检索结果-内蒙古大学图书馆

Learning stochastic majority votes by minimizing a PAC-bayes generalization bound 21

学校读者我要写书评

暂无评论

Learning stochastic majority votes by minimizing a PAC-bayes...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Valentina Zantedeschi Paul Viallard Emilie Morvant Rémi Emonet Amaury Habrard Pascal Germain Benjamin Guedj Inria Lille - Nord Europe research centre France and The Inria London Programme France and UK and University College London Department of Computer Science Centre for Artificial Intelligence UK Univ Lyon UJM-Saint-Etienne CNRS Institut d Optique Graduate School Laboratoire Hubert Curien UMR 5516 Saint-Etienne France Département d'informatique et de génie logiciel Université Laval Québec Canada

ISBN: (纸本)9781713845393

We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dirichlet distributions: this allows for a closed-form and differentiable expression for the expected risk, which then turns the generalization bound into a tractable training objective. The resulting stochastic majority vote learning algorithm achieves state-of-the-art accuracy and benefits from (non-vacuous) tight generalization bounds, in a series of numerical experiments when compared to competing algorithms which also minimize PAC-Bayes objectives – both with uninformed (data-independent) and informed (data-dependent) priors.

关键词：

Candidate eclipsing binary systems with a δ Scuti star in Northern TESS field

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Aliçavuş, Filiz Kahraman Gümüs, D. Kırmızıtas, Ö. Ekinci, Ö. Çavuş, S. Kaya, Y.T. Aliçavuş, F. Çanakkale Onsekiz Mart University Faculty of Sciences and Arts Physics Department Çanakkale17100 Turkey Çanakkale Onsekiz Mart University Astrophysics Research Center Ulup0131nar ̆ Observatory ÇanakkaleTR-17100 Turkey Istanbul University Institute of Graduate Studies in Science Programme of Astronomy and Space Sciences Beyazıt Istanbul34116 Turkey Çanakkale Onsekiz Mart University School of Graduate Studies Department of Physics ÇanakkaleTR-17100 Turkey Çanakkale Onsekiz Mart University School of Graduate Studies Department of Space Sciences and Technologies ÇanakkaleTR-17100 Turkey Çanakkale Onsekiz Mart University Faculty of Engineering Computer Engineering ÇanakkaleTR-17100 Turkey

Existence of pulsating stars in eclipsing binaries have been known for decades. These types of objects are extremely valuable systems for astronomical studies as they exhibit both eclipsing and pulsation variations. The eclipsing binaries are the only way to directly measure the mass and radius of stars with a good accuracy (≤ 1%), while the pulsations are a unique way to probe the stellar interior via oscillation frequencies. There are different types of pulsating stars existing in eclipsing binaries. One of them is the δ Scuti variables. Currently, the known number of δ Scuti stars in eclipsing binaries is around 90 according to the latest catalog of these variables. An increasing number of these kinds of variables is important to understand the stellar structure, evolution and the effect of binarity on the pulsations. Therefore, in this study, we focus on discovering new eclipsing binaries with δ Scuti component(s). We searched for the northern TESS field with a visual inspection by following some criteria such as light curve shape, the existence of pulsation like variations in the out-of-eclipse light curve and the Teff values of the targets. As a result of these criteria, we determined some targets. The TESS light curves of the selected targets first were removed from the binarity and frequency analysis was performed on the residuals. The luminosity, absolute and bolometric magnitudes of the targets were calculated as well. To find how much of these parameters represent the primary binary component (more luminous) we also computed the flux density ratio of the systems by utilizing the area of the eclipses. In addition, the positions of the systems in the H-R diagram was examined considering the flux density ratios. As a consequence of the investigation, we defined 38 candidates δ Scuti and also one Maia variable in eclipsing binary systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Stars

Learning stochastic majority votes by minimizing a PAC-bayes generalization bound

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zantedeschi, Valentina Viallard, Paul Morvant, Emilie Emonet, Rémi Habrard, Amaury Germain, Pascal Guedj, Benjamin Inria Lille - Nord Europe research centre France The Inria London Programme France University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom Univ Lyon UJM-Saint-Etienne CNRS Institut d Optique Graduate School Laboratoire Hubert Curien UMR 5516 Saint-EtienneF-42023 France Département d’informatique et de génie logiciel Université Laval Québec Canada

