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arXiv 2025年

作者： Pylypenko, U. Wyrzykowski, L. Mikolajczyk, P.J. Kotysz, K. Zieliński, P. Ihanec, N. Wicker, M. Ratajczak, M. Sitek, M. Howil, K. Jablońska, M. Kaczmarek, Z. Kruszyńska, K. Udalski, A. Damljanovic, G. Stojanovic, M. Jovanovic, M.D. Kvernadze, T. Kvaratskhelia, O. Zejmo, M. Brincat, S.M. Qvam, J.K.T. Güver, T. Bachelet, E. Rybicki, K.A. Garofalo, A. Hardy, L. Zdanavicius, J. Pakstiene, E. Zola, S. Kurowski, S. Reichart, D.E. Davidson, J.W. Burgaz, U. Rivet, J.P. Jelinek, M. Popowicz, A. Esenoglu, H.H. Sonbas, E. Carrasco, J.M. Awiphan, S. Tasuya, O. Godunova, V. Simon, A. Fukui, A. Galdies, C. Bakowska, K. Hofbauer, P. Gurgul, A. Joachimczyk, B. Dominik, M. Tsapras, Y. Street, R.A. Hundertmark, M. Bozza, V. Rota, P. Cassan, A. Wambsganss, J. Figuera Jaimes, R. Astronomical Observatory University of Warsaw Al. Ujazdowskie 4 Warsaw00-478 Poland Astrophysics Division National Centre for Nuclear Research Pasteura 7 Warsaw02-093 Poland Astronomical Institute University of Wroclaw ul. Mikolaja Kopernika 11 Wroclaw51-622 Poland Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń Grudziadzka 5 Toruń87-100 Poland Faculty of Mathematics and Computer Science Jagiellonian University Lojasiewicza 6 Kraków30-348 Poland Research School of Astronomy and Astrophysics Australian National University Mount Stromlo Observatory Cotter Road Weston CreekACT2611 Australia Zentrum für Astronomie der Universität Heidelberg Astronomisches Rechen-Institut Mönchhofstr. 12-14 Heidelberg69120 Germany Las Cumbres Observatory Global Telescope Network 6740 Cortona Drive suite 102 GoletaCA93117 United States Astronomical Observatory Volgina 7 Belgrade11060 Serbia E.Kharadze Georgian National Astrophysical Observatory Abastumani0301 Georgia Janusz Gil Institute of Astronomy University of Zielona Góra Lubuska 2 Zielona GóraPL-65-265 Poland Flarestar Obsevatory Fl.5 Ent.B Silver Jubilee Apt George Tayar Street San GwannSGN 3160 Malta Horten Videregaende Skole Strandpromenaden 33 Horten3183 Norway Istanbul University Faculty of Science Department of Astronomy and Spaces Sciences Istanbul34119 Turkey Istanbul University Observatory Research and Application Center Istanbul University Istanbul34119 Turkey Department of Physics Adıyaman University Adıyaman02040 Turkey Department of Physics The George Washington University WashingtonDC20052 United States IPAC Mail Code 100-22 Caltech 1200 E. California Blvd. PasadenaCA91125 United States Department of Particle Physics and Astrophysics Weizmann Institute of Science Rehovot76100 Israel INAF-Osservatorio di Astrofisica e Scienza dello Spazio Via Gobetti 93/3 BolognaI-40129 Italy Department of Physics and Astronom

Context. Microlensing events provide a unique way to detect and measure the masses of isolated, non-luminous objects, particularly dark stellar remnants. Under certain conditions, it is possible to measure the mass of these objects using photometry alone, specifically when a microlensing light curve displays a finite-source (FS) effect. This effect generally occurs in highly magnified light curves, i.e. when the source and the lens are very well aligned. Aims. In this study, we analyse Gaia Alerts and Gaia Data Release 3 datasets, identifying four moderate-to-high-magnification microlensing events without a discernible FS effect. The absence of this effect suggests a large Einstein radius, implying substantial lens masses. Methods. In each event, we constrain the FS effect and therefore establish lower limits for angular Einstein radius and lens mass. Additionally, we use the DarkLensCode software to obtain mass, distance, and brightness distribution for the lens based on the Galactic model. Results. Our analysis established lower mass limits of ∼ 0.2 M☉ for one lens and ∼ 0.3 − 0.5 M☉ for two others. DarkLensCode analysis supports these findings, estimating lens masses in the range of∼ 0.42 − 1.66 M☉ and dark lens probabilities exceeding 60%. These results strongly indicate that the lenses are stellar remnants, such as white dwarfs or neutron stars. Conclusions. While further investigations are required to confirm the nature of these lenses, we demonstrate a straightforward yet effective approach to identifying stellar remnant candidates. © 2025, CC BY.

