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arXiv 2018年

作者： Li, Wenbin Liu, Yanfang Huo, Jing Shi, Yinghuan Gao, Yang Wang, Lei Luo, Jiebo The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The College of Mathematics and Information Engineering Longyan University 364012 China The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Computing and Information Technology University of Wollongong Australia The Department of Computer Science University of Rochester RochesterNY14611 United States

Online metric learning has been widely applied in classification and retrieval. It can automatically learn a suitable metric from data by restricting similar instances to be separated from dissimilar instances with a given margin. However, the existing online metric learning algorithms have limited performance in real-world classifications, especially when data distributions are complex. To this end, this paper proposes a multilayer framework for online metric learning to capture the nonlinear similarities among instances. Different from the traditional online metric learning, which can only learn one metric space, the proposed Multi-Layer Online Metric Learning (MLOML) takes an online metric learning algorithm as a metric layer and learns multiple hierarchical metric spaces, where each metric layer follows a nonlinear layers for the complicated data distribution. Moreover, the forward propagation (FP) strategy and backward propagation (BP) strategy are employed to train the hierarchical metric layers. To build a metric layer of the proposed MLOML, a new Mahalanobis-based Online Metric Learning (MOML) algorithm is presented based on the passive-aggressive strategy and one-pass triplet construction strategy. Furthermore, in a progressively and nonlinearly learning way, MLOML has a stronger learning ability than traditional online metric learning in the case of limited available training data. To make the learning process more explainable and theoretically guaranteed, theoretical analysis is provided. The proposed MLOML enjoys several nice properties, indeed learns a metric progressively, and performs better on the benchmark datasets. Extensive experiments with different settings have been conducted to verify these properties of the proposed MLOML. Copyright © 2018, The Authors. All rights reserved.

关键词： Multilayers

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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

arXiv

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arXiv 2022年

作者： Srivastava, Aarohi Rastogi, Abhinav Rao, Abhishek Shoeb, Abu Awal Md Abid, Abubakar Fisch, Adam Brown, Adam R. Santoro, Adam Gupta, Aditya Garriga-Alonso, Adrià Kluska, Agnieszka Lewkowycz, Aitor Agarwal, Akshat Power, Alethea Ray, Alex Warstadt, Alex Kocurek, Alexander W. Safaya, Ali Tazarv, Ali Xiang, Alice Parrish, Alicia Nie, Allen Hussain, Aman Askell, Amanda Dsouza, Amanda Slone, Ambrose Rahane, Ameet Iyer, Anantharaman S. Andreassen, Anders Madotto, Andrea Santilli, Andrea Stuhlmüller, Andreas Dai, Andrew La, Andrew Lampinen, Andrew Zou, Andy Jiang, Angela Chen, Angelica Vuong, Anh Gupta, Animesh Gottardi, Anna Norelli, Antonio Venkatesh, Anu Gholamidavoodi, Arash Tabassum, Arfa Menezes, Arul Kirubarajan, Arun Mullokandov, Asher Sabharwal, Ashish Herrick, Austin Efrat, Avia Erdem, Aykut Karakaş, Ayla Ryan Roberts, B. Loe, Bao Sheng Zoph, Barret Bojanowski, Bartlomiej Özyurt, Batuhan Hedayatnia, Behnam Neyshabur, Behnam Inden, Benjamin Stein, Benno Ekmekci, Berk Lin, Bill Yuchen Howald, Blake Orinion, Bryan Diao, Cameron Dour, Cameron Stinson, Catherine Argueta, Cedrick Ramírez, César Ferri Singh, Chandan Rathkopf, Charles Meng, Chenlin Baral, Chitta Wu, Chiyu Callison-Burch, Chris Waites, Chris Voigt, Christian Manning, Christopher D. Potts, Christopher Ramirez, Cindy Rivera, Clara E. Siro, Clemencia Raffel, Colin Ashcraft, Courtney Garbacea, Cristina Sileo, Damien Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Dan Freeman, Daniel Khashabi, Daniel Levy, Daniel González, Daniel Moseguí Perszyk, Danielle Hernandez, Danny Chen, Danqi Ippolito, Daphne Gilboa, Dar Dohan, David Drakard, David University of Notre Dame United States Google United States Rutgers University United States Stanford University United States MIT United States DeepMind United Kingdom University of Cambridge United Kingdom University of Amsterdam Netherlands Google Research Blueshift Team NeuralSpace New York University United States Cornell University United States Koç University Turkey University of California Irvine United States Duke Kunshan University China Anthropic United States Georgia Tech United States KNC Neural Calculus National Public School - HSR Layout HKUST Hong Kong Sapienza University of Rome Italy Ought Google Research United States UC Berkeley United States Thapar University India Amazon United States Microsoft United States University of Pennsylvania United States Imperial College London United Kingdom Allen Instituite for AI Tel Aviv University Yale University United States Wroclaw University of Science and Technology Poland Blueshift Max Planck Institute for Mathematics in the Sciences Germany Bauhaus-Universität Weimar Germany Thomson Reuters Special Services University of Southern California United States Rice University United States Queen's University Kingston Canada Princeton University United States Universitat Politècnica de València Spain Juelich Research Center Germany Arizona State University United States Carnegie Mellon University United States Karlsruhe Institute of Technology Germany UNC Chapel Hill United States University of Michigan United States KU Leuven Belgium Harvard University United States ML Collective University of Virginia United States University of Edinburgh United Kingdom Research Institutes of Sweden Sweden University of Washington United States Facebook AI Research United States Mila Canada Landskape AI Umeå University Sweden Brain Team OpenAI Brown University United States MIT Media Lab United States Hacettepe University Turkey Saarland University Germany National University of Singapore Singapore Strathmore University

