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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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Influence of Large-scaled reclamation on the Cold Wave in Coastal Area of Jiangsu Province

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IOP Conference Series: Earth and Environmental Science 2019年第1期326卷

作者： Hongsheng Cao Xishan Pan Chunhui Li Yan Wang Shaopeng Wang Nanjing Hydraulic Research Institute Nanjing China Nanjing R&D Tech Group Co. Ltd. Nanjing China Tidal Flat Research Centre of Jiangsu Province Nanjing China Key Laboratory of Coastal Disaster and Defence of Ministry of Education Hohai University Nanjing China School of Marine Sciences Nanjing University of Information Science & Technology Nanjing China National Marine Data and Information Service Tianjin China

Taking the large-scaled reclamation in Jiangsu Sea as study background, on the premise of large-scaled area (East China Sea) having a good accuracy. The cold wave of Jiangsu Sea is simulated by the nested model of SWAN, and analysing the Hs (significant wave height) near the reclamation area extracted from the results. It is found that the cold wave field around the reclamation area is influenced remarkably by large-scaled reclamation. The large change of wave field is mainly concentrated in the southern part of central Jiangsu province, and it has a significant influence on the wave field. due to the effect of covering, the wave height of the southern sea area of dongsha, Gaoni and Lengjiasha reclamation area decreases greatly, and the decline can be as much as 30%. due to the extent of reclamation is small, the change of wave field in the northern part of Jiangsu province is not obvious, and the range of change is not obvious.

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Label-free high-resolution proteomic analysis of cerebrospinal fluid in Alzheimer's disease

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IBrO reports 2019年 6卷 S223-S224页

作者： Sun Ah Park Jin Myung Jung Jun Sung Park Jeong Ho Lee Bumhee Park Hyung Jun Kim Jeong-Ho Park Won Seok Chae Jee Hyang Jeong Seong Hye Choi Je-Hyun Baek Ajou University School of Medicine Suwon Republic of Korea Department of Data Science The University of Suwon Suwon Republic of Korea Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Soonchunhyang University Bucheon Hospital Bucheon Republic of Korea Ewha Womans University Mokdong Hospital Seoul Republic of Korea Inha University School of Medicine Incheon Republic of Korea R&D Center for Clinical Mass Spectrometry Seegene Medical Foundation Seoul Republic of Korea

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External exposome and incident asthma across the life course in 14 European cohorts: a prospective analysis within the EXPANSE project

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The Lancet regional Health - Europe 2025年

作者： Yu, Zhebin Kress, Sara Blay, Natalia Gregor, Petr Kukk, Hanna-Maria Leskien, Miriam Majewska, renata Oosterwegel, Max J. Szabó, daniel ten Have, Margreet Klánová, Jana Mikeš, Ondřej Bergström, Anna Bussalleu, Alonso de Cid, rafael dalecka, Andrea dadvand, Payam van dorsselaer, Saskia Fischer, Krista de Hoogh, Kees Koppelman, Gerard H. Kronberg, Jaanika Metspalu, Andres Milani, Lili Esko, Tõnu Metspalu, Mait Lakerveld, Jeroen Ljungman, Petter Merid, Simon Kebede Macek, Pawel Manczuk, Marta Merritt, Anne-Sophie Pac, Agnieszka Palta, Priit Pershagen, Göran Peters, Annette Pikhart, Hynek Saucy, Apolline Schikowski, Tamara Shen, Youchen Standl, Marie Tonne, Cathryn Vermeulen, roel Vlaanderen, Jelle Vonk, Judith M. Wolf, Kathrin Ek, Carl Henrik Gruzieva, Olena Gehring, Ulrike Melén, Erik Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden IUF – Leibniz Research Institute for Environmental Medicine Düsseldorf Germany Genomes for Life-GCAT Lab CORE Program Germans Trias I Pujol Research Institute (IGTP) Badalona Spain Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP) Badalona Spain RECETOX Faculty of Science Masaryk University Brno Czech Republic Institute of Genomics University of Tartu Estonia Institute of Mathematics and Statistics University of Tartu Estonia Institute of Epidemiology Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg Germany Institute for Medical Information Processing Biometry and Epidemiology Medical Faculty Ludwig-Maximilians-Universität München Munich Germany Chair of Epidemiology and Preventive Medicine Jagiellonian University Medical College Kraków Poland Institute for Risk Assessment Sciences Utrecht University Utrecht Netherlands Netherlands Institute of Mental Health and Addiction Utrecht Netherlands Centre for Occupational and Environmental Medicine Region Stockholm Stockholm Sweden Swiss Tropical and Public Health Institute Allschwil Switzerland University of Basel Basel Switzerland ISGlobal Barcelona Spain Universitat Pompeu Fabra (UPF) Barcelona Spain CIBER Epidemiología y Salud Pública Spain University of Groningen University Medical Center Groningen Beatrix Children's Hospital Department of Pediatric Pulmonology and Pediatric Allergology Groningen Netherlands University of Groningen University Medical Center Groningen Groningen Research Institute for Asthma and COPD (GRIAC) Groningen Netherlands Department of Epidemiology and Data Science Amsterdam UMC Location Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Cardiology Danderyd Hospital Stockholm Sweden Department of Clinical Sciences and Education Södersjukhuset Karolinska Institutet Stockholm Sweden Collegium Medicum Jan Koc

