolutions than LM.

An electron transporting blue emitter for OLED

NASA Astrophysics Data System (ADS)

Qi, Boyuan; Luo, Jiaxiu; Li, Suyue; Xiao, Lixin; Sun, Wenfang; Chen, Zhijian; Qu, Bo; Gong, Qihuang

2010-11-01

After the premier commercialization of OLED in 1997, OLED has been considered as the candidate for the next generation of flat panel display. In comparison to liquid crystal display (LCD) and plasma display panel (PDP), OLED exhibits promising merits for display, e.g., flexible, printable, micro-buildable and multiple designable. Although many efforts have been made on electroluminescent (EL) materials and devices, obtaining highly efficient and pure blue light is still a great challenge. In order to improve the emission efficiency and purity of the blue emission, a new bipolar blue light emitter, 2,7-di(2,2':6',2"-terpyridine)- 2,7-diethynyl-9,9-dioctyl-9H-fluorene (TPEF), was designed and synthesized. A blue OLED was obtained with the configuration of ITO/PEDOT/PVK:CBP:TPEF/LiF/Al. The device exhibits a turn-on voltage of 9 V and a maximum brightness of 12 cd/m2 at 15 V. The device gives a deep blue emission located at 420 nm with the Commission Internationale de l'Eclairage (CIE) coordinates of (0.17, 0.10). We also use TPEF as electron transporting material in the device of ITO/PPV/TPEF/LiF/Al, the turn-on voltage is 3 V. It is proved the current in the device was enhanced indeed by using the new material.

Neptune in False Color

NASA Image and Video Library

1996-01-29

In this false color image of Neptune, objects that are deep in the atmosphere are blue, while those at higher altitudes are white. The image was taken by Voyager 2 wide-angle camera through an orange filter and two different methane filters. http://photojournal.jpl.nasa.gov/catalog/PIA00051

The Spatial Distribution of Resolved Young Stars in Blue Compact Dwarf Galaxies

NASA Astrophysics Data System (ADS)

Murphy, K.; Crone, M. M.

2002-12-01

We present the first results from a survey of the distribution of resolved young stars in Blue Compact Dwarf Galaxies. In order to identify the dominant physical processes driving star formation in these puzzling galaxies, we use a multi-scale cluster-finding algorithm to quantify the characteristic scales and properties of star-forming regions, from sizes smaller than 10 pc up to the size of each entire galaxy. This project was partially funded by the Lubin Chair at Skidmore College.

Engineering PFLOTRAN for Scalable Performance on Cray XT and IBM BlueGene Architectures

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

Mills, Richard T; Sripathi, Vamsi K; Mahinthakumar, Gnanamanika

We describe PFLOTRAN - a code for simulation of coupled hydro-thermal-chemical processes in variably saturated, non-isothermal, porous media - and the approaches we have employed to obtain scalable performance on some of the largest scale supercomputers in the world. We present detailed analyses of I/O and solver performance on Jaguar, the Cray XT5 at Oak Ridge National Laboratory, and Intrepid, the IBM BlueGene/P at Argonne National Laboratory, that have guided our choice of algorithms.

Deep Space Climate Observatory (DSCOVR) lifted off from Cape Canaveral

NASA Image and Video Library

2015-02-13

KSC-2015-1342 (02/11/2015) --- Backdropped by a bright blue sky, the SpaceX Falcon 9 rocket carrying NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, soars away from Space Launch Complex 40 at Cape Canaveral Air Force Station in Florida. Liftoff occurred at 6:03 p.m. EST. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force, and will maintain the nation's real-time solar wind monitoring capabilities. To learn more about DSCOVR, visit www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Ben Smegelsky..

Towards ecosystem based management and monitoring of the deep Mediterranean, North-East Atlantic and Beyond

NASA Astrophysics Data System (ADS)

Grehan, Anthony J.; Arnaud-Haond, Sophie; D'Onghia, Gianfranco; Savini, Alessandra; Yesson, Chris

2017-11-01

The deep sea covers 65% of the earth's surface and 95% of the biosphere but only a very small fraction (less than 0.0001%) of this has been explored (Rogers et al., 2015; Taylor and Roterman, 2017). However, current knowledge indicates that the deep ocean is characterized by a high level of biodiversity and by the presence of important biological and non-renewable resources. As well as vast flat and muddy plains, the topography of the deep ocean contains a variety of complex and heterogeneous seafloor features, such as canyons, seamounts, cold seeps, hydrothermal vents and biogenic (deep-water coral) reefs and sponge bioherms that harbour an unquantified and diverse array of organisms. The deep sea, despite its remoteness, provides a variety of supporting, provisioning, regulating and cultural, ecosystem goods and services (Thurber et al., 2014). The recent push for 'Blue Growth', to unlock the potential of seas and oceans (European Commission, 2017) has increased the focus on the potential to exploit resources in the deep-sea and consequently the need for improved management (Thurber et al., 2014).

Tropical Storm Ernesto over Cuba

NASA Technical Reports Server (NTRS)

2006-01-01

[figure removed for brevity, see original site] Microwave Image

These infrared, microwave, and visible images were created with data retrieved by the Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite.

Infrared Image Because infrared radiation does not penetrate through clouds, AIRS infrared images show either the temperature of the cloud tops or the surface of the Earth in cloud-free regions. The lowest temperatures (in purple) are associated with high, cold cloud tops that make up the top of the storm. In cloud-free areas the AIRS instrument will receive the infrared radiation from the surface of the Earth, resulting in the warmest temperatures (orange/red).

Microwave Image In the AIRS microwave imagery, deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. On the other hand, land appears much warmer due to its high radiation emissivity.

Microwave radiation from Earth's surface and lower atmosphere penetrates most clouds to a greater or lesser extent depending upon their water vapor, liquid water and ice content. Precipitation, and ice crystals found at the cloud tops where strong convection is taking place, act as barriers to microwave radiation. Because of this barrier effect, the AIRS microwave sensor detects only the radiation arising at or above their location in the atmospheric column. Where these barriers are not present, the microwave sensor detects radiation arising throughout the air column and down to the surface. Liquid surfaces (oceans, lakes and rivers) have 'low emissivity' (the signal isn't as strong) and their radiation brightness temperature is therefore low. Thus the ocean also appears 'low temperature' in the AIRS microwave images and is assigned the color blue. Therefore deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. Land appears much warmer due to its high radiation emissivity.

The Atmospheric Infrared Sounder Experiment, with its visible, infrared, and microwave detectors, provides a three-dimensional look at Earth's weather. Working in tandem, the three instruments can make simultaneous observations all the way down to the Earth's surface, even in the presence of heavy clouds. With more than 2,000 channels sensing different regions of the atmosphere, the system creates a global, 3-D map of atmospheric temperature and humidity and provides information on clouds, greenhouse gases, and many other atmospheric phenomena. The AIRS Infrared Sounder Experiment flies onboard NASA's Aqua spacecraft and is managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif., under contract to NASA. JPL is a division of the California Institute of Technology in Pasadena.

Retrieval of aerosol properties and water leaving radiance from multi-angle spectro-polarimetric measurement over coastal waters

NASA Astrophysics Data System (ADS)

Gao, M.; Zhai, P.; Franz, B. A.; Hu, Y.; Knobelspiesse, K. D.; Xu, F.; Ibrahim, A.

2017-12-01

Ocean color remote sensing in coastal waters remains a challenging task due to the complex optical properties of aerosols and ocean water properties. It is highly desirable to develop an advanced ocean color and aerosol retrieval algorithm for coastal waters, to advance our capabilities in monitoring water quality, improve our understanding of coastal carbon cycle dynamics, and allow for the development of more accurate circulation models. However, distinguishing the dissolved and suspended material from absorbing aerosols over coastal waters is challenging as they share similar absorption spectrum within the deep blue to UV range. In this paper we report a research algorithm on aerosol and ocean color retrieval with emphasis on coastal waters. The main features of our algorithm include: 1) combining co-located measurements from a hyperspectral ocean color instrument (OCI) and a multi-angle polarimeter (MAP); 2) using the radiative transfer model for coupled atmosphere and ocean system (CAOS), which is based on the highly accurate and efficient successive order of scattering method; and 3) incorporating a generalized bio-optical model with direct accounting of the total absorption of phytoplankton, CDOM and non-algal particles(NAP), and the total scattering of phytoplankton and NAP for improved description of ocean light scattering. The non-linear least square fitting algorithm is used to optimize the bio-optical model parameters and the aerosol optical and microphysical properties including refractive indices and size distributions for both fine and coarse modes. The retrieved aerosol information is used to calculate the atmospheric path radiance, which is then subtracted from the OCI observations to obtain the water leaving radiance contribution. Our work aims to maximize the use of available information from the co-located dataset and conduct the atmospheric correction with minimal assumptions. The algorithm will contribute to the success of current MAP instruments, such as the Research Scanning Polarimeter (RSP), and future ocean color missions, such as the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission, by enabling retrieval of ocean biogeochemical properties under optically-complex atmospheric and oceanic conditions.

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

NASA Astrophysics Data System (ADS)

Choi, Myungje; Kim, Jhoon; Lee, Jaehwa; Kim, Mijin; Park, Young-Je; Jeong, Ukkyo; Kim, Woogyung; Hong, Hyunkee; Holben, Brent; Eck, Thomas F.; Song, Chul H.; Lim, Jae-Hyun; Song, Chang-Keun

2016-04-01

The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements made to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm together with validation results during the Distributed Regional Aerosol Gridded Observation Networks - Northeast Asia 2012 campaign (DRAGON-NE Asia 2012 campaign). The evaluation during the spring season over East Asia is important because of high aerosol concentrations and diverse types of Asian dust and haze. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single-scattering albedo (SSA) at 440 nm, Ångström exponent (AE) between 440 and 860 nm, and aerosol type. The aerosol models are created based on a global analysis of the Aerosol Robotic Networks (AERONET) inversion data, and covers a broad range of size distribution and absorptivity, including nonspherical dust properties. The Cox-Munk ocean bidirectional reflectance distribution function (BRDF) model is used over ocean, and an improved minimum reflectance technique is used over land. Because turbid water is persistent over the Yellow Sea, the land algorithm is used for such cases. The aerosol products are evaluated against AERONET observations and MODIS Collection 6 aerosol products retrieved from Dark Target (DT) and Deep Blue (DB) algorithms during the DRAGON-NE Asia 2012 campaign conducted from March to May 2012. Comparison of AOD from GOCI and AERONET resulted in a Pearson correlation coefficient of 0.881 and a linear regression equation with GOCI AOD = 1.083 × AERONET AOD - 0.042. The correlation between GOCI and MODIS AODs is higher over ocean than land. GOCI AOD shows better agreement with MODIS DB than MODIS DT. The other GOCI YAER products (AE, FMF, and SSA) show lower correlation with AERONET than AOD, but still show some skills for qualitative use.

GOCI Yonsei Aerosol Retrieval (YAER) Algorithm and Validation During the DRAGON-NE Asia 2012 Campaign

NASA Technical Reports Server (NTRS)

Choi, Myungje; Kim, Jhoon; Lee, Jaehwa; Kim, Mijin; Park, Young-Je; Jeong, Ukkyo; Kim, Woogyung; Hong, Hyunkee; Holben, Brent; Eck, Thomas F.;

Sub-microradian pointing for deep space optical telecommunications network

NASA Technical Reports Server (NTRS)

Ortiz, G.; Lee, S.; Alexander, J.

2001-01-01

This presentation will cover innovative hardware, algorithms, architectures, techniques and recent laboratory results that are applicable to all deep space optical communication links, such as the Mars Telecommunication Network to future interstellar missions.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

Versatile benzimidazole/triphenylamine hybrids: efficient nondoped deep-blue electroluminescence and good host materials for phosphorescent emitters.

PubMed

Gong, Shaolong; Zhao, Yongbiao; Wang, Meng; Yang, Chuluo; Zhong, Cheng; Qin, Jingui; Ma, Dongge

2010-09-03

Two new bipolar compounds, N,N,N',N'-tetraphenyl-5'-(1-phenyl-1H-benzimidazol-2-yl)-1,1':3',1''-terphenyl-4,4''-diamine (1) and N,N,N',N'-tetraphenyl-5'-(1-phenyl-1H-benzimidazol-2-yl)-1,1':3',1''-terphenyl-3,3''-diamine (2), were synthesized and characterized, and their thermal, photophysical, and electrochemical properties were investigated. Compounds 1 and 2 possess good thermal stability with high glass-transition temperatures of 109-129 degrees C and thermal decomposition temperatures of 501-531 degrees C. The fluorescence quantum yield of 1 (0.52) is higher than that of 2 (0.16), which could be attributed to greater pi conjugation between the donor and acceptor moieties. A nondoped deep-blue fluorescent organic light-emitting diode (OLED) using 1 as the blue emitter displays high performance, with a maximum current efficiency of 2.2 cd A(-1) and a maximum external efficiency of 2.9 % at the CIE coordinates of (0.17, 0.07) that are very close to the National Television System Committee's blue standard (0.15, 0.07). Electrophosphorescent devices using the two compounds as host materials for green and red phosphor emitters show high efficiencies. The best performance of a green phosphorescent device was achieved using 2 as the host, with a maximum current efficiency of 64.3 cd A(-1) and a maximum power efficiency of 68.3 lm W(-1); whereas the best performance of a red phosphorescent device was achieved using 1 as the host, with a maximum current efficiency of 11.5 cd A(-1), and a maximum power efficiency of 9.8 lm W(-1). The relationship between the molecular structures and optoelectronic properties are discussed.

Global Map of Epithermal Neutrons

NASA Technical Reports Server (NTRS)

2002-01-01

Observations by NASA's 2001 Mars Odyssey spacecraft show a global view of Mars in intermediate-energy, or epithermal, neutrons. Soil enriched by hydrogen is indicated by the deep blue colors on the map, which show a low intensity of epithermal neutrons. Progressively smaller amounts of hydrogen are shown in the colors light blue, green, yellow and red. The deep blue areas in the polar regions are believed to contain up to 50 percent water ice in the upper one meter (three feet) of the soil. Hydrogen in the far north is hidden at this time beneath a layer of carbon dioxide frost (dry ice). Light blue regions near the equator contain slightly enhanced near-surface hydrogen, which is most likely chemically or physically bound because water ice is not stable near the equator. The view shown here is a map of measurements made during the first three months of mapping using the neutron spectrometer instrument, part of the gamma ray spectrometer instrument suite. The central meridian in this projection is zero degrees longitude. Topographic features are superimposed on the map for geographic reference.

NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. Investigators at Arizona State University in Tempe, the University of Arizona in Tucson, and NASA's Johnson Space Center, Houston, operate the science instruments. The gamma-ray spectrometer was provided by the University of Arizona in collaboration with the Russian Aviation and Space Agency, which provided the high-energy neutron detector, and the Los Alamos National Laboratories, New Mexico, which provided the neutron spectrometer. Lockheed Martin Astronautics, Denver, is the prime contractor for the project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

Prey-mediated behavioral responses of feeding blue whales in controlled sound exposure experiments.

PubMed

Friedlaender, A S; Hazen, E L; Goldbogen, J A; Stimpert, A K; Calambokidis, J; Southall, B L

2016-06-01

Behavioral response studies provide significant insights into the nature, magnitude, and consequences of changes in animal behavior in response to some external stimulus. Controlled exposure experiments (CEEs) to study behavioral response have faced challenges in quantifying the importance of and interaction among individual variability, exposure conditions, and environmental covariates. To investigate these complex parameters relative to blue whale behavior and how it may change as a function of certain sounds, we deployed multi-sensor acoustic tags and conducted CEEs using simulated mid-frequency active sonar (MFAS) and pseudo-random noise (PRN) stimuli, while collecting synoptic, quantitative prey measures. In contrast to previous approaches that lacked such prey data, our integrated approach explained substantially more variance in blue whale dive behavioral responses to mid-frequency sounds (r2 = 0.725 vs. 0.14 previously). Results demonstrate that deep-feeding whales respond more clearly and strongly to CEEs than those in other behavioral states, but this was only evident with the increased explanatory power provided by incorporating prey density and distribution as contextual covariates. Including contextual variables increases the ability to characterize behavioral variability and empirically strengthens previous findings that deep-feeding blue whales respond significantly to mid-frequency sound exposure. However, our results are only based on a single behavioral state with a limited sample size, and this analytical framework should be applied broadly across behavioral states. The increased capability to describe and account for individual response variability by including environmental variables, such as prey, that drive foraging behavior underscores the importance of integrating these and other relevant contextual parameters in experimental designs. Our results suggest the need to measure and account for the ecological dynamics of predator-prey interactions when studying the effects of anthropogenic disturbance in feeding animals.

Tracing the assembly history of NGC 1395 through its Globular Cluster System

NASA Astrophysics Data System (ADS)

Escudero, Carlos G.; Faifer, Favio R.; Smith Castelli, Analía V.; Forte, Juan C.; Sesto, Leandro A.; González, Nélida M.; Scalia, María C.

2018-03-01

We used deep Gemini-South/GMOS g΄r΄i΄z΄ images to study the globular cluster (GC) system of the massive elliptical galaxy NGC 1395, located in the Eridanus supergroup. The photometric analysis of the GC candidates reveals a clear colour bimodality distribution, indicating the presence of `blue' and `red' GC subpopulations. While a negative radial colour gradient is detected in the projected spatial distribution of the red GCs, the blue GCs display a shallow colour gradient. The blue GCs also display a remarkable shallow and extended surface density profile, suggesting a significant accretion of low-mass satellites in the outer halo of the galaxy. In addition, the slope of the projected spatial distribution of the blue GCs in the outer regions of the galaxy, is similar to that of the X-ray halo emission. Integrating up to 165 kpc the profile of the projected spatial distribution of the GCs, we estimated a total GC population and specific frequency of 6000 ± 1100 and SN = 7.4 ± 1.4, respectively. Regarding NGC 1395 itself, the analysis of the deep Gemini/GMOS images shows a low surface brightness umbrella-like structure indicating, at least, one recent merger event. Through relations recently published in the literature, we obtained global parameters, such as Mstellar = 9.32 × 1011 M⊙ and Mh = 6.46 × 1013 M⊙. Using public spectroscopic data, we derive stellar population parameters of the central region of the galaxy by the full spectral fitting technique. We have found that this region seems to be dominated for an old stellar population, in contrast to findings of young stellar populations from the literature.

A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

NASA Astrophysics Data System (ADS)

Fan, J.; Li, Q.; Hou, J.; Feng, X.; Karimian, H.; Lin, S.

2017-10-01

Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.

86. Round Meadow Creek Viaduct. This steel girder bridge, built ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

86. Round Meadow Creek Viaduct. This steel girder bridge, built in 1939, has a reinforced concrete deck and piers. It is an example of a major in-line, or straight, viaduct over a deep ravine. - Blue Ridge Parkway, Between Shenandoah National Park & Great Smoky Mountains, Asheville, Buncombe County, NC

Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

PubMed

Brown, James M; Campbell, J Peter; Beers, Andrew; Chang, Ken; Ostmo, Susan; Chan, R V Paul; Dy, Jennifer; Erdogmus, Deniz; Ioannidis, Stratis; Kalpathy-Cramer, Jayashree; Chiang, Michael F

2018-05-02

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The decision to treat is primarily based on the presence of plus disease, defined as dilation and tortuosity of retinal vessels. However, clinical diagnosis of plus disease is highly subjective and variable. To implement and validate an algorithm based on deep learning to automatically diagnose plus disease from retinal photographs. A deep convolutional neural network was trained using a data set of 5511 retinal photographs. Each image was previously assigned a reference standard diagnosis (RSD) based on consensus of image grading by 3 experts and clinical diagnosis by 1 expert (ie, normal, pre-plus disease, or plus disease). The algorithm was evaluated by 5-fold cross-validation and tested on an independent set of 100 images. Images were collected from 8 academic institutions participating in the Imaging and Informatics in ROP (i-ROP) cohort study. The deep learning algorithm was tested against 8 ROP experts, each of whom had more than 10 years of clinical experience and more than 5 peer-reviewed publications about ROP. Data were collected from July 2011 to December 2016. Data were analyzed from December 2016 to September 2017. A deep learning algorithm trained on retinal photographs. Receiver operating characteristic analysis was performed to evaluate performance of the algorithm against the RSD. Quadratic-weighted κ coefficients were calculated for ternary classification (ie, normal, pre-plus disease, and plus disease) to measure agreement with the RSD and 8 independent experts. Of the 5511 included retinal photographs, 4535 (82.3%) were graded as normal, 805 (14.6%) as pre-plus disease, and 172 (3.1%) as plus disease, based on the RSD. Mean (SD) area under the receiver operating characteristic curve statistics were 0.94 (0.01) for the diagnosis of normal (vs pre-plus disease or plus disease) and 0.98 (0.01) for the diagnosis of plus disease (vs normal or pre-plus disease). For diagnosis of plus disease in an independent test set of 100 retinal images, the algorithm achieved a sensitivity of 93% with 94% specificity. For detection of pre-plus disease or worse, the sensitivity and specificity were 100% and 94%, respectively. On the same test set, the algorithm achieved a quadratic-weighted κ coefficient of 0.92 compared with the RSD, outperforming 6 of 8 ROP experts. This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.

Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network.

PubMed

Al-Fatlawi, Ali H; Fatlawi, Hayder K; Sai Ho Ling

2017-07-01

Daily physical activities monitoring is benefiting the health care field in several ways, in particular with the development of the wearable sensors. This paper adopts effective ways to calculate the optimal number of the necessary sensors and to build a reliable and a high accuracy monitoring system. Three data mining algorithms, namely Decision Tree, Random Forest and PART Algorithm, have been applied for the sensors selection process. Furthermore, the deep belief network (DBN) has been investigated to recognise 33 physical activities effectively. The results indicated that the proposed method is reliable with an overall accuracy of 96.52% and the number of sensors is minimised from nine to six sensors.

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

NASA Astrophysics Data System (ADS)

Fotin, Sergei V.; Yin, Yin; Haldankar, Hrishikesh; Hoffmeister, Jeffrey W.; Periaswamy, Senthil

2016-03-01

Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 +/- 0:040 to 0:893 +/- 0:033 for suspicious ROIs; and from 0:852 +/- 0:065 to 0:930 +/- 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

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

PubMed Central

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

2018-01-01

Purpose To automatically and accurately segment geographic atrophy (GA) in spectral-domain optical coherence tomography (SD-OCT) images by constructing a voting system with deep neural networks without the use of retinal layer segmentation. Methods An automatic GA segmentation method for SD-OCT images based on the deep network was constructed. The structure of the deep network was composed of five layers, including one input layer, three hidden layers, and one output layer. During the training phase, the labeled A-scans with 1024 features were directly fed into the network as the input layer to obtain the deep representations. Then a soft-max classifier was trained to determine the label of each individual pixel. Finally, a voting decision strategy was used to refine the segmentation results among 10 trained models. Results Two image data sets with GA were used to evaluate the model. For the first dataset, our algorithm obtained a mean overlap ratio (OR) 86.94% ± 8.75%, absolute area difference (AAD) 11.49% ± 11.50%, and correlation coefficients (CC) 0.9857; for the second dataset, the mean OR, AAD, and CC of the proposed method were 81.66% ± 10.93%, 8.30% ± 9.09%, and 0.9952, respectively. The proposed algorithm was capable of improving over 5% and 10% segmentation accuracy, respectively, when compared with several state-of-the-art algorithms on two data sets. Conclusions Without retinal layer segmentation, the proposed algorithm could produce higher segmentation accuracy and was more stable when compared with state-of-the-art methods that relied on retinal layer segmentation results. Our model may provide reliable GA segmentations from SD-OCT images and be useful in the clinical diagnosis of advanced nonexudative AMD. Translational Relevance Based on the deep neural networks, this study presents an accurate GA segmentation method for SD-OCT images without using any retinal layer segmentation results, and may contribute to improved understanding of advanced nonexudative AMD. PMID:29302382

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

PubMed

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

2018-01-01

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

Programming Deep Brain Stimulation for Parkinson's Disease: The Toronto Western Hospital Algorithms.

PubMed

Picillo, Marina; Lozano, Andres M; Kou, Nancy; Puppi Munhoz, Renato; Fasano, Alfonso

2016-01-01

Deep brain stimulation (DBS) is an established and effective treatment for Parkinson's disease (PD). After surgery, a number of extensive programming sessions are performed to define the most optimal stimulation parameters. Programming sessions mainly rely only on neurologist's experience. As a result, patients often undergo inconsistent and inefficient stimulation changes, as well as unnecessary visits. We reviewed the literature on initial and follow-up DBS programming procedures and integrated our current practice at Toronto Western Hospital (TWH) to develop standardized DBS programming protocols. We propose four algorithms including the initial programming and specific algorithms tailored to symptoms experienced by patients following DBS: speech disturbances, stimulation-induced dyskinesia and gait impairment. We conducted a literature search of PubMed from inception to July 2014 with the keywords "deep brain stimulation", "festination", "freezing", "initial programming", "Parkinson's disease", "postural instability", "speech disturbances", and "stimulation induced dyskinesia". Seventy papers were considered for this review. Based on the literature review and our experience at TWH, we refined four algorithms for: (1) the initial programming stage, and management of symptoms following DBS, particularly addressing (2) speech disturbances, (3) stimulation-induced dyskinesia, and (4) gait impairment. We propose four algorithms tailored to an individualized approach to managing symptoms associated with DBS and disease progression in patients with PD. We encourage established as well as new DBS centers to test the clinical usefulness of these algorithms in supplementing the current standards of care. Copyright © 2016 Elsevier Inc. All rights reserved.

In situ spectral response of the Arabian Gulf and Sea of Oman coastal waters to bio-optical properties.

PubMed

Al Shehhi, Maryam R; Gherboudj, Imen; Ghedira, Hosni

2017-10-01

Mapping of Chlorophyll-a (Chl-a) over the coastal waters of the Arabian Gulf and the Sea of Oman using the satellite-based observations, such as MODIS (Moderate Resolution Imaging Spectro-radiometer), has shown inferior performance (Chl-a overestimation) than that of deep waters. Studies in the region have shown that this poor performance is due to three reasons: (i) water turbidity (sediments re-suspension), and the presence of colored dissolved organic matter (CDOM), (ii) bottom reflectance and (iii) incapability of the existing atmospheric correction models to reduce the effect of the aerosols from the water leaving radiance. Therefore, this work focuses on investigating the sensitivity of the in situ spectral signatures of these coastal waters to the algal (chlorophyll: Chl-a), non-algal (sediments and CDOM) and the bottom reflectance properties, in absence of contributions from the atmosphere. Consequently, the collected in situ spectral signatures will improve our understanding of Arabian Gulf and Sea of Oman water properties. For this purpose, comprehensive field measurements were carried out between 2013 and 2016, over Abu-Dhabi (Arabian Gulf) and Fujairah (Sea of Oman) where unique water quality data were collected. Based on the in situ water spectral analysis, the bottom reflectance (water depth<20m) are found to degrade the performance of the conventional ocean color algorithms more than the sediment-laden waters where these waters increase the R rs at the blue and red ranges. The increasing presence of CDOM markedly decreases the R rs in the blue range, which is conflicting with the effect of Chl-a. Given the inadequate performance of the widely used ocean-color algorithms (OC3: ocean color 3, OC2: ocean color 2) in retrieving Chl-a in these very shallow coastal waters, therefore, a new algorithm is proposed here based on a 3-bands ratio approach using [R rs (656) -1 -R rs (506) -1 ]×R rs (661). The selected optimum bands (656nm, 506nm, and 661nm) from this approach can be used to retrieve the Chl-a more accurately in these coastal Case II (turbid) waters which are close to the bands of the current missions such as Sentinel-3 OLCI (Ocean and Land Colour Instrument), MODIS, VIIRS (Visible Infrared Imaging Radiometer Suite) and LandSat 8. However, more uniformly distributed data over the Arabian Gulf is required to have a highly accurate regional model for Chl-a retrieval. Copyright © 2017 Elsevier B.V. All rights reserved.

Google DeepMind and healthcare in an age of algorithms.

PubMed

Powles, Julia; Hodson, Hal

2017-01-01

Data-driven tools and techniques, particularly machine learning methods that underpin artificial intelligence, offer promise in improving healthcare systems and services. One of the companies aspiring to pioneer these advances is DeepMind Technologies Limited, a wholly-owned subsidiary of the Google conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health project: a collaboration with the Royal Free London NHS Foundation Trust, to assist in the management of acute kidney injury. Initially received with great enthusiasm, the collaboration has suffered from a lack of clarity and openness, with issues of privacy and power emerging as potent challenges as the project has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this article draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age.

Enhanced Higgs boson to τ(+)τ(-) search with deep learning.