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance);performance is remarkably similar across model classes, though with benefits from sparsity;tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics;social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting. Copyright © 2022, The Authors. All rights reserved.

关键词： Software design

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Childhood adversities and their associations with mental disorders in the World Mental Health International College Student surveys initiative

引用

Psychiatry research 2025年 351卷

作者： Mathilde M Husky Sue Lee Nancy A Sampson Shelby Borowski Yesica Albor Ahmad N Alhadi Jordi Alonso Nouf K Al-Saud Yasmin A Altwaijri Claes Andersson Lukoye Atwoli Caroline Ayuya Muaka Patricia M Báez-Mansur Laura Ballester Jason Bantjes Harald Baumeister Marcus Bendtsen Corina Benjet Anne H Berman Ronny Bruffaerts Paula Carrasco Silver C N Chan Irina F Cohut María Anabell Covarrubias Díaz Couder Paula Cristóbal-Narváez Marcelo A Crockett Pim Cuijpers Oana A David Dong Dong David D Ebert Carlos G Forero Jorge Gaete Margalida Gili Raúl Gutiérrez-García Josep Maria Haro Penelope Hasking Xanthe Hunt Florence Jaguga Leontien Jansen Álvaro I Langer Irene Léniz Yan Liu Christine Lochner Scarlett Mac-Ginty Vania Martínez Andre Mason Muthoni Mathai Margaret McLafferty Elaine K Murray Catherine M Musyoka Cătălin Nedelcea Daniel Núñez Siobhan M O'Neill José A Piqueras Codruta A Popescu Charlene Rapsey Kealagh Robinson Tiscar Rodriguez-Jimenez Wylene Saal Damian Scarf Oi-Ling Siu Dan J Stein Sascha Y Struijs Cristina T Tomoiaga Karla Patricia Valdés-García Eunice Vargas-Contreras Shelby Vereecke Daniel V Vigo Angel Y Wang Samuel Y S Wong Ronald C Kessler Active Team Bordeaux Population Health Research Center INSERM U1219 University of Bordeaux France. Department of Health Care Policy Harvard Medical School Boston MA USA. Center for Global Mental Health Research Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz Mexico City Mexico. Department of Psychiatry College of Medicine King Saud University Riyadh Saudi Arabia SABIC Psychological Health Research & Applications Chair (SPHRAC) Department of Psychiatry College of Medicine King Saud University Riyadh Saudi Arabia. Health Services Research Group Hospital del Mar Research Institute Barcelona Spain Department of Medicine and Life Sciences Pompeu Fabra University (UPF) Barcelona Spain Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública Instituto de Salud Carlos III (CIBERESP ISCIII) Madrid Spain. Biostatistics Epidemiology and Scientific Computing Department King Faisal Specialist Hospital and Research Centre Riyadh Saudi Arabia. Department of Criminology Malmö University Malmö Sweden. Department of Medicine Medical College East Africa the Aga Khan University Nairobi Kenya Brain and Mind Institute the Aga Khan University Nairobi Kenya Department of Mental Health and Behavioural Sciences Moi University School of Medicine Eldoret Kenya. Department of Psychology & Counselling Daystar University Nairobi Kenya. Coordinación de Desarrollo Académico y Servicios Educativos Universidad la Salle Ciudad Victoria Ciudad Victoria Mexico. Mental Health Alcohol Substance Use and Tobacco Research Unit South African Medical Research Council Cape Town South Africa Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa. Department of Clinical Psychology and Psychotherapy Ulm University Ulm Germany. Department of Health Medicine and Caring Sciences Linköping University Linkoping Sweden. Department of Psychology Uppsala University Uppsala Sweden. Center for Public Health Psychiatry Department of Neuros