Background: The joint impact of exposure to multiple urban environmental factors on asthma remains unclear. Methods: We analysed data from 14 European cohorts to assess the impact of the urban exposome on asthma incidence across the life course. We linked three external exposome domains (air pollution, built environment, ambient temperature) to the participants’ home addresses at baseline. We performed k-means clustering within each domain and assessed associations of clusters with asthma adjusting for potentially relevant covariates in cohort-specific analyses, with subsequent separate meta-analyses for birth and adult cohorts. An environmental risk score using a coefficient-weighted sum approach was used to assess the impact of combining the three domains. Findings: A total of 7428 incident asthma cases were identified among 349,037 participants (from birth up to age 70+). Overall, we observed higher risks of asthma for clusters characterized by high particulate matter and nitrogen dioxide exposure in adults (Or_meta = 1.13, 95%CI:1.01–1.25), and clusters characterized by high built-up area and low levels of greenness in both children and adults (Or_meta = 1.36, 95%CI: 1.14–1.64 for birth cohorts and Or_meta = 1.15, 95%CI: 1.03–1.28 for adult cohorts, respectively). The joint exposure using the environment risk score combining the three domains was consistently associated with higher risks of incident asthma (Or_meta = 1.13, 95%CI: 1.07–1.20 for birth cohorts, Or_meta = 1.15, 95%CI: 1.10–1.20 for adult cohorts per 20% increase). On average 11.6% of the incident asthma cases could be attributed to environmental risk score above cohort-specific median levels. Interpretation: Multiple environmental exposures jointly contribute to incident asthma risk across the life course. Urban planning accounting for these factors may help mitigate asthma development. Funding: This study was funded by the European Union's Horizon 2

关键词： Asthma Cohort Exposome Life course

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Correction: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-d consortium

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Journal of neuroengineering and rehabilitation 2024年第1期21卷 71页

