检索结果-内蒙古大学图书馆

Search for continuous gravitational waves from 20 accreting millisecond x-ray pulsars in O3 LIGO data

Physical Review D 2022年第2期105卷 022002-022002页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain T. Akutsu S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer L. Cadonati G. Cagnoli C. Cahillane J. Calderón Bustillo J. D. Callaghan T. A. Callister E. Ca LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière F-69622 Villeurbanne France Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Un

Results are presented of searches for continuous gravitational waves from 20 accreting millisecond x-ray pulsars with accurately measured spin frequencies and orbital parameters, using data from the third observing run of the Advanced LIGO and Advanced Virgo detectors. The search algorithm uses a hidden Markov model, where the transition probabilities allow the frequency to wander according to an unbiased random walk, while the J-statistic maximum-likelihood matched filter tracks the binary orbital phase. Three narrow subbands are searched for each target, centered on harmonics of the measured spin frequency. The search yields 16 candidates, consistent with a false alarm probability of 30% per subband and target searched. These candidates, along with one candidate from an additional target-of-opportunity search done for SAX J1808.4−3658, which was in outburst during one month of the observing run, cannot be confidently associated with a known noise source. Additional follow-up does not provide convincing evidence that any are a true astrophysical signal. When all candidates are assumed nonastrophysical, upper limits are set on the maximum wave strain detectable at 95% confidence, h095%. The strictest constraint is h095%=4.7×10−26 from IGR J17062−6143. Constraints on the detectable wave strain from each target lead to constraints on neutron star ellipticity and r-mode amplitude, the strictest of which are ε95%=3.1×10−7 and α95%=1.8×10−5 respectively. This analysis is the most comprehensive and sensitive search of continuous gravitational waves from accreting millisecond x-ray pulsars to date.

关键词： Gravitational waves Binary stars Neutron stars & pulsars

来源：评论

学校读者我要写书评

暂无评论

An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

引用

arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

Overview of KAGRA : Data transfer and management

引用

Progress of Theoretical and Experimental Physics 2023年第10期2023卷

作者： Akutsu, T Ando, M Arai, K Arai, Y Araki, S Araya, A Aritomi, N Asada, H Aso, Y Bae, S Bae, Y Baiotti, L Bajpai, R Barton, M A Cannon, K Cao, Z Capocasa, E Chan, M Chen, C Chen, K Chen, Y Chiang, C-Y Chu, H Chu, Y-K Eguchi, S Enomoto, Y Flaminio, R Fujii, Y Fujikawa, Y Fukunaga, M Fukushima, M Gao, D Ge, G Ha, S Hagiwara, A Haino, S Han, W-B Hasegawa, K Hattori, K Hayakawa, H Hayama, K Himemoto, Y Hiranuma, Y Hirata, N Hirose, E Hong, Z Hsieh, B Huang, G-Z Huang, H-Y Huang, P Huang, Y-C Huang, Y Hui, D C Y Ide, S Ikenoue, B Imam, S Inayoshi, K Inoue, Y Ioka, K Ito, K Itoh, Y Izumi, K Jeon, C Jin, H-B Jung, K Jung, P Kaihotsu, K Kajita, T Kakizaki, M Kamiizumi, M Kanda, N Kang, G Kawaguchi, K Kawai, N Kawasaki, T Kim, C Kim, J Kim, J C Kim, W S Kim, Y-M Kimura, N Kita, N Kitazawa, H Kobayashi, Y Kojima, Y Kokeyama, K Komori, K Kong, A K H Kotake, K Kozakai, C Kozu, R Kumar, R Kume, J Kuo, C Kuo, H-S Kuromiya, Y Kuroyanagi, S Kusayanagi, K Kwak, K Lee, H K Lee, H W Lee, R Leonardi, M Li, K L Lin, L C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H L Liu, G C Luo, L-W Majorana, E Marchio, M Michimura, Y Mio, N Miyakawa, O Miyamoto, A Miyazaki, Y Miyo, K Miyoki, S Mori, Y Morisaki, S Moriwaki, Y Nagano, K Nagano, S Nakamura, K Nakano, H Nakano, M Nakashima, R Nakayama, Y Narikawa, T Naticchioni, L Negishi, R Quynh, L Nguyen Ni, W-T Nishizawa, A Nozaki, S Obuchi, Y Ogaki, W Oh, J J Oh, K Oh, S H Ohashi, M Ohashi, T Ohishi, N Ohkawa, M Ohta, H Okutani, Y Okutomi, K Oohara, K Ooi, C Oshino, S Otabe, S Pan, K Pang, H Parisi, A Park, J Arellano, F E Peña Pinto, I Sago, N Saito, S Saito, Y Sakai, K Sakai, Y Sakuno, Y Sasaki, Y Sato, S Sato, T Sawada, T Sekiguchi, T Sekiguchi, Y Shao, L Shibagaki, S Shimizu, R Shimoda, T Shimode, K Shinkai, H Shishido, T Shoda, A Somiya, K Son, E J Sotani, H Sugimoto, R Suresh, J Suzuki, T Tagoshi, H Takahashi, H Takahashi, R Takamori, A Takano, S Takeda, H Takeda, M Tanaka, H Tanaka, K Tanaka, T Tanioka, S Martin, E N Tapia San Telada, S Tomaru, T Tomigami, Y Tomura, T Travasso, F Trozzo, L Tsang, T Ts Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Hirosaki University 3 Bunkyo-cho Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University No. 19 Xinjiekou Street Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Rd. Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy Nation

KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located in Kamioka, Gifu prefecture, Japan. In this article, we describe the KAGRA data management system, i.e., recording of data, transfer from the KAGRA experiment site to computing resources, as well as data distribution to tier sites, including international sites in Taiwan and Korea. The amount of KAGRA data exceeded 1.0 PiB and increased by about 1.5 TB per day during operation in 2020. Our system has succeeded in data management, and has achieved performance that can withstand observations after 2023, that is, a transfer rate of 20 MB s-1or more and file storage of sufficient capacity for petabyte class. We also discuss the sharing of data between the global gravitational-wave detector network with other experiments, namely LIGO and Virgo. The latency, which consists of calculation of calibrated strain data and transfer time within the global network, is very important from the view of multi-messenger astronomy using gravitational waves. Real-time calbrated data delivered from the KAGRA detector site and other detectors to our computing system arrive with about 4–15 seconds of latency. These latencies are sufficiently short compared to the time taken for gravitational wave event search computations. We also established a high-latency exchange of offline calibrated data that was aggregated with a better accuracy compared with real-time data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

arXiv

引用

arXiv 2020年

作者： Marinescu, Razvan V. Oxtoby, Neil P. Young, Alexandra L. Bron, Esther E. Toga, Arthur W. Weiner, Michael W. Barkhof, Frederik Fox, Nick C. Eshaghi, Arman Ansart, Manon Durrleman, Stanley Lu, Pascal Iddi, Samuel Li, Dan Thompson, Wesley K. Donohue, Michael C. Nahon, Aviv Levy, Yarden Halbersberg, Dan Cohen, Mariya Liao, Huiling Li, Tengfei Yu, Kaixian Zhu, Hongtu Tamez-Peña, José G. Ismail, Aya Wood, Timothy Bravo, Hector Corrada Nguyen, Minh Sun, Nanbo Feng, Jiashi Thomas Yeo, B.T. Chen, Gang Qi, Ke Chen, Shiyang Qiu, Deqiang Buciuman, Ionut Kelner, Alex Pop, Raluca Rimocea, Denisa Ghazi, Mostafa M. Nielsen, Mads Ourselin, Sebastien Sørensen, Lauge Venkatraghavan, Vikram Liu, Keli Rabe, Christina Manser, Paul Hill, Steven M. Howlett, James Huang, Zhiyue Kiddle, Steven Mukherjee, Sach Rouanet, Anaïs Taschler, Bernd Tom, Brian D.M. White, Simon R. Faux, Noel Sedai, Suman de Velasco Oriol, Javier Clemente, Edgar E.V. Estrada, Karol Aksman, Leon Altmann, Andre Stonnington, Cynthia M. Wang, Yalin Wu, Jianfeng Devadas, Vivek Fourrier, Clementine Raket, Lars Lau Sotiras, Aristeidis Erus, Guray Doshi, Jimit Davatzikos, Christos Vogel, Jacob Doyle, Andrew Tam, Angela Diaz-Papkovich, Alex Jammeh, Emmanuel Koval, Igor Moore, Paul Lyons, Terry J. Gallacher, John Tohka, Jussi Ciszek, Robert Jedynak, Bruno Pandya, Kruti Bilgel, Murat Engels, William Cole, Joseph Golland, Polina Klein, Stefan Alexander, Daniel C. Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Netherlands Laboratory of NeuroImaging University of Southern California United States Center for Imaging of Neurodegenerative Diseases University of California San Francisco United States Department of Radiology and Nuclear Medicine VU Medical Centre Netherlands Dementia Research Centre The UK Dementia Research Institute UCL Queen Square Institute of Neurology United Kingdom Institut du Cerveau et de la Moelle épinière Paris France Alzheimer's Therapeutic Research Institute University of Southern California United States Department of Statistics and Actuarial Science University of Ghana Ghana Department of Family Medicine and Public Health University of California San Diego United States Ben Gurion University of