PURPOSE:This study investigates associations of childhood adversities (CAs) with lifetime prevalence, 12-month prevalence, and 12-month persistence of mental disorders in a large cross-national sample of university students. METHODS:data came from epidemiologic surveys carried out by the World Mental Health International College Student (WMH-ICS)Initiative across 18 countries (n=60,719). The web-based surveys screened for lifetime and 12-month prevalence and age-of-onset of common DSM-5 disorders (Major Depressive Disorder, Bipolar I/II Disorder, Generalized Anxiety Disorder, Panic Disorder, Posttraumatic Stress Disorder, Alcohol and Drug Use disorders, Attention-Deficit/Hyperactivity Disorder) and five types of CAs (family dysfunction, emotional abuse, physical abuse, sexual abuse, neglect). Multivariable Poisson regression models estimated associations of CA type, number, and frequency with disorders. RESULTS:The majority of incoming students reported exposure to at least one CA (64.9%), including 50.0 % family dysfunction, 42.2 % emotional abuse, 21.2 % physical abuse, 18.8 % neglect, and 5.0 % sexual abuse. Lifetime and 12-month disorders were significantly associated with CAs in multivariable models, although associations with disorder persistence were weaker. Population attributable risk proportions of 12-month disorders associated with CAs were in the range of 40.7-61.0 % for anxiety and mood disorders and 13.5-55.2 % for substance use disorders. CONCLUSION:Six out of ten university students arrive at university having been exposed to CAs. These students have substantially higher risk of mental disorders than other students, primarily due to associations with lifetime risk rather than persistence. Given the considerable distress and impairment caused by mental disorders, these results underscore the need for primary and secondary prevention efforts.

关键词： ADHD Anxiety Childhood adversities Depression Mental disorders Persistence Substance use disorder University students

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Euclid preparation. XLIV. Modelling spectroscopic clustering on mildly nonlinear scales in beyond-ΛCDM models

