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arXiv 2022年

作者： Abusleme, Angel Adam, Thomas Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano Akram, Muhammad Aleem, Abid Alexandros, Tsagkarakis An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin de André, João Pedro Athayde Marcondes Auguste, Didier Bai, Weidong Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Bergnoli, Antonio Birkenfeld, Thilo Blin, Sylvie Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Busto, Jose Butorov, Ilya Cabrera, Anatael Caccianiga, Barbara Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callegari, Riccardo Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Chan, Chi Chang, Jinfan Chang, Yun Chen, Guoming Chen, Pingping Chen, Po-An Chen, Shaomin Chen, Xurong Chen, Yixue Chen, Yu Chen, Zhiyuan Chen, Zikang Cheng, Jie Cheng, Yaping Cheng, Yu Chin Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Molla, Marta Colomer Di Lorenzo, Selma Conforti Corti, Daniele Corso, Flavio Dal Dalager, Olivia De La Taille, Christophe Deng, Zhi Deng, Ziyan Depnering, Wilfried Diaz, Marco Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Dusini, Stefano Dvorak, Martin Enqvist, Timo Enzmann, Heike Fabbri, Andrea Fan, Donghua Fan, Lei Fang, Jian Fang, Wenxing Fargetta, Marco Fedoseev, Dmitry Fei, Zhengyong Feng, Li-Cheng Feng, Qichun Ford, Richard Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Giammarchi, Marco Giudice, Nunzio Gonchar, Maxim Gong, Guanghua Gong, Hui Gornushkin, Yuri Göttel, Alexandre Grassi, Marco Gromov, Vasily Gu, Minghao Gu, Xiaofei Gu, Yu Guan, Mengyun Guan, Yuduo Guardone, Nunzio Guo, Cong G Yerevan Physics Institute Yerevan Armenia Université Libre de Bruxelles Brussels Belgium Universidade Estadual de Londrina Londrina Brazil Pontificia Universidade Catolica do Rio de Janeiro Rio de Janeiro Brazil ANID Chile Pontificia Universidad Católica de Chile Santiago Chile Universidad Tecnica Federico Santa Maria Valparaiso Chile Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Normal University Beijing China China Institute of Atomic Energy Beijing China Institute of High Energy Physics Beijing China North China Electric Power University Beijing China School of Physics Peking University Beijing China Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Jilin University Changchun China College of Electronic Science and Engineering National University of Defense Technology Changsha China Chongqing University Chongqing China Dongguan University of Technology Dongguan China Jinan University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China The Radiochemistry and Nuclear Chemistry Group University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan China Key Laboratory of Particle Physics and Particle Irradiation Ministry of Education Shandong University Qingdao China Institute of Modern Physics Chinese Academy of Sciences Lanzhou China Nanjing University Nanjing China Guangxi University Nanning China East China University of Science and Technology Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China Nankai University Tianjin China Wuhan University Wuhan China Xi’an Jiaotong University Xi'An China Xiamen University Xiamen China School of Physics and Microelectronics Zhengzhou University Zhengzhou China Institute of Physics National Yang Ming Chiao Tung Universit

Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20 kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3 % at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision technology Co. (NNVT) and 5,000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK). © 2022, CC BY.

关键词： Photons

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An Image Inpainting Method Based on Adaptive Fuzzy Switching Median

An Image Inpainting Method Based on Adaptive Fuzzy Switching...

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National Foundation for Science and technology Development Conference on Information and computer Science (NICS)

作者： Nguyen Van Son Dang N. H. Thanh Ugur Erkan V. B. Surya Prasath Ballistic Research Laboratory Military Weapon Institute Hanoi Vietnam Department of Information Technology Hue College of Industry VN Department of Computer Engineering Karamanoglu Mehmetbey University Karaman Turkey Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati USA

ISBN: (数字)9781728151632

ISBN: (纸本)9781728151649

Image inpainting is an important problem of image processing that has many applications. The goal of the image inpainting problem is to restore or fill the corrupted or missing regions of image. In this paper, we propose an image inpainting method based on the adaptive fuzzy switching median. The adaptive fuzzy switching median is to provide an accurate estimation for the values of corrupted pixels when there are many corrupted pixels on image. In the experiments, we implement the proposed method on an open dataset of real natural images. We utilize standard image quality assessment metrics such as the peak signal-to-noise ratio metric and the structured similarity metric to compare the inpainting result of the proposed method with other similar inpainting methods to prove its effectiveness. The proposed inpainting method is also extended to process colorful images.