the Negev Beersheba Israel The University of Texas Health Science Center at Houston Houston United States Instituto Tecnológico y de Estudios Superiores de Monterrey Monterrey Mexico University of Maryland College Park United States National University of Singapore Singapore Singapore Medical College of Wisconsin Milwaukee United States Emory University Atlanta United States Georgia Institute of Technology Atlanta United States Vasile Lucaciu National College Baia Mare Romania Biomediq A/S Denmark Cerebriu A/S Denmark University of Copenhagen Denmark School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom Genentech United States MRC Biostatistics Unit University of Cambridge United Kingdom IBM Research Australia Melbourne Australia Brandeis University Waltham United States Mayo Clinic ScottsdaleAZ United States H. Lundbeck A/S Denmark Clinical Memory Research Unit Department of Clinical Sciences Malmo Lund University Lund Sweden Center

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADASCog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 perce

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

DTI changes of thalamic subregions in genetic frontotemporal dementia: findings from the GENFI cohort

引用

Alzheimer's & Dementia 2023年第S24期19卷

作者： Sonja Soskic Henry F. J. Tregidgo Emily G. Todd Arabella Bouzigues David M Cash Lucy L. Russell David L Thomas Ian B Malone John C. van Swieten Lize C. Jiskoot Harro Seelaar Barbara Borroni Daniela Galimberti Raquel Sanchez-Valle Robert Laforce Jr. Fermin Moreno Matthis Synofzik Caroline Graff Mario Masellis Carmela Tartaglia James B. Rowe Rik Vandenberghe Elizabeth Finger Fabrizio Tagliavini Alexandre Mendonca Isabel Santana Christopher R. Butler Simon Ducharme Alexander Gerhard Johannes Levin Markus Otto Sandro Sorbi Isabelle Le Ber Florence Pasquier Daniel C Alexander Juan Eugenio Iglesias Jonathan D. Rohrer Martina Bocchetta the GENetic Frontotemporal dementia Initiative (GENFI) Centre for Medical Image Computing University College London London United Kingdom SonjaSoskic Centre for Medical Image Computing University College London London United Kingdom. Dementia Research Centre Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology University College London London United Kingdom UCL Centre for Medical Image Computing London United Kingdom Neuroradiological Academic Unit Department of Brain Repair and Rehabilitation UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Neurology and Alzheimer Center Erasmus Medical Center Rotterdam Netherlands Centre for Neurodegenerative Disorders Neurology Unit Department of Clinical and Experimental Sciences University of Brescia Brescia Italy Department of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milan Italy Neurology Department Hospital Clinic Institut d'Investigacions Biomediques Barcelona Spain Clinique Interdisciplinaire de Mémoire Département des Sciences Neurologiques CHU de Québec and Faculté de Médecine Université Laval Quebec City QC Canada Hospital Universitario Donostia San Sebastian Spain Division Translational Genomics of Neurodegenerative Diseases Hertie Institute for Clinical Brain Research (HIH) University of Tübingen Tübingen Germany German Center for Neurodegenerative Diseases (DZNE) Tübingen Germany Karolinska Institutet Department NVS Division of Neurogeriatrics Stockholm Sweden Unit for Hereditary Dementia Theme Inflammation and Aging Karolinska University Hospital-Solna Stockholm Sweden Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada Toronto Western Hospital Tanz Centre for Research in Neurodegenerative Disease Toronto ON Canada Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and Brain Sci