作者： M Encarna Micó-Amigo Tecla Bonci Anisoara Paraschiv-Ionescu Martin Ullrich Cameron Kirk Abolfazl Soltani Arne Küderle Eran Gazit Francesca Salis Lisa Alcock Kamiar Aminian Clemens Becker Stefano Bertuletti Philip Brown Ellen Buckley Alma Cantu Anne-Elie Carsin Marco Caruso Brian Caulfield Andrea Cereatti Lorenzo Chiari Ilaria d'Ascanio Bjoern Eskofier Sara Fernstad Marcel Froehlich Judith Garcia-Aymerich Clint Hansen Jeffrey M Hausdorff Hugo Hiden Emily Hume Alison Keogh Felix Kluge Sarah Koch Walter Maetzler dimitrios Megaritis Arne Mueller Martijn Niessen Luca Palmerini Lars Schwickert Kirsty Scott Basil Sharrack Henrik Sillén david Singleton Beatrix Vereijken Ioannis Vogiatzis Alison J Yarnall Lynn rochester Claudia Mazzà Silvia del din Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne UK. Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine The University of Sheffield Sheffield UK. Laboratory of Movement Analysis and Measurement Ecole Polytechnique Federale de Lausanne Lausanne Switzerland. Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen Nürnberg Erlangen Germany. Center for the Study of Movement Cognition and Mobility Neurological Institute Tel Aviv Sourasky Medical Center Tel Aviv Israel. Department of Biomedical Sciences University of Sassari Sassari Italy. Department of Electronics and Telecommunications Politecnico di Torino Turin Italy. National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC) Newcastle University and The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. Robert Bosch Gesellschaft für Medizinische Forschung Stuttgart Germany. The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. School of Computing Newcastle University Newcastle Upon Tyne UK. Barcelona Institute for Global Health (ISGlobal) Barcelona Spain. Universitat Pompeu Fabra Barcelona Catalonia Spain. CIBER Epidemiología y Salud Pública (CIBERESP) Madrid Spain. Insight Centre for Data Analytics University College Dublin Dublin Ireland. School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland. Department of Electrical Electronic and Information Engineering «Guglielmo Marconi» University of Bologna Bologna Italy. Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV) University of Bologna Bologna Italy. Grünenthal GmbH Aachen Germany. Department of Neurology University Medical Center Schleswig-Holstein Campus Kiel Kiel Germany. Sagol School of Neuroscien

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Advancing image understanding in poor visibility environments: A collective benchmark study

arXiv

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arXiv 2019年

作者： Yang, Wenhan Yuan, Ye ren, Wenqi Liu, Jiaying Scheirer, Walter J. Wang, Zhangyang Zhang, Taiheng Zhong, Qiaoyong Xie, di Pu, Shiliang Zheng, Yuqiang Qu, Yanyun Xie, Yuhong Chen, Liang Li, Zhonghao Hong, Chen Jiang, Hao Yang, Siyuan Liu, Yan Qu, Xiaochao Wan, Pengfei Zheng, Shuai Zhong, Minhui Su, Taiyi He, Lingzhi Guo, Yandong Zhao, Yao Zhu, Zhenfeng Liang, Jinxiu Wang, Jingwen Chen, Tianyi Quan, Yuhui Xu, Yong Liu, Bo Liu, Xin Sun, Qi Lin, Tingyu Li, Xiaochuan Lu, Feng Gu, Lin Zhou, Shengdi Cao, Cong Zhang, Shifeng Chi, Cheng Zhuang, Chubin Lei, Zhen Li, Stan Z. Wang, Shizheng Liu, ruizhe Yi, dong Zuo, Zheming Chi, Jianning Wang, Huan Wang, Kai Liu, Yixiu Gao, Xingyu Chen, Zhenyu Guo, Chang Li, Yongzhou Zhong, Huicai Huang, Jing Guo, Heng Yang, Jianfei Liao, Wenjuan Yang, Jiangang Zhou, Liguo Feng, Mingyue Qin, Likun Wangxuan Institute of Computer Technology Peking University Beijing100080 China Department of Computer Science and Engineering Texas A&M University TX77843 United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mechanical Engineering Zhejiang University Hangzhou310027 China Hikvision Research Institute Hangzhou310051 China Mtlab Meitu Inc. Beijing100080 China Insitute of Information Science Beijing Jiaotong University Beijing100044 China Department of Computer Science and Technology Tongji University Shanghai201804 China XPENGMOTORS Beijing China School of Computer Science and Engineering at South China University of Technology Guangzhou510006 China Tencent AI Lab Shenzhen518000 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Northeastern University Shenyang110819 China Nanyang Technological University Singapore639798 Big Data Center State Grid Corporation of China Beijing China Winsense Inc. Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Informatics Technical University of Munich Garching85748 Germany Institute of Microelectronics Chinese Academy of Sciences Beijing100190 China China Electric Power Research Institute Beijing100031 China Westlake University Hangzhou310024 China Department of Computer Science Durham University Durham United Kingdom Australian National University ActonACT0200 Australia Sunway-AI Co. Ltd Zhuhai China Chinese Academy of Sciences R&D Center for Internet of Things Wuxi214200 China