PubMed

Baldi, P; Sadowski, P; Whiteson, D

2015-03-20

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Stellar Vampires Unmasked

NASA Astrophysics Data System (ADS)

2006-10-01

Astronomers have found possible proofs of stellar vampirism in the globular cluster 47 Tucanae. Using ESO's Very Large Telescope, they found that some hot, bright, and apparently young stars in the cluster present less carbon and oxygen than the majority of their sisters. This indicates that these few stars likely formed by taking their material from another star. "This is the first detection of a chemical signature clearly pointing to a specific scenario to form so-called 'Blue straggler stars' in a globular cluster", said Francesco Ferraro, from the Astronomy Department of Bologna University (Italy) and lead-author of the paper presenting the results. Blue stragglers are unexpectedly young-looking stars found in stellar aggregates, such as globular clusters, which are known to be made up of old stars. These enigmatic objects are thought to be created in either direct stellar collisions or through the evolution and coalescence of a binary star system in which one star 'sucks' material off the other, rejuvenating itself. As such, they provide interesting constraints on both binary stellar evolution and star cluster dynamics. To date, the unambiguous signatures of either stellar traffic accidents or stellar vampirism have not been observed, and the formation mechanisms of Blue stragglers are still a mystery. The astronomers used ESO's Very Large Telescope to measure the abundance of chemical elements at the surface of 43 Blue straggler stars in the globular cluster 47 Tucanae [1]. They discovered that six of these Blue straggler stars contain less carbon and oxygen than the majority of these peculiar objects. Such an anomaly indicates that the material at the surface of the blue stragglers comes from the deep interiors of a parent star [2]. Such deep material can reach the surface of the blue straggler only during the mass transfer process occurring between two stars in a binary system. Numerical simulations indeed show that the coalescence of stars should not result in anomalous abundances. ESO PR Photo 37/06 ESO PR Photo 37/06 Abundances in Blue Straggler Stars In the core of a globular cluster, stars are packed extremely close to each other: more than 4000 stars are found in the innermost light-year-sized cube of 47 Tucanae. Thus, stellar collisions are thought to be very frequent and the collision channel for the formation of blue stragglers should be extremely efficient. The chemical signature detected by these observations demonstrates that also the binary mass-transfer scenario is fully active even in a high-density cluster like 47 Tuc. "Our discovery is therefore a fundamental step toward the solution of the long-standing mystery of blue straggler formation in globular clusters," said Ferraro. Measurements of so many faint stars are only possible since the advent of 8-m class telescopes equipped with multiplexing capability spectrographs. In this case, the astronomers used the FLAMES/Giraffe instrument that allows the simultaneous observation of up to 130 targets at a time, making it ideally suited for surveying individual stars in closely populated fields.

Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

2016-01-01

This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

Antimicrobial photodynamic therapy as an adjunct for treatment of deep carious lesions-A systematic review.

PubMed

Cieplik, Fabian; Buchalla, Wolfgang; Hellwig, Elmar; Al-Ahmad, Ali; Hiller, Karl-Anton; Maisch, Tim; Karygianni, Lamprini

2017-06-01

For deep carious lesions, a more conservative treatment modality ("selective caries removal") has been proposed, where only the heavily contaminated dentine is removed. In this regard, effective adjuncts for cavity disinfection such as the antimicrobial photodynamic therapy (aPDT) can be valuable clinically prior to definitive restoration. Therefore, the aim of this study was to systematically assess clinical studies on the effectiveness of aPDT as a supplementary tool in the treatment of deep caries lesions. Searches were performed in four databases (PubMed, EMBASE, ISI Web of Science, ClinicalTrials.gov) from 1st January, 2011 until 21st June, 2016 for search terms relevant to the observed parameters, pathological condition, intervention and anatomic entity. The pooled information was evaluated according to PRISMA guidelines. At first, 1651 articles were recovered, of which 1249 full-text articles were evaluated, 270 articles thereof were reviewed for eligibility and finally 6 articles met all inclusion criteria. The aPDT protocols involved Methylene Blue, Toluidine Blue and aluminium-chloride-phthalocyanine as photosensitizers and diode lasers, light-emitting diodes and halogen light-sources. The data from five reports, utilizing both culture-dependent and -independent methods, disclosed significant reduction of cariogenic bacterial load after mechanical caries removal with adjunct aPDT. As these studies exhibit some methodological limitations, e.g. lack of positive controls, this systematic review can support the application of aPDT to a limited extent only in terms of reducing the microbial load in deep carious lesions before restorative treatment. Copyright © 2017 Elsevier B.V. All rights reserved.

Cosmic Accretion and Galaxy Co-Evolution: Lessons from the Extended Chandra Deep Field South

NASA Astrophysics Data System (ADS)

Urry, C. Megan

2011-05-01

The Chandra deep fields reveal that most cosmic accretion onto supermassive black holes is obscured by gas and dust. The GOODS and MUSYC multiwavelength data show that many X-ray-detected AGN are faint and red (or even undetectable) in the optical but bright in the infrared, as is characteristic of obscured sources. (N.B. The ECDFS is most sensitive to the AGN that constitute the X-ray background, namely, moderate luminosity AGN, with log Lx=43-44, at moderate redshifts, 0.5

Space Science

NASA Image and Video Library

2002-04-01

This picture of the galaxy UGC 10214 was was taken by the Advanced Camera for Surveys (ACS), which was installed aboard the Hubble Space Telescope (HST) in March 2002 during HST Servicing Mission 3B (STS-109 mission). Dubbed the "Tadpole," this spiral galaxy is unlike the textbook images of stately galaxies. Its distorted shape was caused by a small interloper, a very blue, compact galaxy visible in the upper left corner of the more massive Tadpole. The Tadpole resides about 420 million light-years away in the constellation Draco. Seen shining through the Tadpole's disk, the tiny intruder is likely a hit-and-run galaxy that is now leaving the scene of the accident. Strong gravitational forces from the interaction created the long tail of debris, consisting of stars and gas that stretch our more than 280,000 light-years. The galactic carnage and torrent of star birth are playing out against a spectacular backdrop: a "wallpaper pattern" of 6,000 galaxies. These galaxies represent twice the number of those discovered in the legendary Hubble Deep Field, the orbiting observatory's "deepest" view of the heavens, taken in 1995 by the Wide Field and planetary camera 2. The ACS picture, however, was taken in one-twelfth of the time it took to observe the original HST Deep Field. In blue light, ACS sees even fainter objects than were seen in the "deep field." The galaxies in the ACS picture, like those in the deep field, stretch back to nearly the begirning of time. Credit: NASA, H. Ford (JHU), G. Illingworth (USCS/LO), M. Clampin (STScI), G. Hartig (STScI), the ACS Science Team, and ESA.

Blue light hazard optimization for white light-emitting diode sources with high luminous efficacy of radiation and high color rendering index

NASA Astrophysics Data System (ADS)

Zhang, Jingjing; Guo, Weihong; Xie, Bin; Yu, Xingjian; Luo, Xiaobing; Zhang, Tao; Yu, Zhihua; Wang, Hong; Jin, Xing

2017-09-01

Blue light hazard of white light-emitting diodes (LED) is a hidden risk for human's photobiological safety. Recent spectral optimization methods focus on maximizing luminous efficacy and improving color performances of LEDs, but few of them take blue hazard into account. Therefore, for healthy lighting, it's urgent to propose a spectral optimization method for white LED source to exhibit low blue light hazard, high luminous efficacy of radiation (LER) and high color performances. In this study, a genetic algorithm with penalty functions was proposed for realizing white spectra with low blue hazard, maximal LER and high color rendering index (CRI) values. By simulations, white spectra from LEDs with low blue hazard, high LER (≥297 lm/W) and high CRI (≥90) were achieved at different correlated color temperatures (CCTs) from 2013 K to 7845 K. Thus, the spectral optimization method can be used for guiding the fabrication of LED sources in line with photobiological safety. It is also found that the maximum permissible exposure duration of the optimized spectra increases by 14.9% than that of bichromatic phosphor-converted LEDs with equal CCT.

Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas.

PubMed

Liu, Yanpeng; Li, Yibin; Ma, Xin; Song, Rui

2017-03-29

In the pattern recognition domain, deep architectures are currently widely used and they have achieved fine results. However, these deep architectures make particular demands, especially in terms of their requirement for big datasets and GPU. Aiming to gain better results without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size; therefore, it can extracts more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features' dimensions are reduced by Principal Component Analysis (PCA) and we apply several classifiers to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames compared with neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. As a result, the salient areas found from different subjects are the same size. In addition, the gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

Blue-green phosphor for fluorescent lighting applications

DOEpatents

Srivastava, Alok; Comanzo, Holly; Manivannan, Venkatesan; Setlur, Anant Achyut

2005-03-15

A fluorescent lamp including a phosphor layer including Sr.sub.4 Al.sub.14 O.sub.25 :Eu.sup.2+ (SAE) and at least one of each of a red, green and blue emitting phosphor. The phosphor layer can optionally include an additional, deep red phosphor and a yellow emitting phosphor. The resulting lamp will exhibit a white light having a color rendering index of 90 or higher with a correlated color temperature of from 2500 to 10000 Kelvin. The use of SAE in phosphor blends of lamps results in high CRI light sources with increased stability and acceptable lumen maintenance over, the course of the lamp life.

Deep-Blue Fluorescent Particles via Microwave Heating of Polyacrylonitrile Dispersions.

PubMed

Go, Dennis; Jurásková, Alena; Hoffmann, Andreas; Kapiti, Gent; Kuehne, Alexander J C

2017-03-01

This study presents a new method to produce fluorescent particles. Established methods are based on the incorporation of conjugated dye molecules into dielectric polymer matrices or preparation of colloids, which are composed of fluorescent conjugated polymer. By contrast, this study presents a method where dielectric polyacrylonitrile is exposed to microwave radiation leading to an intramolecular cyclization reaction producing π-conjugated segments, which fluoresce blue. During this conversion, the particles shrink in diameter but as an ensemble they retain their monodispersity. This work investigates the optimal reaction conditions and characterizes the optical properties. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The impact of privacy protection filters on gender recognition

NASA Astrophysics Data System (ADS)

Ruchaud, Natacha; Antipov, Grigory; Korshunov, Pavel; Dugelay, Jean-Luc; Ebrahimi, Touradj; Berrani, Sid-Ahmed

2015-09-01

Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or even outperform human visual system in its ability to detect and recognize objects and their features. In this paper, we focus on the specific task of gender recognition in images when they have been processed by privacy protection filters (e.g., blurring, masking, and pixelization) applied at different strengths. Assuming a privacy protection scenario, we compare the performance of state of the art deep learning algorithms with a subjective evaluation obtained via crowdsourcing to understand how privacy protection filters affect both machine and human vision.

Ab initio calculations of deep-level carrier nonradiative recombination rates in bulk semiconductors.

PubMed

Shi, Lin; Wang, Lin-Wang

2012-12-14

Nonradiative carrier recombination is of both applied and fundamental interest. Here a novel algorithm is introduced to calculate such a deep level nonradiative recombination rate using the ab initio density functional theory. This algorithm can calculate the electron-phonon coupling constants all at once. An approximation is presented to calculate the phonon modes for one impurity in a large supercell. The neutral Zn impurity site together with a N vacancy is considered as the carrier-capturing deep impurity level in bulk GaN. Its capture coefficient is calculated as 5.57 × 10(-10)cm(3)/s at 300 K. We found that there is no apparent onset of such a nonradiative process as a function of temperature.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

PubMed

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Deep seawater inherent optical properties in the Southern Ionian Sea

NASA Astrophysics Data System (ADS)

Riccobene, G.; Capone, A.; Aiello, S.; Ambriola, M.; Ameli, F.; Amore, I.; Anghinolfi, M.; Anzalone, A.; Avanzini, C.; Barbarino, G.; Barbarito, E.; Battaglieri, M.; Bellotti, R.; Beverini, N.; Bonori, M.; Bouhadef, B.; Brescia, M.; Cacopardo, G.; Cafagna, F.; Caponetto, L.; Castorina, E.; Ceres, A.; Chiarusi, T.; Circella, M.; Cocimano, R.; Coniglione, R.; Cordelli, M.; Costa, M.; Cuneo, S.; D'Amico, A.; de Bonis, G.; de Marzo, C.; de Rosa, G.; de Vita, R.; Distefano, C.; Falchini, E.; Fiorello, C.; Flaminio, V.; Fratini, K.; Gabrielli, A.; Galeotti, S.; Gandolfi, E.; Grimaldi, A.; Habel, R.; Leonora, E.; Lonardo, A.; Longo, G.; Lo Presti, D.; Lucarelli, F.; Maccioni, E.; Margiotta, A.; Martini, A.; Masullo, R.; Megna, R.; Migneco, E.; Mongelli, M.; Montaruli, T.; Morganti, M.; Musumeci, M.; Nicolau, C. A.; Orlando, A.; Osipenko, M.; Osteria, G.; Papaleo, R.; Pappalardo, V.; Petta, C.; Piattelli, P.; Raffaelli, F.; Raia, G.; Randazzo, N.; Reito, S.; Ricco, G.; Ripani, M.; Rovelli, A.; Ruppi, M.; Russo, G. V.; Russo, S.; Russo, S.; Sapienza, P.; Sedita, M.; Schuller, J.-P.; Shirokov, E.; Simeone, F.; Sipala, V.; Spurio, M.; Taiuti, M.; Terreni, G.; Trasatti, L.; Urso, S.; Valente, V.; Vicini, P.

2007-02-01

The NEMO (NEutrino Mediterranean Observatory) Collaboration has been carrying out since 1998 an evaluation programme of deep sea sites suitable for the construction of the future Mediterranean km3 Čerenkov neutrino telescope. We investigated the seawater optical and oceanographic properties of several deep sea marine areas close to the Italian Coast. Inherent optical properties (light absorption and attenuation coefficients) have been measured as a function of depth using an experimental apparatus equipped with standard oceanographic probes and the commercial transmissometer AC9 manufactured by WETLabs. This paper reports on the visible light absorption and attenuation coefficients measured in deep seawater of a marine region located in the Southern Ionian Sea, 60 100 km SE of Capo Passero (Sicily). Data show that blue light absorption coefficient is about 0.015 m-1 (corresponding to an absorption length of 67 m) close to the one of optically pure water and it does not show seasonal variation.

KSC-05PD-0137

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. After a perfect liftoff at 1:47 p.m. EST today from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., the Boeing Delta II rocket with Deep Impact spacecraft aboard soars through the clear blue sky. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

Earth observations taken from Space Shuttle Columbia during STS-80 mission

NASA Image and Video Library

1996-12-03

STS080-742-070 (19 Nov.-7 Dec. 1996) --- A view of the Tongue of the Ocean in the Bahama Islands east of Florida. The lines leading from the flat bottom of the Great Bahama Bank, leading into the Tongue, are caused by rapid transfer of ocean water caused by both temperature changes in the water and hurricanes that periodically cross the area. The water is about 30 feet deep on the Great Bahama Bank, and nearly a mile deep in the tongue. To the left is the Exuma Sound, over a mile deep, and a series of islands along its edge with Great Exuma Island the easiest to see. Green Cay, the small dot lower left, leaving a wake to the southeast of light colored coral. The deep blue area to the top right center is the southeastern edge of the Great Bahama Bank.

Quantification of early cutaneous manifestations of chronic venous insufficiency by automated analysis of photographic images: Feasibility and technical considerations.

PubMed

Becker, François; Fourgeau, Patrice; Carpentier, Patrick H; Ouchène, Amina

2018-06-01

We postulate that blue telangiectasia and brownish pigmentation at ankle level, early markers of chronic venous insufficiency, can be quantified for longitudinal studies of chronic venous disease in Caucasian people. Objectives and methods To describe a photographic technique specially developed for this purpose. The pictures were acquired using a dedicated photo stand to position the foot in a reproducible way, with a normalized lighting and acquisition protocol. The image analysis was performed with a tool developed using algorithms optimized to detect and quantify blue telangiectasia and brownish pigmentation and their relative surface in the region of interest. To test the short-term reproducibility of the measures. Results The quantification of the blue telangiectasia and of the brownish pigmentation using an automated digital photo analysis is feasible. The short-term reproducibility is good for blue telangiectasia quantification. It is a less accurate for the brownish pigmentation. Conclusion The blue telangiectasia of the corona phlebectatica and the ankle flare can be assessed using a clinimetric approach based on the automated digital photo analysis.

The Efficacy of Blue-Green Infrastructure for Pluvial Flood Prevention under Conditions of Deep Uncertainty

NASA Astrophysics Data System (ADS)

Babovic, Filip; Mijic, Ana; Madani, Kaveh

2017-04-01

Urban areas around the world are growing in size and importance; however, cities experience elevated risks of pluvial flooding due to the prevalence of impermeable land surfaces within them. Urban planners and engineers encounter a great deal of uncertainty when planning adaptations to these flood risks, due to the interaction of multiple factors such as climate change and land use change. This leads to conditions of deep uncertainty. Blue-Green (BG) solutions utilise natural vegetation and processes to absorb and retain runoff while providing a host of other social, economic and environmental services. When utilised in conjunction with Decision Making under Deep Uncertainty (DMDU) methodologies, BG infrastructure provides a flexible and adaptable method of "no-regret" adaptation; resulting in a practical, economically efficient, and socially acceptable solution for flood risk mitigation. This work presents the methodology for analysing the impact of BG infrastructure in the context of the Adaptation Tipping Points approach to protect against pluvial flood risk in an iterative manner. An economic analysis of the adaptation pathways is also conducted in order to better inform decision-makers on the benefits and costs of the adaptation options presented. The methodology was applied to a case study in the Cranbrook Catchment in the North East of London. Our results show that BG infrastructure performs better under conditions of uncertainty than traditional grey infrastructure.

Synthesis and electroluminescence property of new hexaphenylbenzene derivatives including amine group for blue emitters

PubMed Central

2013-01-01

Three new blue-emitting compounds of 5P-VA, 5P-VTPA, and 5P-DVTPA for organic light-emitting diode (OLED) based on hexaphenylbenzene moiety were demonstrated. Physical properties by the change of the substitution groups of the synthesized materials were systematically examined. Photoluminescence spectrum of the synthesized materials showed maximum emitting wavelengths of about 400 to 447 nm in solution state and 451 to 461 nm in film state, indicating deep blue emission color. OLED devices were fabricated by the synthesized compounds using vacuum deposit process as an emitting layer. The device structure was ITO/2-TNATA 60 nm/ NPB 15 nm/ EML 35 nm/ TPBi 20 nm/ LiF 1 nm/ Al 200 nm. External quantum efficiencies and CIE values of 5P-VA, 5P-VTPA, and 5P-DVTPA were 1.89%, 3.59%, 3.34%, and (0.154, 0.196), (0.150, 0.076), (0.148, 0.120), respectively. 5P-VTPA and 5P-DVTPA exhibited superior highly blue quality and thermal property such as high Td of 448°C and 449°C. PMID:24134333

Spectrum Project

NASA Image and Video Library

2017-10-16

Inside the Spectrum prototype unit, organisms in a Petri plate are exposed to blue excitation lighting. The device works by exposing organisms to different colors of fluorescent light while a camera records what's happening with time-lapse photography. Results from the Spectrum project will shed light on which living things are best suited for long-duration flights into deep space.

Novel bioluminescent coelenterazine derivatives with imidazopyrazinone C-6 extended substitution for Renilla luciferase.

PubMed

Jiang, Tianyu; Yang, Xiaofeng; Yang, Xingye; Yuan, Mingliang; Zhang, Tianchao; Zhang, Huateng; Li, Minyong

2016-06-21

Two series of novel coelenterazine analogues (alkynes and triazoles) with imidazopyrazinone C-6 extended substitution have been designed and synthesized successfully for the extension of bioluminescent substrates. After extensive evaluation, some compounds display excellent bioluminescence properties compared with DeepBlueC in cellulo, thus becoming potential molecules for bioluminescence techniques.

White/blue-emitting, water-dispersible CdSe quantum dots prepared by counter ion-induced polymer collapse

NASA Astrophysics Data System (ADS)

Wang, Jing; Goh, Jane Betty; Goh, M. Cynthia; Giri, Neeraj Kumar; Paige, Matthew F.

2015-09-01

The synthesis and characterization of water-dispersible, luminescent CdSe/ZnS semiconductor quantum dots that exhibit nominal "white" fluorescence emission and have potential applications in solid-state lighting is described. The nanomaterials, prepared through counter ion-induced collapse and UV cross-linking of high-molecular weight polyacrylic acid in the presence of appropriate aqueous inorganic ions, were of ∼2-3 nm diameter and could be prepared in gram quantities. The quantum dots exhibited strong luminescence emission in two bands, the first in the blue-region (band edge) of the optical spectrum and the second, a broad emission in the red-region (attributed to deep trap states) of the optical spectrum. Because of the relative strength of emission of the band edge and deep trap state luminescence, it was possible to achieve visible white luminescence from the quantum dots in aqueous solution and in dried, solid films. The optical spectroscopic properties of the nanomaterials, including ensemble and single-molecule spectroscopy, was performed, with results compared to other white-emitting quantum dot systems described previously in the literature.

Distinguishing triplet energy transfer and trap-assisted recombination in multi-color organic light-emitting diode with an ultrathin phosphorescent emissive layer

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

Xue, Qin, E-mail: xueqin19851202@163.com; Liu, Shouyin; Xie, Guohua

2014-03-21

An ultrathin layer of deep-red phosphorescent emitter tris(1-phenylisoquinoline) iridium (III) (Ir(piq){sub 3}) is inserted within different positions of the electron blocking layer fac-tris (1-phenylpyrazolato-N,C{sup 2′})-iridium(III) (Ir(ppz){sub 3}) to distinguish the contribution of the emission from the triplet exciton energy transfer/diffusion from the adjacent blue phosphorescent emitter and the trap-assisted recombination from the narrow band-gap emitter itself. The charge trapping effect of the narrow band-gap deep-red emitter which forms a quantum-well-like structure also plays a role in shaping the electroluminescent characteristics of multi-color organic light-emitting diodes. By accurately controlling the position of the ultrathin sensing layer, it is considerably easy tomore » balance the white emission which is quite challenging for full-color devices with multiple emission zones. There is nearly no energy transfer detectable if 7 nm thick Ir(ppz){sub 3} is inserted between the blue phosphorescent emitter and the ultrathin red emitter.« less

Beyond the usual mapping functions in GPS, VLBI and Deep Space tracking.

NASA Astrophysics Data System (ADS)

Barriot, Jean-Pierre; Serafini, Jonathan; Sichoix, Lydie

2014-05-01

We describe here a new algorithm to model the water contents of the atmosphere (including ZWD) from GPS slant wet delays relative to a single receiver. We first make the assumption that the water vapor contents are mainly governed by a scale height (exponential law), and secondly that the departures from this decaying exponential can be mapped as a set of low degree 3D Zernike functions (w.r.t. space) and Tchebyshev polynomials (w.r.t. time.) We compare this new algorithm with previous algorithms known as mapping functions in GPS, VLBI and Deep Space tracking and give an example with data acquired over a one day time span at the Geodesy Observatory of Tahiti.

Adapting MODIS Dust Mask Algorithm to Suomi NPP VIIRS for Air Quality Applications

NASA Astrophysics Data System (ADS)

Ciren, P.; Liu, H.; Kondragunta, S.; Laszlo, I.

2012-12-01

Despite pollution reduction control strategies enforced by the Environmental Protection Agency (EPA), large regions of the United States are often under exceptional events such as biomass burning and dust outbreaks that lead to non-attainment of particulate matter standards. This has warranted the National Weather Service (NWS) to provide smoke and dust forecast guidance to the general public. The monitoring and forecasting of dust outbreaks relies on satellite data. Currently, Aqua/MODIS (MODerate resolution Imaging Spectrometer) and Terra/MODIS provide measurements needed to derive dust mask and Aerosol Optical Thickness (AOT) products. The newly launched Suomi NPP VIIRS (Visible/Infrared Imaging Radiometer Suite) instrument has a Suspended Matter (SM) product that indicates the presence of dust, smoke, volcanic ash, sea salt, and unknown aerosol types in a given pixel. The algorithm to identify dust is different over land and ocean but for both, the information comes from AOT retrieval algorithm. Over land, the selection of dust aerosol model in the AOT retrieval algorithm indicates the presence of dust and over ocean a fine mode fraction smaller than 20% indicates dust. Preliminary comparisons of VIIRS SM to CALIPSO Vertical Feature Mask (VFM) aerosol type product indicate that the Probability of Detection (POD) is at ~10% and the product is not mature for operational use. As an alternate approach, NESDIS dust mask algorithm developed for NWS dust forecast verification that uses MODIS deep blue, visible, and mid-IR channels using spectral differencing techniques and spatial variability tests was applied to VIIRS radiances. This algorithm relies on the spectral contrast of dust absorption at 412 and 440 nm and an increase in reflectivity at 2.13 μm when dust is present in the atmosphere compared to a clear sky. To avoid detecting bright desert surface as airborne dust, the algorithm uses the reflectances at 1.24 μm and 2.25 μm to flag bright pixels. The algorithm flags pixels that fall into the glint region so sun glint is not picked up as dust. The algorithm also has a spatial variability test that uses reflectances at 0.86 μm to screen for clouds over water. Analysis of one granule for a known dust event on May 2, 2012 shows that the agreement between VIIRS and MODIS is 82% and VIIRS and CALIPSO is 71%. The probability of detection for VIIRS when compared to MODIS and CALIPSO is 53% and 45% respectively whereas the false alarm ratio for VIIRS when compared to MODIS and CALIPSO is 20% and 37% respectively. The algorithm details, results from the test cases, and the use of the dust flag product in NWS applications will be presented.

Long-term detection of Parkinsonian tremor activity from subthalamic nucleus local field potentials.

PubMed

Houston, Brady; Blumenfeld, Zack; Quinn, Emma; Bronte-Stewart, Helen; Chizeck, Howard

2015-01-01

Current deep brain stimulation paradigms deliver continuous stimulation to deep brain structures to ameliorate the symptoms of Parkinson's disease. This continuous stimulation has undesirable side effects and decreases the lifespan of the unit's battery, necessitating earlier replacement. A closed-loop deep brain stimulator that uses brain signals to determine when to deliver stimulation based on the occurrence of symptoms could potentially address these drawbacks of current technology. Attempts to detect Parkinsonian tremor using brain signals recorded during the implantation procedure have been successful. However, the ability of these methods to accurately detect tremor over extended periods of time is unknown. Here we use local field potentials recorded during a deep brain stimulation clinical follow-up visit 1 month after initial programming to build a tremor detection algorithm and use this algorithm to detect tremor in subsequent visits up to 8 months later. Using this method, we detected the occurrence of tremor with accuracies between 68-93%. These results demonstrate the potential of tremor detection methods for efficacious closed-loop deep brain stimulation over extended periods of time.

Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

PubMed

Zheng, Yu-Jun; Sheng, Wei-Guo; Sun, Xing-Ming; Chen, Sheng-Yong

2017-12-01

Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

Comparative histometric analysis of the effects of high-intensity focused ultrasound and radiofrequency on skin.

PubMed

Suh, Dong Hye; Choi, Jeong Hwee; Lee, Sang Jun; Jeong, Ki-Heon; Song, Kye Yong; Shin, Min Kyung

2015-01-01

High-intensity focused ultrasound (HIFU) and radiofrequency (RF) are used for non-invasive skin tightening. Neocollagenesis and neoelastogenesis have been reported to have a mechanism of controlled thermal injury. To compare neocollagenesis and neoelastogenesis in each layer of the dermis after each session of HIFU and monopolar RF. We analyzed the area fraction of collagen and elastic fibers using the Masson's Trichrome and Victoria blue special stains, respectively, before and after 2 months of treatments. Histometric analyses were performed in each layer of the dermis, including the papillary dermis, and upper, mid, and deep reticular dermis. Monopolar RF led to neocollagenesis in the papillary dermis, and upper, mid, and deep reticular dermis, and neoelastogenesis in the papillary dermis, and upper and mid reticular dermis. HIFU led to neocollagenesis in the mid and deep reticular dermis and neoelastogenesis in the deep reticular dermis. Among these treatment methods, HIFU showed the highest level of neocollagenesis and neoelastogenesis in the deep reticular dermis. HIFU affects deep tissues and impacts focal regions. Monopolar RF also affects deep tissues, but impacts diffuse regions. We believe these data provide further insight into effective skin tightening.

Focused Crawling of the Deep Web Using Service Class Descriptions

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

Rocco, D; Liu, L; Critchlow, T

2004-06-21

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

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

PubMed

Hatipoglu, Nuh; Bilgin, Gokhan

2017-10-01

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

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

PubMed

Chen, C L Philip; Liu, Zhulin

2018-01-01

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

Simulations of deep galaxy fields. 1: Monte Carlo simulations of optical and near-infrared counts

NASA Technical Reports Server (NTRS)

Chokshi, Arati; Lonsdale, Carol J.; Mazzei, Paola; De Zotti, Gianfranco

1994-01-01

Monte Carlo simulations of three-dimensional galaxy distributions are performed, following the 1988 prescription of Chokshi & Wright, to study the photometric properties of evolving galaxy populations in the optical and near-infrared bands to high redshifts. In this paper, the first of a series, we present our baseline model in which galaxy numbers are conserved, and in which no explicit 'starburst' population is included. We use the model in an attempt to simultaneously fit published blue and near-infrared photometric and spectroscopic observations of deep fields. We find that our baseline models, with a formation redshift, z(sub f), of 1000, and H(sub 0) = 50, are able to reproduce the blue counts to b(sub j) = 22, independent of the value of Omega(sub 0), and also to provide a satisfactory fit to the observed blue-band redshift distributions, but for no value of Omega(sub 0) do we achieve an acceptable fit to the fainter blue counts. In the K band, we fit the number counts to the limit of the present-day surveys only for an Omega(sub 0) = 0 cosmology. We investigate the effect on the model fits of varying the cosmological parameters H(sub 0), the formation red-shift z(sub f), and the local luminosity function. Changing H(sub 0) does not improve the fits to the observations. However, reducing the epoch of a galaxy formation used in our simulations has a substantial effect. In particular, a model with z(sub f) approximately equal to 5 in a low Omega(sub 0) universe improves the fit to the faintest photometric blue data without any need to invoke a new population of galaxies, substantial merging, or a significant starburst galaxy population. For an Omega(sub 0) = 1 universe, however, reducing z(sub f) is less successful at fitting the blue-band counts and has little effect at all at K. Varying the parameters of the local luminosity function can also have a significant effect. In particular the steep low end slope of the local luminosity function of Franceschini et al. allows an acceptable fit to the b(sub j) less than or equal to 25 counts for Omega(sub 0) = 1, but is incompatible with Omega(sub 0) = 0.