关键词： Switches Microsoft Windows Measurement Image restoration Image quality computer science Harmonic analysis

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Nikkei Stock Market Price Index Prediction Using Machine Learning

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： Lukman A Harahap Rajalida Lipikorn Asanobu Kitamoto Department of Agricultural Engineering Faculty of Agriculture University of Sumatera Utara Medan Indonesia Machine Intelligence and Multimedia Information Technology Laboratory Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Research Organization of Information and Systems Department of Informatics National Institute of Informatics Tokyo Japan

Stock market prediction has always been a difficult process, most of the prediction rely solely on the data of the corresponding stock market. Relationship of gold and oil price with stock market performance has been proven significant in some major world stock index. Prediction of stock market price index using machine learning methods is expected to perform well, with the ability of machine learning method to predict using nonlinear inputs. The methods were commonly able to predict relatively well in predicting the values of Japanese Nikkei 225 and Japanese Nikkei 400 indexes.

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Comparison of Naive Bayes Algorithm and Support Vector Machine using PSO Feature Selection for Sentiment Analysis on E-Wallet Review

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Journal of Physics: Conference Series 2020年第1期1641卷

作者： Dwi Andini Putri Dinar Ajeng Kristiyanti Elly Indrayuni Acmad Nurhadi Denda Rinaldi Hadinata Informatics Engineering STMIK Nusa Mandiri Indonesia Accounting Information System Universitas Bina Sarana Informatika Indonesia Computer Technology Universitas Bina Sarana Informatika Indonesia Management Science IPB University Indonesia

Cashless payment habits have been widely applied to the transportation system, restaurants and shops in the mall area. So, it is normal if the growth of mobile payment services is currently very rapid. The ease of doing transactions and promotional offers in the form of points and cashback in digital wallet applications (e-wallets) is very beneficial for its users. One of the most popular e-wallets is OVO. With so many reviews about OVO customer opinions on social media, there has also been a lot of public opinion. These opinions can produce negative or positive statements. Sentiment analysis is the mining of opinions or text to classify opinions or user reviews, of a brand reviews, product reviews, or service reviews into the category of positive or negative opinion. The methods used in this research are Naive Bayes and SVM. Both of these algorithms are the best algorithms widely used in text classification research. However, both of these algorithms have weaknesses in several parameters. So, in this study Feature Selection is used to improve its performance. The evaluation was carried out using 10-fold cross validation. Measurement accuracy is measured by confusion matrix and ROC curves. This study uses 500 positive reviews and 500 negative reviews as data training. The results of this study indicate that the use of PSO-based Naive Bayes algorithm produces an accuracy value of 93.10 percent with an AUC value of 0.750. While the results of research from the PSO-based SVM algorithm are 91.30 percent with an AUC value of 0.970. Based on these results the accuracy value generated by the Naive Bayes algorithm is classified as Fair Classification and SVM is classified as Excellent Classification. The AUC value generated by the Naive Bayes algorithm is also smaller than SVM. Therefore, in this study found that SVM is the best algorithm in classifying text.

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Quadcriteria Optimization of Binary Classifiers: Error Rates, Coverage, and Complexity 6th

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6th International Conference on Evolutionary Computation, EVOLVE 2015 held in Conjuction with Summer School on Evolutionary Computing in Optimization and Data Mining, ECODAM 2015