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG2+challenge Track 2 competition in IEEE CVPr 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Energy-efficient non-orthogonal transmission under reliability and finite blocklength constraints

arXiv

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arXiv 2017年

作者： Xu, Yanqing Shen, Chao Chang, Tsung-Hui Lin, Shih-Chun Zhao, Yajun Zhu, Gang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Science and Engineering Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Department of ECE National Taiwan University of Science and Technology Taipei Taiwan Algorithm Department Wireless Product R&D Institute ZTE Corporation Shenzhen China

This paper investigates an energy-efficient nonorthogonal transmission design problem for two downlink receivers that have strict reliability and finite blocklength (latency) constraints. The Shannon capacity formula widely used in traditional designs needs the assumption of infinite blocklength and thus is no longer appropriate. We adopt the newly finite blocklength coding capacity formula for explicitly specifying the trade-off between reliability and code blocklength. However, conventional successive interference cancellation (SIC) may become infeasible due to heterogeneous blocklengths. We thus consider several scenarios with different channel conditions and with/without SIC. By carefully examining the problem structure, we present in closed-form the optimal power and code blocklength for energy-efficient transmissions. Simulation results provide interesting insights into conditions for which non-orthogonal transmission is more energy efficient than the orthogonal transmission such as TdMA. Copyright © 2017, The Authors. All rights reserved.

关键词： Energy efficiency

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Corrigendum to ‟On-tissue polysulfide visualization by surface-enhanced raman spectroscopy benefits patients with ovarian cancer to predict post-operative chemosensitivity" [redox Biol. 41 (2021) 101926]

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redox biology 2021年 44卷 102028页

作者： Kazufumi Honda Takako Hishiki Sohei Yamamoto Takehiro Yamamoto Nami Miura Akiko Kubo Mai Itoh Wei-Yu Chen Masashi Takano Tomoyuki Yoshikawa Takahiro Kasamatsu Shinichiro Sonoda Hirotoshi Yoshizawa Seigo Nakamura Yuichiro Itai Megumi Shiota daisuke Koike Masayuki Naya Noriyo Hayakawa Yoshiko Naito Tomomi Matsuura Keiko Iwaisako Toshihiko Masui Shinji Uemoto Kengo Nagashima Yoshinori Hashimoto Tomohiro Sakuma Osamu Matsubara Wilber Huang Tomoaki Ida Takaaki Akaike Yohei Masugi Michiie Sakamoto Tomoyasu Kato Yoshinori Ino Hiroshi Yoshida Hitoshi Tsuda Nobuyoshi Hiraoka Yasuaki Kabe Makoto Suematsu Department of Biomarkers for Early Detection of Cancer National Cancer Center Research Institute Tokyo Japan AMED-CREST Japan Agency for Medical Research and Development (AMED) Japan Department of Bioregulation Graduate School of Medicine Nippon Medical School Japan. Department Biochemistry Keio University School of Medicine Tokyo Japan. Department of Diagnostic Pathology Anjo Kosei Hospital Aichi Japan. Department of Pathology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Pathology Wan Fang Hospital Taipei Medical University Taipei Taiwan. Department of Obstetrics and Gynecology National Defense Medical College Saitama Japan. Department of Clinical Oncology National Defense Medical College Hospital Saitama Japan. Department of Obstetrics and Gynecology Tokyo Metropolitan Bokutoh Hospital Tokyo Japan. Frontier Core Technology Laboratories R&D Management Headquarters FUJIFILM Corporation Ashigara-gun Kanagawa Japan. FUJIFILM Business Expert Corporation Ashigara-gun Kanagawa Japan. Division of Hepato-Biliary-Pancreatic Surgery and Transplantation Department of Surgery Kyoto University 54 Shogoin Kawaracho Sakyo Kyoto Japan Department of Medical Life Systems Faculty of Life and Medical Sciences Doshisha University 1-3 Tatara Miyakodani Kyotanabe city Kyoto Japan. Division of Hepato-Biliary-Pancreatic Surgery and Transplantation Department of Surgery Kyoto University 54 Shogoin Kawaracho Sakyo Kyoto Japan. Research Center for Medical and Health Data Science The Institute of Statistical Mathematics Tokyo Japan. Scientific Imaging Scanner Department System Division Hamamatsu Photonics Hamamatsu Japan. Bioscience Division Mitsui Knowledge Industry Corporation Ltd. Tokyo Japan. Department of Pathology Hiratsuka Kyosai Hospital The Cancer Institute Kanagawa Japan. Abnova Corporation Taipei Taiwan. Department of Environmental Medicine and Molecular Toxicology Tohoku University Graduate School of Medici