Methyl green and nitrotetrazolium blue chloride co-expression in colon tissue: A hyperspectral microscopic imaging analysis

NASA Astrophysics Data System (ADS)

Li, Qingli; Liu, Hongying; Wang, Yiting; Sun, Zhen; Guo, Fangmin; Zhu, Jianzhong

2014-12-01

Histological observation of dual-stained colon sections is usually performed by visual observation under a light microscope, or by viewing on a computer screen with the assistance of image processing software in both research and clinical settings. These traditional methods are usually not sufficient to reliably differentiate spatially overlapping chromogens generated by different dyes. Hyperspectral microscopic imaging technology offers a solution for these constraints as the hyperspectral microscopic images contain information that allows differentiation between spatially co-located chromogens with similar but different spectra. In this paper, a hyperspectral microscopic imaging (HMI) system is used to identify methyl green and nitrotetrazolium blue chloride in dual-stained colon sections. Hyperspectral microscopic images are captured and the normalized score algorithm is proposed to identify the stains and generate the co-expression results. Experimental results show that the proposed normalized score algorithm can generate more accurate co-localization results than the spectral angle mapper algorithm. The hyperspectral microscopic imaging technology can enhance the visualization of dual-stained colon sections, improve the contrast and legibility of each stain using their spectral signatures, which is helpful for pathologist performing histological analyses.

Carbonyl-based blue autofluorescence of proteins and amino acids

PubMed Central

Niyangoda, Chamani; Miti, Tatiana; Breydo, Leonid; Uversky, Vladimir

2017-01-01

Intrinsic protein fluorescence is inextricably linked to the near-UV autofluorescence of aromatic amino acids. Here we show that a novel deep-blue autofluorescence (dbAF), previously thought to emerge as a result of protein aggregation, is present at the level of monomeric proteins and even poly- and single amino acids. Just as its aggregation-related counterpart, this autofluorescence does not depend on aromatic residues, can be excited at the long wavelength edge of the UV and emits in the deep blue. Differences in dbAF excitation and emission peaks and intensities from proteins and single amino acids upon changes in solution conditions suggest dbAF’s sensitivity to both the chemical identity and solution environment of amino acids. Autofluorescence comparable to dbAF is emitted by carbonyl-containing organic solvents, but not those lacking the carbonyl group. This implicates the carbonyl double bonds as the likely source for the autofluorescence in all these compounds. Using beta-lactoglobulin and proline, we have measured the molar extinction coefficients and quantum yields for dbAF in the monomeric state. To establish its potential utility in monitoring protein biophysics, we show that dbAF emission undergoes a red-shift comparable in magnitude to tryptophan upon thermal denaturation of lysozyme, and that it is sensitive to quenching by acrylamide. Carbonyl dbAF therefore provides a previously neglected intrinsic optical probe for investigating the structure and dynamics of amino acids, proteins and, by extension, DNA and RNA. PMID:28542206

The effect of meta coupling on colour purity, quantum yield, and exciton utilizing efficiency in deep-blue emitters from phenanthroimidazole isomers.

PubMed

Wang, Zhiming; Li, Xueying; Zhang, Wanyu; Zhang, Shitong; Li, Hui; Yu, Zhenqiang; Chen, Yanming; Lu, Ping; Chen, Ping

2015-12-21

meta-Coupling isomers usually exhibit bluer emission than do the para-isomers, but the loss of efficiency with respect to photoluminescence (PL) and electroluminescence (EL) is an inevitable result in most cases, particularly for deep blue emitters. In this study, three blue emitting isomers, 4,4'-bis(1-phenyl-phenanthro[9,10-d]imidazol-2-yl)biphenyl (BPPI), 3,4'-bis(1-phenyl-phenanthro[9,10-d]-imidazol-2-yl)biphenyl (L-BPPI) and 3,3'-bis(1-phenyl-phenanthro[9,10-d]-imidazol-2-yl)biphenyl (Z-BPPI), were chosen as model compounds to investigate the essential reason behind the meta-coupling effect due to their different coupling forms, viz. para-para, para-meta, and meta-meta, respectively, in similar dimeric phenanthroimidazole frameworks. A combination of detailed photophysical data, device performance and DFT calculations for the excited state provided valuable information. In particular, the relationship between certain key parameters in calculations as well as PL or EL properties was confirmed, such as oscillator strength and quantum yield, among others, which could effectively reduce the issues related to synthesis and characterisation using prior computer simulations. Good agreement was observed in the results obtained from calculation and experiments, and it was concluded that meta-tuning barely realised improvement in EL, unless some special excited states formed or an exciton conversion channel appeared, as in the case of reverse intersystem crossing.

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Jet-images — deep learning edition

DOE PAGES

de Oliveira, Luke; Kagan, Michael; Mackey, Lester; ...

2016-07-13

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. Finally, this interplay between physically-motivated feature driven tools and supervised learning algorithms is generalmore » and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.« less

Jet-images — deep learning edition

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

de Oliveira, Luke; Kagan, Michael; Mackey, Lester

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. Finally, this interplay between physically-motivated feature driven tools and supervised learning algorithms is generalmore » and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.« less

NASA's next generation all-digital deep space network breadboard receiver

NASA Technical Reports Server (NTRS)

Hinedi, Sami

1993-01-01

This paper describes the breadboard advanced receiver (ARX) that is currently being built for future use in NASA's deep space network (DSN). This receiver has unique requirements in having to operate with very weak signals from deep space probes and provide high quality telemetry and tracking data. The hybrid analog/digital receiver performs multiple functions including carrier, subcarrier and symbol synchronization. Tracking can be achieved for either residual, suppressed or hybrid carriers and for both sinusoidal and square wave subcarriers. System requirements are specified and a functional description of the ARX is presented. The various digital signal processing algorithms used are also discussed and illustrated with block diagrams. Other functions such as time tagged Doppler extraction and monitor/control are also discussed including acquisition algorithms and lock detection schemes.

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

PubMed

Kang, Eunhee; Min, Junhong; Ye, Jong Chul

2017-10-01

Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns. To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach. We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively suppress CT-specific noise. In addition, our CNN is designed with a residual learning architecture for faster network training and better performance. Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X-ray dose. In addition, we show that the wavelet-domain CNN is efficient when used to remove noise from low-dose CT compared to existing approaches. Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 "Low-Dose CT Grand Challenge." To the best of our knowledge, this work is the first deep-learning architecture for low-dose CT reconstruction which has been rigorously evaluated and proven to be effective. In addition, the proposed algorithm, in contrast to existing model-based iterative reconstruction (MBIR) methods, has considerable potential to benefit from large data sets. Therefore, we believe that the proposed algorithm opens a new direction in the area of low-dose CT research. © 2017 American Association of Physicists in Medicine.

Tactical Synthesis Of Efficient Global Search Algorithms

NASA Technical Reports Server (NTRS)

Nedunuri, Srinivas; Smith, Douglas R.; Cook, William R.

2009-01-01

Algorithm synthesis transforms a formal specification into an efficient algorithm to solve a problem. Algorithm synthesis in Specware combines the formal specification of a problem with a high-level algorithm strategy. To derive an efficient algorithm, a developer must define operators that refine the algorithm by combining the generic operators in the algorithm with the details of the problem specification. This derivation requires skill and a deep understanding of the problem and the algorithmic strategy. In this paper we introduce two tactics to ease this process. The tactics serve a similar purpose to tactics used for determining indefinite integrals in calculus, that is suggesting possible ways to attack the problem.

A Geologic Model for Eridania Basin on Ancient Mars

NASA Image and Video Library

2017-10-06

This diagram illustrates an interpretation for the origin of some deposits in the Eridania basin of southern Mars as resulting from seafloor hydrothermal activity more than 3 billion years ago. The ground level depicted is an exaggerated topography of a transect about 280 miles (450 kilometers) long. Blue portions of the diagram depict water-depth estimates and the possibility of ice covering the ancient sea. Thick, clay-rich deposits (green) formed through hydrothermal alteration of volcanic materials in deep water, by this model. Notations indicate deep-water reactions of iron and magnesium ions with silicates, sulfides and carbonates. Deep-seated structural discontinuities could have facilitated the ascent of magma from a mantle source. Chloride deposits formed from evaporation of seawater at higher elevations in the basin. https://photojournal.jpl.nasa.gov/catalog/PIA22060

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

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

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Development of anomaly detection models for deep subsurface monitoring

NASA Astrophysics Data System (ADS)

Sun, A. Y.

2017-12-01

Deep subsurface repositories are used for waste disposal and carbon sequestration. Monitoring deep subsurface repositories for potential anomalies is challenging, not only because the number of sensor networks and the quality of data are often limited, but also because of the lack of labeled data needed to train and validate machine learning (ML) algorithms. Although physical simulation models may be applied to predict anomalies (or the system's nominal state for that sake), the accuracy of such predictions may be limited by inherent conceptual and parameter uncertainties. The main objective of this study was to demonstrate the potential of data-driven models for leakage detection in carbon sequestration repositories. Monitoring data collected during an artificial CO2 release test at a carbon sequestration repository were used, which include both scalar time series (pressure) and vector time series (distributed temperature sensing). For each type of data, separate online anomaly detection algorithms were developed using the baseline experiment data (no leak) and then tested on the leak experiment data. Performance of a number of different online algorithms was compared. Results show the importance of including contextual information in the dataset to mitigate the impact of reservoir noise and reduce false positive rate. The developed algorithms were integrated into a generic Web-based platform for real-time anomaly detection.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

NASA Astrophysics Data System (ADS)

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

PubMed

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-15

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

PubMed Central

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-01-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features. PMID:27079888

Validation of MODIS FLH and In Situ Chlorophyll a from Tampa Bay, Florida (USA)

NASA Technical Reports Server (NTRS)

Fischer, Andrew; MorenoMadrinan, Max J.

2012-01-01

Satellite observation of phytoplankton concentration or chlorophyll-a (chla) is an important characteristic, critically integral to monitoring coastal water quality. However, the optical properties of estuarine and coastal waters are highly variable and complex and pose a great challenge for accurate analysis. Constituents such as suspended solids and dissolved organic matter and the overlapping and uncorrelated absorptions in the blue region of the spectrum renders the blue-green ratio algorithms for estimating chl-a inaccurate. Measurement of suninduced chlorophyll fluorescence, on the other hand, which utilizes the near infrared portion of the electromagnetic spectrum may, provide a better estimate of phytoplankton concentrations. While modelling and laboratory studies have illustrated both the utility and limitations of satellite algorithms based on the sun induced chlorophyll fluorescence signal, few have examined the empirical validity of these algorithms or compared their accuracy against bluegreen ratio algorithms . In an unprecedented analysis using a long term (2003-2011) in situ monitoring data set from Tampa Bay, Florida (USA), we assess the validity of the FLH product from the Moderate Resolution Imaging Spectrometer against a suite of water quality parameters taken in a variety of conditions throughout this large optically complex estuarine system. . Overall, the results show a 106% increase in the validity of chla concentration estimation using FLH over the standard chla estimate from the blue-green OC3M algorithm. Additionally, a systematic analysis of sampling sites throughout the bay is undertaken to understand how the FLH product responds to varying conditions in the estuary and correlations are conducted to see how the relationships between satellite FLH and in situ chlorophyll-a change with depth, distance from shore, from structures like bridges, and nutrient concentrations and turbidity. Such analysis illustrates that the correlations between FLH and in situ chla measurements increases with increasing distance between monitoring sites and structures like bridges and shore. Due probably to confounding factors, expected improvement in the FLH- chla relationship was not clearly noted when increasing depth and distance from shore alone (not including bridges). Correlations between turbidity and nutrient concentrations are discussed further and principle component analyses are employed to address the relationships between the multivariate data sets. A thorough understanding of how satellite FLH algorithms relate to in situ water quality parameters will enhance our understanding of how MODIS s global FLH algorithm can be used empirically to monitor coastal waters worldwide.

A deep learning method for lincRNA detection using auto-encoder algorithm.

PubMed

Yu, Ning; Yu, Zeng; Pan, Yi

2017-12-06

RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly annotated lincRNA data, deep learning methods based on auto-encoder algorithm can exert their capability in knowledge learning in order to capture the useful features and the information correlation along DNA genome sequences for lincRNA detection. As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences.

Development and application of deep convolutional neural network in target detection

NASA Astrophysics Data System (ADS)

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.

First Time Rapid and Accurate Detection of Massive Number of Metal Absorption Lines in the Early Universe Using Deep Neural Network

NASA Astrophysics Data System (ADS)

Zhao, Yinan; Ge, Jian; Yuan, Xiaoyong; Li, Xiaolin; Zhao, Tiffany; Wang, Cindy

2018-01-01

Metal absorption line systems in the distant quasar spectra have been used as one of the most powerful tools to probe gas content in the early Universe. The MgII λλ 2796, 2803 doublet is one of the most popular metal absorption lines and has been used to trace gas and global star formation at redshifts between ~0.5 to 2.5. In the past, machine learning algorithms have been used to detect absorption lines systems in the large sky survey, such as Principle Component Analysis, Gaussian Process and decision tree, but the overall detection process is not only complicated, but also time consuming. It usually takes a few months to go through the entire quasar spectral dataset from each of the Sloan Digital Sky Survey (SDSS) data release. In this work, we applied the deep neural network, or “ deep learning” algorithms, in the most recently SDSS DR14 quasar spectra and were able to randomly search 20000 quasar spectra and detect 2887 strong Mg II absorption features in just 9 seconds. Our detection algorithms were verified with previously released DR12 and DR7 data and published Mg II catalog and the detection accuracy is 90%. This is the first time that deep neural network has demonstrated its promising power in both speed and accuracy in replacing tedious, repetitive human work in searching for narrow absorption patterns in a big dataset. We will present our detection algorithms and also statistical results of the newly detected Mg II absorption lines.

Phototropins But Not Cryptochromes Mediate the Blue Light-Specific Promotion of Stomatal Conductance, While Both Enhance Photosynthesis and Transpiration under Full Sunlight12[C][W][OA

PubMed Central

Boccalandro, Hernán E.; Giordano, Carla V.; Ploschuk, Edmundo L.; Piccoli, Patricia N.; Bottini, Rubén; Casal, Jorge J.

2012-01-01

Leaf epidermal peels of Arabidopsis (Arabidopsis thaliana) mutants lacking either phototropins 1 and 2 (phot1 and phot2) or cryptochromes 1 and 2 (cry1 and cry2) exposed to a background of red light show severely impaired stomatal opening responses to blue light. Since phot and cry are UV-A/blue light photoreceptors, they may be involved in the perception of the blue light-specific signal that induces the aperture of the stomatal pores. In leaf epidermal peels, the blue light-specific effect saturates at low irradiances; therefore, it is considered to operate mainly under the low irradiance of dawn, dusk, or deep canopies. Conversely, we show that both phot1 phot2 and cry1 cry2 have reduced stomatal conductance, transpiration, and photosynthesis, particularly under the high irradiance of full sunlight at midday. These mutants show compromised responses of stomatal conductance to irradiance. However, the effects of phot and cry on photosynthesis were largely nonstomatic. While the stomatal conductance phenotype of phot1 phot2 was blue light specific, cry1 cry2 showed reduced stomatal conductance not only in response to blue light, but also in response to red light. The levels of abscisic acid were elevated in cry1 cry2. We conclude that considering their effects at high irradiances cry and phot are critical for the control of transpiration and photosynthesis rates in the field. The effects of cry on stomatal conductance are largely indirect and involve the control of abscisic acid levels. PMID:22147516

Phototropins but not cryptochromes mediate the blue light-specific promotion of stomatal conductance, while both enhance photosynthesis and transpiration under full sunlight.

PubMed

Boccalandro, Hernán E; Giordano, Carla V; Ploschuk, Edmundo L; Piccoli, Patricia N; Bottini, Rubén; Casal, Jorge J

2012-03-01

Leaf epidermal peels of Arabidopsis (Arabidopsis thaliana) mutants lacking either phototropins 1 and 2 (phot1 and phot2) or cryptochromes 1 and 2 (cry1 and cry2) exposed to a background of red light show severely impaired stomatal opening responses to blue light. Since phot and cry are UV-A/blue light photoreceptors, they may be involved in the perception of the blue light-specific signal that induces the aperture of the stomatal pores. In leaf epidermal peels, the blue light-specific effect saturates at low irradiances; therefore, it is considered to operate mainly under the low irradiance of dawn, dusk, or deep canopies. Conversely, we show that both phot1 phot2 and cry1 cry2 have reduced stomatal conductance, transpiration, and photosynthesis, particularly under the high irradiance of full sunlight at midday. These mutants show compromised responses of stomatal conductance to irradiance. However, the effects of phot and cry on photosynthesis were largely nonstomatic. While the stomatal conductance phenotype of phot1 phot2 was blue light specific, cry1 cry2 showed reduced stomatal conductance not only in response to blue light, but also in response to red light. The levels of abscisic acid were elevated in cry1 cry2. We conclude that considering their effects at high irradiances cry and phot are critical for the control of transpiration and photosynthesis rates in the field. The effects of cry on stomatal conductance are largely indirect and involve the control of abscisic acid levels.

Deep Imaging of the HCG 95 Field. I. Ultra-diffuse Galaxies

NASA Astrophysics Data System (ADS)

Shi, Dong Dong; Zheng, Xian Zhong; Zhao, Hai Bin; Pan, Zhi Zheng; Li, Bin; Zou, Hu; Zhou, Xu; Guo, KeXin; An, Fang Xia; Li, Yu Bin

2017-09-01

We present a detection of 89 candidates of ultra-diffuse galaxies (UDGs) in a 4.9 degree2 field centered on the Hickson Compact Group 95 (HCG 95) using deep g- and r-band images taken with the Chinese Near Object Survey Telescope. This field contains one rich galaxy cluster (Abell 2588 at z = 0.199) and two poor clusters (Pegasus I at z = 0.013 and Pegasus II at z = 0.040). The 89 candidates are likely associated with the two poor clusters, giving about 50-60 true UDGs with a half-light radius {r}{{e}}> 1.5 {kpc} and a central surface brightness μ (g,0)> 24.0 mag arcsec-2. Deep z\\prime -band images are available for 84 of the 89 galaxies from the Dark Energy Camera Legacy Survey (DECaLS), confirming that these galaxies have an extremely low central surface brightness. Moreover, our UDG candidates are spread over a wide range in g - r color, and ˜26% are as blue as normal star-forming galaxies, which is suggestive of young UDGs that are still in formation. Interestingly, we find that one UDG linked with HCG 95 is a gas-rich galaxy with H I mass 1.1× {10}9 M ⊙ detected by the Very Large Array, and has a stellar mass of {M}\\star ˜ 1.8× {10}8 M ⊙. This indicates that UDGs at least partially overlap with the population of nearly dark galaxies found in deep H I surveys. Our results show that the high abundance of blue UDGs in the HCG 95 field is favored by the environment of poor galaxy clusters residing in H I-rich large-scale structures.

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

PubMed Central

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-01-01

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. PMID:27754380

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

PubMed

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-10-13

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy.

PubMed

Yu, Hui; Jing, Wenwen; Iriya, Rafael; Yang, Yunze; Syal, Karan; Mo, Manni; Grys, Thomas E; Haydel, Shelley E; Wang, Shaopeng; Tao, Nongjian

2018-05-15

Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clinical and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm determines if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to determine the minimum inhibitory concentration of pathogens from both bacteria spiked urine and clinical infected urine samples for different antibiotics within 30 min and validate the results with the gold standard broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the solution of increasing drug-resistant infections.

Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.

PubMed

Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling

2018-04-01

In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.

Press Pelease Image - STS-1 - Earth View

NASA Image and Video Library

1981-04-12

S81-30396 (12-14 April 1981) --- A vertical view of Eleuthera Island in the Bahamas and part of the great Bahama Bank, as photographed with a 70mm handheld camera from the space shuttle Columbia in Earth orbit. The light blue of the Bahama Bank contrasts sharply with the darker blue of the deep ocean waters. Astronauts John W. Young, commander, and Robert L. Crippen, pilot, took a series of Earth photos from inside the flight deck of the Columbia, which has windows on its top side, convenient for shooting photographs as the spacecraft flew ?upside down? above Earth. The mission frame ID number is STS001-12-322. Photo credit: NASA

Kerr-gated picosecond Raman spectroscopy and Raman photon migration of equine bone tissue with 400-nm excitation

NASA Astrophysics Data System (ADS)

Morris, Michael D.; Goodship, Allen E.; Draper, Edward R. C.; Matousek, Pavel; Towrie, Michael; Parker, Anthony W.

2004-07-01

We show that Raman spectroscopy with visible lasers, even in the deep blue is possible with time-gated Raman spectroscopy. A 4 picosec time gate allows efficient fluorescence rejection, up to 1000X, and provides almost background-free Raman spectra with low incident laser power. The technology enables spectroscopy with better than 10X higher scattering efficiency than is possible with the NIR (785 nm and 830 nm) lasers that are conventionally used. Raman photon migration is shown to allow depth penetration. We show for the first time that Kerr-gated Raman spectra of bone tissue with blue laser excitation enables both fluorescence rejection and depth penetration.

Fast gain recovery rates with strong wavelength dependence in a non-linear SOA.

PubMed

Cleary, Ciaran S; Power, Mark J; Schneider, Simon; Webb, Roderick P; Manning, Robert J

2010-12-06

We report remarkably fast and strongly wavelength-dependent gain recovery in a single SOA without the aid of an offset filter. Full gain recovery times as short as 9 ps were observed in pump-probe measurements when pumping to the blue wavelength side of a continuous wave probe, in contrast to times of 25 to 30 ps when pumping to the red wavelength side. Experimental and numerical analysis indicate that the long effective length and high gain led to deep saturation of the second half of the SOA by the probe. The consequent absorption of blue-shifted pump pulses in this region resulted in device dynamics analogous to those of the Turbo-Switch.

Dimensions of Intelligent Systems

DTIC Science & Technology

2002-08-01

Keywords: IS, Intelligent Systems, Turing Test, Cognitive Model, situated cognition, BDI, Deep Blue, constructionism 1: Introduction Investigation of...Our social experience provides an implicit, observer bias to assign mentality and intentions to the system in a test and many would argue that...extended the intentional notions of Belief, Desire, and Intention (BDI ) to include social “properties” of Value6

Community Detection on the GPU

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

Naim, Md; Manne, Fredrik; Halappanavar, Mahantesh

We present and evaluate a new GPU algorithm based on the Louvain method for community detection. Our algorithm is the first for this problem that parallelizes the access to individual edges. In this way we can fine tune the load balance when processing networks with nodes of highly varying degrees. This is achieved by scaling the number of threads assigned to each node according to its degree. Extensive experiments show that we obtain speedups up to a factor of 270 compared to the sequential algorithm. The algorithm consistently outperforms other recent shared memory implementations and is only one order ofmore » magnitude slower than the current fastest parallel Louvain method running on a Blue Gene/Q supercomputer using more than 500K threads.« less

Providing Multi-Page Data Extraction Services with XWRAPComposer

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

Liu, Ling; Zhang, Jianjun; Han, Wei

2008-04-30

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

Multi-label spacecraft electrical signal classification method based on DBN and random forest

PubMed Central

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data. PMID:28486479

Multi-label spacecraft electrical signal classification method based on DBN and random forest.

PubMed

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

Self-learning Monte Carlo with deep neural networks

NASA Astrophysics Data System (ADS)

Shen, Huitao; Liu, Junwei; Fu, Liang

2018-05-01

The self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturally incorporated into SLMC, and without any prior knowledge can learn the original model accurately and efficiently. Demonstrated in quantum impurity models, we reduce the complexity for a local update from O (β2) in Hirsch-Fye algorithm to O (β lnβ ) , which is a significant speedup especially for systems at low temperatures.

Automated mammographic breast density estimation using a fully convolutional network.

PubMed

Lee, Juhun; Nishikawa, Robert M

2018-03-01

The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of our algorithm for CC view, MLO view, and CC-MLO-averaged were 0.81, 0.79, and 0.85, respectively, while those of LIBRA were 0.58, 0.71, and 0.69, respectively. For CC view and CC-MLO averaged cases, the difference in rho values between the proposed algorithm and LIBRA showed statistical significance (P < 0.006). In addition, our algorithm provided reliable PD estimates for the left and the right breast (Pearson's ρ > 0.87) and for the MLO and CC views (Pearson's ρ = 0.76). However, LIBRA showed a lower Pearson's rho value (0.66) for both the left and right breasts for the CC view. In addition, our algorithm showed an excellent ability to separate each sub BI-RADS breast density class (statistically significant, p-values = 0.0001 or less); only one comparison pair, density 1 and density 2 in the CC view, was not statistically significant (P = 0.54). However, LIBRA failed to separate breasts in density 1 and 2 for both the CC and MLO views (P > 0.64). We have developed a new deep learning based algorithm for breast density segmentation and estimation. We showed that the proposed algorithm correlated well with BI-RADS density assessments by radiologists and outperformed an existing state of the art algorithm. © 2018 American Association of Physicists in Medicine.

Retrospective Derivation and Validation of an Automated Electronic Search Algorithm to Identify Post Operative Cardiovascular and Thromboembolic Complications

PubMed Central

Tien, M.; Kashyap, R.; Wilson, G. A.; Hernandez-Torres, V.; Jacob, A. K.; Schroeder, D. R.

2015-01-01

Summary Background With increasing numbers of hospitals adopting electronic medical records, electronic search algorithms for identifying postoperative complications can be invaluable tools to expedite data abstraction and clinical research to improve patient outcomes. Objectives To derive and validate an electronic search algorithm to identify postoperative thromboembolic and cardiovascular complications such as deep venous thrombosis, pulmonary embolism, or myocardial infarction within 30 days of total hip or knee arthroplasty. Methods A total of 34 517 patients undergoing total hip or knee arthroplasty between January 1, 1996 and December 31, 2013 were identified. Using a derivation cohort of 418 patients, several iterations of a free-text electronic search were developed and refined for each complication. Subsequently, the automated search algorithm was validated on an independent cohort of 2 857 patients, and the sensitivity and specificities were compared to the results of manual chart review. Results In the final derivation subset, the automated search algorithm achieved a sensitivity of 91% and specificity of 85% for deep vein thrombosis, a sensitivity of 96% and specificity of 100% for pulmonary embolism, and a sensitivity of 100% and specificity of 95% for myocardial infarction. When applied to the validation cohort, the search algorithm achieved a sensitivity of 97% and specificity of 99% for deep vein thrombosis, a sensitivity of 97% and specificity of 100% for pulmonary embolism, and a sensitivity of 100% and specificity of 99% for myocardial infarction. Conclusions The derivation and validation of an electronic search strategy can accelerate the data abstraction process for research, quality improvement, and enhancement of patient care, while maintaining superb reliability compared to manual review. PMID:26448798

Programming Deep Brain Stimulation for Tremor and Dystonia: The Toronto Western Hospital Algorithms.

PubMed

Picillo, Marina; Lozano, Andres M; Kou, Nancy; Munhoz, Renato Puppi; Fasano, Alfonso

2016-01-01

Deep brain stimulation (DBS) is an effective treatment for essential tremor (ET) and dystonia. After surgery, a number of extensive programming sessions are performed, mainly relying on neurologist's personal experience as no programming guidelines have been provided so far, with the exception of recommendations provided by groups of experts. Finally, fewer information is available for the management of DBS in ET and dystonia compared with Parkinson's disease. Our aim is to review the literature on initial and follow-up DBS programming procedures for ET and dystonia and integrate the results with our current practice at Toronto Western Hospital (TWH) to develop standardized DBS programming protocols. We conducted a literature search of PubMed from inception to July 2014 with the keywords "balance", "bradykinesia", "deep brain stimulation", "dysarthria", "dystonia", "gait disturbances", "initial programming", "loss of benefit", "micrographia", "speech", "speech difficulties" and "tremor". Seventy-six papers were considered for this review. Based on the literature review and our experience at TWH, we refined three algorithms for management of ET, including: (1) initial programming, (2) management of balance and speech issues and (3) loss of stimulation benefit. We also depicted algorithms for the management of dystonia, including: (1) initial programming and (2) management of stimulation-induced hypokinesia (shuffling gait, micrographia and speech impairment). We propose five algorithms tailored to an individualized approach to managing ET and dystonia patients with DBS. We encourage the application of these algorithms to supplement current standards of care in established as well as new DBS centers to test the clinical usefulness of these algorithms in supplementing the current standards of care. Copyright © 2016 Elsevier Inc. All rights reserved.

Deep Interactive Learning with Sharkzor

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

None

Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user’s mental model. Initially, images uploaded by the user are spread out on a canvas. The user then interacts with the images to impute their mental model into the applications algorithmic underpinnings. Methods of interaction within Sharkzor’s user interface and user experience support three primary user tasks: triage, organize and automate. The user triages the large pile of overlapping images by moving images of interest into proximity. The user then organizes said images into meaningful groups. Aftermore » interacting with the images and groups, deep learning helps to automate the user’s interactions. The loop of interaction, automation, and response by the user allows the system to quickly make sense of large amounts of data.« less

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), attach a strap during installation of the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Defense Satellite Communications Systems Processing Facility (DPF) at Cape Canaveral Air Station (CCAS) finish installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched Oct. 25 aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), maneuver the ion propulsion engine into place before installation on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight- tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), install an ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Defense Satellite Communications Systems Processing Facility (DPF) at Cape Canaveral Air Station (CCAS) make adjustments while installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight- tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched Oct. 25 aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), make adjustments while installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight- tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Defense Satellite Communication Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), move to the workstand the second conical section leaf of the payload transportation container for Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

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

NASA Technical Reports Server (NTRS)

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.

Deep neural networks to enable real-time multimessenger astrophysics

NASA Astrophysics Data System (ADS)

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.

A unique form of light reflector and the evolution of signalling in Ovalipes (Crustacea: Decapoda: Portunidae)

PubMed Central

Parker, A. R.; Mckenzie, D. R.; Ahyong, S. T.