关键词： White dwarfs

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MARKOV DECISION PROCESSES WITH UNCERTAIN TRANSITION RATES: SENSITIVITY AND MAX HYPHEN MIN control

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Asian Journal of control 2008年第2期6卷

作者： Suresh Kalyanasundaram Edwin K. P. Chong Ness B. Shroff Motorola India Electronics Limited No. 66/1 Plot 5 Bagmane Techpark C. V. Raman Nagar Post Bangalore 560 093 India. Department of Electrical and Computer Engineering Colorado State University Fort Collins CO 80523-1373 USA. Professor Edwin K. P. Chong received the B.E.(Hons.) degree with First Class Honors from the University of Adelaide South Australia in 1987 graduating top of his class and the M.A. and Ph.D. degrees in 1989 and 1991 respectively both from Princeton University where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991 where he was named a University Faculty Scholar in 1999 and promoted to Full Professor in 2001. Since August 2001 he has been a Professor of Electrical and Computer Engineering and Professor of Mathematics at Colorado State University. His current interests are in communication networks and optimization methods. He coauthored the best-selling book An Introduction to Optimization 2nd Edition Wiley-Interscience 2001. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He coauthored a paper that was awarded Best Paper in the journal Computer Networks 2003. Professor Chong is a Fellow of the IEEE. He was founding chairman of the IEEE Control Systems Society Technical Committee on Discrete Event Systems and until recently served as an IEEE Control Systems Society Distinguished Lecturer. He has been on the editorial board of the IEEE Transactions on Automatic Control. He is currently on the editorial board of the journal Computer Networks. He has also served on the organizing committees of several international conferences. He has been on the program committees for the IEEE Conference on Decision and Control the American Control Conference the IEEE International Symposium on Intelligent Control IEEE Symposium on Computers and Communications and the IEEE Global Telecommunications Conference. He has also served in the executive committees for the IEEE Co

Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain transition rates represented as compact sets. We first consider the problem of sensitivity analysis where the aim is to quantify the range of uncertainty of the average per-unit-time reward given the range of uncertainty of the transition rates. We then develop solution techniques for the problem of obtaining the max-min optimal policy, which maximizes the worst-case average per-unit-time reward. In each of these problems, we distinguish between systems that can have their transition rates chosen independently and those where the transition rates depend on each other. Our solution techniques are applicable to Markov decision processes with fixed but unknown transition rates and to those with time-varying transition rates.

关键词： Markov decision processes sensitivity max-min control call admission control policy iteration

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Royal Academy of Medicine in Ireland - Section of Bioengineering - Proceedings of meeting held in Dundalk, Co. Louth, in February 1998.

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IRISH JOURNAL OF MEDICAL SCIENCE 1998年第4期167卷 256-276页

