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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Model transformations via XSLT 17

Model transformations via XSLT

引用

17th Annual International Workshop on Databases, Texts, Specifications, and Objects, DATESO 2017

作者： Pavlát, Jakub Richta, Karel Richta, Tomáš Janoušek, Vladimír Czech Technical University in Prague Faculty of Electrical Engineering Dept. of Computer Science Karlovo nám. 13 Praha 2121 35 Czech Republic Brno Institute of Technology Faculty of Information Technology Dept. of Intelligent Systems Božetěchova 2 Brno612 66 Czech Republic

ISBN: (纸本)9788001061381

Many present systems are conceived as a set of autonomous agents that communicate together in solving problems. Petri nets are commonly used for a specification of parallel systems. An interesting question is whether we can implement arbitrary system by a set of Petri nets agents. These agents could be specified by Petri nets, but such a description is not sufficiently user-friendly. Our idea is to define these agents by classical workflow models and then transform them into a set of Petri nets. Such transformation would support development of software systems, whose specification is based on classical workflow models, but the implementation is based on Petri nets. Each net of the resulting system is translated into a specific target representation called Petri Nets Byte Code (PNBC). These codes are interpreted by the special Petri Nets Virtual Machines (PNVM), which are installed on all nodes of the system, under the Petri Nets Operating System (PNOS). This paper deals with the one step of this process, which is an implementation of required transformations from workflow model into Petri nets with the help of XSLT. © K. Richta, P. Moravec (Eds.): Dateso 2017.

关键词： Petri nets

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Highly structured graphene polyethylene nanocomposites

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AIP Conference Proceedings 2019年第1期2065卷

作者： Karolina Gaska Roland Kádár Xiangdong Xu Stanislaw Gubanski Christian Müller Santosh Pandit Venkata R. S. S. Mokkapati Ivan Mijakovic Andrzej Rybak Artur Siwek Magnus Svensson aChalmers University of Technology Dept. of Industrial and Materials Science Division of Engineering Materials Polymeric Materials and Composites SE 412 96 Göteborg Sweden bChalmers University of Technology Dept. of Electrical Engineering Division of Electric Power Engineering High Voltage Engineering SE 412 96 Göteborg Sweden cChalmers University of Technology Dept. of Chemistry and Chemical Engineering Division of Applied Chemistry Polymer Technology SE 412 96 Göteborg Sweden dChalmers University of Technology Dept. of Biology and Biological Engineering Division of Systems and Synthetic Biology SE 412 Göteborg Sweden eABB Corporate Research Center PL 31038 Krakow Poland fWellspect HealthCare SE 43121 Mölndal Sweden

This research presents an overview of the properties of highly structured, low density polyethylene-graphene nanoplatelets (LDPE-GnP). The influence of nanofiller content, size and processing conditions on the material properties have been investigated. Therefore, rheological and thermal nanocomposite properties were investigated. So-called dry-coating method has been used in order to prepare masterbatches which were thereafter extruded by means of single screw extruder resulting in a strong anisotropy in the extruded samples. Graphene nanoplatelets were oriented in the extrusion direction for all shear rates and flow histories investigated, as confirmed by scanning electron microscopy. The rheological percolation was determined via nonlinear parameters to be around 11wt%. Thermal conductivity measurements revealed strong anisotropy with in-plane conductivity increasing with GnP content.