arXiv

引用

arXiv 2023年

作者： Bose, B. Carrilho, P. Marinucci, M. Moretti, C. Pietroni, M. Carella, E. Piga, L. Wright, B.S. Vernizzi, F. Carbone, C. Casas, S. D’Amico, G. Frusciante, N. Koyama, K. Pace, F. Pourtsidou, A. Baldi, M. de la Bella, L.F. Fiorini, B. Giocoli, C. Lombriser, L. Aghanim, N. Amara, A. Andreon, S. Auricchio, N. Bardelli, S. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Cardone, V.F. Carretero, J. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Costille, A. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Farina, M. Farrens, S. Ferriol, S. Fosalba, P. Frailis, M. Franceschi, E. Galeotta, S. Garilli, B. Gillis, B. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hormuth, F. Hornstrup, A. Jahnke, K. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Medinaceli, E. Meneghetti, M. Meylan, G. Moresco, M. Moscardini, L. Mota, D.F. Munari, E. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sapone, D. Sartoris, B. Schneider, P. Secroun, A. Seidel, G. Seiffert, M. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Starck, J.-L. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valentijn, E.A. Valenziano, L. Vassallo, T. Veropalumbo, A. Wang, Y. Weller, J. Zamorani, G. Zoubian, J. Zucca, E. Biviano, A. Bozzo, E. Burigana, C. Colodro-Conde, C. Di Ferdinando, D. Graciá-Carpio, J. Mauri, N. Neissner, C. Sakr, Z. Scottez, V. Tenti, M. Viel, M. Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Viale delle Scienze 7/A Parma43124 Italy Technion Israel Institute of Technology Israel SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INFN Gruppo Collegato di Parma Viale delle Scienze 7/A Parma43124 Italy Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy School of Physics and Astronomy Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Institut de Physique Théorique CEA CNRS Université Paris-Saclay Gif-sur-YvetteCedex 91191 France RWTH Aachen University Aachen52056 Germany Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Dipartimento di Fisica Università degli Studi di Torino Via P. Giuria 1 Torino10125 Italy INFN-Sezione di Torino Via P. Giuria 1 Torino10125 Italy Pino Torinese10025 Italy Higgs Centre for Theoretical Physics School of Physics and Astronomy The University of Edinburgh EdinburghEH9 3FD United Kingdom Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bologna Via Irnerio 46 Bologna40126 Italy Université de Genève Département de Ph

Context. The Euclid space satellite mission will measure the large-scale clustering of galaxies at an unprecedented precision, providing a unique probe of modifications to the ΛCDM model. Aims. We investigated the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in the context of modified gravity and exotic dark energy scenarios. We examined the normal branch of the Dvali–Gabadadze–Porrati model, the Hu–Sawicki f(R) model, a slowly evolving dark energy model, an interacting dark energy model, and massive neutrinos. For each, we tested approximations for the perturbative kernel calculations, including the omission of screening terms and the use of perturbative kernels based on the Einstein–de Sitter universe;we explored different infrared-resummation schemes, tracer bias models and a linear treatment of massive neutrinos;we investigated various approaches for dealing with redshift-space distortions and modelling the mildly nonlinear scales, namely the Taruya–Nishimishi–Saito © 2023, CC BY.

关键词： Dark energy

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Context-aware video recommendation by mining users' view preferences based on access points 27

Context-aware video recommendation by mining users' view pre...

引用

27th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2017

作者： Zhang, Jiaming Zhou, Yipeng Wu, Di Yang, Chunfeng School of Data and Computer Science Sun Yat-sen University Guangzhou Guangdong China Institute for Telecommunications Research University of South Australia Australia Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangzhou Guangdong China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China Tencent Inc. China

ISBN: (纸本)9781450350037

With the astronomical growth of online video population, the video recommendation system is crucial for users to locate videos fitting their interests. The collaborative filtering (CF) realizing personalized recommendation by analyzing users' historical view records is currently the most prevalent algorithm adopted by existing systems. Nevertheless, video recommendation performance can be improved for most cases, particularly the one with cold-start problem, if additional information is involved. In this article, we propose an AP-based Context-Aware (APCA) recommendation scheme on top of the traditional factor-based CF algorithm by utilizing the information of access points which has not been explored yet by existing works. The underlying principle is that users' view preferences are expected to be different with different contexts, e.g., hotel, home, public areas, which can be inferred by mining the information of access points via which users launch video requests. With the data collected from Tencent Video, a leading online video provider in China, we present a measurement study to show user view preferences in different contexts before our APCA algorithm is introduced. Experiments are executed driven by the trace data collected from Tencent Video to validate the effectiveness of our scheme in improving recommendation performance. © 2017 ACM.

关键词： Collaborative filtering

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Two‐Dimensional Perovskite Oxynitride K2LaTa2O6N with an H+/K+Exchangeability in Aqueous Solution Forming a Stable Photocatalyst for Visible‐Light H2Evolution