作者： [Anonymous] Orthopaedic Research Unit The Queen’s University of Belfast School of Biomedical Sciences The Queen’s University of Belfast Howmedica International Ltd. Raheen Industrial Estate Limerick Department of Mechanical Engineering Trinity College Dublin 2 Mater Hospital Dublin 7 Orthopaedic Research Laboratory Århus University Hospital Denmark University of Limerick Limerick Limerick Regional Hospital Raheen Limerick Boston Scientific Ireland Ltd. Galway Department of Mechanical Engineering University College Dublin Dublin Department of Electrical and Electronic Engineering University College Dublin Dublin Bioengineering Research Centre Department of Mechanical Engineering University College Dublin Dublin Northern Ireland Bio-Engineering Centre (NIBEC) School of Electrical and Mechanical Engineering University of Ulster Newtownabbey Department of Mechanical & Manufacturing Engineering The Ashby Building The Queen’s University of Belfast Johnson and Johnson Professional Orthopaedic Division Queensway England Department of Cardiology and Department of Medical Physics & Bioengineering St. James’s Hospital Dublin 8 Burke House Ellison Street Castlebar Royal College of Surgeons in Ireland Department of Surgery Beaumont Hospital Dublin Department of Electronic and Electrical Engineering Trinity College Dublin 2 Department of Aeronautical Engineering The Queen’s University of Belfast Vascular Unit Belfast City Hospital Department of Medical Physics and Bioengineering St. James’s Hospital Dublin 8 Centre for Biomedical Electronics Department of Electronic and Computer Engineering University of Limerick Limerick Department of Mechanical and Aeronautical Engineering University of Limerick Limerick School of Health Sciences University of Ulster Ireland School of Physiotherapy University of Ulster Newtownabbey Clinical Engineering NI Medical Physics Agency Belfast and Radiology Royal Victoria Hospital Musgrave Park Hospital University of Limerick Biomedical Institute (ULBMI) Uni

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Special Issue on Variable Structure System control — New Designs and Applications

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Asian Journal of control 2008年第4期5卷

作者： Prof. Jian-Xin Xu Prof. Leonid Fridman Department of Electrical and Computer Eng. National University of Singapore 4 Engineering Drive 3 Singapore 117576 Tel +65 6874-2566 Fax +65 6779-1103 Dr Jian-Xin Xu received his Bachelor degree from Zhejiang University China in 1982. He attended the University of Tokyo Japan where he received his Master's and Ph.D. degrees in 1986 and 1989 respectively. All his degrees are in Electrical Engineering. He worked for one year in the Hitachi research Laboratory Japan and for more than one year in Ohio State University U.S.A. as a Visiting Scholar. In 1991 he joined the National University of Singapore and is currently an associate professor in the Department of Electrical Engineering. His research interests lie in the fields of learning control variable structure control fuzzy logic control discontinuous signal processing and applications to motion control and process control problems. He is the associate editor of Asian Journal of Control member of TC on variable structure systems and sliding mode control of IEEE Control Systems Society and a senior member of IEEE. He has produced more than 90 peer-refereed journal papers near 160 technical papers in conference proceedings and authored/edited 4 books. Division de Estudios de Posgrado Facultad de Ingenieria National Autonomous University of Mexico DEP-FI UNAM Edificio “A” Circuito Exterior Ciudad Universitaria A. P. 70–256 C.P.04510 Mexico D.F. Mexico Tel +52 55 56223014 Fax +52 55 56161719 Dr. Leonid M. Fridman received his M.S in mathematics from Kuibyshev (Samara) State University Russia Ph.D. in Applied Mathematics from Institute of Control Science (Moscow) and Dr. of Science degrees in Control Science from Moscow State University of Mathematics and Electronics in 1976 1988 and 1998 respectively. In 1976–1999 Dr. Fridman was with the Department of Mathematics at the Samara State Architecture and Civil Engineering Academy Samara Russia. In 2000–2002 he was with the Department of Postgraduate Study and Investigations at the Chihuahu

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology & Evolution 2025年 1-12页