Background Atrophy of thalamic subregions has been observed across the spectrum of frontotemporal dementia (FTD). To gain better insight into underlying tissue changes, we investigated how thalamic subregional fractional anisotropy (FA) and mean diffusivity (MD) derived from diffusion tensor imaging (DTI) are altered in genetic FTD. Method We used our newly developed thalamus segmentation tool, which jointly combines structural and diffusion input MRI data, to segment thalami and extract thalamic subregional FA and MD values for 163 genetic mutation carriers and 126 non-carriers with suitable 3T MRI data from the GENetic FTD Initiative (GENFI). Mutation carriers were divided according to their genetic diagnosis and CDR®+NACC FTLD global scores into presymptomatic/prodromal (≤0.5: 41 C9orf72 , 59 GRN , 34 MAPT ) and symptomatic (≥1: 8 C9orf72 , 11 GRN , 10 MAPT ) groups. Mean FA and MD values for thalamic subregions were obtained using diffusion tensors interpolated in the log domain and weighted by segmentation posterior probabilities. Thalamic subregional FA and MD values for presymptomatic and symptomatic mutation carriers within each genetic group were compared with non-carriers using analysis of covariance with bootstrapping, where age, scanner type, and sex were covariates. We corrected for multiple comparisons and calculated percentage changes in adjusted FA and MD mean values for mutation carriers relative to non-carriers. Result The only significant change at the presymptomatic stage was found for C9orf72 expansion carriers, who showed FA reduction in the intralaminar subregion (5%) (Figure 1, Table 1). In symptomatic C9orf72 expansion carriers, FA was reduced in the laterodorsal (21%), lateral posterior (13%), anteroventral (13%) and intralaminar (11%) subregions. Symptomatic MAPT mutation carriers also showed FA reduction in the laterodorsal (15%) and anteroventral (11%) subregions. No significant FA reductions were found for GRN mutation carriers and no s

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparison of Laguerre-Gaussian, Hermite–Gaussian and linearly polarized modes in SDM over FMF with electrical nonlinear equalizer

引用

AIP Conference Proceedings 2020年第1期2203卷

作者： Alaa Fareed Alaan Ghazi Aras Al-Dawoodi S. A. Aljunid Syed Zulkarnain Syed Idrus C. B. M. Rashidi Angela Amphawan Ahmed M. Fakhrudeen Ibrahim Eskandar Ibrahim Fadhel 1School of Computing Universiti Utara Malaysia Malaysia 2School of Human Development & Techno-communication Universiti Malaysia Perlis Malaysia 3School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia 4Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq 5Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts USA 6Networks Department College of Computer Science and Information Technology Kirkuk University Iraq 7Department of Computer and Information Systems National Institute of Administrative Science Yemen

The development of communication technologies shows that fiber optics is in the main communication improvement in the last decade. Fiber optics is moved from laboratory to be a reality. Transmission of the data using the light as a signal carrier is a revolutionary improvement in the communication era. However, to enable this technology several obstacles should be addressed, such as achieving the longest distance transmission with less possible bit error rate (BER). One of the solutions was performed by careful selection of the optical modes profiles with the help of the nonlinear electrical equalizer (NLEE) to solve the problem of the mode coupling. This paper investigates on the compensation of different modes profiles in optical communication systems over few-mode fiber (FMF) based on Space Division Multiplexer (SDM) with an electrical non-linear equalizer utilized to equalize nonlinear distortions. The result shows that the LG mode achieved the best transmission distance comparing to the other modes which make it the best option to be used. Basically, the evaluation of the system has been done based on standard evaluation metrics such as BER and Eye Diagram. These results can optimize the data transmission quality.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