1998-01-01

The first demonstration, to our knowledge, of an evolutionary shift in communication mode in animals is presented. Some species of Ovalipes display spectacular iridescence resulting from multilayer reflectors in the cuticle. This reflector is unique in animals because each layer is corrugated and slightly out of phase with adjacent layers. Solid layers are separated from fluid layers in the reflector by side branches acting as support struts. An effect of this reflector is that blue light is reflected over a 'broad' angle around a plane parallel to the sea floor when the host crab is resting. Species of Ovalipes all possess stridulatory structures. The shallow-water species with the best developed stridulatory structures are non-iridescent and use sound as a signal. Deep-water species possess poorly developed stridulatory structures and display iridescence from most regions of the body. In deep water, where incident light is blue, light display is highly directional in contrast to sound produced via stridulation. Sound and light display probably perform the same function of sexual signalling in Ovalipes, although the directional signal is less likely to attract predators. Deep-water species of Ovalipes appear to have evolved towards using light in conspecific signalling. This change from using sound to using light reflects the change in habitat light properties, perhaps the hunting mechanisms of cohabitees, and its progression is an indicator of phylogeny. The changes in sexual signalling mechanisms, following spatial–geographical isolation, may have promoted speciation in Ovalipes.

The Next Era: Deep Learning in Pharmaceutical Research.

PubMed

Ekins, Sean

2016-11-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

The diagnostic management of upper extremity deep vein thrombosis: A review of the literature.

PubMed

Kraaijpoel, Noémie; van Es, Nick; Porreca, Ettore; Büller, Harry R; Di Nisio, Marcello

2017-08-01

Upper extremity deep vein thrombosis (UEDVT) accounts for 4% to 10% of all cases of deep vein thrombosis. UEDVT may present with localized pain, erythema, and swelling of the arm, but may also be detected incidentally by diagnostic imaging tests performed for other reasons. Prompt and accurate diagnosis is crucial to prevent pulmonary embolism and long-term complications as the post-thrombotic syndrome of the arm. Unlike the diagnostic management of deep vein thrombosis (DVT) of the lower extremities, which is well established, the work-up of patients with clinically suspected UEDVT remains uncertain with limited evidence from studies of small size and poor methodological quality. Currently, only one prospective study evaluated the use of an algorithm, similar to the one used for DVT of the lower extremities, for the diagnostic workup of clinically suspected UEDVT. The algorithm combined clinical probability assessment, D-dimer testing and ultrasonography and appeared to safely and effectively exclude UEDVT. However, before recommending its use in routine clinical practice, external validation of this strategy and improvements of the efficiency are needed, especially in high-risk subgroups in whom the performance of the algorithm appeared to be suboptimal, such as hospitalized or cancer patients. In this review, we critically assess the accuracy and efficacy of current diagnostic tools and provide clinical guidance for the diagnostic management of clinically suspected UEDVT. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba

NASA Astrophysics Data System (ADS)

Antuña-Marrero, Juan Carlos; Cachorro Revilla, Victoria; García Parrado, Frank; de Frutos Baraja, Ángel; Rodríguez Vega, Albeth; Mateos, David; Estevan Arredondo, René; Toledano, Carlos

2018-04-01

In the present study, we report the first comparison between the aerosol optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra (AODt) and Aqua (AODa) satellites and those measured using a sun photometer (AODSP) at Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and Aqua data includes AOD derived with both deep blue (DB) and dark target (DT) algorithms from MODIS Collection 6. Combined Terra and Aqua (AODta) data were also considered. Assuming an interval of ±30 min around the overpass time and an area of 25 km around the sun photometer site, two coincidence criteria were considered: individual pairs of observations and both spatial and temporal mean values, which we call collocated daily means. The usual statistics (root mean square error, RMSE; mean absolute error, MAE; median bias, BIAS), together with linear regression analysis, are used for this comparison. Results show very similar values for both coincidence criteria: the DT algorithm generally displays better statistics and higher homogeneity than the DB algorithm in the behaviour of AODt, AODa, AODta compared to AODSP. For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same patterns, (c) BIAS for both Terra and Aqua presents positive and negative values but its absolute values are lower for the DT algorithm; (d) combined AODta data also give lower values of these three statistical indicators for the DT algorithm; (e) both algorithms present good correlations for comparing AODt, AODa, AODta vs. AODSP, with a slight overestimation of satellite data compared to AODSP, (f). The DT algorithm yields better figures with slopes of 0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual monthly means of AODta establish a first climatology that is more comparable to that given by the sun photometer and their statistical evaluation reveals better agreement with AODSP for the DT algorithm. Results of the AE comparison showed similar results to those reported in the literature concerning the two algorithms' capacity for retrieval. A comparison between broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer observations at the Camagüey site and three other meteorological stations in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As expected, RMSE values are higher than those from the MODIS-sun photometer comparison, but within the same order of magnitude. Results from the BAOD derived from solar radiation measurements demonstrate its reliability in describing climatological AOD series estimates.

Deep linear autoencoder and patch clustering-based unified one-dimensional coding of image and video

NASA Astrophysics Data System (ADS)

Li, Honggui

2017-09-01

This paper proposes a unified one-dimensional (1-D) coding framework of image and video, which depends on deep learning neural network and image patch clustering. First, an improved K-means clustering algorithm for image patches is employed to obtain the compact inputs of deep artificial neural network. Second, for the purpose of best reconstructing original image patches, deep linear autoencoder (DLA), a linear version of the classical deep nonlinear autoencoder, is introduced to achieve the 1-D representation of image blocks. Under the circumstances of 1-D representation, DLA is capable of attaining zero reconstruction error, which is impossible for the classical nonlinear dimensionality reduction methods. Third, a unified 1-D coding infrastructure for image, intraframe, interframe, multiview video, three-dimensional (3-D) video, and multiview 3-D video is built by incorporating different categories of videos into the inputs of patch clustering algorithm. Finally, it is shown in the results of simulation experiments that the proposed methods can simultaneously gain higher compression ratio and peak signal-to-noise ratio than those of the state-of-the-art methods in the situation of low bitrate transmission.

Teaching Real-World Applications of Business Statistics Using Communication to Scaffold Learning

ERIC Educational Resources Information Center

Green, Gareth P.; Jones, Stacey; Bean, John C.

2015-01-01

Our assessment research suggests that quantitative business courses that rely primarily on algorithmic problem solving may not produce the deep learning required for addressing real-world business problems. This article illustrates a strategy, supported by recent learning theory, for promoting deep learning by moving students gradually from…

Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras

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

Cui, Yonggang; Thomas, Maikael A.

We propose the development of proven deep learning algorithms to flag objects and events of interest in Next Generation Surveillance System (NGSS) surveillance to make IAEA image review more efficient. Video surveillance is one of the core monitoring technologies used by the IAEA Department of Safeguards when implementing safeguards at nuclear facilities worldwide. The current image review software GARS has limited automated functions, such as scene-change detection, black image detection and missing scene analysis, but struggles with highly cluttered backgrounds. A cutting-edge algorithm to be developed in this project will enable efficient and effective searches in images and video streamsmore » by identifying and tracking safeguards relevant objects and detect anomalies in their vicinity. In this project, we will develop the algorithm, test it with the IAEA surveillance cameras and data sets collected at simulated nuclear facilities at BNL and SNL, and implement it in a software program for potential integration into the IAEA’s IRAP (Integrated Review and Analysis Program).« less

Dreaming of Atmospheres

NASA Astrophysics Data System (ADS)

Waldmann, I. P.

2016-04-01

Here, we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as the “dreams” of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.

3.1 W narrowband blue external cavity diode laser

NASA Astrophysics Data System (ADS)

Peng, Jue; Ren, Huaijin; Zhou, Kun; Li, Yi; Du, Weichuan; Gao, Songxin; Li, Ruijun; Liu, Jianping; Li, Deyao; Yang, Hui

2018-03-01

We reported a high-power narrowband blue diode laser which is suitable for subsequent nonlinear frequency conversion into the deep ultraviolet (DUV) spectral range. The laser is based on an external cavity diode laser (ECDL) system using a commercially available GaN-based high-power blue laser diode emitting at 448 nm. Longitudinal mode selection is realized by using a surface diffraction grating in Littrow configuration. The diffraction efficiency of the grating was optimized by controlling the polarization state of the laser beam incident on the grating. A maximum optical output power of 3.1 W in continuous-wave operation with a spectral width of 60 pm and a side-mode suppression ratio (SMSR) larger than 10 dB at 448.4 nm is achieved. Based on the experimental spectra and output powers, the theoretical efficiency and output power of the subsequent nonlinear frequency conversion were calculated according to the Boyd- Kleinman theory. The single-pass conversion efficiency and output power is expected to be 1.9×10-4 and 0.57 mW, respectively, at the 3.1 W output power of the ECDL. The high-power narrowband blue diode laser is very promising as pump source in the subsequent nonlinear frequency conversion.

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during DRAGON-NE Asia 2012 campaign

NASA Astrophysics Data System (ADS)

Choi, M.; Kim, J.; Lee, J.; Kim, M.; Park, Y. Je; Jeong, U.; Kim, W.; Holben, B.; Eck, T. F.; Lim, J. H.; Song, C. K.

2015-09-01

The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorology Satellites (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm over ocean and land together with validation results during the DRAGON-NE Asia 2012 campaign. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type from selected aerosol models in calculating AOD. Assumed aerosol models are compiled from global Aerosol Robotic Networks (AERONET) inversion data, and categorized according to AOD, FMF, and SSA. Nonsphericity is considered, and unified aerosol models are used over land and ocean. Different assumptions for surface reflectance are applied over ocean and land. Surface reflectance over the ocean varies with geometry and wind speed, while surface reflectance over land is obtained from the 1-3 % darkest pixels in a 6 km × 6 km area during 30 days. In the East China Sea and Yellow Sea, significant area is covered persistently by turbid waters, for which the land algorithm is used for aerosol retrieval. To detect turbid water pixels, TOA reflectance difference at 660 nm is used. GOCI YAER products are validated using other aerosol products from AERONET and the MODIS Collection 6 aerosol data from "Dark Target (DT)" and "Deep Blue (DB)" algorithms during the DRAGON-NE Asia 2012 campaign from March to May 2012. Comparison of AOD from GOCI and AERONET gives a Pearson correlation coefficient of 0.885 and a linear regression equation with GOCI AOD =1.086 × AERONET AOD - 0.041. GOCI and MODIS AODs are more highly correlated over ocean than land. Over land, especially, GOCI AOD shows better agreement with MODIS DB than MODIS DT because of the choice of surface reflectance assumptions. Other GOCI YAER products show lower correlation with AERONET than AOD, but are still qualitatively useful.

A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head.

PubMed

Devalla, Sripad Krishna; Chin, Khai Sing; Mari, Jean-Martial; Tun, Tin A; Strouthidis, Nicholas G; Aung, Tin; Thiéry, Alexandre H; Girard, Michaël J A

2018-01-01

To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH). A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for one eye of each of 100 subjects (40 healthy and 60 glaucoma). All images were enhanced using adaptive compensation. A custom deep learning network was then designed and trained with the compensated images to digitally stain (i.e., highlight) six tissue layers of the ONH. The accuracy of our algorithm was assessed (against manual segmentations) using the dice coefficient, sensitivity, specificity, intersection over union (IU), and accuracy. We studied the effect of compensation, number of training images, and performance comparison between glaucoma and healthy subjects. For images it had not yet assessed, our algorithm was able to digitally stain the retinal nerve fiber layer + prelamina, the RPE, all other retinal layers, the choroid, and the peripapillary sclera and lamina cribrosa. For all tissues, the dice coefficient, sensitivity, specificity, IU, and accuracy (mean) were 0.84 ± 0.03, 0.92 ± 0.03, 0.99 ± 0.00, 0.89 ± 0.03, and 0.94 ± 0.02, respectively. Our algorithm performed significantly better when compensated images were used for training (P < 0.001). Besides offering a good reliability, digital staining also performed well on OCT images of both glaucoma and healthy individuals. Our deep learning algorithm can simultaneously stain the neural and connective tissues of the ONH, offering a framework to automatically measure multiple key structural parameters of the ONH that may be critical to improve glaucoma management.

Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.

PubMed

Ehteshami Bejnordi, Babak; Mullooly, Maeve; Pfeiffer, Ruth M; Fan, Shaoqi; Vacek, Pamela M; Weaver, Donald L; Herschorn, Sally; Brinton, Louise A; van Ginneken, Bram; Karssemeijer, Nico; Beck, Andrew H; Gierach, Gretchen L; van der Laak, Jeroen A W M; Sherman, Mark E

2018-06-13

The breast stromal microenvironment is a pivotal factor in breast cancer development, growth and metastases. Although pathologists often detect morphologic changes in stroma by light microscopy, visual classification of such changes is subjective and non-quantitative, limiting its diagnostic utility. To gain insights into stromal changes associated with breast cancer, we applied automated machine learning techniques to digital images of 2387 hematoxylin and eosin stained tissue sections of benign and malignant image-guided breast biopsies performed to investigate mammographic abnormalities among 882 patients, ages 40-65 years, that were enrolled in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project. Using deep convolutional neural networks, we trained an algorithm to discriminate between stroma surrounding invasive cancer and stroma from benign biopsies. In test sets (928 whole-slide images from 330 patients), this algorithm could distinguish biopsies diagnosed as invasive cancer from benign biopsies solely based on the stromal characteristics (area under the receiver operator characteristics curve = 0.962). Furthermore, without being trained specifically using ductal carcinoma in situ as an outcome, the algorithm detected tumor-associated stroma in greater amounts and at larger distances from grade 3 versus grade 1 ductal carcinoma in situ. Collectively, these results suggest that algorithms based on deep convolutional neural networks that evaluate only stroma may prove useful to classify breast biopsies and aid in understanding and evaluating the biology of breast lesions.

Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing.

PubMed

Thyreau, Benjamin; Sato, Kazunori; Fukuda, Hiroshi; Taki, Yasuyuki

2018-01-01

The hippocampus is a particularly interesting target for neuroscience research studies due to its essential role within the human brain. In large human cohort studies, bilateral hippocampal structures are frequently identified and measured to gain insight into human behaviour or genomic variability in neuropsychiatric disorders of interest. Automatic segmentation is performed using various algorithms, with FreeSurfer being a popular option. In this manuscript, we present a method to segment the bilateral hippocampus using a deep-learned appearance model. Deep convolutional neural networks (ConvNets) have shown great success in recent years, due to their ability to learn meaningful features from a mass of training data. Our method relies on the following key novelties: (i) we use a wide and variable training set coming from multiple cohorts (ii) our training labels come in part from the output of the FreeSurfer algorithm, and (iii) we include synthetic data and use a powerful data augmentation scheme. Our method proves to be robust, and it has fast inference (<30s total per subject), with trained model available online (https://github.com/bthyreau/hippodeep). We depict illustrative results and show extensive qualitative and quantitative cohort-wide comparisons with FreeSurfer. Our work demonstrates that deep neural-network methods can easily encode, and even improve, existing anatomical knowledge, even when this knowledge exists in algorithmic form. Copyright © 2017 Elsevier B.V. All rights reserved.

Properties of Zn-doped GaN. I - Photoluminescence

NASA Technical Reports Server (NTRS)

Pankove, J. I.; Berkeyheiser, J. E.; Miller, E. A.

1974-01-01

It is shown that zinc in gallium nitride forms an efficient radiative center emitting blue light at 2.86 plus or minus 0.02 eV and acts as a deep acceptor that can render the crystal insulating. A systematic variation of growth conditions indicates that an optimization of the photoluminescence efficiency is possible. Under nonoptimal conditions lower photon energy emission is obtained.

The Devil and the Deep Blue Sea: Dyadic Narcissism and the Problem of Individuation

ERIC Educational Resources Information Center

Sprince, Jenny

2009-01-01

This paper addresses issues of infantile gender identity as they are demonstrated through group processes amongst the carers of disturbed adolescents. It uses this and other clinical material to explore gender narcissism--both male and female. It examines how such narcissism is linked to sado-masochism, and how it can impede a healthy development…

Pharmacological Treatment of Neonatal Opiate Withdrawal: Between the Devil and the Deep Blue Sea

PubMed Central

Liu, Anthony; Björkman, Tracey; Stewart, Caroline; Nanan, Ralph

2011-01-01

Illicit drug use with opiates in pregnancy is a major global health issue with neonatal withdrawal being a common complication. Morphine is the main pharmacological agent administered for the treatment of neonatal withdrawal. In the past, morphine has been considered by and large inert in terms of its long-term effects on the central nervous system. However, recent animal and clinical studies have demonstrated that opiates exhibit significant effects on the growing brain. This includes direct dose-dependent effects on reduction in brain size and weight, protein, DNA, RNA, and neurotransmitters—possibly as a direct consequence of a number of opiate-mediated systems that influence neural cell differentiation, proliferation, and apoptosis. At this stage, we are stuck between the devil and the deep blue sea. There are no real alternatives to pharmacological treatment with opiates and other drugs for neonatal opiate withdrawal and opiate addiction in pregnant women. However, pending further rigorous studies examining the potential harmful effects of opiate exposure in utero and the perinatal period, prolonged use of these agents in the neonatal period should be used judiciously, with caution, and avoided where possible. PMID:21760818

Randomized in vivo evaluation of photodynamic antimicrobial chemotherapy on deciduous carious dentin

NASA Astrophysics Data System (ADS)

Steiner-Oliveira, Carolina; Longo, Priscila Larcher; Aranha, Ana Cecília Corrêa; Ramalho, Karen Müller; Mayer, Marcia Pinto Alves; de Paula Eduardo, Carlos

2015-10-01

The aim of this randomized in vivo study was to compare antimicrobial chemotherapies in primary carious dentin. Thirty-two participants ages 5 to 7 years underwent partial caries removal from deep carious dentin lesions in primary molars and were subsequently divided into three groups: control [chlorhexidine and resin-modified glass ionomer cement (RMGIC)], LEDTB [photodynamic antimicrobial chemotherapy (PACT) with light-emitting diode associated with toluidine blue solution and RMGIC], and LMB [PACT with laser associated with methylene blue solution and RMGIC]. The participants were submitted to initial clinical and radiographic examinations. Demographic features and biofilm, gingival, and DMFT/DMFS indexes were evaluated, in addition to clinical and radiographic followups at 6 and 12 months after treatments. Carious dentin was collected before and after each treatment, and the number of Streptococcus mutans, Streptococcus sobrinus, Lactobacillus casei, Fusobacterium nucleatum, Atopobium rimae, and total bacteria was established by quantitative polymerase chain reaction. No signs of pain or restoration failure were observed. All therapies were effective in reducing the number of microorganisms, except for S. sobrinus. No statistical differences were observed among the protocols used. All therapies may be considered as effective modern approaches to minimal intervention for the management of deep primary caries treatment.

Optoelectronic device physics and technology of nitride semiconductors from the UV to the terahertz.

PubMed

Moustakas, Theodore D; Paiella, Roberto

2017-10-01

This paper reviews the device physics and technology of optoelectronic devices based on semiconductors of the GaN family, operating in the spectral regions from deep UV to Terahertz. Such devices include LEDs, lasers, detectors, electroabsorption modulators and devices based on intersubband transitions in AlGaN quantum wells (QWs). After a brief history of the development of the field, we describe how the unique crystal structure, chemical bonding, and resulting spontaneous and piezoelectric polarizations in heterostructures affect the design, fabrication and performance of devices based on these materials. The heteroepitaxial growth and the formation and role of extended defects are addressed. The role of the chemical bonding in the formation of metallic contacts to this class of materials is also addressed. A detailed discussion is then presented on potential origins of the high performance of blue LEDs and poorer performance of green LEDs (green gap), as well as of the efficiency reduction of both blue and green LEDs at high injection current (efficiency droop). The relatively poor performance of deep-UV LEDs based on AlGaN alloys and methods to address the materials issues responsible are similarly addressed. Other devices whose state-of-the-art performance and materials-related issues are reviewed include violet-blue lasers, 'visible blind' and 'solar blind' detectors based on photoconductive and photovoltaic designs, and electroabsorption modulators based on bulk GaN or GaN/AlGaN QWs. Finally, we describe the basic physics of intersubband transitions in AlGaN QWs, and their applications to near-infrared and terahertz devices.

Optoelectronic device physics and technology of nitride semiconductors from the UV to the terahertz

NASA Astrophysics Data System (ADS)

Moustakas, Theodore D.; Paiella, Roberto

2017-10-01

This paper reviews the device physics and technology of optoelectronic devices based on semiconductors of the GaN family, operating in the spectral regions from deep UV to Terahertz. Such devices include LEDs, lasers, detectors, electroabsorption modulators and devices based on intersubband transitions in AlGaN quantum wells (QWs). After a brief history of the development of the field, we describe how the unique crystal structure, chemical bonding, and resulting spontaneous and piezoelectric polarizations in heterostructures affect the design, fabrication and performance of devices based on these materials. The heteroepitaxial growth and the formation and role of extended defects are addressed. The role of the chemical bonding in the formation of metallic contacts to this class of materials is also addressed. A detailed discussion is then presented on potential origins of the high performance of blue LEDs and poorer performance of green LEDs (green gap), as well as of the efficiency reduction of both blue and green LEDs at high injection current (efficiency droop). The relatively poor performance of deep-UV LEDs based on AlGaN alloys and methods to address the materials issues responsible are similarly addressed. Other devices whose state-of-the-art performance and materials-related issues are reviewed include violet-blue lasers, ‘visible blind’ and ‘solar blind’ detectors based on photoconductive and photovoltaic designs, and electroabsorption modulators based on bulk GaN or GaN/AlGaN QWs. Finally, we describe the basic physics of intersubband transitions in AlGaN QWs, and their applications to near-infrared and terahertz devices.

Jupiter in True and False Color

NASA Image and Video Library

2001-01-23

These color composite frames of the mid-section of Jupiter were of narrow angle images acquired on December 31, 2000, a day after Cassini's closest approach to the planet. The smallest features in these frames are roughly ~ 60 kilometers. The left is natural color, composited to yield the color that Jupiter would have if seen by the naked eye. The right frame is composed of 3 images: two were taken through narrow band filters centered on regions of the spectrum where the gaseous methane in Jupiter's atmosphere absorbs light, and the third was taken in a red continuum region of the spectrum, where Jupiter has no absorptions. The combination yields an image whose colors denote the height of the clouds. Red regions are deep water clouds, bright blue regions are high haze (like the blue covering the Great Red Spot). Small, intensely bright white spots are energetic lightning storms which have penetrated high into the atmosphere where there is no opportunity for absorption of light: these high cloud systems reflect all light equally. The darkest blue regions -- for example, the long linear regions which border the northern part of the equatorial zone, are the very deep "hot spots', seen in earlier images, from which Jovian thermal emission is free to escape to space. This is the first time that global images of Jupiter in all the methane and attendant continuum filters have been acquired by a spacecraft. From images like these, the stratigraphy of Jupiter's dynamic atmosphere will be determined. http://photojournal.jpl.nasa.gov/catalog/PIA02877

Migration Pathways, Behavioural Thermoregulation and Overwintering Grounds of Blue Sharks in the Northwest Atlantic

PubMed Central

Campana, Steven E.; Dorey, Anna; Fowler, Mark; Joyce, Warren; Wang, Zeliang; Yashayaev, Igor

2011-01-01

The blue shark Prionace glauca is the most abundant large pelagic shark in the Atlantic Ocean. Although recaptures of tagged sharks have shown that the species is highly migratory, migration pathways towards the overwintering grounds remain poorly understood. We used archival satellite pop-up tags to track 23 blue sharks over a mean period of 88 days as they departed the coastal waters of North America in the autumn. Within 1–2 days of entering the Gulf Stream (median date of 21 Oct), all sharks initiated a striking diel vertical migration, taking them from a mean nighttime depth of 74 m to a mean depth of 412 m during the day as they appeared to pursue vertically migrating squid and fish prey. Although functionally blind at depth, calculations suggest that there would be a ∼2.5-fold thermoregulatory advantage to swimming and feeding in the markedly cooler deep waters, even if there was any reduced foraging success associated with the extreme depth. Noting that the Gulf Stream current speeds are reduced at depth, we used a detailed circulation model of the North Atlantic to examine the influence of the diving behaviour on the advection experienced by the sharks. However, there was no indication that the shark diving resulted in a significant modification of their net migratory pathway. The relative abundance of deep-diving sharks, swordfish, and sperm whales in the Gulf Stream and adjacent waters suggests that it may serve as a key winter feeding ground for large pelagic predators in the North Atlantic. PMID:21373198

Relationship Between Surface Reflectance in the Visible and Mid-IR used in MODIS Aerosol Algorithm-Theory

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Gobron, Nadine; Pinty, Bernard; Widlowski, Jean-Luc; Verstraete, Michel M.; Lau, William K. M. (Technical Monitor)

2002-01-01

Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument that flies in polar orbit on the Terra platform, are used to derive the aerosol optical thickness and properties over land and ocean. The relationships between visible reflectance (at blue, rho(sub blue), and red, rho(sub red)) and mid-infrared (at 2.1 microns, rho(sub 2.1)) are used in the MODIS aerosol retrieval algorithm to derive global distribution of aerosols over the land. These relations have been established from a series of measurements indicating that rho(sub blue) is approximately 0.5 rho(sub red) is approximately 0.25 rho(sub 2.1). Here we use a model to describe the transfer of radiation through a vegetation canopy composed of randomly oriented leaves to assess the theoretical foundations for these relationships. Calculations for a wide range of leaf area indices and vegetation fractions show that rho(sub blue) is consistently about 1/4 of rho(sub 2.1) as used by MODIS for the whole range of analyzed cases, except for very dark soils, such as those found in burn scars. For its part, the ratio rho(sub red)/rho(sub 2.1) varies from less than the empirically derived value of 1/2 for dense and dark vegetation, to more than 1/2 for bright mixture of soil and vegetation. This is in agreement with measurements over uniform dense vegetation, but not with measurements over mixed dark scenes. In the later case the discrepancy is probably mitigated by shadows due to uneven canopy and terrain on a large scale. It is concluded that the value of this ratio should ideally be made dependent on the land cover type in the operational processing of MODIS data, especially over dense forests.

Salivary fistula: Blue dye testing as part of an algorithm for early diagnosis

PubMed Central

Kiong, Kimberley L.; Tan, Ngian Chye; Skanthakumar, Thakshayeni; Teo, Constance E.H.; Soo, Khee Chee; Tan, Hiang Khoon; Roche, Elizabeth; Yee, Kaisin

2017-01-01

Objective Orocutaneous and pharyngocutaneous fistula (OPCF) is a debilitating complication of head and neck surgery for squamous cell carcinoma (SCC), resulting in delayed adjuvant treatment and prolonged hospitalization. As yet, there is no established test that can help in prompt and accurate diagnosis of OPCF. This study aims to determine the accuracy of bedside blue dye testing and its role as part of an algorithm for early diagnosis. We also analyze the risk factors predisposing to OPCF. Study Design Retrospective cohort study from 2012 to 2014. Methods Patients with head and neck SCC who underwent major resection and reconstruction, at risk of OPCF, were included. Results of blue‐dye and video‐fluoroscopic swallow‐studies (VFSS) testing for OPCF were recorded. For the patients that were noted to develop OPCF, the length of time to diagnosis of fistula and subsequent mode of management were examined. Results Of the 93 patients in this study, 25 (26.9%) developed OPCF. Advanced T‐classification (T3/T4) was the only significant predisposing risk factor (p = 0.013). The sensitivity and specificity of the bedside blue dye testing was found to be 36.4% and 100%, respectively. The test positive patients were diagnosed with OPCF at a median of postoperative day (POD) 9.5 as compared to POD 13 for the test negative patients (p = 0.001). Early diagnosis was associated with faster fistula resolution with treatment. Conclusion Blue dye testing is a simple bedside test that can assist in the early diagnosis of OPCF in patients, allowing treatment to be instituted earlier with improved outcomes. Level of Evidence 3 PMID:29299509

Photochemical eradication of methicillin-resistant Staphylococcus aureus by blue light activation of riboflavin.

PubMed

Makdoumi, Karim; Goodrich, Ray; Bäckman, Anders

2017-08-01

To compare elimination of methicillin-resistant Staphylococcus aureus (MRSA) by exposure of blue light alone and with riboflavin. A reference strain of MRSA was cultured and diluted in PBS with and without riboflavin (0.01%). Fifteen microlitre was added on a microscope slide, creating a fluid layer with a thickness of around 400 microns. Both of the bacterial suspensions were exposed to blue light, and the effect between exposure with and without riboflavin was compared. Evaluation involved two different wavelengths (412 and 450 nm) of blue light with a lower (5.4 J/cm 2 ) and higher dose (approximately 28.5 J/cm 2 ). The effect of 412 nm light was also evaluated for a thicker fluid layer (1.17 mm). After exposure, colony-forming units (CFUs) were determined for each solution. All measurements were repeated eight times. The reductions in bacteria were similar for both wavelengths. With riboflavin, a statistically significant elimination was observed for both 412 and 450 nm (p < 0.001). At both dosages, the mean reduction was more pronounced with the presence of riboflavin than without it. Using the higher dose, CFU reduction was 99% and 98%, respectively, for 412 and 450 nm light. The bactericidal efficacy was high also in the deeper fluid layer (93%, higher dose). Riboflavin enhanced the antibacterial effect on the exposed MRSA strain of blue light for both 412 and 450 nm blue light. This indicates that blue light could be considered for possible implementation in deep corneal infections. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

3D reconstruction of synapses with deep learning based on EM Images

NASA Astrophysics Data System (ADS)

Xiao, Chi; Rao, Qiang; Zhang, Dandan; Chen, Xi; Han, Hua; Xie, Qiwei

2017-03-01

Recently, due to the rapid development of electron microscope (EM) with its high resolution, stacks delivered by EM can be used to analyze a variety of components that are critical to understand brain function. Since synaptic study is essential in neurobiology and can be analyzed by EM stacks, the automated routines for reconstruction of synapses based on EM Images can become a very useful tool for analyzing large volumes of brain tissue and providing the ability to understand the mechanism of brain. In this article, we propose a novel automated method to realize 3D reconstruction of synapses for Automated Tapecollecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) with deep learning. Being different from other reconstruction algorithms, which employ classifier to segment synaptic clefts directly. We utilize deep learning method and segmentation algorithm to obtain synaptic clefts as well as promote the accuracy of reconstruction. The proposed method contains five parts: (1) using modified Moving Least Square (MLS) deformation algorithm and Scale Invariant Feature Transform (SIFT) features to register adjacent sections, (2) adopting Faster Region Convolutional Neural Networks (Faster R-CNN) algorithm to detect synapses, (3) utilizing screening method which takes context cues of synapses into consideration to reduce the false positive rate, (4) combining a practical morphology algorithm with a suitable fitting function to segment synaptic clefts and optimize the shape of them, (5) applying the plugin in FIJI to show the final 3D visualization of synapses. Experimental results on ATUM-SEM images demonstrate the effectiveness of our proposed method.