作者： Stephen Pringle Jessica C. Fisher Gail E. Austen Jake E. Bicknell Nicolas J. Deere Mohammad S. Farhadinia Charlie J. Gardner Richard A. Griffiths Daniel J. Ingram Matthew J. Struebig Natalie Yoh Zoe G. Davies Martin Dallimer Mark A. Goddard Katherine C. R. Baldock Miranda T. Prendergast-Miller Léni K. Le Goff Emma Hart Simón C. Smith Simon J. Langdale Sara-Adela Abad Marc Ancrenaz Fabio Angeoletto Fernando Auat Cheein Joseph J. Bailey Lindsay F. Banin Cristina Banks-Leite Aliyu S. Barau Reshu Bashyal Adam J. Bates Jon Bielby Serge Wich Christopher D. Williams Petra Bosilj Marc Hanheide Emma R. Bush Simon J. Butler Dan Carpenter Christopher F. Clements Antoine Cully Kendi F. Davies Brett A. Melbourne Michael Dodd Rosie Drinkwater Don A. Driscoll Guillaume Dutilleux Mads Dyrmann David P. Edwards Aisyah Faruk Richard Field Franziska Schrodt Robert J. Fletcher Chris W. Foster Richard Fox Richard M. Francksen Aldina M. A. Franco Alison M. Gainsbury Ioanna Giorgi Giovanni L. Masala Salua Hamaza Matt W. Hayward Marcus Hedblom Thorunn Helgason Sui P. Heon Kevin A. Hughes Edmund R. Hunt George Jackson-Mills Kelly Jowett Timothy H. Keitt Laura N. Kloepper Stephanie Kramer-Schadt Jim Labisko Frédéric Labrosse Jenna Lawson Nicolas Lecomte Ricardo F. de Lima Nick A. Littlewood Harry H. Marshall Lindsay C. Maskell Eleni Matechou Barbara Mazzolai Alistair McConnell Aslan Miriyev Eric Djomo Nana Alessandro Ossola Sarah Papworth Catherine L. Parr Ana Payo-Payo Gad Perry Nathalie Pettorelli Marcus Rowcliffe Rajeev Pillay Simon G. Potts Lan Qie Carl D. Soulsbury Persie Rolley-Parnell Stephen J. Rossiter Heather Rumble Jon P. Sadler Christopher J. Sandom Asiem Sanyal Sarab S. Sethi Adi Shabrani Robert Siddall Robbert P. H. Snep Margaret C. Stanley Philip A. Stephens P. J. Stephenson Matthew Studley Martin Svátek Gilbert Tang Nicholas K. Taylor Kate D. L. Umbers Robert J. Ward Patrick J. C. White Mark J. Whittingham Ibrahim B. Yakubu Syed A. R. Zaidi Anna Zmarz Joeri A. Zwerts Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK Centre for Environmental Policy Imperial College London London UK Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Synthotech Ltd Milner Court Hornbeam Square Harrogate UK Mechanical Engineering Department University College London London UK HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brasil Department of Engineering Harper Adams University Newport UK Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge UK Centre for Ecology and Hydrology Penicuik UK Department of Life Sciences Imperial College London Silwood Park Campus Ascot UK Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire UK School of Biological and Environmental Sciences Liverpool John Moores University Liverpool UK Lincoln Centre for Autonomous Systems University of Lincoln Lincoln UK Royal Botanic Garden Edinburgh Edinburgh UK School of Biological Sciences University of East Anglia Norwich Research Park Norwich UK Digital Ecology Limited Bristol UK School of Biological Sciences University of Bristol Bristol UK Department of Computing Imperial College London London UK Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA Faculty of Science Technology Engineering and Mathematics (STEM) The Open University Walton Hall Milton Keynes UK Palaeogenomics Group Faculty of Veterinary Medicine Ludwig Maximilian University Munich Munich Germany School of Life and Environmental Sciences

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively.

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Measurements of power and current components in unbalanced and distorted three‐phase systems

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European Transactions on Electrical Power 1993年第1期3卷 75-83页

作者： Andria, G. Salvatore, L. Savino, M. Trotta, A. Dr. Gregorio Andria (1956) AEI received the M. S. degree in Electrical Engineering from the State University of Bari/Italy in 1981 and the Ph. D. degree in Electrical Engineering in 1987 from the same University. From 1981 to 1983 he was working in the Electrotechnics and Electronics Department of the University of Bari as a member of the research team on electrical measurements. From 1984 to 1986 he was a Doctoral Fellow and currently he is a researcher in the same department. His research interests are in the fields of electrical and electronic measurements on components and systems including digital measurements for the analysis of electrical quantities in non-sinusoidal systems and the design of integrated optical sensors for measurement and control of non-electrical physical quantities. (Department of Electrotechnics and Electronics Faculty of Engineering polytechnic of Bovia E. Orabona 4 1-70125 Bari. Italy T +3980/242266 Fax + 3980/242410) Prof. Luigi Salvatore (1945) AEI received the degree in electrical engineering from the University of Bari/Italy in 1970. Since 1976 he has worked in the Electrotechnical and Electronic Department of the same University as a member of the research team on electrical machines. From 1983 to 1987 he was a researcher of electrical machines in the same department. Since 1987 he has been an Associate Professor of electrical machines at the University of Bari. At the present time his research interests include the control monitoring and diagnostics of AC drives and the areas of signal processing anddigital measurements on power electronics systems. (Department of Electrotechnics and Electronics Faculty of Engineering Polytechnic of Bari via E. Orabona 4 I-70125 Bari Italy T + 3980/242258 Fax + 3980/242410) Prof. Mario Savino (1947) AEI received the degree in Electrical Engineering from the University of Barif Italy in 1971. Since then he has been working in the Electrotechnical Institute of Bari until 1973 as researcher from 1973 to 1982 as Assistant Profe