引用

The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spectral properties of hyperbolic nano-networks with tunable aggregation of simplexes

arXiv

引用

arXiv 2019年

作者： Dankulov, Marija Mitrović Tadić, Bosiljka Melnik, Roderick Department of Theoretical Physics Jožef Stefan Institute Jamova 39 Ljubljana Slovenia Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia Complexity Science Hub Vienna Josephstadterstrasse 39 Vienna Austria MS2Discovery Interdisciplinary Research Institute M2NeT Laboratory Department of Mathematics Wilfrid Laurier University 75 University Ave W WaterlooONN2L 3C5 Canada BCAM - Basque Center for Applied Mathematics Alameda de Mazarredo 14 BilbaoE-48009 Spain

Cooperative self-assembly can result in complex nano-networks with new hyperbolic geometry. However, the relation between the hyperbolicity and spectral and dynamical features of these structures remains unclear. Using the model of aggregation of simplexes introduced in I [Sci. Rep., 8:1987, 2018], here we study topological and spectral properties of a large class of self-assembled structures consisting of monodisperse building blocks (cliques of size n = 3, 4, 5, 6) which self-assemble via sharing the geometrical shapes of a lower order. The size of the shared sub-structure is tunned by varying the chemical affinity ν such that for significant positive ν sharing the largest face is the most probable, while for ν 4 can be reached for n ≥ 5 and sufficiently large ν. For the aggregates of triangles and tetrahedra, the spectral dimension remains in the range ds ∈ [2, 4), as well as for the higher cliques at vanishing affinity. On the other end, for ν Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

Anomaly Detection Based on Spatio-Temporal and Sparse Featur...

引用

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Laisen Nie Huizhi Wang Shimin Gong Zhaolong Ning Mohammad S. Obaidat Kuei-Fang Hsiao School of Electronics and Information Northwestern Polytechnical University Xi'an P.R.China Qingdao Research Institute of Northwestern Polytechnical University Qingdao P.R.China School of Intelligent Systems Engineering Sun Yat-sen University China State Key Laboratory for Novel Software Technology Nanjing University China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China Department of Information Management Ming Chuan University Taiwan

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance for anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

关键词： Anomaly detection Feature extraction Vehicular ad hoc networks Kernel Estimation Security Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Mycorrhizal symbioses and tree diversity in global forest communities