Hurricane Dean

NASA Technical Reports Server (NTRS)

2007-01-01

Location: The coast of Mexico from Manzanillo to Mazatlan Categorization: Tropical Depression Sustained Winds: 35 mph (56 km/hr)

[figure removed for brevity, see original site] [figure removed for brevity, see original site] Infrared ImageMicrowave Image

[figure removed for brevity, see original site] Click on the image to access AIRS Weather Snapshot for Hurricane Dean

Infrared Images Because infrared radiation does not penetrate through clouds, AIRS infrared images show either the temperature of the cloud tops or the surface of the Earth in cloud-free regions. The lowest temperatures (in purple) are associated with high, cold cloud tops that make up the top of the storm. In cloud-free areas the AIRS instrument will receive the infrared radiation from the surface of the Earth, resulting in the warmest temperatures (orange/red).

Microwave Images In the AIRS microwave imagery, deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. On the other hand, land appears much warmer due to its high radiation emissivity.

Microwave radiation from Earth's surface and lower atmosphere penetrates most clouds to a greater or lesser extent depending upon their water vapor, liquid water and ice content. Precipitation, and ice crystals found at the cloud tops where strong convection is taking place, act as barriers to microwave radiation. Because of this barrier effect, the AIRS microwave sensor detects only the radiation arising at or above their location in the atmospheric column. Where these barriers are not present, the microwave sensor detects radiation arising throughout the air column and down to the surface. Liquid surfaces (oceans, lakes and rivers) have 'low emissivity' (the signal isn't as strong) and their radiation brightness temperature is therefore low. Thus the ocean also appears 'low temperature' in the AIRS microwave images and is assigned the color blue. Therefore deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. Land appears much warmer due to its high radiation emissivity.

Visible/Near-Infrared Images The AIRS instrument suite contains a sensor that captures radiation in four bands of the visible/near-infrared portion of the electromagetic spectrum. Data from three of these bands are combined to create 'visible' images similar to a snapshot taken with your camera.

The Atmospheric Infrared Sounder Experiment, with its visible, infrared, and microwave detectors, provides a three-dimensional look at Earth's weather. Working in tandem, the three instruments can make simultaneous observations all the way down to the Earth's surface, even in the presence of heavy clouds. With more than 2,000 channels sensing different regions of the atmosphere, the system creates a global, 3-D map of atmospheric temperature and humidity and provides information on clouds, greenhouse gases, and many other atmospheric phenomena. The AIRS Infrared Sounder Experiment flies onboard NASA's Aqua spacecraft and is managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif., under contract to NASA. JPL is a division of the California Institute of Technology in Pasadena.

Hurricane Felix

NASA Technical Reports Server (NTRS)

2007-01-01

[figure removed for brevity, see original site] Microwave Image

These infrared and microwave images were created with data retrieved by the Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite, and show the remnants of the former Hurricane Felix over Central America.

Infrared Images Because infrared radiation does not penetrate through clouds, AIRS infrared images show either the temperature of the cloud tops or the surface of the Earth in cloud-free regions. The lowest temperatures (in purple) are associated with high, cold cloud tops that make up the top of the storm. In cloud-free areas the AIRS instrument will receive the infrared radiation from the surface of the Earth, resulting in the warmest temperatures (orange/red).

Microwave Images In the AIRS microwave imagery, deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. On the other hand, land appears much warmer due to its high radiation emissivity.

Microwave radiation from Earth's surface and lower atmosphere penetrates most clouds to a greater or lesser extent depending upon their water vapor, liquid water and ice content. Precipitation, and ice crystals found at the cloud tops where strong convection is taking place, act as barriers to microwave radiation. Because of this barrier effect, the AIRS microwave sensor detects only the radiation arising at or above their location in the atmospheric column. Where these barriers are not present, the microwave sensor detects radiation arising throughout the air column and down to the surface. Liquid surfaces (oceans, lakes and rivers) have 'low emissivity' (the signal isn't as strong) and their radiation brightness temperature is therefore low. Thus the ocean also appears 'low temperature' in the AIRS microwave images and is assigned the color blue. Therefore deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. Land appears much warmer due to its high radiation emissivity.

Visible/Near-Infrared Images The AIRS instrument suite contains a sensor that captures radiation in four bands of the visible/near-infrared portion of the electromagetic spectrum. Data from three of these bands are combined to create 'visible' images similar to a snapshot taken with your camera.

The Atmospheric Infrared Sounder Experiment, with its visible, infrared, and microwave detectors, provides a three-dimensional look at Earth's weather. Working in tandem, the three instruments can make simultaneous observations all the way down to the Earth's surface, even in the presence of heavy clouds. With more than 2,000 channels sensing different regions of the atmosphere, the system creates a global, 3-D map of atmospheric temperature and humidity and provides information on clouds, greenhouse gases, and many other atmospheric phenomena. The AIRS Infrared Sounder Experiment flies onboard NASA's Aqua spacecraft and is managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif., under contract to NASA. JPL is a division of the California Institute of Technology in Pasadena.

Tropical Storm Erin

NASA Technical Reports Server (NTRS)

2007-01-01

Location: The Atlantic Ocean 210 miles south of Galveston, Texas Categorization: Tropical Storm Sustained Winds: 40 mph (60 km/hr)

[figure removed for brevity, see original site] [figure removed for brevity, see original site] Infrared ImageMicrowave Image

Infrared Images Because infrared radiation does not penetrate through clouds, AIRS infrared images show either the temperature of the cloud tops or the surface of the Earth in cloud-free regions. The lowest temperatures (in purple) are associated with high, cold cloud tops that make up the top of the storm. In cloud-free areas the AIRS instrument will receive the infrared radiation from the surface of the Earth, resulting in the warmest temperatures (orange/red).

Microwave Images In the AIRS microwave imagery, deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. On the other hand, land appears much warmer due to its high radiation emissivity.

Microwave radiation from Earth's surface and lower atmosphere penetrates most clouds to a greater or lesser extent depending upon their water vapor, liquid water and ice content. Precipitation, and ice crystals found at the cloud tops where strong convection is taking place, act as barriers to microwave radiation. Because of this barrier effect, the AIRS microwave sensor detects only the radiation arising at or above their location in the atmospheric column. Where these barriers are not present, the microwave sensor detects radiation arising throughout the air column and down to the surface. Liquid surfaces (oceans, lakes and rivers) have 'low emissivity' (the signal isn't as strong) and their radiation brightness temperature is therefore low. Thus the ocean also appears 'low temperature' in the AIRS microwave images and is assigned the color blue. Therefore deep blue areas in storms show where the most precipitation occurs, or where ice crystals are present in the convective cloud tops. Outside of these storm regions, deep blue areas may also occur over the sea surface due to its low radiation emissivity. Land appears much warmer due to its high radiation emissivity.

Visible/Near-Infrared Images The AIRS instrument suite contains a sensor that captures radiation in four bands of the visible/near-infrared portion of the electromagetic spectrum. Data from three of these bands are combined to create 'visible' images similar to a snapshot taken with your camera.

The Atmospheric Infrared Sounder Experiment, with its visible, infrared, and microwave detectors, provides a three-dimensional look at Earth's weather. Working in tandem, the three instruments can make simultaneous observations all the way down to the Earth's surface, even in the presence of heavy clouds. With more than 2,000 channels sensing different regions of the atmosphere, the system creates a global, 3-D map of atmospheric temperature and humidity and provides information on clouds, greenhouse gases, and many other atmospheric phenomena. The AIRS Infrared Sounder Experiment flies onboard NASA's Aqua spacecraft and is managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif., under contract to NASA. JPL is a division of the California Institute of Technology in Pasadena.

Profiling and quantification of grain anthocyanins in purple pericarp × blue aleurone wheat crosses by high-performance thin-layer chromatography and densitometry.

PubMed

Böhmdorfer, Stefan; Oberlerchner, Josua Timotheus; Fuchs, Christina; Rosenau, Thomas; Grausgruber, Heinrich

2018-01-01

Anthocyanins are abundant secondary metabolites responsible for most blue to blue-black, and red to purple colors of various plant organs. In wheat grains, anthocyanins are accumulated in the pericarp and/or aleurone layer. Anthocyanin pigmented wheat grains can be processed into functional foods with potential health benefits due to the antioxidant properties of the anthocyanins. The grain anthocyanin content can be increased by pyramidizing the different genes responsible for the accumulation of anthocyanins in the different grain layers. Our objective was to develop a high-performance thin-layer chromatography (HPTLC) method that allows the determination of both the anthocyanin profile and the total pigment concentration. Thereby, selection of breeding lines with significantly higher grain anthocyanin content from purple pericarp × blue aleurone wheat crosses should become more efficient than selection based on only visual scoring of grain color and the unspecific determination of anthocyanin concentration by UV/Vis spectroscopy. A wide variability in the grain anthocyanin content was observed in breeding lines and check varieties. The highest concentration of anthocyanins was observed in deep purple (i.e. combination of the purple pericarp and blue aleurone genetics) grained breeding lines, followed by blue aleurone and purple pericarp genotypes. Determination of the total anthocyanin content was included into the chromatographic analysis, rendering an additional photometric analysis unnecessary. Ten target zones were identified in anthocyanin pigmented wheat grains; four of these zones were typically for blue aleurone types, five for purple pericarp types, and one (i.e. kuromanin glucoside) was characteristic for both. Chemometrics applied to the anthocyanin profile recorded by scanning densitometry revealed that peak heights and peak areas are highly correlated and that seven out of the ten target zones were responsible for about 90% of the total variation in the germplasm. Multivariate analysis of these seven target zones allowed not only a separation of the genetic material into purple, blue and deep purple grained genotypes, but also the identification of genotypes with a specific anthocyanin pattern. Thereby, the original classification by visual scoring was overruled in about one-third of the breeding lines. The presented HPTLC method with à côté calibration allowed the profiling of the pigments and quantification of wheat grain anthocyanin content in a single analysis, replacing UV/Vis spectroscopy with subsequent HPLC analysis. Moreover, no sample preparation apart from extraction and filtration is required, and more than 15 samples can be evaluated in one analysis run, corresponding to several dozens of samples per day. Hence, the method fulfills the requirements for screening methods in early generations of a plant breeding program such as high-throughput, small sample size, high repeatability, fast determination, and reasonable costs per sample. Combined with multivariate statistical analysis, the anthocyanin pattern allowed the validation of the genetic background in the offspring of purple × blue wheat crosses and, therefore, the efficient selection of genotypes exhibiting both the cyanidin and delphinidin aglycon.

Earth observations taken from shuttle orbiter Columbia

NASA Image and Video Library

1995-10-27

STS073-702-051 (27 October 1995) --- Photographed by the crew aboard the Space Shuttle Columbia, this detailed scene of East Caicos Island highlights the shallow tropical waters typical of the Bahamas region. The contrast between the light blue shallow water and dark blue deep water marks a sharp difference (hundreds of meters) in water depth. The shallow marine regions include sandbars and tidal channels (just right of center). The coastline of the island is low and swampy, and is also greatly influenced by the tides. Further offshore, the darker regions in the slightly deeper watermark sea grass and algae beds. This sensitive submarine environment can be mapped from space because the waters are so clear. Chains of clouds forming off islands and headlands, mark the downwind direction.

Holographic recording in a doubly doped lithium niobate crystal with two wavelengths: a blue laser diode and a green laser

NASA Astrophysics Data System (ADS)

Komori, Yuichi; Ishii, Yukihiro

2010-08-01

A doubly-doped LiNbO3 (LN) crystal has been well used as a nonvolatile two-wavelength recording material. By using two levels of the crystal, two-kind holograms can be recorded on one crystal; a hologram is recorded with a 405-nm blue laser diode (LD) for a deep Mn level, and another hologram is with a 532-nm green laser for a shallow Fe level. The recording capacity doubles. A 780-nm LD is non-volatile reconstructing source since the LD line is insensitive to both levels. Multiplexed reconstructed images are demonstrated by using a sharp angular selectivity of a volume LN crystal keeping Bragg condition with spherical reconstructions.

Ultraviolet/blue light-emitting diodes based on single horizontal ZnO microrod/GaN heterojunction.

PubMed

Du, Chia-Fong; Lee, Chen-Hui; Cheng, Chao-Tsung; Lin, Kai-Hsiang; Sheu, Jin-Kong; Hsu, Hsu-Cheng

2014-01-01

We report electroluminescence (EL) from single horizontal ZnO microrod (MR) and p-GaN heterojunction light-emitting diodes under forward and reverse bias. EL spectra were composed of two blue emissions centered at 431 and 490 nm under forward biases, but were dominated by a ultraviolet (UV) emission located at 380 nm from n-ZnO MR under high reverse biases. Light-output-current characteristic of the UV emission reveals that the rate of radiative recombination is faster than that of the nonradiative recombination. Highly efficient ZnO excitonic recombination at reverse bias is caused by electrons tunneling from deep-level states near the n-ZnO/p-GaN interface to the conduction band in n-ZnO.

Opportunities and obstacles for deep learning in biology and medicine.

PubMed

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.

Opportunities and obstacles for deep learning in biology and medicine

PubMed Central

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

A novel application of deep learning for single-lead ECG classification.

PubMed

Mathews, Sherin M; Kambhamettu, Chandra; Barner, Kenneth E

2018-06-04

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. Copyright © 2018. Published by Elsevier Ltd.

Detecting atrial fibrillation by deep convolutional neural networks.

PubMed

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

2018-02-01

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

Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yu, Yongtao; Li, Jonathan; Wen, Chenglu; Guan, Haiyan; Luo, Huan; Wang, Cheng

2016-03-01

This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

STScI-PRC02-11a FARAWAY GALAXIES PROVIDE A STUNNING 'WALLPAPER' BACKDROP FOR A RUNAWAY GALAXY

NASA Technical Reports Server (NTRS)

2002-01-01

Against a stunning backdrop of thousands of galaxies, this odd-looking galaxy with the long streamer of stars appears to be racing through space, like a runaway pinwheel firework. This picture of the galaxy UGC 10214 was taken by the Advanced Camera for Surveys (ACS), which was installed aboard NASA's Hubble Space Telescope in March during Servicing Mission 3B. Dubbed the 'Tadpole,' this spiral galaxy is unlike the textbook images of stately galaxies. Its distorted shape was caused by a small interloper, a very blue, compact galaxy visible in the upper left corner of the more massive Tadpole. The Tadpole resides about 420 million light-years away in the constellation Draco. Seen shining through the Tadpole's disk, the tiny intruder is likely a hit-and-run galaxy that is now leaving the scene of the accident. Strong gravitational forces from the interaction created the long tail of debris, consisting of stars and gas that stretch out more than 280,000 light-years. Numerous young blue stars and star clusters, spawned by the galaxy collision, are seen in the spiral arms, as well as in the long 'tidal' tail of stars. Each of these clusters represents the formation of up to about a million stars. Their color is blue because they contain very massive stars, which are 10 times hotter and 1 million times brighter than our Sun. Once formed, the star clusters become redder with age as the most massive and bluest stars exhaust their fuel and burn out. These clusters will eventually become old globular clusters similar to those found in essentially all halos of galaxies, including our own Milky Way. Two prominent clumps of young bright blue stars in the long tail are separated by a 'gap' -- a section that is fainter than the rest of the tail. These clumps of stars will likely become dwarf galaxies that orbit in the Tadpole's halo. The galactic carnage and torrent of star birth are playing out against a spectacular backdrop: a 'wallpaper pattern' of 6,000 galaxies. These galaxies represent twice the number of those discovered in the legendary Hubble Deep Field, the orbiting observatory's 'deepest' view of the heavens, taken in 1995 by the Wide Field and Planetary Camera 2. The ACS picture, however, was taken in one-twelfth the time it took to observe the original Hubble Deep Field. In blue light, ACS sees even fainter objects than were seen in the 'deep field.' The galaxies in the ACS picture, like those in the deep field, stretch back to nearly the beginning of time. They are a myriad of shapes and represent fossil samples of the universe's 13-billion-year evolution. The ACS image is so sharp that astronomers can identify distant colliding galaxies, the 'building blocks' of galaxies, an exquisite 'Whitman's Sampler' of galaxies, and many extremely faraway galaxies. ACS made this observation on April 1 and 9, 2002. The color image is constructed from three separate images taken in near-infrared, orange, and blue filters. Credit: NASA, H. Ford (JHU), G. Illingworth (USCS/LO), M.Clampin (STScI), G. Hartig (STScI), the ACS Science Team, and ESA The ACS Science Team: (H. Ford, G. Illingworth, M. Clampin, G. Hartig, T. Allen, K. Anderson, F. Bartko, N. Benitez, J. Blakeslee, R. Bouwens, T. Broadhurst, R. Brown, C. Burrows, D. Campbell, E. Cheng, N. Cross, P. Feldman, M. Franx, D. Golimowski, C. Gronwall, R. Kimble, J. Krist, M. Lesser, D. Magee, A. Martel, W. J. McCann, G. Meurer, G. Miley, M. Postman, P. Rosati, M. Sirianni, W. Sparks, P. Sullivan, H. Tran, Z. Tsvetanov, R. White, and R. Woodruff)

Quicklook Constituent Abundance and Stretch Parameter Retrieval for the Juno Microwave Radiometer using Neural Networks

NASA Astrophysics Data System (ADS)

Bellotti, A.; Steffes, P. G.

2016-12-01

The Juno Microwave Radiometer (MWR) has six channels ranging from 1.36-50 cm and the ability to peer deep into the Jovian atmosphere. An Artifical Neural Network algorithm has been developed to rapidly perform inversion for the deep abundance of ammonia, the deep abundance of water vapor, and atmospheric "stretch" (a parameter that reflects the deviation from a wet adiabate in the higher atmosphere). This algorithm is "trained" by using simulated emissions at the six wavelengths computed using the Juno atmospheric microwave radiative transfer (JAMRT) model presented by Oyafuso et al. (This meeting). By exploiting the emission measurements conducted at six wavelengths and at various incident angles, the neural network can provide preliminary results to a useful precison in a computational method hundreds of times faster than conventional methods. This can quickly provide important insights into the variability and structure of the Jovian atmosphere.

A measurement of multi-jet rates in deep-inelastic scattering at HERA

NASA Astrophysics Data System (ADS)

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

1994-03-01

Multi-jet production is observed in deep-inelastic electron proton scattering with the H1 detector at HERA. Jet rates for momentum transfers squared up to 500 GeV2 are determined using the JADE jet clustering algorithm. They are found to be in agreement with predictions from QCD based models.

A Deep Learning-Based Method for Similar Patient Question Retrieval in Chinese.

PubMed

Tang, Guo Yu; Ni, Yuan; Xie, Guo Tong; Fan, Xin Li; Shi, Yan Ling

2017-01-01

The online patient question and answering (Q&A) system, either as a website or a mobile application, attracts an increasing number of users in China. Patients will post their questions and the registered doctors then provide the corresponding answers. A large amount of questions with answers from doctors are accumulated. Instead of awaiting the response from a doctor, the newly posted question could be quickly answered by finding a semantically equivalent question from the Q&A achive. In this study, we investigated a novel deep learning based method to retrieve the similar patient question in Chinese. An unsupervised learning algorithm using deep neural network is performed on the corpus to generate the word embedding. The word embedding was then used as the input to a supervised learning algorithm using a designed deep neural network, i.e. the supervised neural attention model (SNA), to predict the similarity between two questions. The experimental results showed that our SNA method achieved P@1 = 77% and P@5 = 84%, which outperformed all other compared methods.

An improved multi-domain convolution tracking algorithm

NASA Astrophysics Data System (ADS)

Sun, Xin; Wang, Haiying; Zeng, Yingsen

2018-04-01

Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

Red to far-red multispectral fluorescence image fusion for detection of fecal contamination on apples

USDA-ARS?s Scientific Manuscript database

This research developed a multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet/blue LED excitation for detection of fecal contamination on Golden Delicious apples. Using a hyperspectral line-scan imaging system consisting of an EMCCD camera, spectrograph, an...

Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability.

PubMed

Santos, José; Monteagudo, Ángel

2017-03-27

The canonical code, although prevailing in complex genomes, is not universal. It was shown the canonical genetic code superior robustness compared to random codes, but it is not clearly determined how it evolved towards its current form. The error minimization theory considers the minimization of point mutation adverse effect as the main selection factor in the evolution of the code. We have used simulated evolution in a computer to search for optimized codes, which helps to obtain information about the optimization level of the canonical code in its evolution. A genetic algorithm searches for efficient codes in a fitness landscape that corresponds with the adaptability of possible hypothetical genetic codes. The lower the effects of errors or mutations in the codon bases of a hypothetical code, the more efficient or optimal is that code. The inclusion of the fitness sharing technique in the evolutionary algorithm allows the extent to which the canonical genetic code is in an area corresponding to a deep local minimum to be easily determined, even in the high dimensional spaces considered. The analyses show that the canonical code is not in a deep local minimum and that the fitness landscape is not a multimodal fitness landscape with deep and separated peaks. Moreover, the canonical code is clearly far away from the areas of higher fitness in the landscape. Given the non-presence of deep local minima in the landscape, although the code could evolve and different forces could shape its structure, the fitness landscape nature considered in the error minimization theory does not explain why the canonical code ended its evolution in a location which is not an area of a localized deep minimum of the huge fitness landscape.

Deep kernel learning method for SAR image target recognition

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Object-Oriented Image Clustering Method Using UAS Photogrammetric Imagery

NASA Astrophysics Data System (ADS)

Lin, Y.; Larson, A.; Schultz-Fellenz, E. S.; Sussman, A. J.; Swanson, E.; Coppersmith, R.

2016-12-01

Unmanned Aerial Systems (UAS) have been used widely as an imaging modality to obtain remotely sensed multi-band surface imagery, and are growing in popularity due to their efficiency, ease of use, and affordability. Los Alamos National Laboratory (LANL) has employed the use of UAS for geologic site characterization and change detection studies at a variety of field sites. The deployed UAS equipped with a standard visible band camera to collect imagery datasets. Based on the imagery collected, we use deep sparse algorithmic processing to detect and discriminate subtle topographic features created or impacted by subsurface activities. In this work, we develop an object-oriented remote sensing imagery clustering method for land cover classification. To improve the clustering and segmentation accuracy, instead of using conventional pixel-based clustering methods, we integrate the spatial information from neighboring regions to create super-pixels to avoid salt-and-pepper noise and subsequent over-segmentation. To further improve robustness of our clustering method, we also incorporate a custom digital elevation model (DEM) dataset generated using a structure-from-motion (SfM) algorithm together with the red, green, and blue (RGB) band data for clustering. In particular, we first employ an agglomerative clustering to create an initial segmentation map, from where every object is treated as a single (new) pixel. Based on the new pixels obtained, we generate new features to implement another level of clustering. We employ our clustering method to the RGB+DEM datasets collected at the field site. Through binary clustering and multi-object clustering tests, we verify that our method can accurately separate vegetation from non-vegetation regions, and are also able to differentiate object features on the surface.

[Medical computer-aided detection method based on deep learning].

PubMed

Tao, Pan; Fu, Zhongliang; Zhu, Kai; Wang, Lili

2018-03-01

This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

Efficient collective swimming by harnessing vortices through deep reinforcement learning.

PubMed

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.

The Next Era: Deep Learning in Pharmaceutical Research

PubMed Central

Ekins, Sean

2016-01-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule’s properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique. PMID:27599991

Deep learning in color: towards automated quark/gluon jet discrimination

DOE PAGES

Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.

2017-01-25

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. Here, to establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, themore » deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.« less

Deep learning in color: towards automated quark/gluon jet discrimination

NASA Astrophysics Data System (ADS)

Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.

2017-01-01

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

Deep learning in color: towards automated quark/gluon jet discrimination

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

Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. Here, to establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, themore » deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.« less

WRF-Chem Model Simulations of Arizona Dust Storms

NASA Astrophysics Data System (ADS)

Mohebbi, A.; Chang, H. I.; Hondula, D.

2017-12-01

The online Weather Research and Forecasting model with coupled chemistry module (WRF-Chem) is applied to simulate the transport, deposition and emission of the dust aerosols in an intense dust outbreak event that took place on July 5th, 2011 over Arizona. Goddard Chemistry Aerosol Radiation and Transport (GOCART), Air Force Weather Agency (AFWA), and University of Cologne (UoC) parameterization schemes for dust emission were evaluated. The model was found to simulate well the synoptic meteorological conditions also widely documented in previous studies. The chemistry module performance in reproducing the atmospheric desert dust load was evaluated using the horizontal field of the Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectro (MODIS) radiometer Terra/Aqua and Aerosol Robotic Network (AERONET) satellites employing standard Dark Target (DT) and Deep Blue (DB) algorithms. To assess the temporal variability of the dust storm, Particulate Matter mass concentration data (PM10 and PM2.5) from Arizona Department of Environmental Quality (AZDEQ) ground-based air quality stations were used. The promising performance of WRF-Chem indicate that the model is capable of simulating the right timing and loading of a dust event in the planetary-boundary-layer (PBL) which can be used to forecast approaching severe dust events and to communicate an effective early warning.

Nutrient and sediment transport in streams of the Lake Tahoe basin: a 30-year retrospective

Treesearch

Robert Coats

2004-01-01

Lake Tahoe, widely renowned for its astounding clarity and deep blue color, lies at an elevation of 1,898 meters (m) in the central Sierra Nevada, astride the California-Nevada border. The volume of the lake is 156 cubic kilometers (km3), and its surface area is 501 square kilometers (km2), 38 percent of the total basin...

Going-to-the-Sun Road: A Model of Landscape Engineering. Teaching with Historic Places.

ERIC Educational Resources Information Center

Metcalf, Fay

Soaring mountain peaks, glaciers, deep-blue lakes, and lush forests delight the senses of visitors who drive on Going-to-the-Sun Road through Glacier National Park in northwestern Montana. The construction of the Going-to-the-Sun-Road, dedicated in 1933, made this experience available to the many visitors who come to the park by car. Building this…

Polar Maps of Thermal and Epithermal Neutrons

NASA Technical Reports Server (NTRS)

2002-01-01

Observations by NASA's 2001 Mars Odyssey spacecraft show views of the polar regions of Mars in thermal neutrons (top) and epithermal neutrons (bottom). In these maps, deep blue indicates a low amount of neutrons, and red indicates a high amount. Thermal neutrons are sensitive to the presence of hydrogen and the presence of carbon dioxide, in this case 'dry ice' frost. The red area in the upper right map indicates that about one meter (three feet) of carbon dioxide frost covers the surface around the north pole, as it does every Mars winter in the polar regions. An enhancement of thermal neutrons close to the south pole, seen as a light green color on the upper left map, indicates the presence of residual carbon dioxide in the south polar cap, even though the annual frost dissipated from that region during southern summer. Soil enriched with hydrogen is indicated by the deep blue colors on the epithermal maps (bottom), showing a low intensity of epithermal neutrons. The deep blue areas in the polar regions are believed to contain up to 50 percent water ice in the upper one meter (three feet) of the soil. The views shown here are of measurements made during the first three months of mapping using the neutron spectrometer instrument, part of the gamma ray spectrometer instrument suite. Topographic features are superimposed on the map for geographic reference.

NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. Investigators at Arizona State University in Tempe, the University of Arizona in Tucson, and NASA's Johnson Space Center, Houston, operate the science instruments. The gamma-ray spectrometer was provided by the University of Arizona in collaboration with the Russian Aviation and Space Agency, which provided the high-energy neutron detector, and the Los Alamos National Laboratories, New Mexico, which provided the neutron spectrometer. Lockheed Martin Astronautics, Denver, is the prime contractor for the project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

Atmospheric Motion in Jupiter Northern Hemisphere

NASA Image and Video Library

2000-09-25

True-color (left) and false-color (right) mosaics of Jupiter's northern hemisphere between 10 and 50 degrees latitude. Jupiter's atmospheric motions are controlled by alternating eastward and westward bands of air between Jupiter's equator and polar regions. The direction and speed of these bands influences the color and texture of the clouds seen in this mosaic. The high and thin clouds are represented by light blue, deep clouds are reddish, and high and thick clouds are white. A high haze overlying a clear, deep atmosphere is represented by dark purple. This image was taken by NASA's Galileo spacecraft on April 3, 1997 at a distance of 1.4 million kilometers (.86 million miles). http://photojournal.jpl.nasa.gov/catalog/PIA03000

[Venous Doppler color echography: importance and inconveniences].

PubMed

Laroche, J P; Dauzat, M; Muller, G; Janbon, C

1993-01-01

Color Doppler is a technique which performs a real-time opacification of the vascular system with blue indicating reverse flow and red indicating forward flow (directional color coding). In venous pathology, the use of color Doppler improves significantly the anatomical evaluation of the inferior vena cava, the iliac vein, the deep femoral vein, and the sural system. Color Doppler facilitates the study of deep venous thrombosis (providing useful information to differentiate ancient from most recent thrombus) and also the study of post-thrombotic conditions (assessment of reverse flow, repermeation phenomena). Finally, color Doppler produces a better insight for the study of varicose veins, especially with regard to mapping, identification of communicante veins, and study of the external saphenous vein.

Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database.