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Sketched subspace clustering

arXiv

引用

arXiv 2017年

作者： Traganitis, Panagiotis A. Giannakis, Georgios B. Dept. of Electrical and Computer Engineering Digital Technology Center University of Minnesota MinneapolisMN55455 United States

The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth labels, is an important tool for drawing inferences from data. Subspace clustering (SC) is a relatively recent method that is able to successfully classify nonlinearly separable data in a multitude of settings. In spite of their high clustering accuracy, SC methods incur prohibitively high computational complexity when processing large volumes of high-dimensional data. Inspired by random sketching approaches for dimensionality reduction, the present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored for large volumes of high-dimensional data. Sketch-SC accelerates the computationally heavy parts of state-of-the-art SC approaches by compressing the data matrix across both dimensions using random projections, thus enabling fast and accurate large-scale SC. Performance analysis as well as extensive numerical tests on real data corroborate the potential of Sketch-SC and its competitive performance relative to state-of-the-art scalable SC approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Clustering algorithms

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Photonic integrated circuit-based imaging system for SPIDER

Photonic integrated circuit-based imaging system for SPIDER

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2017 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2017

作者： Badham, Katherine Kendrick, Richard L. Wuchenich, Danielle Ogden, Chad Chriqui, Guy Duncan, Alan Thurman, Samuel T. Coherent, Lockheed Martin Yoo, S.J.B. Su, Tiehui Lai, Weicheng Chun, Jaeyi Li, Siwei Liu, Guangyao Lockheed Martin Advanced Technology Center Palo AltoCA United States Department of Technologies 135 South Taylor Avenue LouisvilleCO United States Dept. of Electrical and Computer Engineering University of California at Davis DavisCA United States

ISBN: (纸本)9781509062904

The Lockheed Martin Advanced technology center and the University of California at Davis (UC Davis) has developed an interferometric imaging system based on photonic integrated circuits (PICs). This approach could enable a new hardware architecture with a 10x to 100x reduction in size and weight compared to traditional imaging systems, particularly targeted for space applications. The current PIC design consists of a threelayer system of waveguides, arrayed waveguide gratings (AWGs) and multi-mode interferometers (MMIs) that link 12 interferometric baselines to measure the phase and amplitude of the mutual intensity of an extended object. A series of lenslets couple light into 24 input waveguides on one edge of the PIC. On the opposite side there are 206 interferometer output ports that provide the fringe data for creating images. In this paper we will describe the photonic integrated circuit design and the testbed used to create the first images of extended scenes. We will also summarize the image reconstruction steps and present the final images. © 2017 IEEE. All rights reserved.

关键词： Timing circuits

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ALESS-JWST: Joint (sub-)kiloparsec JWST and ALMA imaging of z ∼ 3 submillimeter galaxies reveals heavily obscured bulge formation events

arXiv

引用

arXiv 2024年

作者： Hodge, Jacqueline A. da Cunha, Elisabete Kendrew, Sarah Li, Juno Smail, Ian Westoby, Bethany A. Nayak, Omnarayani Swinbank, Mark A. Chen, Chian-Chou Walter, Fabian van der Werf, Paul Cracraft, Misty Battisti, Andrew Brandt, Willian N. Rivera, Gabriela Calistro Chapman, Scott C. Cox, Pierre Dannerbauer, Helmut Decarli, Roberto Castillo, Marta Frias Greve, Thomas R. Knudsen, Kirsten K. Leslie, Sarah Menten, Karl M. Rybak, Matus Schinnerer, Eva Wardlow, Julie L. Weiss, Axel Leiden Observatory Leiden University P.O. Box 9513 Leiden2300 RA Netherlands International Centre for Radio Astronomy Research University of Western Australia 35 Stirling Hwy Crawley26WA 6009 Australia Australia ESA Office Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Centre for Extragalactic Astronomy Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD United States No. 1 Sec. 4 Roosevelt Road Taipei106216 Taiwan Max–Planck Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Research School of Astronomy and Astrophysics Australian National University Cotter Road Weston CreekACT2611 Australia Department of Astronomy and Astrophysics 525 Davey Lab The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics 104 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Institute of Communications and Navigation Wessling Germany Karl-Schwarzschild-Straße 2 M¨unchen Garching85748 Germany Department of Physics and Atmospheric Science Dalhousie University 6310 Coburg Road HalifaxB3H 4R2 Canada National Research Council Herzberg Astronomy and Astrophysics 5071 West Saanich Road VictoriaV9E 2E7 Canada Department of Physics and Astronomy University of British Columbia 6225 Agricultural Road VancouverV6T 1Z1 Canada Eureka Scientific Inc OaklandCA94602 United States Sorbonne Université UPMC Université Paris 6 CNRS UMR 7095 Institut d’Astrophysique de Paris 98bis boulevard Arago Paris75014 France 39020 Spain INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna via Gobetti 93/3 BolognaI-40129 Italy De