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Worldwide Soundscapes: A Synthesis of Passive Acoustic Monitoring Across realms

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Global Ecology and Biogeography 2025年第5期34卷

作者： Kevin F. A. darras rodney A. rountree Steven L. Van Wilgenburg Anna F. Cord Frederik Pitz Youfang Chen Lijun dong Agnès rocquencourt Camille desjonquères Patrick Mauritz diaz Tzu-Hao Lin Théophile Turco Louise Emmerson Tom Bradfer-Lawrence Amandine Gasc Sarah Marley Marcus Salton Laura Schillé Paul J. Wensveen Shih-Hung Wu Adriana C. Acero-Murcia Orlando Acevedo-Charry Matyáš Adam Jacopo Aguzzi Irmak Akoglu M. Clara P. Amorim Mina Anders Michel André Alexandre Antonelli Leandro Aparecido do Nascimento Giulliana Appel Stephanie Archer Christos Astaras Andrey Atemasov Jamieson Atkinson Joël Attia Emanuel Baltag Luc Barbaro Fritjof Basan Carly Batist Julio Ernesto Baumgarten Just T. Bayle Sempere Kristen Bellisario Asaf Ben david Oded Berger-Tal Frédéric Bertucci Matthew G. Betts Iqbal S. Bhalla Thiago Bicudo Marta Bolgan Sara Bombaci Gerard Bota Martin Boullhesen robert A. Briers Susannah Buchan Michal Budka Katie Burchard Giuseppa Buscaino Alice Calvente Marconi Campos-Cerqueira Maria Isabel Carvalho Gonçalves Maria Ceraulo Maite Cerezo-Araujo Gunnar Cerwén Adams A. Chaskda Maria Chistopolova Christopher W. Clark Kieran d. Cox Benjamin Cretois Chapin Czarnecki Luis P. da Silva Wigna da Silva Laurence H. de Clippele david de la Haye Ana Silvia de Oliveira Tissiani devin de Zwaan M. Eugenia degano Jessica deichmann Joaquin del rio Christian devenish ricardo díaz-delgado Pedro diniz dorgival diógenes Oliveira-Júnior Thiago dorigo Saskia dröge Marina duarte Adam duarte Kerry dunleavy robert dziak Simon Elise Hiroto Enari Haruka S. Enari Florence Erbs Britas Klemens Eriksson Pınar Ertör-Akyazi Nina C. Ferrari Luane Ferreira Abram B. Fleishman Paulo J. Fonseca Bárbara Freitas Nicholas r. Friedman Jérémy S. P. Froidevaux Svetlana Gogoleva Carolina Gonzaga José Miguel González Correa Eben Goodale Benjamin Gottesman Ingo Grass Jack Greenhalgh Jocelyn Gregoire Samuel Haché Jonas Hagge William Halliday Antonia Hammer Tara Hanf-dressler Sylvain Haupert Samara Haver Becky Heath daniel Hending Jose Hernandez-Blanco dennis Higgs EFNO ECODIV INRAE Domaine des Barres Nogent-sur-Vernisson France Sustainable Agricultural Systems & Engineering Lab School of Engineering Westlake University Hangzhou Zhejiang China Chair of Computational Landscape Ecology Faculty of Environmental Sciences Dresden University of Technology Dresden Germany Biology Department University of Victoria Victoria British Columbia Canada The Fish Listener Waguoit Massachusetts USA Terrestrial Unit Prairie Region Canadian Wildlife Service Environment & Climate Change Canada Prairie & Northern Wildlife Research Centre Saskatoon Saskatchewan Canada Agro-ecological Modeling Group Faculty of Agriculture University of Bonn Bonn Germany Marine Mammal and Marine Bioacoustics Laboratory Department of Deep Sea Science Institute of Deep-sea Science and Engineering Chinese Academy of Sciences Sanya Hainan China Institut de Systématique Évolution Biodiversité Muséum National d’Histoire Naturelle Paris France Université Grenoble Alpes Université Savoie Mont Blanc CNRS LECA Grenoble France Climate Change and Biodiversity Hanns R. Neumann Stiftung Indonesia Muaradua Indonesia Biodiversity Research Center Academia Sinica Nankang Taipei Taiwan ENES Bioacoustics Research Laboratory University of Saint-Etienne CRNL CNRS UMR 5292 Inserm UMR_S 1028 Saint-Etienne France Australian Antarctic Division Science Branch Department of Climate Change Energy Environment and Water Channel Highway Kingston Tasmania Australia Biological and Environmental Sciences University of Stirling Stirling Scotland Centre for Conservation Science RSPB Edinburgh UK IMBE Aix Marseille Univ Avignon Univ CNRS IRD Aix-en-Provence France Scotland's Rural College Craibstone Estate Aberdeen UK BIOGECO INRAE University of Bordeaux Cestas France Westman Islands Research Centre University of Iceland Vestmannaeyjar Iceland Qigu Research Center Taiwan Biodiversity Research Institute Nantou County Taiwan Programa de Pós-graduação em Ecologia