关键词： Stochastic systems

Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Hu, Mengjiao Jiang, Xudong Sim, Kang Zhou, Juan Helen Guan, Cuntai Interdisciplinary Graduate Programme NTU Institute for Health Technologies Nanyang Technological University Singapore Center for Sleep and Cognition Department of Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore School of Electrical & Electronic Engineering Nanyang Technological University Singapore Singapore Singapore Translational Magnetic Resonance Research Yong Loo Lin School of Medicine National University of Singapore Singapore School of Computer Science and Engineering School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Deep learning has been successfully applied to recognizing both natural images and medical images. However, there remains a gap in recognizing 3D neuroimaging data, especially for psychiatric diseases such as schizophrenia and depression that have no visible alteration in specific slices. In this study, we propose to process the 3D data by a 2+1D framework so that we can exploit the powerful deep 2D Convolutional Neural Network (CNN) networks pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition. Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics (grey matter, white matter, and cerebrospinal fluid) are decomposed to 2D slices according to neighboring voxel positions and inputted to 2D CNN models pre-trained on the ImageNet to extract feature maps from three views (axial, coronal, and sagittal). Global pooling is applied to remove redundant information as the activation patterns are sparsely distributed over feature maps. Channel-wise and slice-wise convolutions are proposed to aggregate the contextual information in the third view dimension unprocessed by the 2D CNN model. Multi-metric and multi-view information are fused for final prediction. Our approach outperforms handcrafted feature-based machine learning, deep feature approach with a support vector machine (SVM) classifier and 3D CNN models trained from scratch with better cross-validation results on publicly available Northwestern University Schizophrenia Dataset and the results are replicated on another independent dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Spider Plot Model for Analysis of Individual Appraisal Performance Towards Career Planning in Organizations

学校读者我要写书评

暂无评论

Journal of Physics: Conference Series 2019年第1期1179卷

作者： Muhamad Djufri Abdul Malek Bin A Tambi Mustafa Mamat Sukono Budiono Ismanto Hadi Akademi Keuangan dan Bisnis Indonesia Internasional (AKBII) Bandung Indonesia Faculty of Economics and Management Science Universiti Sultan Zainal Abidin Malaysia Master Programme in Management Post Graduate Programme Universitas Narotama Surabaya Indonesia Faculty of Informatice and Computer Science Universiti Sultan Zainal Abidin Malaysia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Malaysia

Career planning at the organizational level must be translated into career planning at the individual employee level. Performance appraisal can be used more broadly so that it can be the main tool for companies in employee career planning. The purpose of this study is to measure how much influence the Performance Evaluation of Career Planning from employees who have occupied managerial levels goes to the next career level. In this paper, analysis of individual employee performance is carried out using the Spider Plot model. The results of analyzing individual performance from managers in different units can explain quickly and easily the appearance of the Spider Plot diagram. So, it is expected that individual career planning can be easily mapped through performance appraisal so that it can be used in career planning.

关键词：

Technology Roadmap for Flexible Sensors

学校读者我要写书评

暂无评论

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

Generalized penalty for circular coordinate representation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Luo, Hengrui Patania, Alice Kim, Jisu Vejdemo-Johansson, Mikael Lawrence Berkeley National Laboratory 1 Cyclotron Rd BerkeleyCA94720 United States Department of Statistics College of Arts and Sciences Ohio State University 1958 Neil Ave ColumbusOH43210 United States Indiana University 1001 IN-45/46 E SR Bypass BloomingtonIN47408 United States DataShape Team Inria Saclay France LMO Université Paris-Saclay Bâtiment 307 Faculté des Sciences d'Orsay Université Paris-Saclay rue Michel Magat Orsay Île-De-France 91400 France Department of Mathematics CUNY College of Staten Island Computer Science Programme CUNY Graduate Center 2800 Victory Boulevard 1S-215 Staten IslandNY10314 United States

MSC Codes 55N31, 62R40, 68T09Topological Data Analysis (TDA) provides novel approaches that allow us to analyze the geometrical shapes and topological structures of a dataset. As one important application, TDA can be used for data visualization and dimension reduction. We follow the framework of circular coordinate representation, which allows us to perform dimension reduction and visualization for high-dimensional datasets on a torus using persistent cohomology. In this paper, we propose a method to adapt the circular coordinate framework to take into account the roughness of circular coordinates in change-point and high-dimensional applications. To do that, we use a generalized penalty function instead of an L2 penalty in the traditional circular coordinate algorithm. We provide simulation experiments and real data analyses to support our claim that circular coordinates with generalized penalty will detect the change in high-dimensional datasets under different sampling schemes while preserving the topological structures. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