作者： Basto-Fernandes, Vitor Yevseyeva, Iryna Ruano-Ordás, David Zhao, Jiaqi Fdez-Riverola, Florentino Méndez, José Ramón Emmerich, Michael T. M. University Institute of Lisbon ISTAR-IUL Av. Das Forcas Armadas Lisboa1649-026 Portugal School of Technology and Management Computer Science and Communications Research Centre Polytechnic Institute of Leiria Leiria2411-901 Portugal School of Computer Science and Informatics Faculty of Technology Cyber Technology Institute De Montfort University Gateway House The Gateway LeicesterLE1 9BH United Kingdom Informatics Engineering School University of Vigo Campus As Lagoas S/N Ourense32004 Spain The School of Computer Science and Technology China University of Mining and Technology No 1 Daxue Road XuzhouJiangsu221116 China Multicriteria Optimization Design and Analytics Group LIACS Leiden University Niels Bohrweg 1 Leiden2333 CA Netherlands

ISBN: (纸本)9783319697086

This paper presents a 4-objective evolutionary multiobjective optimization study for optimizing the error rates (false positives, false negatives), reliability, and complexity of binary classifiers. The example taken is the email anti-spam filtering problem. The two major goals of the optimization is to minimize the error rates that is the false negative rate and the false positive rate. Our approach discusses three-way classification, that is the binary classifier can also not classify an instance in cases where there is not enough evidence to assign the instance to one of the two classes. In this case the instance is marked as suspicious but still presented to the user. The number of unclassified (suspicious) instances should be minimized, as long as this does not lead to errors. This will be termed the coverage objective. The set (ensemble) of rules needed for the anti-spam filter to operate in optimal conditions is addressed as a fourth objective. All objectives stated above are in general conflicting with each other and that is why we address the problem as a 4-objective (quadcriteria) optimization problem. We assess the performance of a set of state-of-the-art evolutionary multiobjective optimization algorithms. These are NSGA-II, SPEA2, and the hypervolume indicator-based SMS-EMOA. Focusing on the anti-spam filter optimization, statistical comparisons on algorithm performance are provided on several benchmarks and a range of performance indicators. Moreover, the resulting 4-D Pareto hyper-surface is discussed in the context of binary classifier optimization. © 2018, Springer International Publishing AG.

关键词： Classification (of information)

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 — Ex vivo imaging: added value and acquisition

arXiv

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arXiv 2022年

作者： Schilling, Kurt G. Grussu, Francesco Ianus, Andrada Hansen, Brian Howard, Amy F.D. Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Allan Johnson, G. Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Re

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio (SNR) and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY.

关键词： Image acquisition

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The ECFA Early Career Researcher's Panel: composition, structure, and activities, 2021-2022

arXiv

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arXiv 2022年

作者： Geanta, Andrei Alexandru Amendola, Chiara Apolinario, Liliana Arling, Jan-Hendrik Ashkenazi, Adi Augsten, Kamil Bagnaschi, Emanuele Bakos, Evelin Barak, Liron Bastos, Diogo Benato, Giovanni Bilin, Bugra Kraljevic, Neven Blaskovic Brenner, Lydia Brizioli, Francesco Camper, Antoine Camplani, Alessandra Vidal, Xabier Cid Dag, Hüseyin de Almeida Dias, Flavia Degens, Jordy Diociaiuti, Eleonora Dufour, Laurent Dunne, Katherine Erhardt, Filip Ghinescu, Stefan-Alexandru Gouskos, Loukas Herzan, Andrej Hinger, Viktoria Hiti, Bojan Ilg, Armin Irles, Adrián Jarkovská, Kateřina Jovicevic, Jelena Keszeghova, Lucia Kirschenmann, Henning Klaver, Suzanne Konstantinou, Sotiroulla Kuich, Magdalena Kumari, Neelam Gajdošová, Katarína Křížková Lelek, Aleksandra Lorenz, Jeanette Carvalho, Ana Luisa Major, Péter Malczewski, Jakub Mancini, Giada Martikainen, Laura Maurice, Émilie Mee, Seán Milosevic, Vukasin Moravcova, Zuzana Valero, Laura Moreno Moureaux, Louis Mäntysaari, Heikki Nikiforou, Nikiforos Otarid, Younes Pitt, Michael Placinta, Vlad-Mihai Räuber, Géraldine Ripellino, Giulia Roberts, Bryn Šantelj, Luka Schramm, Steven Shopova, Mariana Skovpen, Kirill Smolkovič, Aleks Sokmen, Gamze Sznajder, Pawel Gomez, Lourdes Urda Waldron, Abigail Victoria Williams, Sarah Zaccolo, Valentina Zardoshti, Nima Zeyen, Manuel Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest-Magurele Romania IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Laboratório de Instrumentação e Física Experimental de Partículas - LIP Lisboa Portugal Deutsches Elektronen-Synchrotron DESY Hamburg Germany Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague Prague Czech Republic CERN Geneva Switzerland Institute of Physics University of Belgrade Belgrade Serbia INFN Laboratori Nazionali del Gran Sasso L'Aquila Italy MAX IV Laboratory Lund University Lund Sweden Nikhef National Institute for Subatomic Physics University of Amsterdam Amsterdam Netherlands INFN Sezione di Perugia Perugia Italy Department of Physics University of Oslo Oslo Norway Niels Bohr Institute University of Copenhagen Copenhagen Denmark Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Physics Bursa Technical University Bursa Turkey INFN Laboratori Nazionali di Frascati Frascati Italy Department of Physics Stockholm University Stockholm Sweden Physics Department Faculty of Science University of Zagreb Zagreb Croatia Slovak Academy of Sciences Bratislava Slovakia Paul Scherrer Institut Villigen Switzerland Department of Experimental Particle Physics Jožef Stefan Institute Department of Physics University of Ljubljana Ljubljana Slovenia Physik-Institut University of Zürich Zürich Switzerland IFIC Universitat de València and CSIC València Spain Faculty of Mathematics and Physics Charles University Prague Czech Republic Faculty of Mathematics Physics and Informatics Comenius University Bratislava Slovakia Department of Physics University of Helsinki Helsinki Finland Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands University of Cyprus Nicosia Cyprus University of Warsaw Wa