The paper deals with the instantaneous power theory in three‐phase circuits by using the instantaneous time phasors of voltage and current. Particularly it is shown that the instantaneous components of the current time‐phasor in a rotating reference frame p‐q, oriented in such a way that the instantaneous voltage time‐phasor points to the p‐axis direction, represent the active and reactive currents. These components give rise to instantaneous real and imaginary powers, respectively. The spectral analysis of the above‐mentioned time‐phasors gives the harmonic symmetrical components of positive and negative sequence of voltage and current three‐phase systems. The line‐by‐line product of the voltage sequence spectrum by the complex conjugate of the current sequence spectrum gives the DC components of the instantaneous real and imaginary powers. The paper shows also that the instantaneous active and reactive currents are different from those defined on grounds of the Fryze's theory, which can be determined from the voltage and current sequence spectra. Copyright © 1993 John Wiley & Sons, Ltd.

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The status of KAGRA underground cryogenic gravitational wave telescope 15

The status of KAGRA underground cryogenic gravitational wave...

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15th International Conference on Topics in Astroparticle and Underground Physics, TAUP 2017

作者： Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Barton, M.A. Cannon, K. Craig, K. Creus, W. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Furuhata, T. Haino, S. Hasegawa, K. Hashino, K. Hayama, K. Hirobayashi, S. Hirose, E. Hsieh, A.H. Inoue, Y. Ioka, K. Itoh, Y. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kambara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawai, N. Kawamura, S. Kim, C. Kim, H. Kim, J. Kim, Y. Kimura, N. Kinugawa, T. Kirii, S. Kitaoka, Y. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kumar, R. Lee, H. Lee, H. Liu, Y. Luca, N. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Miyakawa, O. Miyamoto, T. Miyamoto, A. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Narikawa, T. Quynh, L.Nguyen Ni, W.-T. Ochi, T. Oh, J. Oh, S. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Alleano, F.E.P. Pinto, I. Sago, N. Saijo, M. Saito, Y. Sakai, K. Sakai, Y. Sasaki, Y. Sasaki, M. Sato, S. Sato, T. Sekiguchi, Y. Seto, N. Shibata, M. Shimoda, T. Shinkai, H. Shoda, A. Somiya, K. Son, E. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueki, S. Ueno, K. Ushiba, T. Van Putten, M.H.P.M. Vocca, H. Wada, S. Wakamatsu, T. Yamada, T. Yamamoto, S. Yamamoto, T. Yamamoto, K. Yamamoto, A. Yokoyama, J. Yokozawa, T. Yoon, T.H. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Department of Physics University of Tokyo Bunkyo Tokyo113-0033 Japan Earthquake Research Institute University of Tokyo Bunkyo Tokyo113-0032 Japan Department of Advanced Physics Hirosaki University Aomori Hirosaki036-8561 Japan National Astronomical Observatory of Japan Hida Gifu506-1205 Japan Department of Physics Tokyo Institute of Technology Meguro Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Hida Gifu506-1205 Japan Research Center for the Early Universe University of Tokyo Bunkyo Tokyo113-0033 Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama Toyama930-8555 Japan Faculty of Engineering University of Toyama Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Sakyo Kyoto606-8502 Japan Department of Physics Osaka City University Sumiyosi Osaka558-8585 Japan Department of Physics Osaka University Osaka Toyonaka560-0043 Japan Korea Institute of Science and Technology Information Yuseong Daejeon 34141 Korea Republic of Yuseong Daejeon34055 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Computer Simulation Inje University Gimhae Gyeongsangnam50834 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of High Energy Accelerator Research Organization Ibaraki Tsukuba305-0801 Japan Department of Physical Science Hiroshima University Higashihiroshima Hiroshima903-0213 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka814-0180 Japan Department of Advanced Materials Science University of Tokyo Chiba Kashiwa277-8582 Japan Istituto Nazionale di Fisica Nucleare Sapienza University Roma00185 Italy Department o