PubMed

Chen-Ying Hung; Wei-Chen Chen; Po-Tsun Lai; Ching-Heng Lin; Chi-Chun Lee

2017-07-01

Electronic medical claims (EMCs) can be used to accurately predict the occurrence of a variety of diseases, which can contribute to precise medical interventions. While there is a growing interest in the application of machine learning (ML) techniques to address clinical problems, the use of deep-learning in healthcare have just gained attention recently. Deep learning, such as deep neural network (DNN), has achieved impressive results in the areas of speech recognition, computer vision, and natural language processing in recent years. However, deep learning is often difficult to comprehend due to the complexities in its framework. Furthermore, this method has not yet been demonstrated to achieve a better performance comparing to other conventional ML algorithms in disease prediction tasks using EMCs. In this study, we utilize a large population-based EMC database of around 800,000 patients to compare DNN with three other ML approaches for predicting 5-year stroke occurrence. The result shows that DNN and gradient boosting decision tree (GBDT) can result in similarly high prediction accuracies that are better compared to logistic regression (LR) and support vector machine (SVM) approaches. Meanwhile, DNN achieves optimal results by using lesser amounts of patient data when comparing to GBDT method.

Speckle contrast optical tomography: A new method for deep tissue three-dimensional tomography of blood flow

PubMed Central

Varma, Hari M.; Valdes, Claudia P.; Kristoffersen, Anna K.; Culver, Joseph P.; Durduran, Turgut

2014-01-01

A novel tomographic method based on the laser speckle contrast, speckle contrast optical tomography (SCOT) is introduced that allows us to reconstruct three dimensional distribution of blood flow in deep tissues. This method is analogous to the diffuse optical tomography (DOT) but for deep tissue blood flow. We develop a reconstruction algorithm based on first Born approximation to generate three dimensional distribution of flow using the experimental data obtained from tissue simulating phantoms. PMID:24761306

Generation of short and intense attosecond pulses

NASA Astrophysics Data System (ADS)

Khan, Sabih Ud Din

Extremely broad bandwidth attosecond pulses (which can support 16as pulses) have been demonstrated in our lab based on spectral measurements, however, compensation of intrinsic chirp and their characterization has been a major bottleneck. In this work, we developed an attosecond streak camera using a multi-layer Mo/Si mirror (bandwidth can support ˜100as pulses) and position sensitive time-of-flight detector, and the shortest measured pulse was 107.5as using DOG, which is close to the mirror bandwidth. We also developed a PCGPA based FROG-CRAB algorithm to characterize such short pulses, however, it uses the central momentum approximation and cannot be used for ultra-broad bandwidth pulses. To facilitate the characterization of such pulses, we developed PROOF using Fourier filtering and an evolutionary algorithm. We have demonstrated the characterization of pulses with a bandwidth corresponding to ˜20as using synthetic data. We also for the first time demonstrated single attosecond pulses (SAP) generated using GDOG with a narrow gate width from a multi-cycle driving laser without CE-phase lock, which opens the possibility of scaling attosecond photon flux by extending the technique to peta-watt class lasers. Further, we generated intense attosecond pulse trains (APT) from laser ablated carbon plasmas and demonstrated ˜9.5 times more intense pulses as compared to those from argon gas and for the first time demonstrated a broad continuum from a carbon plasma using DOG. Additionally, we demonstrated ˜100 times enhancement in APT from gases by switching to 400 nm (blue) driving pulses instead of 800 nm (red) pulses. We measured the ellipticity dependence of high harmonics from blue pulses in argon, neon and helium, and developed a simple theoretical model to numerically calculate the ellipticity dependence with good agreement with experiments. Based on the ellipticity dependence, we proposed a new scheme of blue GDOG which we predict can be employed to extract intense SAP from an APT driven by blue laser pulses. We also demonstrated compression of long blue pulses into >240 microJ broad-bandwidth pulses using neon filled hollow core fiber, which is the highest reported pulse energy of short blue pulses. However, compression of phase using chirp mirrors is still a technical challenge.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

In the Defense Satellite Communications Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), workers place an anti-static blanket over the lower portion of Deep Space 1, to protect the spacecraft during transport to the launch pad. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

In the Defense Satellite Communications Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), after covering the lower portion of Deep Space 1, workers adjust the anti-static blanket covering the upper portion. The blanket will protect the spacecraft during transport to the launch pad. Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Enhanced detection of myeloperoxidase activity in deep tissues through luminescent excitation of near-infrared nanoparticles.

PubMed

Zhang, Ning; Francis, Kevin P; Prakash, Arun; Ansaldi, Daniel

2013-04-01

A previous study reported the use of luminol for the detection of myeloperoxidase (MPO) activity using optical imaging in infiltrating neutrophils under inflammatory disease conditions. The detection is based on a photon-emitting reaction between luminol and an MPO metabolite. Because of tissue absorption and scattering, however, luminol-emitted blue light can be efficiently detected from superficial inflammatory foci only. In this study we report a chemiluminescence resonance energy transfer (CRET) methodology in which luminol-generated blue light excites nanoparticles to emit light in the near-infrared spectral range, resulting in remarkable improvement of MPO detectability in vivo. CRET caused a 37-fold increase in luminescence emission over luminol alone in detecting MPO activity in lung tissues after lipopolysaccharide challenge. We demonstrated a dependence of the chemiluminescent signal on MPO activity using MPO-deficient mice. In addition, co-administration of 4-aminobenzoic acid hydrazide (4-ABAH), an irreversible inhibitor of MPO, significantly attenuated luminescent emission from inflamed lungs. Inhibition of nitric oxide synthase with a nonspecific inhibitor, L-NAME, had no effect on luminol-mediated chemiluminescence production. Pretreatment of mice with MLN120B, a selective inhibitor of IKK-2, resulted in suppression of neutrophil infiltration to the lung tissues and reduction of MPO activity. We also demonstrated that CRET can effectively detect MPO activity at deep tissue tumor foci due to tumor development-associated neutrophil infiltration. We developed a sensitive MPO detection methodology that provides a means for visualizing and quantifying oxidative stress in deep tissue. This method is amenable to rapid evaluation of anti-inflammatory agents in animal models.

Robust Deep Semantics for Language Understanding

DTIC Science & Technology

focus on five areas: deep learning, textual inferential relations, relation and event extraction by distant supervision , semantic parsing and...ontology expansion, and coreference resolution. As time went by, the program focus converged towards emphasizing technologies for knowledge base...natural logic methods for text understanding, improved mention coreference algorithms, and the further development of multilingual tools in CoreNLP.

Deep Space 1 is encapsulated on launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

On Launch Pad 17A at Cape Canaveral Air Station, released from its protective payload transportation container, Deep Space 1 waits to have the fairing attached before launch. Targeted for launch aboard a Boeing Delta 7326 rocket on Oct. 25, Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Defense Satellite Communication Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), begin attaching the conical section leaves of the payload transportation container on Deep Space 1 before launch, targeted for Oct. 25 aboard a Boeing Delta 7326 rocket from Launch Pad 17A. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight- tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

KSC-98pc1261

NASA Image and Video Library

1998-10-07

KENNEDY SPACE CENTER, FLA. -- Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), attach a strap during installation of the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October

KSC-98pc1262

NASA Image and Video Library

1998-10-07

KENNEDY SPACE CENTER, FLA. -- Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), make adjustments while installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October

KSC-98pc1264

NASA Image and Video Library

1998-10-07

KENNEDY SPACE CENTER, FLA. -- Workers in the Defense Satellite Communications Systems Processing Facility (DPF) at Cape Canaveral Air Station (CCAS) make adjustments while installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched Oct. 25 aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS

KSC-98pc1260

NASA Image and Video Library

1998-10-07

KENNEDY SPACE CENTER, FLA. -- Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), install an ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October

KSC-98pc1265

NASA Image and Video Library

1998-10-07

KENNEDY SPACE CENTER, FLA. -- Workers in the Defense Satellite Communications Systems Processing Facility (DPF) at Cape Canaveral Air Station (CCAS) finish installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched Oct. 25 aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS

KSC-98pc1263

NASA Image and Video Library

1998-10-07

KENNEDY SPACE CENTER, FLA. -- Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), maneuver the ion propulsion engine into place before installation on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October

KSC-98pc1314

NASA Image and Video Library

1998-10-10

KENNEDY SPACE CENTER, FLA. -- Workers in the Defense Satellite Communication Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), move to the workstand the second conical section leaf of the payload transportation container for Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS

DREAMING OF ATMOSPHERES

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

Waldmann, I. P., E-mail: ingo@star.ucl.ac.uk

Here, we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as themore » “dreams” of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.« less

Measuring the bioactivity and molecular conformation of typically globular proteins with phenothiazine-derived methylene blue in solid and in solution: A comparative study using photochemistry and computational chemistry.

PubMed

Ding, Fei; Xie, Yong; Peng, Wei; Peng, Yu-Kui

2016-05-01

Methylene blue is a phenothiazine agent, that possesses a diversity of biomedical and biological therapeutic purpose, and it has also become the lead compound for the exploitation of other pharmaceuticals such as chlorpromazine and the tricyclic antidepressants. However, the U.S. Food and Drug Administration has acquired cases of detrimental effects of methylene blue toxicities such as hemolytic anemia, methemoglobinemia and phototoxicity. In this work, the molecular recognition of methylene blue by two globular proteins, hemoglobin and lysozyme was characterized by employing fluorescence, circular dichroism (CD) along with molecular modeling at the molecular scale. The recognition of methylene blue with proteins appears fluorescence quenching via static type, this phenomenon does cohere with time-resolved fluorescence lifetime decay that nonfluorescent protein-drug conjugate formation has a strength of 10(4)M(-1), and the primary noncovalent bonds, that is hydrogen bonds, π-conjugated effects and hydrophobic interactions were operated and remained adduct stable. Meantime, the results of far-UV CD and synchronous fluorescence suggest that the α-helix of hemoglobin/lysozyme decreases from 78.2%/34.7% (free) to 58.7%/23.8% (complex), this elucidation agrees well with the elaborate description of three-dimensional fluorescence showing the polypeptide chain of proteins partially destabilized upon conjugation with methylene blue. Furthermore, both extrinsic fluorescent indicator and molecular modeling clearly exhibit methylene blue is situated within the cavity constituted by α1, β2 and α2 subunits of hemoglobin, while it was located at the deep fissure on the lysozyme surface and Trp-62 and Trp-63 residues are nearby. With the aid of computational analyses and combining the wet experiments, it can evidently be found that the recognition ability of proteins for methylene blue is patterned upon the following sequence: lysozyme

Deep frequency modulation interferometry.

PubMed

Gerberding, Oliver

2015-06-01

Laser interferometry with pm/Hz precision and multi-fringe dynamic range at low frequencies is a core technology to measure the motion of various objects (test masses) in space and ground based experiments for gravitational wave detection and geodesy. Even though available interferometer schemes are well understood, their construction remains complex, often involving, for example, the need to build quasi-monolithic optical benches with dozens of components. In recent years techniques have been investigated that aim to reduce this complexity by combining phase modulation techniques with sophisticated digital readout algorithms. This article presents a new scheme that uses strong laser frequency modulations in combination with the deep phase modulation readout algorithm to construct simpler and easily scalable interferometers.

Manipulation of Thermally Activated Delayed Fluorescence of Blue Exciplex Emission: Fully Utilizing Exciton Energy for Highly Efficient Organic Light Emitting Diodes with Low Roll-Off.

PubMed

Wang, Zixing; Wang, Hedan; Zhu, Jun; Wu, Peng; Shen, Bowen; Dou, Dehai; Wei, Bin

2017-06-28

The application of exciplex energy has become a unique way to achieve organic light-emitting diodes (OLEDs) with high efficiencies, low turn-on voltage, and low roll-off. Novel δ-carboline derivatives with high triplet energy (T 1 ≈ 2.92 eV) and high glass transition temperature (T g ≈ 153 °C) were employed to manipulate exciplex emissions in this paper. Deep blue (peak at 436 nm) and pure blue (peak at 468 nm) thermally activated delayed fluorescence (TADF) of exciplex OLEDs were demonstrated by utilizing them as emitters with the maximum current efficiency (CE) of 4.64 cd A -1 , power efficiency (PE) of 2.91 lm W -1 , and external quantum efficiency (EQE) of 2.36%. Highly efficient blue phosphorescent OLEDs doped with FIrpic showed a maximum CE of 55.6 cd A -1 , PE of 52.9 lm W -1 , and EQE of 24.6% respectively with very low turn on voltage at 2.7 V. The devices still remain high CE of 46.5 cd A -1 at 100 cd m -2 , 45.4 cd A -1 at 1000 cd m -2 and 42.3 cd A -1 at 5000 cd m -2 with EQE close to 20% indicating low roll-off. Manipulating blue exciplex emissions by chemical structure gives an ideal strategy to fully utilize all exciton energies for lighting of OLEDs.

Long-lived and highly efficient green and blue phosphorescent emitters and device architectures for OLED displays

NASA Astrophysics Data System (ADS)

Eickhoff, Christian; Murer, Peter; Geßner, Thomas; Birnstock, Jan; Kröger, Michael; Choi, Zungsun; Watanabe, Soichi; May, Falk; Lennartz, Christian; Stengel, Ilona; Münster, Ingo; Kahle, Klaus; Wagenblast, Gerhard; Mangold, Hannah

2015-09-01

In this paper, two OLED device concepts are introduced. First, classical phosphorescent green carbene emitters with unsurpassed lifetime, combined with low voltage and high efficiency are presented and the associated optimized OLED stacks are explained. Second, a path towards highly efficient, long-lived deep blue systems is shown. The high efficiencies can be reached by having the charge-recombination on the phosphorescent carbene emitter while at the same time short emissive lifetimes are realized by fast energy transfer to the fluorescent emitter, which eventually allows for higher OLED stability in the deep blue. Device architectures, materials and performance data are presented showing that carbene type emitters have the potential to outperform established phosphorescent green emitters both in terms of lifetime and efficiency. The specific class of green emitters under investigation shows distinctly larger electron affinities (2.1 to 2.5 eV) and ionization potentials (5.6 to 5.8 eV) as compared to the "standard" emitter Ir(ppy)3 (5.0/1.6 eV). This difference in energy levels requires an adopted OLED design, in particular with respect to emitter hosts and blocking layers. Consequently, in the diode setup presented here, the emitter species is electron transporting or electron trapping. For said green carbene emitters, the typical peak wavelength is 525 nm yielding CIE color coordinates of (x = 0.33, y = 0.62). Device data of green OLEDs are shown with EQEs of 26 %. Driving voltage at 1000 cd/m2 is below 3 V. In an optimized stack, a device lifetime of LT95 > 15,000 h (1000 cd/m2) has been reached, thus fulfilling AMOLED display requirements.

Harmful algal bloom smart device application: using image analysis and machine learning techniques for classification of harmful algal blooms

EPA Science Inventory

Northern Kentucky University and the U.S. EPA Office of Research Development in Cincinnati Agency are collaborating to develop a harmful algal bloom detection algorithm that estimates the presence of cyanobacteria in freshwater systems by image analysis. Green and blue-green alg...

Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array.

PubMed

Wang, Liang; Yang, Die; Fang, Cheng; Chen, Zuliang; Lesniewski, Peter J; Mallavarapu, Megharaj; Naidu, Ravendra

2015-01-01

Sodium potassium absorption ratio (SPAR) is an important measure of agricultural water quality, wherein four exchangeable cations (K(+), Na(+), Ca(2+) and Mg(2+)) should be simultaneously determined. An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. In this paper, a solid contact ISE array, based on a Prussian blue modified glassy carbon electrode (PB-GCE), was applied with a novel chemometric strategy. One of the most popular independent component analysis (ICA) methods, the fast fixed-point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. This geneticICA can be implemented as a data preprocessing method to improve the prediction accuracy of the Back-propagation neural network (BPNN). The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained. Copyright © 2014 Elsevier B.V. All rights reserved.

Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.

PubMed

Prevedello, Luciano M; Erdal, Barbaros S; Ryu, John L; Little, Kevin J; Demirer, Mutlu; Qian, Songyue; White, Richard D

2017-12-01

Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non-contrast material-enhanced head computed tomographic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI). Materials and Methods This HIPAA-compliant retrospective study was completed after institutional review board approval. A training and validation dataset of noncontrast-enhanced head CT examinations that comprised 100 examinations of HMH, 22 of SAI, and 124 of noncritical findings was obtained resulting in 2583 representative images. Examinations were processed by using a convolutional neural network (deep learning) using two different window and level configurations (brain window and stroke window). AI algorithm performance was tested on a separate dataset containing 50 examinations with HMH findings, 15 with SAI findings, and 35 with noncritical findings. Results Final algorithm performance for HMH showed 90% (45 of 50) sensitivity (95% confidence interval [CI]: 78%, 97%) and 85% (68 of 80) specificity (95% CI: 76%, 92%), with area under the receiver operating characteristic curve (AUC) of 0.91 with the brain window. For SAI, the best performance was achieved with the stroke window showing 62% (13 of 21) sensitivity (95% CI: 38%, 82%) and 96% (27 of 28) specificity (95% CI: 82%, 100%), with AUC of 0.81. Conclusion AI using deep learning demonstrates promise for detecting critical findings at noncontrast-enhanced head CT. A dedicated algorithm was required to detect SAI. Detection of SAI showed lower sensitivity in comparison to detection of HMH, but showed reasonable performance. Findings support further investigation of the algorithm in a controlled and prospective clinical setting to determine whether it can independently screen noncontrast-enhanced head CT examinations and notify the interpreting radiologist of critical findings. © RSNA, 2017 Online supplemental material is available for this article.

Ultraviolet/blue light-emitting diodes based on single horizontal ZnO microrod/GaN heterojunction

PubMed Central

2014-01-01

We report electroluminescence (EL) from single horizontal ZnO microrod (MR) and p-GaN heterojunction light-emitting diodes under forward and reverse bias. EL spectra were composed of two blue emissions centered at 431 and 490 nm under forward biases, but were dominated by a ultraviolet (UV) emission located at 380 nm from n-ZnO MR under high reverse biases. Light-output-current characteristic of the UV emission reveals that the rate of radiative recombination is faster than that of the nonradiative recombination. Highly efficient ZnO excitonic recombination at reverse bias is caused by electrons tunneling from deep-level states near the n-ZnO/p-GaN interface to the conduction band in n-ZnO. PMID:25232299

Systematics of Alkali Metals in Pore Fluids from Serpentinite Mud Volcanoes: IODP Expedition 366

NASA Astrophysics Data System (ADS)

Wheat, C. G.; Ryan, J.; Menzies, C. D.; Price, R. E.; Sissmann, O.

2017-12-01

IODP Expedition 366 focused, in part, on the study of geo­chemical cycling, matrix alteration, material and fluid transport, and deep biosphere processes within the subduction channel in the Mariana forearc. This was accomplished through integrated sampling of summit and flank regions of three active serpentinite mud volcanoes (Yinazao (Blue Moon), Asùt Tesoro (Big Blue), and Fantangisña (Celestial) Seamounts). These edifices present a transect of depths to the Pacific Plate, allowing one to characterize thermal, pressure and compositional effects on processes that are associated with the formation of serpentinite mud volcanoes and continued activity below and within them. Previous coring on ODP Legs 125 and 195 at two other serpentinite mud volcanoes (Conical and South Chamorro Seamounts) and piston, gravity, and push cores from several other Mariana serpentinite mud volcanoes add to this transect of sites where deep-sourced material is discharged at the seafloor. Pore waters (149 samples) were squeezed from serpentinite materials to determine the composition of deep-sourced fluid and to assess the character, extent, and effect of diagenetic reactions and mixing with seawater on the flanks of the seamounts as the serpentinite matrix weathers. In addition two Water Sampler Temperature Tool (WSTP) fluid samples were collected within two of the cased boreholes, each with at least 30 m of screened casing that allows formations fluids to discharge into the borehole. Shipboard results for Na and K record marked seamount-to-seamount differences in upwelling summit fluids, and complex systematics in fluids obtained from flank sites. Here we report new shore-based Rb and Cs measurements, two elements that have been used to constrain the temperature of the deep-sourced fluid. Data are consistent with earlier coring and drilling expeditions, resulting in systematic changes with depth (and by inference temperature) to the subduction channel.

Graph theoretical stable allocation as a tool for reproduction of control by human operators

NASA Astrophysics Data System (ADS)

van Nooijen, Ronald; Ertsen, Maurits; Kolechkina, Alla

2016-04-01

During the design of central control algorithms for existing water resource systems under manual control it is important to consider the interaction with parts of the system that remain under manual control and to compare the proposed new system with the existing manual methods. In graph theory the "stable allocation" problem has good solution algorithms and allows for formulation of flow distribution problems in terms of priorities. As a test case for the use of this approach we used the algorithm to derive water allocation rules for the Gezira Scheme, an irrigation system located between the Blue and White Niles south of Khartoum. In 1925, Gezira started with 300,000 acres; currently it covers close to two million acres.

KSC-98pc1334

NASA Image and Video Library

1998-10-12

KENNEDY SPACE CENTER, FLA. -- On Launch Pad 17A at Cape Canaveral Air Station, Deep Space 1 is viewed from above after installation on a Boeing Delta 7326 rocket . Targeted for launch on Oct. 25, Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999

KSC-98pc1354

NASA Image and Video Library

1998-10-16

KENNEDY SPACE CENTER, FLA. -- On Launch Pad 17A at Cape Canaveral Air Station, workers maneuver the second half of the fairing to encapsulate Deep Space 1, targeted for launch aboard a Boeing Delta II rocket on Oct. 24. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999

KSC-98pc1335

NASA Image and Video Library

1998-10-12

KENNEDY SPACE CENTER, FLA. -- On Launch Pad 17A at Cape Canaveral Air Station, Deep Space 1 is uncovered after installation on a Boeing Delta 7326 rocket. Targeted for launch on Oct. 25, Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999

KSC-98pc1355

NASA Image and Video Library

1998-10-16

KENNEDY SPACE CENTER, FLA. -- On Launch Pad 17A at Cape Canaveral Air Station, workers check make a final check of the fairing encapsulating Deep Space 1, which is targeted for launch aboard a Boeing Delta II rocket on Oct. 24. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999

KSC-98pc1331

NASA Image and Video Library

1998-10-12

KENNEDY SPACE CENTER, FLA. -- On Launch Pad 17A at Cape Canaveral Air Station, Deep Space 1 is lowered in the white room for installation on a Boeing Delta 7326 rocket . The spacecraft is targeted for launch on Oct. 25. Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999

KSC-98pc1333

NASA Image and Video Library

1998-10-12

KENNEDY SPACE CENTER, FLA. -- On Launch Pad 17A at Cape Canaveral Air Station, workers remove the transportation canister around Deep Space 1 after installation on a Boeing Delta 7326 rocket . Targeted for launch on Oct. 25, Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999

KSC-98pc1346

NASA Image and Video Library

1998-10-16

KENNEDY SPACE CENTER, FLA. -- On Launch Pad 17A at Cape Canaveral Air Station, workers begin encapsulating Deep Space 1 with the fairing (right side). Targeted for launch aboard a Boeing Delta 7326 rocket on Oct. 25, Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999

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

NASA Astrophysics Data System (ADS)

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.

Projection decomposition algorithm for dual-energy computed tomography via deep neural network.

PubMed

Xu, Yifu; Yan, Bin; Chen, Jian; Zeng, Lei; Li, Lei

2018-03-15

Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decomposition function. This work aims to establish and validate a data-driven algorithm for estimation of the decomposition function. A deep neural network (DNN) consisting of two sub-nets is proposed to solve the projection decomposition problem. The compressing sub-net, substantially a stack auto-encoder (SAE), learns a compact representation of energy spectrum. The decomposing sub-net with a two-layer structure fits the nonlinear transform between energy projection and basic material thickness. The proposed DNN not only delivers image with lower standard deviation and higher quality in both simulated and real data, and also yields the best performance in cases mixed with photon noise. Moreover, DNN costs only 0.4 s to generate a decomposition solution of 360 × 512 size scale, which is about 200 times faster than the competing algorithms. The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.

Remote sensing estimation of colored dissolved organic matter (CDOM) in optically shallow waters

NASA Astrophysics Data System (ADS)

Li, Jiwei; Yu, Qian; Tian, Yong Q.; Becker, Brian L.

2017-06-01

It is not well understood how bottom reflectance of optically shallow waters affects the algorithm performance of colored dissolved organic matters (CDOM) retrieval. This study proposes a new algorithm that considers bottom reflectance in estimating CDOM absorption from optically shallow inland or coastal waters. The field sampling was conducted during four research cruises within the Saginaw River, Kawkawlin River and Saginaw Bay of Lake Huron. A stratified field sampling campaign collected water samples, determined the depth at each sampling location and measured optical properties. The sampled CDOM absorption at 440 nm broadly ranged from 0.12 to 8.46 m-1. Field sample analysis revealed that bottom reflectance does significantly change water apparent optical properties. We developed a CDOM retrieval algorithm (Shallow water Bio-Optical Properties algorithm, SBOP) that effectively reduces uncertainty by considering bottom reflectance in shallow waters. By incorporating the bottom contribution in upwelling radiances, the SBOP algorithm was able to explain 74% of the variance of CDOM values (RMSE = 0.22 and R2 = 0.74). The bottom effect index (BEI) was introduced to efficiently separate optically shallow and optically deep waters. Based on the BEI, an adaptive approach was proposed that references the amount of bottom effect in order to identify the most suitable algorithm (optically shallow water algorithm [SBOP] or optically deep water algorithm [QAA-CDOM]) to improve CDOM estimation (RMSE = 0.22 and R2 = 0.81). Our results potentially help to advance the capability of remote sensing in monitoring carbon pools at the land-water interface.

Predicting the Shifts of Absorption Maxima of Azulene Derivatives Using Molecular Modeling and ZINDO CI Calculations of UV-Vis Spectra

ERIC Educational Resources Information Center

Patalinghug, Wyona C.; Chang, Maharlika; Solis, Joanne

2007-01-01

The deep blue color of azulene is drastically changed by the addition of substituents such as CH[subscript 3], F, or CHO. Computational semiempirical methods using ZINDO CI are used to model azulene and azulene derivatives and to calculate their UV-vis spectra. The calculated spectra are used to show the trends in absorption band shifts upon…

Large Magellanic Cloud helium-rich peculiar blue supergiants and SN 1987A

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

Tuchman, Y.; Wheeler, J.C.

1990-11-01

The theoretical distribution of massive stars in the H-R diagram is compared to the revised data of Fitzpatrick and Garmany for the LMC. Preferred models of about 20 M solar masses undergo a thermal contraction at T(eff) about 35,000 K at the end of core hydrogen burning but reestablish thermal equilibrium to the red of the main sequence at T(eff) about 20,000 K after ignition of a hydrogen-burning shell. They then evolve on a nuclear time scale to T(eff) about 6000 K where they lose thermal equilibrium and jump to the Hayashi track. The theoretical and observed distributions agree withmore » two significant exceptions: the blue thermal contraction gap is overpopulated compared to the theory, and there is a ledge crossing the center of the H-R diagram. The hypothesis that some of the observed stars in the blue gap are secondaries that have accreted helium-rich matter from deep within the hydrogen envelope of a red supergiant primary is explored. Some preliminary observational justification is given. 27 refs.« less

Toolkits and Libraries for Deep Learning.

PubMed

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

2017-08-01

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

Stratification-Based Outlier Detection over the Deep Web.

PubMed

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

2016-01-01

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

Stratification-Based Outlier Detection over the Deep Web

PubMed Central

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

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. PMID:27313603

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Applying a Hidden Markov Model-Based Event Detection and Classification Algorithm to Apollo Lunar Seismic Data

NASA Astrophysics Data System (ADS)

Knapmeyer-Endrun, B.; Hammer, C.

2014-12-01

The seismometers that the Apollo astronauts deployed on the Moon provide the only recordings of seismic events from any extra-terrestrial body so far. These lunar events are significantly different from ones recorded on Earth, in terms of both signal shape and source processes. Thus they are a valuable test case for any experiment in planetary seismology. In this study, we analyze Apollo 16 data with a single-station event detection and classification algorithm in view of NASA's upcoming InSight mission to Mars. InSight, scheduled for launch in early 2016, has the goal to investigate Mars' internal structure by deploying a seismometer on its surface. As the mission does not feature any orbiter, continuous data will be relayed to Earth at a reduced rate. Full range data will only be available by requesting specific time-windows within a few days after the receipt of the original transmission. We apply a recently introduced algorithm based on hidden Markov models that requires only a single example waveform of each event class for training appropriate models. After constructing the prototypes we detect and classify impacts and deep and shallow moonquakes. Initial results for 1972 (year of station installation with 8 months of data) indicate a high detection rate of over 95% for impacts, of which more than 80% are classified correctly. Deep moonquakes, which occur in large amounts, but often show only very weak signals, are detected with less certainty (~70%). As there is only one weak shallow moonquake covered, results for this event class are not statistically significant. Daily adjustments of the background noise model help to reduce false alarms, which are mainly erroneous deep moonquake detections, by about 25%. The algorithm enables us to classify events that were previously listed in the catalog without classification, and, through the combined use of long period and short period data, identify some unlisted local impacts as well as at least two yet unreported deep moonquakes.

Remote Sensing Reflectance and Inherent Optical Properties in the Mid-mesohaline Chesapeake Bay

NASA Technical Reports Server (NTRS)

Tzortziou, Maria; Subramaniam, Ajit; Herman, Jay R.; Gallegos, Charles L.; Neal, Patrick J.; Harding, Lawrence W., Jr.