Aim The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean). Location Worldwide, 12,343 sites, all ecosystem types. Time Period 1991 to present. Major Taxa Studied All soniferous taxa. Methods We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms. results Terrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm 2 ) than aquatic sampling (0.3 and 1.8 sites/Mkm 2 in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds. Main Conclusions PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio-temporal replication in freshwater habitats. Overall, this

关键词： ArU automated sound recorder biodiversity conservation biology ecoacoustics IUCN GET realm Passive Acoustic Monitoring phenology soundscape ecology

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Simulate Human Saccadic Scan-Paths in Target Searching

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International Journal of Intelligence Science 2016年第1期6卷 1-9页

作者： Lijuan duan Jun Miao david M. W. Powers Jili Gu Laiyun Qing Beijing Key Laboratory of Trusted Computing Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data College of Computer Science and Technology Beijing University of Technology Beijing China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing Technology CAS Beijing China School of Computer Science Engineering & Maths Flinders University of South Australia Adelaide South Australia Beijing Samsung Telecom R&D Center Beijing China University of Chinese Academy of Sciences Beijing China

Human saccade is a dynamic process of information pursuit. There are many methods using either global context or local context cues to model human saccadic scan-paths. In contrast to them, this paper introduces a model for gaze movement control using both global and local cues. To test the performance of this model, an experiment is done to collect human eye movement data by using an SMI iVIEW X Hi-Speed eye tracker with a sampling rate of 1250 Hz. The experiment used a two-by-four mixed design with the location of the targets and the four initial positions. We compare the saccadic scan-paths generated by the proposed model against human eye movement data on a face benchmark dataset. Experimental results demonstrate that the simulated scan-paths by the proposed model are similar to human saccades in term of the fixation order, Hausdorff distance, and prediction accuracy for both static fixation locations and dynamic scan-paths.

关键词： Saccadic Scan-Paths Eye Movement Fixation Locations dynamic Scan-Paths

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