KAGRA is a 3-km interferometric gravitational wave telescope located in the Kamioka mine in Japan. It is the first km-class gravitational wave telescope constructed underground to reduce seismic noise, and the first km-class telescope to use cryogenic cooling of test masses to reduce thermal noise. The construction of the infrastructure to house the interferometer in the tunnel, and the initial phase operation of the interferometer with a simple 3-km Michelson configuration have been completed. The first cryogenic operation is expected in 2018, and the observing runs with a full interferometer are expected in 2020s. The basic interferometer configuration and the current status of KAGRA are described. © Published under licence by IOP Publishing Ltd.

关键词： Interferometers

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Developing a prototype concurrent design tool for composite topside structures

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NAVAL ENGINEERS JOURNAL 1997年第3期109卷 279-290页

作者： Dirlik, S Hambric, S Azarm, S Marquardt, M Hellman, A Bartlett, S Castelli, V Steve Dirlik:began his career at the Naval Surface Warfare Center Carderock Division in 1981 as an aerospace engineer co-op student. He completed his bachelor of science degree in aeropace and ocean engineering from Virginia Polytechnic Institute and State University in 1985 and his master of science degree in aerospace engineering from the University of Maryland in 1990. Mr. Dirlik recently completed a master of science program in applied physics at The Johns Hopins University and currently works in the Radar Cross Section and Target Physics Branch of the Signatures Directorate at the Naval Surface Warfare Center Corderoke Division. He has worked on Navy low observable programs since 1990. Stephen Hambric:is a research associate at the Applied Research Laboratory at The Pennsylvania State University. He received his B. S. and M.S. degree in mechanical engineering from Virginia Polytechnic Institute and State University and his D. Sc. in mechnical engineering from the George Washington University. He has worked on several computer aided multidiscikplinary design and optimization projects over the years including an automated propeller design system and a structural acoustic optimization capability. Dr. Shpour Azarm:is currently working as an associate professor with the Design and Manufacturing Group of the Department of Mechanical Engineering at the University of maryland at College Park. Dr Azarm's expertise is in the areas of optimization-based designed and concurrent design and optimization of multidisciplinary systems. He was a consultant at Black & Decker Corporation (summer 1996) and worked as a Navy senior summer faculty fellow (summer 1995) and a NASA summer faculty fellow (summer 1994). He was a visiting scientist at NASA Langley Research Center for Multidisciplinary Analysis and Applied Structural Optimzatiom at the University of Siegen in Germany (spring 1992) and the Design Institute of the Technical University of Denmark (summer 1990). Dr Azarm was an associate technical editor of the ASME Jour

A prototype concurrent engineering tool has been developed for the preliminary design of composite topside structures for modern navy warships. This tool, named GELS for the Concurrent Engineering of Layered Structures, provides designers with an immediate assessment of the impacts of their decisions on several disciplines which are important to the performance of a modern naval topside structure, including electromagnetic interference effects (EMI), radar cross section (RCS), structural integrity, cost, and weight. Preliminary analysis modules in each of these disciplines are integrated to operate from a common set of design variables and a common materials database. Performance in each discipline and an overall fitness function for the concept are then evaluated. A graphical user interface (GUI) is used to define requirements and to display the results from the technical analysis modules. Optimization techniques, including feasible sequential quadratic programming (FSQP) and exhaustive search are used to modify the design variables to satisfy all requirements simultaneously. The development of this tool, the technical modules, and their integration are discussed noting the decisions and compromises required to develop and integrate the modules into a prototype conceptual design tool.

关键词： Warships

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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