We present JWST NIRCam imaging targeting 13 z∼3 infrared-luminous (LIR ∼ 5×1012L) galaxies from the ALESS survey with uniquely deep, high-resolution (0.08′′–0.16′′) ALMA 870µm imaging. The 2.0-4.4µm (observed frame) NIRCam imaging reveals the rest-frame near-infrared stellar emission in these submillimeter-selected galaxies (SMGs) at the same (sub-)kpc resolution as the 870µm dust continuum. The newly revealed stellar morphologies show striking similarities with the dust continuum morphologies at 870µm, with the centers and position angles agreeing for most sources, clearly illustrating that the spatial offsets reported previously between the 870µm and HST morphologies were due to strong differential dust obscuration. The F444W sizes are 78±21% larger than those measured at 870µm, in contrast to recent results from hydrodynamical simulations that predict larger 870µm sizes. We report evidence for significant dust obscuration in F444W for the highest-redshift sources, emphasizing the importance of longer-wavelength MIRI imaging. The majority of the sources show evidence that they are undergoing mergers/interactions, including tidal tails/plumes—some of which are also detected at 870µm. We find a clear correlation between NIRCam colors and 870µm surface brightness on ∼1 kpc scales, indicating that the galaxies are primarily red due to dust—not stellar age—and we show that the dust structure on ∼kpc-scales is broadly similar to that in nearby galaxies. Finally, we find no strong stellar bars in the rest-frame near-infrared, suggesting the extended bar-like features seen at 870µm are highly obscured and/or gas-dominated structures that are likely early precursors to significant bulge growth. © 2024, CC BY.

关键词： Dust

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Tensor-generative adversarial network with two-dimensional sparse coding: Application to real-time indoor localization

arXiv

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arXiv 2017年

作者： Zhu, Chenxiao Xu, Lingqing Liu, Xiao-Yang Qian, Feng Center for Information Geoscience University of Electronic Science and Technology of China Dept. of Electrical Engineering Columbia University Dept. of Computer Science and Engineering Shanghai Jiao Tong University

Localization technology is important for the development of indoor location-based services (LBS). Global Positioning System (GPS) becomes invalid in indoor environments due to the non-line-of-sight issue, so it is urgent to develop a real-time high-accuracy localization approach for smartphones. However, accurate localization is challenging due to issues such as real-time response requirements, limited fingerprint samples and mobile device storage. To address these problems, we propose a novel deep learning architecture: Tensor-Generative Adversarial Network (TGAN). We first introduce a transform-based 3D tensor to model fingerprint samples. Instead of those passive methods that construct a fingerprint database as a prior, our model applies artificial neural network with deep learning to train network classifiers and then gives out estimations. Then we propose a novel tensor-based super-resolution scheme using the generative adversarial network (GAN) that adopts sparse coding as the generator network and a residual learning network as the discriminator. Further, we analyze the performance of tensor-GAN and implement a trace-based localization experiment, which achieves better performance. Compared to existing methods for smartphones indoor positioning, that are energy-consuming and high demands on devices, TGAN can give out an improved solution in localization accuracy, response time and implementation complexity. Copyright © 2017, The Authors. All rights reserved.

关键词： Indoor positioning systems

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An automated Reconfigurable-Computing Environment for accelerating software applications

An automated Reconfigurable-Computing Environment for accele...