2006-01-01

We used an extensive set of bio-optical data and radiative transfer (RT) model simulations of radiation fields to investigate relationships between inherent optical properties and remotely sensed quantities in the optically complex, mid-mesohaline Chesapeake Bay waters. Field observations showed that the chlorophyll algorithms used by the MODIS (MODerate resolution Imaging Spectroradiometer) ocean color sensor (i.e. Chlor_a, chlor_MODIS, chlor_a_3 products) do not perform accurately in these Case 2 waters. This is because, when applied to waters with high concentrations of chlorophyll, all MODIS algorithms are based on empirical relationships between chlorophyll concentration and blue-green wavelength remote sensing reflectance (Rrs) ratios that do not account for the typically strong blue-wavelength absorption by non-covarying, dissolved and non-algal particulate components. Stronger correlation was observed between chlorophyll concentration and Rrs ratios in the red (i.e. Rrs(677)/Rrs(554)) where dissolved and non-algal particulate absorption become exponentially smaller. Regionally-specific algorithms that are based on the phytoplankton optical properties in the red wavelength region provide a better basis for satellite monitoring of phytoplankton blooms in these Case 2 waters. Good optical closure was obtained between independently measured Rrs spectra and the optical properties of backscattering, b(sub b), and absorption, a, over the wide range of in-water conditions observed in the Chesapeake Bay. Observed variability in the quantity f/Q (proportionality factor in the relationship between Rrs and the water inherent optical properties ratio b(sub b)/(a+b(sub b)) was consistent with RT model calculations for the specific measurement geometry and water bio-optical characteristics. Data and model results showed that f/Q values in these Case 2 coastal waters are not considerably different from those estimated in previous studies for Case 1 waters. Variation in surface backscattering significantly affected Rrs magnitude across the visible spectrum and was most strongly correlated (R(sup 2)=0.88) with observed variability in Rrs at 670 nm. Surface values of particulate backscattering were strongly correlated with non-algal particulate absorption, a(sub nap), in the blue wavelengths (R(sup 2)=0.83). These results, along with the measured values of backscattering fraction magnitude and non-algal particulate absorption spectral slope, suggest that suspended non-algal particles with high inorganic content are the major water constituents regulating b(sub b) variability in the mid-mesohaline Chesapeake Bay. Remote retrieval of surface b(sub b) and (a(sub nap), from Rrs(670) can be used in regionally-specific satellite algorithms to separate contribution by non-algal particles and dissolved organic matter to total light absorption in the blue, and monitor non-algal suspended particle concentration and distribution in these Case 2 waters.

Deep Learning in Intermediate Microeconomics: Using Scaffolding Assignments to Teach Theory and Promote Transfer

ERIC Educational Resources Information Center

Green, Gareth P.; Bean, John C.; Peterson, Dean J.

2013-01-01

Intermediate microeconomics is typically viewed as a theory and tools course that relies on algorithmic problems to help students learn and apply economic theory. However, the authors' assessment research suggests that algorithmic problems by themselves do not encourage students to think about where the theory comes from, why the theory is…

Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

PubMed

Khan, Faisal Nadeem; Zhong, Kangping; Zhou, Xian; Al-Arashi, Waled Hussein; Yu, Changyuan; Lu, Chao; Lau, Alan Pak Tao

2017-07-24

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

Cathodoluminescence, laser ablasion inductively coupled plasma mass spectrometry, electron probe microanalysis and electron paramagnetic resonance analyses of natural sphalerite

USGS Publications Warehouse

Karakus, M.; Hagni, R.D.; Koenig, A.; Ciftc, E.

2008-01-01

Natural sphalerite associated with copper, silver, lead-zinc, tin and tungsten deposits from various world-famous mineral deposits have been studied by cathodoluminescence (CL), laser ablasion inductively coupled plasma mass spectrometry (LA-ICP-MS), electron probe microanalysis (EPMA) and electron paramagnetic resonance (EPR) to determine the relationship between trace element type and content and the CL properties of sphalerite. In general, sphalerite produces a spectrum of CL colour under electron bombardment that includes deep blue, turquoise, lime green, yellow-orange, orange-red and dull dark red depending on the type and concentration of trace quantities of activator ions. Sphalerite from most deposits shows a bright yellow-orange CL colour with ??max centred at 585 nm due to Mn2+ ion, and the intensity of CL is strongly dependent primarily on Fe2+ concentration. The blue emission band with ??max centred at 470-490 nm correlates with Ga and Ag at the Tsumeb, Horn Silver, Balmat and Kankoy mines. Colloform sphalerite from older well-known European lead-zinc deposits and late Cretaceous Kuroko-type VMS deposits of Turkey shows intense yellowish CL colour and their CL spectra are characterised by extremely broad emission bands ranging from 450 to 750 nm. These samples are characterised by low Mn (<10 ppm) and Ag (<1 ppm), and they are enriched in Tl (1-30 ppm) and Pb (80-1500 ppm). Strong green CL is produced by sphalerite from the Balmat-Edwards district. Amber, lime-green and red-orange sphalerite produced weak orange-red CL at room temperatures, with several emission bands centred at 490, 580, 630, 680, 745, with ??max at 630 nm being the strongest. These emission bands are well correlated with trace quantities of Sn, In, Cu and Mn activators. Sphalerite from the famous Ogdensburg and Franklin mines exhibited brilliant deep blue and orange CL colours and the blue CL may be related to Se. Cathodoluminescence behaviour of sphalerite serves to characterise ore types and help detect technologically important trace elements.

Chemical characterization of soil organic matter in a Chesapeake Bay salt marsh: analyzing microbial and vegetation inputs to SOM

NASA Astrophysics Data System (ADS)

Bye, E.; Schreiner, K. M.; Abdulla, H. A.; Minor, E. C.; Guntenspergen, G. R.

2017-12-01

Coastal wetlands play a critical role in the global carbon cycle. These ecosystems sequester and store carbon, known as "blue carbon," at a rate two or three orders of magnitude larger than other terrestrial ecosystems, such as temperate, tropical, and boreal forests. Anthropogenic changes to the climate are threatening blue carbon stores in coastal wetland ecosystems. To understand and predict how these important carbon stores will be affected by anthropogenic climate changes, it is necessary to understand the formation and preservation of soil organic matter (SOM) in these ecosystems. This study will present organic geochemical data from two sediment cores collected from the Smithsonian Environmental Research Center site on a salt marsh in Maryland along the Chesapeake Bay. One core is from a location that recently transitioned from a C4 to C3 plant regime, currently dominated by the sedge Shoenplectis americanus. The second core is from a C4 plant (Spartina patens) dominated location in the marsh. The organic geochemistry of these 100 cm deep sediment cores was studied through multiple bulk analyses including stable isotopes, elemental ratios, Fourier-transform infrared spectroscopy (FTIR), solid-state magic-angle-spinning Nuclear Magnetic Resonance (NMR), and compound specific lignin-phenol analysis. By using comprehensive chemical characterization techniques, this study aims to discern between vegetation- and microbially-derived inputs to SOM in blue carbon ecosystems. The results show a general increase in the aromatic content with a concomitant decrease of carbohydrates with depth in both cores. However, substantial differences between the two cores, indicates differing inputs and/or stabilization mechanisms within SOM formed from different vegetation regimes. Further compound specific work will help to elucidate the specific source of compounds within each compound class, in surface and deep SOM, and additionally can help provide evidence for different models of SOM formation and stabilization. Taken together, these results will shed new light on our understanding of how vegetation and microbially-derived compounds are integrated into SOM in blue carbon stores, including differences and commonalities among different vegetation regimes.

A novel handheld device for use in remote patient monitoring of heart failure patients--design and preliminary validation on healthy subjects.

PubMed

Pollonini, Luca; Rajan, Nithin O; Xu, Shuai; Madala, Sridhar; Dacso, Clifford C

2012-04-01

Remote patient monitoring (RPM) holds great promise for reducing the burden of congestive heart failure (CHF). Improved sensor technology and effective predictive algorithms can anticipate sudden decompensation events. Enhanced telemonitoring systems would promote patient independence and facilitate communication between patients and their physicians. We report the development of a novel hand-held device, called Blue Box, capable of collecting and wirelessly transmitting key cardiac parameters derived from three integrated biosensors: 2 lead electrocardiogram (ECG), photoplethysmography and bioelectrical impedance (bioimpedance). Blue Box measurements include time intervals between consecutive ECG R-waves (RR interval), time duration of the ECG complex formed by the Q, R and S waves (QRS duration), bioimpedance, heart rate and systolic time intervals. In this study, we recruited 24 healthy subjects to collect several parameters measured by Blue Box and assess their value in correlating with cardiac output measured with Echo-Doppler. Linear correlation between the heart rate measured with Blue Box and cardiac output from Echo-Doppler had a group average correlation coefficient of 0.80. We found that systolic time intervals did not improve the model significantly. However, STIs did inversely correlate with increasing workloads.

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

PubMed

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

2017-08-01

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

Demosaicking algorithm for the Kodak-RGBW color filter array

NASA Astrophysics Data System (ADS)

Rafinazari, M.; Dubois, E.

2015-01-01

Digital cameras capture images through different Color Filter Arrays and then reconstruct the full color image. Each CFA pixel only captures one primary color component; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the two unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. The least-Squares Luma-Chroma demultiplexing method is a state of the art demosaicking method for the Bayer CFA. In this paper we develop a new demosaicking algorithm using the Kodak-RGBW CFA. This particular CFA reduces noise and improves the quality of the reconstructed images by adding white pixels. We have applied non-adaptive and adaptive demosaicking method using the Kodak-RGBW CFA on the standard Kodak image dataset and the results have been compared with previous work.

Limnology of Blue Mesa, Morrow Point, and Crystal Reservoirs, Curecanti National Recreation area, during 1999, and a 25-year retrospective of nutrient conditions in Blue Mesa Reservoir, Colorado

USGS Publications Warehouse

Bauch, Nancy J.; Malick, Matt

2003-01-01

The U.S. Geological Survey and the National Park Service conducted a water-quality investigation in Curecanti National Recreation Area in Colorado from April through December 1999. Current (as of 1999) limnological characteristics, including nutrients, phytoplankton, chlorophyll-a, trophic status, and the water quality of stream inflows and reservoir outflows, of Blue Mesa, Morrow Point, and Crystal Reservoirs were assessed, and a 25-year retrospective of nutrient conditions in Blue Mesa Reservoir was conducted. The three reservoirs are in a series on the Gunnison River, with an upstream to downstream order of Blue Mesa, Morrow Point, and Crystal Reservoirs. Physical properties and water-quality samples were collected four times during 1999 from reservoir, inflow, and outflow sites in and around the recreation area. Samples were analyzed for nutrients, phytoplankton and chlorophyll-a (reservoir sites only), and suspended sediment (stream inflows only). Nutrient concentrations in the reservoirs were low; median total nitrogen and phosphorus concentrations were less than 0.4 and 0.06 milligram per liter, respectively. During water-column stratification, samples collected at depth had higher nutrient concentrations than photic-zone samples. Phytoplankton community and density were affected by water temperature, nutrients, and water residence time. Diatoms were the dominant phytoplankton throughout the year in Morrow Point and Crystal Reservoirs and during spring and early winter in Blue Mesa Reservoir. Blue-green algae were dominant in Blue Mesa Reservoir during summer and fall. Phytoplankton density was highest in Blue Mesa Reservoir and lowest in Crystal Reservoir. Longer residence times and warmer temperatures in Blue Mesa Reservoir were favorable for phytoplankton growth and development. Shorter residence times and cooler temperatures in the downstream reservoirs probably limited phytoplankton growth and development. Median chlorophyll-a concentrations were higher in Blue Mesa Reservoir than Morrow Point or Crystal Reservoirs. Blue Mesa Reservoir was mesotrophic in upstream areas and oligotrophic downstream. Both Morrow Point and Crystal Reservoirs were oligotrophic. Trophic-state index values were determined for total phosphorus, chlorophyll-a, and Secchi depth for each reservoir by the Carlson method; all values ranged between 29 and 55. Only the upstream areas in Blue Mesa Reservoir had total phosphorus and chlorophyll-a indices above 50, reflecting mesotrophic conditions. Nutrient inflows to Blue Mesa Reservoir, which were derived primarily from the Gunnison River, varied on a seasonal basis, whereas nutrient inflows to Morrow Point and Crystal Reservoirs, which were derived primarily from deep water releases from the respective upstream reservoir, were steady throughout the sampling period. Total phosphorus concentrations were elevated in many stream inflows. A comparison of current (as of 1999) and historical nutrient, chlorophyll-a, and trophic conditions in Blue Mesa Reservoir and its tributaries indicated that the trophic status in Blue Mesa Reservoir has not changed over the last 25 years, and more recent nutrient enrichment has not occurred.

Deep HST Photometry of NGC 6388: Age and Horizontal Branch Luminosity

NASA Technical Reports Server (NTRS)

Stetson, Peter B.; Catelan, M.; Pritzl, Barton J.; Smith, Horace A.; Kinemuchi, Karen; Layden, Andrew C.; Sweigart, Allen V.; Rich, R. M.

2006-01-01

We present the first deep color-magnitude diagram (CMD) of the Galactic globular cluster NGC 6388, obtained with the Hubble Space Telescope, that is able to reach the main-sequence turnoff point of the cluster. From a detailed comparison between the cluster CMD and that of 47 Tucanae (NGC 104), we find that the bulk of the stars in these two clusters have nearly the same age and chemical composition. On the other hand, our results indicate that the blue horizontal branch and RR Lyrae components in NGC 6388 are intrinsically over-luminous, which must be due to one or more, still undetermined, non-canonical second parameter(s) affecting a relatively minor fraction of the stars in NGC 6388.

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

PubMed

Lu, Huijuan; Wei, Shasha; Zhou, Zili; Miao, Yanzi; Lu, Yi

2015-01-01

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Quantum neuromorphic hardware for quantum artificial intelligence

NASA Astrophysics Data System (ADS)

Prati, Enrico

2017-08-01

The development of machine learning methods based on deep learning boosted the field of artificial intelligence towards unprecedented achievements and application in several fields. Such prominent results were made in parallel with the first successful demonstrations of fault tolerant hardware for quantum information processing. To which extent deep learning can take advantage of the existence of a hardware based on qubits behaving as a universal quantum computer is an open question under investigation. Here I review the convergence between the two fields towards implementation of advanced quantum algorithms, including quantum deep learning.

Fiber Orientation Estimation Guided by a Deep Network.

PubMed

Ye, Chuyang; Prince, Jerry L

2017-09-01

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted ℓ 1 -norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.

In vivo lymph node mapping by Cadmium Tellurium quantum dots in rats.

PubMed

Si, Chengshuai; Zhang, Yunpeng; Lv, Xianbo; Yang, Wuli; Ran, Zhipeng; Sun, Peng

2014-12-01

Intraoperative lymph node mapping (LNM) is highly significant for many surgeries in patients with cancer. Many types of tracers are currently used, but the ideal method has not yet been identified. We aimed to identify a stable lymphatic drainage pathway in an animal model and compared the effects of quantum dots (QD), a new fluorescent tracer, with those of methylene blue in intraoperative LNM. Indian ink (0.2 mL) was subcutaneously injected into the plantar metatarsal regions of six Sprague-Dawley rats. After 2 wk of incubation and subsequent dissection, the potentially stained LNs were examined pathologically to identify the lymphatic drainage pathway. After applying anesthesia, 0.1 mL methylene blue (2%) and QD (1 mg/mL) were injected into the plantar metatarsal regions of six rats for intraoperative LNM. The QD group was observed with a near-infrared imaging system, and the methylene blue group was directly observed. Drainages were recorded at 5, 10, 30, 60, and 120 min and at 1 d. Two three-level drainage pathways, that is, a peripheral drainage (popliteal LNs, inguinal LNs, and axillary LNs) and a central drainage (popliteal lymph node [LN], iliac LN, and renal LN) pathways were identified. Both methylene blue and QD stained the sentinel lymph node (SLNs) quickly, but methylene blue was difficult to identify in the deep tissues and the LNs beyond the SLN. Furthermore, the blue-stained LNs remain dyed for only 2 h. In contrast, the QDs exhibited high target-to-background ratios in both the SLNs and the following LNs. Additionally, the fluorescence lasted from 5 min-1 d after injection. An ideal lymphatic drainage model was found. QDs are excellent tracers for intraoperative LNM compared with methylene blue. Near infrared fluorescent imaging is a promising LNM method for clinical practice. Copyright © 2014 Elsevier Inc. All rights reserved.

Uncovering the overlapping community structure of complex networks by maximal cliques

NASA Astrophysics Data System (ADS)

Li, Junqiu; Wang, Xingyuan; Cui, Yaozu

2014-12-01

In this paper, a unique algorithm is proposed to detect overlapping communities in the un-weighted and weighted networks with considerable accuracy. The maximal cliques, overlapping vertex, bridge vertex and isolated vertex are introduced. First, all the maximal cliques are extracted by the algorithm based on the deep and bread searching. Then two maximal cliques can be merged into a larger sub-graph by some given rules. In addition, the proposed algorithm successfully finds overlapping vertices and bridge vertices between communities. Experimental results using some real-world networks data show that the performance of the proposed algorithm is satisfactory.

Defining probabilities of bowel resection in deep endometriosis of the rectum: Prediction with preoperative magnetic resonance imaging.

PubMed

Perandini, Alessio; Perandini, Simone; Montemezzi, Stefania; Bonin, Cecilia; Bellini, Gaia; Bergamini, Valentino

2018-02-01

Deep endometriosis of the rectum is a highly challenging disease, and a surgical approach is often needed to restore anatomy and function. Two kinds of surgeries may be performed: radical with segmental bowel resection or conservative without resection. Most patients undergo magnetic resonance imaging (MRI) before surgery, but there is currently no method to predict if conservative surgery is feasible or whether bowel resection is required. The aim of this study was to create an algorithm that could predict bowel resection using MRI images, that was easy to apply and could be useful in a clinical setting, in order to adequately discuss informed consent with the patient and plan the an appropriate and efficient surgical session. We collected medical records from 2010 to 2016 and reviewed the MRI results of 52 patients to detect any parameters that could predict bowel resection. Parameters that were reproducible and with a significant correlation to radical surgery were investigated by statistical regression and combined in an algorithm to give the best prediction of resection. The calculation of two parameters in MRI, impact angle and lesion size, and their use in a mathematical algorithm permit us to predict bowel resection with a positive predictive value of 87% and a negative predictive value of 83%. MRI could be of value in predicting the need for bowel resection in deep endometriosis of the rectum. Further research is required to assess the possibility of a wider application of this algorithm outside our single-center study. © 2017 Japan Society of Obstetrics and Gynecology.

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

PubMed

Fang, Shih-Hau; Tsao, Yu; Hsiao, Min-Jing; Chen, Ji-Ying; Lai, Ying-Hui; Lin, Feng-Chuan; Wang, Chi-Te

2018-03-19

Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms. This study retrospectively collected 60 normal voice samples and 402 pathological voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary teaching hospital. We extracted Mel frequency cepstral coefficients from 3-second samples of a sustained vowel. The performances of three machine learning algorithms, namely, deep neural network (DNN), support vector machine, and Gaussian mixture model, were evaluated based on a fivefold cross-validation. Collective cases from the voice disorder database of MEEI (Massachusetts Eye and Ear Infirmary) were used to verify the performance of the classification mechanisms. The experimental results demonstrated that DNN outperforms Gaussian mixture model and support vector machine. Its accuracy in detecting voice pathologies reached 94.26% and 90.52% in male and female subjects, based on three representative Mel frequency cepstral coefficient features. When applied to the MEEI database for validation, the DNN also achieved a higher accuracy (99.32%) than the other two classification algorithms. By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples. Based on this pilot study, future research may proceed to explore more application of DNN from laboratory and clinical perspectives. Copyright © 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Distributed Wind Research | Wind | NREL

Science.gov Websites

evaluation, and improve wind turbine and wind power plant performance. A photo of a snowy road leading to a single wind turbine surrounded by snow-covered pine trees against blue sky. Capabilities NREL's power plant and small wind turbine development. Algorithms and programs exist for simulating, designing

Development of a Simple Multispectral Algorithm Using a Hyperspectral Line-Scan Imaging System for Detection of Fecal Contamination on Apples

USDA-ARS?s Scientific Manuscript database

Foodborne diseases are of serious concern for public health. It is necessary to develop fast and reliable non-destructive detection methods to improve food product monitoring for the food industry. This research was conducted to investigate hyperspectral fluorescence imaging using violet/blue LED ex...

Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography.

PubMed

Venhuizen, Freerk G; van Ginneken, Bram; Liefers, Bart; van Asten, Freekje; Schreur, Vivian; Fauser, Sascha; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I

2018-04-01

We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.

Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography

PubMed Central

Venhuizen, Freerk G.; van Ginneken, Bram; Liefers, Bart; van Asten, Freekje; Schreur, Vivian; Fauser, Sascha; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I.

2018-01-01

We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies. PMID:29675301

Ad hoc cost analysis of the new gastrointestinal bleeding algorithm in patients with ventricular assist device.

PubMed

Hirose, Hitoshi; Sarosiek, Konrad; Cavarocchi, Nicholas C

2014-01-01

Gastrointestinal bleed (GIB) is a known complication in patients receiving nonpulsatile ventricular assist devices (VAD). Previously, we reported a new algorithm for the workup of GIB in VAD patients using deep bowel enteroscopy. In this new algorithm, patients underwent fewer procedures, received less transfusions, and took less time to make the diagnosis than the traditional GIB algorithm group. Concurrently, we reviewed the cost-effectiveness of this new algorithm compared with the traditional workup. The procedure charges for the diagnosis and treatment of each episode of GIB was ~ $2,902 in the new algorithm group versus ~ $9,013 in the traditional algorithm group (p < 0.0001). Following the new algorithm in VAD patients with GIB resulted in fewer transfusions and diagnostic tests while attaining a substantial cost savings per episode of bleeding.

Automatic Classification of volcano-seismic events based on Deep Neural Networks.

NASA Astrophysics Data System (ADS)

Titos Luzón, M.; Bueno Rodriguez, A.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Seismic monitoring of active volcanoes is a popular remote sensing technique to detect seismic activity, often associated to energy exchanges between the volcano and the environment. As a result, seismographs register a wide range of volcano-seismic signals that reflect the nature and underlying physics of volcanic processes. Machine learning and signal processing techniques provide an appropriate framework to analyze such data. In this research, we propose a new classification framework for seismic events based on deep neural networks. Deep neural networks are composed by multiple processing layers, and can discover intrinsic patterns from the data itself. Internal parameters can be initialized using a greedy unsupervised pre-training stage, leading to an efficient training of fully connected architectures. We aim to determine the robustness of these architectures as classifiers of seven different types of seismic events recorded at "Volcán de Fuego" (Colima, Mexico). Two deep neural networks with different pre-training strategies are studied: stacked denoising autoencoder and deep belief networks. Results are compared to existing machine learning algorithms (SVM, Random Forest, Multilayer Perceptron). We used 5 LPC coefficients over three non-overlapping segments as training features in order to characterize temporal evolution, avoid redundancy and encode the signal, regardless of its duration. Experimental results show that deep architectures can classify seismic events with higher accuracy than classical algorithms, attaining up to 92% recognition accuracy. Pre-training initialization helps these models to detect events that occur simultaneously in time (such explosions and rockfalls), increase robustness against noisy inputs, and provide better generalization. These results demonstrate deep neural networks are robust classifiers, and can be deployed in real-environments to monitor the seismicity of restless volcanoes.

Deep Learning and Its Applications in Biomedicine.

PubMed

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Scaling deep learning on GPU and knights landing clusters

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

You, Yang; Buluc, Aydin; Demmel, James

Training neural networks has become a big bottleneck. For example, training ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. We use both self-host Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From the algorithm aspect, we focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. We redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD,more » and Hogwild EASGD are faster than existing counter-part methods (Async SGD, Async MSGD, and Hogwild SGD) in all comparisons. Sync EASGD achieves 5.3X speedup over original EASGD on the same platform. We achieve 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.« less

Properties of the numerical algorithms for problems of quantum information technologies: Benefits of deep analysis

NASA Astrophysics Data System (ADS)

Chernyavskiy, Andrey; Khamitov, Kamil; Teplov, Alexey; Voevodin, Vadim; Voevodin, Vladimir

2016-10-01

In recent years, quantum information technologies (QIT) showed great development, although, the way of the implementation of QIT faces the serious difficulties, some of which are challenging computational tasks. This work is devoted to the deep and broad analysis of the parallel algorithmic properties of such tasks. As an example we take one- and two-qubit transformations of a many-qubit quantum state, which are the most critical kernels of many important QIT applications. The analysis of the algorithms uses the methodology of the AlgoWiki project (algowiki-project.org) and consists of two parts: theoretical and experimental. Theoretical part includes features like sequential and parallel complexity, macro structure, and visual information graph. Experimental part was made by using the petascale Lomonosov supercomputer (Moscow State University, Russia) and includes the analysis of locality and memory access, scalability and the set of more specific dynamic characteristics of realization. This approach allowed us to obtain bottlenecks and generate ideas of efficiency improvement.

High-speed phosphor-LED wireless communication system utilizing no blue filter

NASA Astrophysics Data System (ADS)

Yeh, C. H.; Chow, C. W.; Chen, H. Y.; Chen, J.; Liu, Y. L.; Wu, Y. F.

2014-09-01

In this paper, we propose and investigate an adaptively 84.44 to 190 Mb/s phosphor-LED visible light communication (VLC) system at a practical transmission distance. Here, we utilize the orthogonal-frequency-division-multiplexing quadrature-amplitude-modulation (OFDM-QAM) modulation with power/bit-loading algorithm in proposed VLC system. In the experiment, the optimal analogy pre-equalization design is also performed at LED-Tx side and no blue filter is used at the Rx side for extending the modulation bandwidth from 1 MHz to 30 MHz. In addition, the corresponding free space transmission lengths are between 75 cm and 2 m under various data rates of proposed VLC. And the measured bit error rates (BERs) of < 3.8×10-3 [forward error correction (FEC) limit] at different transmission lengths and measured data rates can be also obtained. Finally, we believe that our proposed scheme could be another alternative VLC implementation in practical distance, supporting < 100 Mb/s, using commercially available LED and PD (without optical blue filtering) and compact size.

Single-shot color fringe projection for three-dimensional shape measurement of objects with discontinuities.

PubMed

Dai, Meiling; Yang, Fujun; He, Xiaoyuan

2012-04-20

A simple but effective fringe projection profilometry is proposed to measure 3D shape by using one snapshot color sinusoidal fringe pattern. One color fringe pattern encoded with a sinusoidal fringe (as red component) and one uniform intensity pattern (as blue component) is projected by a digital video projector, and the deformed fringe pattern is recorded by a color CCD camera. The captured color fringe pattern is separated into its RGB components and division operation is applied to red and blue channels to reduce the variable reflection intensity. Shape information of the tested object is decoded by applying an arcsine algorithm on the normalized fringe pattern with subpixel resolution. In the case of fringe discontinuities caused by height steps, or spatially isolated surfaces, the separated blue component is binarized and used for correcting the phase demodulation. A simple and robust method is also introduced to compensate for nonlinear intensity response of the digital video projector. The experimental results demonstrate the validity of the proposed method.

Is It Possible to Distinguish Between Dust and Salt Aerosol Over Waters with Unknown Chlorophyll Concentrations Using Spectral Remote Sensing?

NASA Technical Reports Server (NTRS)

Levy, R. C.; Kaufman, Y. J.

1999-01-01

Atmospheric aerosol has uncertain impacts on the global climate system, as well as on atmospheric and bio-geo-chemical processes of regional and local scales. EOS-MODIS is one example of a satellite sensor designed to improve understanding of the aerosols' type, size and distribution at all temporal and spatial scales. Ocean scientists also plan to use data from EOS-MODIS to assess the temporal and spatial coverage of in-water chlorophyll. MODIS is the first sensor planned to observe the combined ocean-atmosphere system with a wide spectral range (from 410 to 2200 nm). Dust aerosol and salt aerosol have similar spectral signals for wavelengths longer than 550 nm, but because dust selectively absorbs blue light, they have divergent signals in the blue wavelength regions (412 to 490 nm). Chlorophyll also selectively absorbs blue radiation, so that varying chlorophyll concentrations produces a highly varying signal in the blue regions, but less variability in the green, and almost no signal in the red to mid-infrared regions. Thus, theoretically, it may be difficult to differentiate dust and salt in the presence of unknown chlorophyll in the ocean. This study attempts to address the cases in which aerosol and chlorophyll signals can and cannot be separated. For the aerosol spectra, we use the aerosol lookup table from the operational MODIS aerosol-over-ocean algorithm, and for chlorophyll spectra, we use the SeaBAM data set (created for SeaWiFS). We compare the signals using Principal Component Analysis and attempt to retrieve both chlorophyll and aerosol properties using a variant of the operational MODIS aerosol-over-ocean algorithm. Results show that for small optical depths, less than 0.5, it is not possible to differentiate between dust and salt and to determine the chlorophyll concentration at the same time. For larger aerosol optical depths, the chlorophyll signals are comparatively insignificant, and we can hope to distinguish between dust and salt.

Ammonia Clouds on Jupiter

NASA Technical Reports Server (NTRS)

2007-01-01

[figure removed for brevity, see original site] Click on the image for movie of Ammonia Ice Clouds on Jupiter

In this movie, put together from false-color images taken by the New Horizons Ralph instrument as the spacecraft flew past Jupiter in early 2007, show ammonia clouds (appearing as bright blue areas) as they form and disperse over five successive Jupiter 'days.' Scientists noted how the larger cloud travels along with a small, local deep hole.

Gulf Coast Deep Water Port Facilities study. Appendix B. North Central Gulf Hydrobiological Zones.

DTIC Science & Technology

1973-04-01

bottom and surface salinities , but their effect is more noticeable at the surface. Because of variation in these factors along the Gulf Coast... effects of discharge on salinity have been considered above. Numerous streams empty into the Gulf of Mexico along its north central portion but the...1967) investigated various aspects of osmoregulation in blue crabs in Mississippi Sound and adjacent waters and observed that salinity and temperature

Water-Resources Investigations in Tennessee: Programs and Activities of the U.S. Geological Survey, 1992-94

DTIC Science & Technology

1995-01-01

rainfall runoff model, DR&& to Bear Branch watershed, Murfreesboro, Tennessee .......... 37 Seepage and spring inventory reconnaissance and base-flow... bearing rocks in the Valley and Ridge, Blue Ridge, and Piedmont physiographic provinces, and covers parts of eight states from New Jersey to Alabama...100 feet in diameter and about 250 feet deep. It penetrates three water- bearing units of carbonate origin (the shallow aquifer, the Manchester aquifer

Retrieve Optically Thick Ice Cloud Microphysical Properties by Using Airborne Dual-Wavelength Radar Measurements

NASA Technical Reports Server (NTRS)

Wang, Zhien; Heymsfield, Gerald M.; Li, Lihua; Heymsfield, Andrew J.