引用

Angewandte Chemie 2020年第24期132卷

作者： Takayoshi Oshima Tom Ichibha Kenji Oqmhula Keisuke Hibino Hiroto Mogi Shunsuke Yamashita Kotaro Fujii Yugo Miseki Kenta Hongo Daling Lu Ryo Maezono Kazuhiro Sayama Masatomo Yashima Koji Kimoto Hideki Kato Masato Kakihana Hiroshi Kageyama Kazuhiko Maeda Department of Chemistry School of Science Tokyo Institute of Technology 2-12-1-NE-2 Ookayama Meguro-ku Tokyo 152-8550 Japan Japan Society for the Promotion of Science Kojimachi Business Center Building 5-3-1 Kojimachi Chiyoda-ku Tokyo 102-0083 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Research Center for Advanced Measurement and Characterization National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Research Center for Photovoltaics (RCPV) and Global Zero Emission Research Center (GZR) National Institute of Advanced Industrial Science and Technology (AIST) Central 5 1-1-1 Higashi Tsukuba Ibaraki 305-8565 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama 322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan Suzukakedai Materials Analysis Division Technical Department Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan Institute of Multidisciplinary Research for Advanced Materials Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan

Undoped layered oxynitrides have not been considered as promising H 2 ‐evolution photocatalysts because of the low chemical stability of oxynitrides in aqueous solution. Here, we demonstrate the synthesis of a new layered perovskite oxynitride, K 2 LaTa 2 O 6 N, as an exceptional example of a water‐tolerant photocatalyst for H 2 evolution under visible light. The material underwent in‐situ H + /K + exchange in aqueous solution while keeping its visible‐light‐absorption capability. Protonated K 2 LaTa 2 O 6 N, modified with an Ir cocatalyst, exhibited excellent catalytic activity toward H 2 evolution in the presence of I − as an electron donor and under visible light; the activity was six times higher than Pt/ZrO 2 /TaON, one of the best‐performing oxynitride photocatalysts for H 2 evolution. Overall water splitting was also achieved using the Ir‐loaded, protonated K 2 LaTa 2 O 6 N in combination with Cs‐modified Pt/WO 3 as an O 2 evolution photocatalyst in the presence of an I 3 − /I − shuttle redox couple.

关键词： artificial photosynthesis layered materials mixed anion compound solar fuels water splitting

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

TechRxiv

引用

TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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Sequential scheme for locally discriminating bipartite unitary operations without inverses

arXiv

引用

arXiv 2017年

作者： Li, Lvzhou Institute of Computer Science Theory School of Data and Computer Science Sun Yat-sen University Guangzhou The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education 510006 China

Local distinguishability of bipartite unitary operations has recently received much attention. A nontrivial and interesting question concerning this subject is whether there is a sequential scheme for locally discriminating between two bipartite unitary operations, because a sequential scheme usually represents the most economic strategy for discrimination. An affirmative answer to this question was given in the literature, however with two limitations: (i) the unitary operations to be discriminated were limited to act on d ⊗ d, i.e., a two-qudit system, and (ii) the inverses of the unitary operations were assumed to be accessible, although this assumption may be unrealizable in experiment. In this paper, we improve the result by removing the two limitations. Specifically, we show that any two bipartite unitary operations acting on dA ⊗ dB can be locally discriminated by a sequential scheme, without using the inverses of the unitary operations. Therefore, this paper enhances the applicability and feasibility of the sequential scheme for locally discriminating unitary operations. Copyright © 2017, The Authors. All rights reserved.

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LiteBIRD science Goals and Forecasts: Primordial Magnetic Fields