引用

IEEE Southeastcon

作者： Shrikant S. Jadhav Clay Gloster Vance Alford Christopher Doss Youngsoo Kim Dept. of Electrical and Computer Engineering North Carolina A&T State University Greensboro NC Dept. of Computer Systems Technology North Carolina A&T State University Greensboro NC Dept. of Electrical Engineering San Jose State University San Jose CA

In this paper, we present the Reconfigurable-Computing Environment (RCE) toolset for automatically generating VHDL models for implementation of generic applications on a Field Programmable Gate Array (FPGA). The RCE toolset automatically generates the hardware description of an Application Specific Digital Signal Processor (ASDSP) that is loaded onto an FPGA board containing multiple memories connected to an FPGA. We also present, PolyGen, an automated tool that generates scalable floating point polynomial evaluation units. Polynomial evaluation is used as an application to demonstrate the merits of the RCE framework. Our experiments show that the results obtained executing polynomial evaluation using the RCE framework is significantly faster than executing it on a typical server. While the maximum clock rate of the FPGA board (200 MHz) is an order of magnitude slower than a server (3.4 GHz), we achieve approximately 200× speedup. If all the resources on the FPGA board are used it is possible to achieve a potential speedup of 800× using the RCE framework.

关键词： Field programmable gate arrays Hardware Tools Software Multicore processing computers Acceleration

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Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background

arXiv

引用

arXiv 2018年

作者： Ahmadi, Mahdi Emami, Ali Hajabdollahi, Mohsen Soroushmehr, S.M. Reza Karimi, Nader Samavi, Shadrokh Najarian, Kayvan Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Dept. of Information Technology and Elect. Engineering University of Queensland Brisbane Australia Dept. of Compl. Med. and Bioinfo. and Michigan Center for Integrative Research in Critical Care University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Department of Emergency Medicine and Michigan Center for Integrative Research in Critical Care Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States

By increasing the volume of telemedicine information, the need for medical image compression has become more important. In angiographic images, a small ratio of the entire image usually belongs to the vasculature that provides crucial information for diagnosis. Other parts of the image are diagnostically less important and can be compressed with higher compression ratio. However, the quality of those parts affect the visual perception of the image as well. Existing methods compress foreground and background of angiographic images using different techniques. In this paper we first utilize convolutional neural network to segment vessels and then represent a hierarchical block processing algorithm capable of both eliminating the background redundancies and preserving the overall visual quality of angiograms. Copyright © 2018, The Authors. All rights reserved.

关键词： Medical imaging

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Genetic variant analysis of boys with Autism: A pilot study on linking facial phenotype to genotype

Genetic variant analysis of boys with Autism: A pilot study ...

引用

2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

作者： Obafemi-Ajayi, Tayo Settles, Luke Su, Yuqing Germeroth, Cynthia Olbricht, Gayla R. Wunsch, Donald C. Takahashi, T. Nicole Miles, Judith Engineering Program Missouri State University Springfield MO United States Mathematics and Statistics Dept. Missouri University of Science and Technology RollaMO United States Information Technology Missouri University of Science and Technology RollaMO United States Electrical and Computer Eng. Dept. Missouri University of Science and Technology RollaMO United States Thompson Center for Autism and Neurodevelopmental Disorders ColumbiaMO United States

ISBN: (纸本)9781509030491

This work examines the validity of facial phenotypes as Autism Spectrum Disorders (ASD) biomarkers in boys with essential autism. A family-based association analysis framework is presented that uses previously identified facially-delineated (FD) clusters to examine relationship between FD clusters and known ASD genes. The hypothesis is that there are certain genetic variants, single nucleotide polymorphisms (SNP), specific to the FD clusters. Although statistical significance was not established, the results identified some candidate SNPs unique to each of the FD clusters that could indicate an underlying etiological difference. Further, recommendations are provided for larger-scale studies that could utilize the analysis framework presented. © 2017 IEEE.

关键词： Biomarkers

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