Considering discrepancy when calibrating a mechanistic electrophysiology model

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series

学校读者我要写书评

暂无评论

Global Ecology and Biogeography 2025年第5期34卷

作者： Maria Dornelas Laura H. Antão Amanda E. Bates Viviana Brambilla Jonathan M. Chase Cher F. Y. Chow Ada Fontrodona-Eslava Anne E. Magurran Inês S. Martins Faye Moyes Alban Sagouis Samuel Adu-Acheampong Daniel Acquah-Lamptey Dušan Adam Penelope A. Ajani Aitor Albaina Pablo Almaraz Jeongseop An Roger Sigismund Anderson Madelaine Jean Robertson Anderson Alexsander Z. Antunes Ivan Arismendi Linda Armbrecht Pedro Aros-Mardones Sreejith Kalpuzha Ashtamoorthy Narayanan Ayyappan Gal Badihi Joseph J. Bailey Andrew H. Baird Mark Edward Baird Sreekumar Vadakkethil Balakrishnan José António L. Barão-Nóbrega Adi Barash Miguel Barbosa Jos Barlow Claus Bässler Matthieu Beaumont Natalie Beenaerts Tiago Octavio Begot Wallace Beiroz Ricardo Beldade David M. Bell Alecia Bellgrove Jonathan Belmaker Lisandro Benedetti-Cecchi Cassandra E. Benkwitt Pamela Medina-van Berkum Brandon T. Bestelmeyer Matthew G. Betts Maxwell Kelvin Billah Anne D. Bjorkman Magdalena Błażewicz Christopher P. Bloch Shane A. Blowes Antonio Bode Juliano A. Bogoni Thomas Bolger Timothy C. Bonebrake Erik Bonsdorff Roberta Bottarin Luke N. Brokensha Rob W. Brooker Andrew J. Brooks Helge Bruelheide Thiago Almeida Bueno Claire Laguionie Mariana Lopes Campagnoli James Cant Erica Pellegrini Caramaschi Alexandre Caron Tadhg Carroll Tancredi Caruso Juan Carvajal-Quintero Giuseppe Castaldelli Edward Castañeda-Moya Pedro V. Castilho Sonia Zanini Cechin Shahar Chaikin Uchangi Manjunatha Chandrashekara Tory J. Chase Chaolun Allen Chen Jorge José Cherem Sei-Woong Choi Erica M. Christensen Alexander V. Christianini Jackson Wing Four Chu Peter Coad Carl Van Colen Lise Comte Elisabeth J. Cooper J. Hans C. Cornelissen Eddy Cosson Unai Cotano Luc Crevecoeur Shannan Kyle Crow Graeme S. Cumming Vanessa S. Daga Gabriella Damasceno Gergana N. Daskalova Claire H. Davies Robert A. Davis Frank P. Day Sussy De-La-Zerda Amy Elizabeth Deacon Indradatta de Castro-Arrazola Steven Degraer Kharran Deonarinesingh Juan C. Diaz-Ricaurte Christopher R. Dickman Tara Dirilgen Ciaran John Dolan J. Emmet Centre for Biological Diversity School of Biology University of St Andrews Fife UK MARE—Centro de Ciências do Mar e do Ambiente Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Research Centre for Ecological Change Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland Department of Biology University of Victoria Victoria British Columbia Canada German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany Department of Computer Science Martin Luther University Halle-Wittenberg Halle (Saale) Germany Leverhulme Centre for Anthropocene Biodiversity Department of Biology University of York York UK Department of Animal Biology and Conservation Science University of Ghana Accra Ghana Philipps Universität Marburg Marburg Germany Department of Forest Ecology Silva Tarouca Research Institute Brno Czech Republic School of Life Sciences University of Technology Sydney Sydney New South Wales Australia Zoology and Animal Cell Biology Department University of the Basque Country (UPV/EHU) Leioa Spain Grupo de Oceanografía de Ecosistemas Instituto de Ciencias Marinas de Andalucía CSIC Campus Universitario de Puerto Real Puerto Real Spain National Institute of Ecology Seocheon Republic of Korea African Regional Postgraduate Programme in Insect Science University of Ghana Accra Ghana Département de Biologie Université de Sherbrooke Sherbrooke Quebec Canada Núcleo de Conservação da Biodiversidade Instituto de Pesquisas Ambientais Secretaria de Meio Ambiente Infraestrutura e Logística do Estado de São Paulo São Paulo Brazil Department of Fisheries Wildlife and Conservation Sciences Oregon State University Corvallis Oregon USA Institute for Marine and Antarctic Studies Ecology & Biodiversity Centre University of Tasmania Hobart Tasmania Australia Graduate Program in Oceanography Department of Oceanography Faculty of Natural Scienc

Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

关键词： assemblage biodiversity community database ecoinformatics species taxa time