arXiv

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arXiv 2024年

作者： Paoletti, D. Rubino-Martin, J. Shiraishi, M. Molinari, D. Chluba, J. Finelli, F. Baccigalupi, C. Errard, J. Gruppuso, A. Lonappan, A.I. Tartari, A. Allys, E. Anand, A. Aumont, J. Ballardini, M. Banday, A.J. Barreiro, R.B. Bartolo, N. Bersanelli, M. Bortolami, M. Brinckmann, T. Calabrese, E. Campeti, P. Carones, A. Casas, F.J. Cheung, K. Clermont, L. Columbro, F. Conenna, G. Coppolecchia, A. Cuttaia, F. D’Alessandro, G. de Bernardis, P. Torre, S. Della Diego-Palazuelos, P. Eriksen, H.K. Fuskeland, U. Galloni, G. Galloway, M. Gerbino, M. Gervasi, M. Ghigna, T. Giardiello, S. Gimeno-Amo, C. Gjerløw, E. Grupp, F. Hazumi, M. Henrot-Versillé, S. Hergt, L.T. Hivon, E. Ichiki, K. Ishino, H. Kohri, K. Komatsu, E. Krachmalnicoff, N. Lamagna, L. Lattanzi, M. Lembo, M. Levrier, F. López-Caniego, M. Luzzi, G. Martínez-González, E. Masi, S. Matarrese, S. Micheli, S. Migliaccio, M. Monelli, M. Montier, L. Morgante, G. Mousset, L. Nagata, R. Namikawa, T. Natoli, P. Novelli, A. Obata, I. Occhiuzzi, A. Odagiri, K. Pagano, L. Paiella, A. Pascual-Cisneros, G. Piacentini, F. Piccirilli, G. Remazeilles, M. Ritacco, A. Ruiz-Granda, M. Sakurai, Y. Scott, D. Stever, S.L. Sullivan, R.M. Takase, Y. Tassis, K. Terenzi, L. Tristram, M. Vacher, L. van Tent, B. Vielva, P. Wehus, I.K. Weymann-Despres, G. Zannoni, M. Zhou, Y. INAF - OAS Bologna via Piero Gobetti 93/3 Bologna40129 Italy INFN Sezione di Bologna Viale C. Berti Pichat 6/2 Bologna40127 Italy Instituto de Astrofísica de Canarias La Laguna Canary Islands TenerifeE-38200 Spain Tenerife La LagunaE-38206 Spain Suwa University of Science Nagano Chino391-0292 Japan High Performance Computing Department CINECA via Magnanelli 2/3 Casalecchio di Reno40033 Italy Jodrell Bank Centre for Astrophysics Alan Turing Building Department of Physics and Astronomy School of Natural Sciences The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste via Valerio 2 Trieste34127 Italy IFPU Via Beirut 2 Grignano Trieste34151 Italy Université de Paris CNRS Astroparticule et Cosmologie ParisF-75013 France Dipartimento di Fisica Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma00133 Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 Pisa56127 Italy Dipartimento di Fisica Università di Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Laboratoire de Physique de l’École Normale Supérieure ENS Université PSL CNRS Sorbonne Université Université de Paris Paris75005 France IRAP Université de Toulouse CNRS CNES UPS Toulouse France Dipartimento di Fisica e Scienze della Terra Università di Ferrara Via Saragat 1 Ferrara44122 Italy INFN Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Avenida los Castros SN Santander39005 Spain Dipartimento di Fisica e Astronomia "G. Galilei" Universita‘degli Studi di Padova via Marzolo 8 PadovaI-35131 Italy INFN Sezione di Padova via Marzolo 8 PadovaI-35131 Italy INAF Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Dipartimento di Fisica Universita’ degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN Sezione di Milano Via Celoria 16 Milano20133 Italy School of Physics and Astronomy Cardiff University CardiffCF24 3AA United

We present detailed forecasts for the constraints on the characteristics of primordial magnetic fields (PMFs) generated prior to recombination that will be obtained with the LiteBIRD satellite. The constraints are driven by some of the main physical effects of PMFs on the CMB anisotropies: the gravitational effects of magnetically-induced perturbations;the effects on the thermal and ionization history of the Universe;the Faraday rotation imprint on the CMB polarization spectra;and the non-Gaussianities induced in polarization anisotropies. LiteBIRD represents a sensitive probe for PMFs. We explore different levels of complexity, for LiteBIRD data and PMF configurations, accounting for possible degeneracies with primordial gravitational waves from inflation. By exploiting all the physical effects, LiteBIRD will be able to improve the current limit on PMFs at intermediate and large scales coming from Planck. In particular, thanks to its accurate B-mode polarization measurement, LiteBIRD will improve the constraints on infrared configurations for the gravitational effect, giving Bn1BMpc=−2.9 marg1 Mpc p(B2)marg n1BMpc=−2.9 © 2024, CC BY.

关键词： Magnetic fields

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A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification 1st

A Novel Intrusion Detection System Based on Advanced Naive B...

引用

1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Wang, Yunpeng Li, Yuzhou Tian, Daxin Wang, Congyu Wang, Wenyang Hui, Rong Guo, Peng Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD’99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance. © 2018, ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Intrusion detection

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