2005-01-01

An algorithm to retrieve optically thick ice cloud microphysical property profiles is developed by using the GSFC 9.6 GHz ER-2 Doppler Radar (EDOP) and the 94 GHz Cloud Radar System (CRS) measurements aboard the high-altitude ER-2 aircraft. In situ size distribution and total water content data from the CRYSTAL-FACE field campaign are used for the algorithm development. To reduce uncertainty in calculated radar reflectivity factors (Ze) at these wavelengths, coincident radar measurements and size distribution data are used to guide the selection of mass-length relationships and to deal with the density and non-spherical effects of ice crystals on the Ze calculations. The algorithm is able to retrieve microphysical property profiles of optically thick ice clouds, such as, deep convective and anvil clouds, which are very challenging for single frequency radar and lidar. Examples of retrieved microphysical properties for a deep convective clouds are presented, which show that EDOP and CRS measurements provide rich information to study cloud structure and evolution. Good agreement between IWPs derived from an independent submillimeter-wave radiometer, CoSSIR, and dual-wavelength radar measurements indicates accuracy of the IWC retrieved from the two-frequency radar algorithm.

Improved transition path sampling methods for simulation of rare events

NASA Astrophysics Data System (ADS)

Chopra, Manan; Malshe, Rohit; Reddy, Allam S.; de Pablo, J. J.

2008-04-01

The free energy surfaces of a wide variety of systems encountered in physics, chemistry, and biology are characterized by the existence of deep minima separated by numerous barriers. One of the central aims of recent research in computational chemistry and physics has been to determine how transitions occur between deep local minima on rugged free energy landscapes, and transition path sampling (TPS) Monte-Carlo methods have emerged as an effective means for numerical investigation of such transitions. Many of the shortcomings of TPS-like approaches generally stem from their high computational demands. Two new algorithms are presented in this work that improve the efficiency of TPS simulations. The first algorithm uses biased shooting moves to render the sampling of reactive trajectories more efficient. The second algorithm is shown to substantially improve the accuracy of the transition state ensemble by introducing a subset of local transition path simulations in the transition state. The system considered in this work consists of a two-dimensional rough energy surface that is representative of numerous systems encountered in applications. When taken together, these algorithms provide gains in efficiency of over two orders of magnitude when compared to traditional TPS simulations.

Automated Generation of Geo-Referenced Mosaics From Video Data Collected by Deep-Submergence Vehicles: Preliminary Results

NASA Astrophysics Data System (ADS)

Rhzanov, Y.; Beaulieu, S.; Soule, S. A.; Shank, T.; Fornari, D.; Mayer, L. A.

2005-12-01

Many advances in understanding geologic, tectonic, biologic, and sedimentologic processes in the deep ocean are facilitated by direct observation of the seafloor. However, making such observations is both difficult and expensive. Optical systems (e.g., video, still camera, or direct observation) will always be constrained by the severe attenuation of light in the deep ocean, limiting the field of view to distances that are typically less than 10 meters. Acoustic systems can 'see' much larger areas, but at the cost of spatial resolution. Ultimately, scientists want to study and observe deep-sea processes in the same way we do land-based phenomena so that the spatial distribution and juxtaposition of processes and features can be resolved. We have begun development of algorithms that will, in near real-time, generate mosaics from video collected by deep-submergence vehicles. Mosaics consist of >>10 video frames and can cover 100's of square-meters. This work builds on a publicly available still and video mosaicking software package developed by Rzhanov and Mayer. Here we present the results of initial tests of data collection methodologies (e.g., transects across the seafloor and panoramas across features of interest), algorithm application, and GIS integration conducted during a recent cruise to the Eastern Galapagos Spreading Center (0 deg N, 86 deg W). We have developed a GIS database for the region that will act as a means to access and display mosaics within a geospatially-referenced framework. We have constructed numerous mosaics using both video and still imagery and assessed the quality of the mosaics (including registration errors) under different lighting conditions and with different navigation procedures. We have begun to develop algorithms for efficient and timely mosaicking of collected video as well as integration with navigation data for georeferencing the mosaics. Initial results indicate that operators must be properly versed in the control of the video systems as well as maintaining vehicle attitude and altitude in order to achieve the best results possible.

A comparative study of two prediction models for brain tumor progression

NASA Astrophysics Data System (ADS)

Zhou, Deqi; Tran, Loc; Wang, Jihong; Li, Jiang

2015-03-01

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named "Dropout" can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region.

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

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

A survey on deep learning in medical image analysis.

PubMed

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.

Research on Daily Objects Detection Based on Deep Neural Network

NASA Astrophysics Data System (ADS)

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.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

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

FANSe2: a robust and cost-efficient alignment tool for quantitative next-generation sequencing applications.

PubMed

Xiao, Chuan-Le; Mai, Zhi-Biao; Lian, Xin-Lei; Zhong, Jia-Yong; Jin, Jing-Jie; He, Qing-Yu; Zhang, Gong

2014-01-01

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

Wrapped in an anti-static blanket for protection, Deep Space 1 is moved out of the Defense Satellite Communications Systems Processing Facility (DPF) at Cape Canaveral Air Station (CCAS) for its trip to Launch Pad 17A. The spacecraft will be launched aboard a Boeing Delta 7326 rocket on Oct. 25. Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

In the Defense Satellite Communications Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), the lower part of Deep Space 1 is enclosed with the conical section leaves of the payload transportation container prior to its move to Launch Pad 17A. The spacecraft is targeted for launch Oct. 25 aboard a Boeing Delta 7326 rocket. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

KSC-98pc1316

NASA Image and Video Library

1998-10-10

KENNEDY SPACE CENTER, FLA. -- In the Defense Satellite Communications Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), after covering the lower portion of Deep Space 1, workers adjust the anti-static blanket covering the upper portion. The blanket will protect the spacecraft during transport to the launch pad. Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS

KSC-98pc1317

NASA Image and Video Library

1998-10-10

KENNEDY SPACE CENTER, FLA. -- In the Defense Satellite Communications Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), workers place an anti-static blanket over the lower portion of Deep Space 1, to protect the spacecraft during transport to the launch pad. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS

A Nth-order linear algorithm for extracting diffuse correlation spectroscopy blood flow indices in heterogeneous tissues.

PubMed

Shang, Yu; Yu, Guoqiang

2014-09-29

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a N th-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD B ). The purpose of this study is to extend the capability of the N th-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD B in the brain layer with a step decrement of 10% while maintaining αD B values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order ( N  ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The N th-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.

Sci-Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques - 11: Quantification of chest wall motion during deep inspiration breast hold treatments using cine EPID images and a physics based algorithm

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

Alpuche Aviles, Jorge E.; VanBeek, Timothy

Purpose: This work presents an algorithm used to quantify intra-fraction motion for patients treated using deep inspiration breath hold (DIBH). The algorithm quantifies the position of the chest wall in breast tangent fields using electronic portal images. Methods: The algorithm assumes that image profiles, taken along a direction perpendicular to the medial border of the field, follow a monotonically and smooth decreasing function. This assumption is invalid in the presence of lung and can be used to calculate chest wall position. The algorithm was validated by determining the position of the chest wall for varying field edge positions in portalmore » images of a thoracic phantom. The algorithm was used to quantify intra-fraction motion in cine images for 7 patients treated with DIBH. Results: Phantom results show that changes in the distance between chest wall and field edge were accurate within 0.1 mm on average. For a fixed field edge, the algorithm calculates the position of the chest wall with a 0.2 mm standard deviation. Intra-fraction motion for DIBH patients was within 1 mm 91.4% of the time and within 1.5 mm 97.9% of the time. The maximum intra-fraction motion was 3.0 mm. Conclusions: A physics based algorithm was developed and can be used to quantify the position of chest wall irradiated in tangent portal images with an accuracy of 0.1 mm and precision of 0.6 mm. Intra-fraction motion for patients treated with DIBH at our clinic is less than 3 mm.« less

Deep learning for medical image segmentation - using the IBM TrueNorth neurosynaptic system

NASA Astrophysics Data System (ADS)

Moran, Steven; Gaonkar, Bilwaj; Whitehead, William; Wolk, Aidan; Macyszyn, Luke; Iyer, Subramanian S.

2018-03-01

Deep convolutional neural networks have found success in semantic image segmentation tasks in computer vision and medical imaging. These algorithms are executed on conventional von Neumann processor architectures or GPUs. This is suboptimal. Neuromorphic processors that replicate the structure of the brain are better-suited to train and execute deep learning models for image segmentation by relying on massively-parallel processing. However, given that they closely emulate the human brain, on-chip hardware and digital memory limitations also constrain them. Adapting deep learning models to execute image segmentation tasks on such chips, requires specialized training and validation. In this work, we demonstrate for the first-time, spinal image segmentation performed using a deep learning network implemented on neuromorphic hardware of the IBM TrueNorth Neurosynaptic System and validate the performance of our network by comparing it to human-generated segmentations of spinal vertebrae and disks. To achieve this on neuromorphic hardware, the training model constrains the coefficients of individual neurons to {-1,0,1} using the Energy Efficient Deep Neuromorphic (EEDN)1 networks training algorithm. Given the 1 million neurons and 256 million synapses, the scale and size of the neural network implemented by the IBM TrueNorth allows us to execute the requisite mapping between segmented images and non-uniform intensity MR images >20 times faster than on a GPU-accelerated network and using <0.1 W. This speed and efficiency implies that a trained neuromorphic chip can be deployed in intra-operative environments where real-time medical image segmentation is necessary.

Evaluation of Different MODIS AOD Retrieval Algorithms for PM (sub 2.5) Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Crosson, William; Burrows, Erica; Coffield, Shane; Crane, Breanna

2016-01-01

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP (Minority University Research and Education Project) Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns PM (sub 2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM (sub 2.5) varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target versus Deep Blue), Collections (5.1 versus 6) and spatial resolutions (10-kilometers versus 3-kilometers) for cities in the Western, Midwestern and Southeastern U.S. We developed and validated PM (sub 2.5) prediction models using remotely-sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM (sub 2.5) models, especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Our study re-establishes the connection between PM (sub 2.5) and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM (sub 2.5) data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM (sub 2.5).

Evaluation of MODIS columnar aerosol retrievals using AERONET in semi-arid Nevada and California, U.S.A., during the summer of 2012

NASA Astrophysics Data System (ADS)

Loría-Salazar, S. Marcela; Holmes, Heather A.; Patrick Arnott, W.; Barnard, James C.; Moosmüller, Hans

2016-11-01

Satellite characterization of local aerosol pollution is desirable because of the potential for broad spatial coverage, enabling transport studies of pollution from major sources, such as biomass burning events. However, retrieval of quantitative measures of air pollution such as Aerosol Optical Depth (AOD) from satellite measurements is challenging over land because the underlying surface albedo may be heterogeneous in space and time. Ground-based sunphotometer measurements of AOD are unaffected by surface albedo and are crucial in enabling evaluation, testing, and further development of satellite instruments and retrieval algorithms. Columnar aerosol optical properties from ground-based sunphotometers (Cimel CE-318) as part of AERONET and MODIS aerosol retrievals from Aqua and Terra satellites were compared over semi-arid California and Nevada during the summer season of 2012. Sunphotometer measurements were used as a 'ground truth' to evaluate the current state of satellite retrievals in this spatiotemporal domain. Satellite retrieved (MODIS Collection 6) AOD showed the presence of wildfires in northern California during August. During the study period, the dark-target (DT) retrieval algorithm appears to overestimate AERONET AOD by an average factor of 3.85 in the entire study domain. AOD from the deep-blue (DB) algorithm overestimates AERONET AOD by an average factor of 1.64. Low AOD correlation was also found between AERONET, DT, and DB retrievals. Smoke from fires strengthened the aerosol signal, but MODIS versus AERONET AOD correlation hardly increased during fire events (r2∼0.1-0.2 during non-fire periods and r2∼0-0.31 during fire periods). Furthermore, aerosol from fires increased the normalized mean bias (NMB) of MODIS retrievals of AOD (NMB∼23%-154% for non-fire periods and NMB∼77%-196% for fire periods). Ångström Extinction Exponent (AEE) from DB for both Terra and Aqua did not correlate with AERONET observations. High surface reflectance and incorrect aerosol physical parametrizations may still be affecting the DT and DB MODIS AOD retrievals in the semi-arid western U.S.

Evaluation of Different MODIS AOD Retrieval Algorithms for PM2.5 Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

NASA Astrophysics Data System (ADS)

Al-Hamdan, M. Z.; Crosson, W. L.; Burrows, E. C.; Coffield, S.; Crane, B.

2016-12-01

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns (PM2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM2.5 varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), Collections (5.1 vs. 6) and spatial resolutions (10-km vs. 3-km) for cities in the Western, Midwestern and Southeastern United States. We developed and validated PM2.5 prediction models using remotely sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM2.5 models, and especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Finally, our study re-establishes the connection between PM2.5 and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM2.5 data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM2.5.

Rapidly rotating second-generation progenitors for the 'blue hook' stars of ω Centauri.

PubMed

Tailo, Marco; D'Antona, Francesca; Vesperini, Enrico; Di Criscienzo, Marcella; Ventura, Paolo; Milone, Antonino P; Bellini, Andrea; Dotter, Aaron; Decressin, Thibaut; D'Ercole, Annibale; Caloi, Vittoria; Capuzzo-Dolcetta, Roberto

2015-07-16

Horizontal branch stars belong to an advanced stage in the evolution of the oldest stellar galactic population, occurring either as field halo stars or grouped in globular clusters. The discovery of multiple populations in clusters that were previously believed to have single populations gave rise to the currently accepted theory that the hottest horizontal branch members (the 'blue hook' stars, which had late helium-core flash ignition, followed by deep mixing) are the progeny of a helium-rich 'second generation' of stars. It is not known why such a supposedly rare event (a late flash followed by mixing) is so common that the blue hook of ω Centauri contains approximately 30 per cent of the horizontal branch stars in the cluster, or why the blue hook luminosity range in this massive cluster cannot be reproduced by models. Here we report that the presence of helium core masses up to about 0.04 solar masses larger than the core mass resulting from evolution is required to solve the luminosity range problem. We model this by taking into account the dispersion in rotation rates achieved by the progenitors, whose pre-main-sequence accretion disk suffered an early disruption in the dense environment of the cluster's central regions, where second-generation stars form. Rotation may also account for frequent late-flash-mixing events in massive globular clusters.

Synthesis and fluorescence emission properties of 1,3,6,8-tetraarylpyrenes

NASA Astrophysics Data System (ADS)

Hu, Jian-Yong; Feng, Xing; Tomiyasu, Hirotsugu; Seto, Nobuyuki; Rayhan, Ummey; Elsegood, Mark R. J.; Redshaw, Carl; Yamato, Takehiko

2013-09-01

Three types of stable pyrene-based highly fluorescence (blue) compounds, 1-, 1,6-bis, 1,8-bis and 1,3,6,8-tetrakis(7-tert-butylpyrenyl)pyrenes and 1,3,6,8-tetrakis[9,9-bis(3-methylbutyl)-9H-fluoren-2-yl]pyrene, were successfully synthesized via a Pd/Cu-catalysed Suzuki cross-coupling reaction of the corresponding bromopyrenes with 7-tert-butyl-1-pyrenylboronic ester or 2-[9,9-bis(3-methylbutyl)-9H-fluoren-2-yl]-4,4,5,5-tetramethyl[1,3,2]dioxaborolane, respectively. All compounds have good solubility in common organic solvents and high thermal stability with melting points up to 270 °C; the exceptions are the isomeric 1,6-bis-, and 1,8-bispyrenyl-substituted pyrenes. All products show high extinction coefficients of absorption (λmax ≈ 349-396 nm) and high quantum yields (λmax ≈ 432-465 nm; Φf ≈ 0.75-0.99) in dichloromethane solution, and emit strong fluorescence in the visible region ranging from deep-blue to pure-blue on increasing the number of substituents. This data suggests that such systems have promise as blue emitters in organic light-emitting device (OLED) applications (OLED = organic light emitting diode). Crystal structures were determined for 1,3,6,8-tetrakis [9,9-bis(3-methylbutyl)-9H-fluoren-2-yl] pyrene and 1,3,6,8-tetrakis(4-methoxyphenyl)pyrene.

A Deep Learning Algorithm of Neural Network for the Parameterization of Typhoon-Ocean Feedback in Typhoon Forecast Models

NASA Astrophysics Data System (ADS)

Jiang, Guo-Qing; Xu, Jing; Wei, Jun

2018-04-01

Two algorithms based on machine learning neural networks are proposed—the shallow learning (S-L) and deep learning (D-L) algorithms—that can potentially be used in atmosphere-only typhoon forecast models to provide flow-dependent typhoon-induced sea surface temperature cooling (SSTC) for improving typhoon predictions. The major challenge of existing SSTC algorithms in forecast models is how to accurately predict SSTC induced by an upcoming typhoon, which requires information not only from historical data but more importantly also from the target typhoon itself. The S-L algorithm composes of a single layer of neurons with mixed atmospheric and oceanic factors. Such a structure is found to be unable to represent correctly the physical typhoon-ocean interaction. It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D-L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. Therefore, it produces a stable crescent-shaped SSTC distribution, with its large-scale pattern determined mainly by atmospheric factors (e.g., winds) and small-scale features by oceanic factors (e.g., eddies). Sensitivity experiments reveal that the D-L algorithms improve maximum wind intensity errors by 60-70% for four case study simulations, compared to their atmosphere-only model runs.

Multimodality molecular imaging and extracellular vesicle release based genetic profiling with porphyrin nanodroplets (Conference Presentation)

NASA Astrophysics Data System (ADS)

Zemp, Roger J.; Paproski, Robert J.

2017-03-01

For emerging tissue-engineering applications, transplants, and cell-based therapies it is important to assess cell viability and function in vivo in deep tissues. Bioluminescence and fluorescence methods are poorly suited to deep monitoring applications with high resolution and require genetically-engineered reporters which are not always feasible. We report on a method for imaging cell viability using deep, high-resolution photoacoustic imaging. We use an exogenous dye, Resazurin, itself weakly fluorescent until it is reduced from blue to a pink color with bright red fluorescence. Upon cell death fluorescence is lost and an absorption shift is observed. The irreversible reaction of resazurin to resorufin is proportional to aerobic respiration. We detect colorimetric absorption shifts using multispectral photoacoustic imaging and quantify the fraction of viable cells. SKOV-3 cells with and without ±80oC heat treatment were imaged after Resazurin treatment. High 575nm:620nm ratiometric absorption and photoacoustic signals in viable cells were observed with a much lower ratio in low-viability populations.

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

PubMed

Hoseini, Farnaz; Shahbahrami, Asadollah; Bayat, Peyman

2018-02-27

Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques.

SELFI: an object-based, Bayesian method for faint emission line source detection in MUSE deep field data cubes

NASA Astrophysics Data System (ADS)

Meillier, Céline; Chatelain, Florent; Michel, Olivier; Bacon, Roland; Piqueras, Laure; Bacher, Raphael; Ayasso, Hacheme

2016-04-01

We present SELFI, the Source Emission Line FInder, a new Bayesian method optimized for detection of faint galaxies in Multi Unit Spectroscopic Explorer (MUSE) deep fields. MUSE is the new panoramic integral field spectrograph at the Very Large Telescope (VLT) that has unique capabilities for spectroscopic investigation of the deep sky. It has provided data cubes with 324 million voxels over a single 1 arcmin2 field of view. To address the challenge of faint-galaxy detection in these large data cubes, we developed a new method that processes 3D data either for modeling or for estimation and extraction of source configurations. This object-based approach yields a natural sparse representation of the sources in massive data fields, such as MUSE data cubes. In the Bayesian framework, the parameters that describe the observed sources are considered random variables. The Bayesian model leads to a general and robust algorithm where the parameters are estimated in a fully data-driven way. This detection algorithm was applied to the MUSE observation of Hubble Deep Field-South. With 27 h total integration time, these observations provide a catalog of 189 sources of various categories and with secured redshift. The algorithm retrieved 91% of the galaxies with only 9% false detection. This method also allowed the discovery of three new Lyα emitters and one [OII] emitter, all without any Hubble Space Telescope counterpart. We analyzed the reasons for failure for some targets, and found that the most important limitation of the method is when faint sources are located in the vicinity of bright spatially resolved galaxies that cannot be approximated by the Sérsic elliptical profile. The software and its documentation are available on the MUSE science web service (muse-vlt.eu/science).

Human-level control through deep reinforcement learning.

PubMed

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

2015-02-26

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

Human-level control through deep reinforcement learning

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

Sparsity-Based Representation for Classification Algorithms and Comparison Results for Transient Acoustic Signals

DTIC Science & Technology

2016-05-01

large but correlated noise and signal interference (i.e., low -rank interference). Another contribution is the implementation of deep learning...representation, low rank, deep learning 52 Tung-Duong Tran-Luu 301-394-3082Unclassified Unclassified Unclassified UU ii Approved for public release; distribution...Classification of Acoustic Transients 6 3.2 Joint Sparse Representation with Low -Rank Interference 7 3.3 Simultaneous Group-and-Joint Sparse Representation

Deep learning algorithms for detecting explosive hazards in ground penetrating radar data

NASA Astrophysics Data System (ADS)

Besaw, Lance E.; Stimac, Philip J.

2014-05-01

Buried explosive hazards (BEHs) have been, and continue to be, one of the most deadly threats in modern conflicts. Current handheld sensors rely on a highly trained operator for them to be effective in detecting BEHs. New algorithms are needed to reduce the burden on the operator and improve the performance of handheld BEH detectors. Traditional anomaly detection and discrimination algorithms use "hand-engineered" feature extraction techniques to characterize and classify threats. In this work we use a Deep Belief Network (DBN) to transcend the traditional approaches of BEH detection (e.g., principal component analysis and real-time novelty detection techniques). DBNs are pretrained using an unsupervised learning algorithm to generate compressed representations of unlabeled input data and form feature detectors. They are then fine-tuned using a supervised learning algorithm to form a predictive model. Using ground penetrating radar (GPR) data collected by a robotic cart swinging a handheld detector, our research demonstrates that relatively small DBNs can learn to model GPR background signals and detect BEHs with an acceptable false alarm rate (FAR). In this work, our DBNs achieved 91% probability of detection (Pd) with 1.4 false alarms per square meter when evaluated on anti-tank and anti-personnel targets at temperate and arid test sites. This research demonstrates that DBNs are a viable approach to detect and classify BEHs.

99aa/99ac data sets

Science.gov Websites

-redshifted), Observed Flux, Statistical Error (Based on the optimal extraction algorithm of the IRAF packages were acquired using different instrumental settings for the blue and red parts of the spectrum to avoid extracted for systematics checks of the wavelength calibration. Wavelength and flux calibration were applied

Color image encryption by using Yang-Gu mixture amplitude-phase retrieval algorithm in gyrator transform domain and two-dimensional Sine logistic modulation map

NASA Astrophysics Data System (ADS)

Sui, Liansheng; Liu, Benqing; Wang, Qiang; Li, Ye; Liang, Junli

2015-12-01

A color image encryption scheme is proposed based on Yang-Gu mixture amplitude-phase retrieval algorithm and two-coupled logistic map in gyrator transform domain. First, the color plaintext image is decomposed into red, green and blue components, which are scrambled individually by three random sequences generated by using the two-dimensional Sine logistic modulation map. Second, each scrambled component is encrypted into a real-valued function with stationary white noise distribution in the iterative amplitude-phase retrieval process in the gyrator transform domain, and then three obtained functions are considered as red, green and blue channels to form the color ciphertext image. Obviously, the ciphertext image is real-valued function and more convenient for storing and transmitting. In the encryption and decryption processes, the chaotic random phase mask generated based on logistic map is employed as the phase key, which means that only the initial values are used as private key and the cryptosystem has high convenience on key management. Meanwhile, the security of the cryptosystem is enhanced greatly because of high sensitivity of the private keys. Simulation results are presented to prove the security and robustness of the proposed scheme.

Combination of artificial neural network and genetic algorithm method for modeling of methylene blue adsorption onto wood sawdust from water samples.

PubMed

Khajeh, Mostafa; Sarafraz-Yazdi, Ali; Natavan, Zahra Bameri

2016-03-01

The aim of this research was to develop a low price and environmentally friendly adsorbent with abundant of source to remove methylene blue (MB) from water samples. Sawdust solid-phase extraction coupled with high-performance liquid chromatography was used for the extraction and determination of MB. In this study, an experimental data-based artificial neural network model is constructed to describe the performance of sawdust solid-phase extraction method for various operating conditions. The pH, time, amount of sawdust, and temperature were the input variables, while the percentage of extraction of MB was the output. The optimum operating condition was then determined by genetic algorithm method. The optimized conditions were obtained as follows: 11.5, 22.0 min, 0.3 g, and 26.0°C for pH of the solution, extraction time, amount of adsorbent, and temperature, respectively. Under these optimum conditions, the detection limit and relative standard deviation were 0.067 μg L(-1) and <2.4%, respectively. The Langmuir and Freundlich adsorption models were applied to describe the isotherm constant and for the removal and determination of MB from water samples. © The Author(s) 2013.

Simulation-based planning for theater air warfare

NASA Astrophysics Data System (ADS)

Popken, Douglas A.; Cox, Louis A., Jr.

2004-08-01

Planning for Theatre Air Warfare can be represented as a hierarchy of decisions. At the top level, surviving airframes must be assigned to roles (e.g., Air Defense, Counter Air, Close Air Support, and AAF Suppression) in each time period in response to changing enemy air defense capabilities, remaining targets, and roles of opposing aircraft. At the middle level, aircraft are allocated to specific targets to support their assigned roles. At the lowest level, routing and engagement decisions are made for individual missions. The decisions at each level form a set of time-sequenced Courses of Action taken by opposing forces. This paper introduces a set of simulation-based optimization heuristics operating within this planning hierarchy to optimize allocations of aircraft. The algorithms estimate distributions for stochastic outcomes of the pairs of Red/Blue decisions. Rather than using traditional stochastic dynamic programming to determine optimal strategies, we use an innovative combination of heuristics, simulation-optimization, and mathematical programming. Blue decisions are guided by a stochastic hill-climbing search algorithm while Red decisions are found by optimizing over a continuous representation of the decision space. Stochastic outcomes are then provided by fast, Lanchester-type attrition simulations. This paper summarizes preliminary results from top and middle level models.

A New, More Physically Based Algorithm, for Retrieving Aerosol Properties over Land from MODIS

NASA Technical Reports Server (NTRS)

Levy, Robert C.; Kaufman, Yoram J.; Remer, Lorraine A.; Mattoo, Shana

2004-01-01

The MOD Imaging Spectrometer (MODIS) has been successfully retrieving aerosol properties, beginning in early 2000 from Terra and from mid 2002 from Aqua. Over land, the retrieval algorithm makes use of three MODIS channels, in the blue, red and infrared wavelengths. As part of the validation exercises, retrieved spectral aerosol optical thickness (AOT) has been compared via scatterplots against spectral AOT measured by the global Aerosol Robotic NETwork (AERONET). On one hand, global and long term validation looks promising, with two-thirds (average plus and minus one standard deviation) of all points falling between published expected error bars. On the other hand, regression of these points shows a positive y-offset and a slope less than 1.0. For individual regions, such as along the U.S. East Coast, the offset and slope are even worse. Here, we introduce an overhaul of the algorithm for retrieving aerosol properties over land. Some well-known weaknesses in the current aerosol retrieval from MODIS include: a) rigid assumptions about the underlying surface reflectance, b) limited aerosol models to choose from, c) simplified (scalar) radiative transfer (RT) calculations used to simulate satellite observations, and d) assumption that aerosol is transparent in the infrared channel. The new algorithm attempts to address all four problems: a) The new algorithm will include surface type information, instead of fixed ratios of the reflectance in the visible channels to the mid-IR reflectance. b) It will include updated aerosol optical properties to reflect the growing aerosol retrieved from eight-plus years of AERONE". operation. c) The effects of polarization will be including using vector RT calculations. d) Most importantly, the new algorithm does not assume that aerosol is transparent in the infrared channel. It will be an inversion of reflectance observed in the three channels (blue, red, and infrared), rather than iterative single channel retrievals. Thus, this new formulation of the MODIS aerosol retrieval over land includes more physically based surface, aerosol and radiative transfer with fewer potentially erroneous assumptions.

Dust Storm over the Middle East: Retrieval Approach, Source Identification, and Trend Analysis

NASA Astrophysics Data System (ADS)

Moridnejad, A.; Karimi, N.; Ariya, P. A.

2014-12-01

The Middle East region has been considered to be responsible for approximately 25% of the Earth's global emissions of dust particles. By developing Middle East Dust Index (MEDI) and applying to 70 dust storms characterized on MODIS images and occurred during the period between 2001 and 2012, we herein present a new high resolution mapping of major atmospheric dust source points participating in this region. To assist environmental managers and decision maker in taking proper and prioritized measures, we then categorize identified sources in terms of intensity based on extracted indices for Deep Blue algorithm and also utilize frequency of occurrence approach to find the sensitive sources. In next step, by implementing the spectral mixture analysis on the Landsat TM images (1984 and 2012), a novel desertification map will be presented. The aim is to understand how human perturbations and land-use change have influenced the dust storm points in the region. Preliminary results of this study indicate for the first time that c.a., 39 % of all detected source points are located in this newly anthropogenically desertified area. A large number of low frequency sources are located within or close to the newly desertified areas. These severely desertified regions require immediate concern at a global scale. During next 6 months, further research will be performed to confirm these preliminary results.

A-Train Aerosol Observations Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-Sky Estimates

NASA Technical Reports Server (NTRS)

Redemann, J.; Livingston, J.; Shinozuka, Y.; Kacenelenbogen, M.; Russell, P.; LeBlanc, S.; Vaughan, M.; Ferrare, R.; Hostetler, C.; Rogers, R.;