引用

science Advances 2025年第24期11卷

作者： Feng Jiang Xucai Pu Bernhard Schmid Peter B. Reich Jingjing Liang Akane O. Abbasi Jesús Aguirre-Gutiérrez Angelica Maria Almeyda Zambrano Jan Altman Juan Gabriel Álvarez-González Luciana F. Alves Bienvenu H. K. Amani Christian Ammer Gerardo A. Aymard Naveen Babu Kanda Meredith L. Bastian Jean-Francois Bastin Marijn Bauters Pascal Boeckx Svetlana N. Bondarchuk Alexander Bondarev Francis Q. Brearley Sophie Brennan Jaime Briseño-Reyes Eben N. Broadbent Goran Češljar Han Y. H. Chen Chelsea Chisholm WookJin Choi Emil Cienciala Connie J. Clark Alessio Collalti José Javier Corral-Rivas Javid Ahmad Dar Selvadurai Dayanandan Sergio de-Miguel Ashaq Ahmad Dar Géraldine Derroire Ilija Djordjevic Tran Van Do Jiří Doležal Aurélie Dourdain Teresa Eyre Adandé Belarmain Fandohan Lorenzo Frizzera Roberto Cazzolla Gatti Damiano Gianelle M. Socorro González Elizondo Elisa Grieco David J. Harris Andy Hector Bruno Hérault Cang Hui Nobuo Imai Andrzej M. Jagodziński Chengjun Ji Lin Jiang Carlos A. Joly Viktor N. Karminov Kuswata Kartawinata Justin N. Kassi Elizabeth Kearsley Gunnar Keppel Mohammed Latif Khan Carine Klauberg Kirill A. Korznikov Subashree Kothandaraman Florian Kraxner Leonid Krivobokov Dmitry Kucher Amit Kumar Anna Kvashnina Gaia Vaglio Laurin Rodrigo Vieira Leite Moses B. Libalah Ekaterina S. Lonkina Huicui Lu Shan Luo Yuan Luo Emma Mackintosh Andrew R. Marshall Rodolfo Vásquez Martínez Radim Matula William McDonald Ayyappan Narayanan María Guadalupe Nava-Miranda Jagadeesan Naveenkumar Abel Monteagudo Mendoza Stanisław Miścicki Tatyana Moskalyuk Liudmila Mukhortova Sharif A. Mukul Gert-Jan Nabuurs Victor J. Neldner Radovan Nevenic Anny E. N’Guessan Michael Ngugi Alain Paquette Elena I. Parfenova Marc Parren Narayanaswamy Parthasarathy Pablo L. Peri Sebastian Pfautsch Maria T. F. Piedade Galina Polyakova Axel Dalberg Poulsen John R. Poulsen Hans Pretzsch Mirco Rodeghiero Ervan Rutishauser Purabi Saikia Philippe Saner Dmitry Schepaschenko Jochen Schöngart Eric B. Searle Douglas Sheil Zehao Shen Stephanie Shooner Anatoly Sh Institute of Ecology and State Key Laboratory for Vegetation Structure Function and Construction (VegLab) College of Urban and Environmental Sciences Peking University Beijing China. Remote Sensing Laboratories Department of Geography University of Zürich Zürich Switzerland. Department of Forest Resources University of Minnesota St Paul MN 55108 USA. Hawkesbury Institute for the Environment Western Sydney University Penrith New South Wales Australia. Institute for Global Change Biology and School for Environment and Sustainability University of Michigan Ann Arbor MI 48109 USA. Forest Advanced Computing and Artificial Intelligence Laboratory (FACAI) Department of Forestry and Natural Resources Purdue University West Lafayette IN USA. Environmental Change Institute School of Geography and the Environment University of Oxford Oxford UK. Leverhulme Centre for Nature Recovery University of Oxford Oxford UK OX13QY. Spatial Ecology and Conservation (SPEC) Lab Center for Latin American Studies University of Florida Gainesville FL 32611 USA. Institute of Botany of the Czech Academy of Sciences Třeboň Czech Republic. Faculty of Forestry and Wood Sciences Czech University of Life Sciences Prague Czech Republic. Departamento de Ingeniería Agroforestal Universidad de Santiago de Compostela Lugo Spain. Center for Tropical Research Institute of the Environment and Sustainability University of California Los Angeles Los Angeles CA 90095 USA. Université Jean Lorougnon Guédé Unité de Formation et de Recherche en Agroforesterie BP 150 Daloa Ivory Coast. Université Nangui Abrogoua Unité de Formation et de Recherche en Sciences de la Nature 02 BP 801 Abidjan 02 Laboratoire d’Écologie et du Développement durable (LEDD) Abidjian Ivory Coast. Silviculture and Forest Ecology Georg August-University of Göttingen Göttingen Germany. UNELLEZ-Guanare Programa de Ciencias del Agro y el Mar Herbario Universitario (PORT) Portuguesa Venezuela. Jardín Botánico de Bogotá José Cele

Unraveling the mechanisms underlying the maintenance of species diversity is a central pursuit in ecology. It has been hypothesized that ectomycorrhizal (EcM) in contrast to arbuscular mycorrhizal fungi can reduce tree species diversity in local communities, which remains to be tested at the global scale. To address this gap, we analyzed global forest inventory data and revealed that the relationship between tree species richness and EcM tree proportion varied along environmental gradients. Specifically, the relationship is more negative at low latitudes and in moist conditions but is unimodal at high latitudes and in arid conditions. The negative association of EcM tree proportion on species diversity at low latitudes and in humid conditions is likely due to more negative plant-soil microbial interactions in these regions. These findings extend our knowledge on the mechanisms shaping global patterns in plant species diversity from a belowground view.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：