The European Committee for Future Accelerators (ECFA) Early Career Researcher's (ECR) panel, which represents the interests of the ECR community to ECFA, officially began its activities in January 2021. In the first two years, the panel has defined its own internal structure, responded to ECFA requests for feedback, and launched its own initiatives to better understand and support the diverse interests of early career researchers. This report summarises the panel composition and structure, as well as the different activities the panel has been involved with during the first two years of its existence. © 2022, CC BY.

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Designing ecosystems of intelligence from first principles

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Collective Intelligence 2024年第1期3卷

作者： Karl J Friston Maxwell JD Ramstead Alex B Kiefer Alexander Tschantz Christopher L Buckley Mahault Albarracin Riddhi J Pitliya Conor Heins Brennan Klein Beren Millidge Dalton AR Sakthivadivel Toby St Clere Smithe Magnus Koudahl Safae Essafi Tremblay Capm Petersen Kaiser Fung Jason G Fox Steven Swanson Dan Mapes Gabriel René VERSES Research Lab Los Angeles CA USA (KJF MJDR ABK AT CLB MA RJP CH BK BM DARS TSCS MK SET CP KF JGF SS DM GR) Wellcome Centre for Human Neuroimaging University College London London UK (KJF MJDR) Department of Philosophy Monash University Melbourne VIC Australia (ABK) Department of Informatics University of Sussex Brighton UK (CLB) Department of Computer Science Université du Québec à Montréal Montréal QC Canada (MA) Department of Experimental Psychology University of Oxford Oxford UK (RJP TSCS) Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz Germany (CH) Department of Biology University of Konstanz Konstanz Germany (CH) Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany (CH) Network Science Institute Northeastern University Boston MA USA (BK) Brain Network Dynamics Unit University of Oxford Oxford UK (BM) Department of Mathematics Stony Brook University New York NY USA (DARS) Department of Physics and Astronomy Stony Brook University New York NY USA (DARS) Department of Biomedical Engineering Stony Brook University New York NY USA (DARS) Topos Institute Berkeley CA USA (TSCS) Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands (MK) Department of Philosophy Université du Québec à Montréal Montréal QC Canada (SET)

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call “shared intelligence.” This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology.

关键词： Active inference free energy principle artificial intelligence belief updating belief propagation

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Comparative and demographic analysis of orang-utan genomes (vol 469, pg 529, 2011)

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NATURE 2022年第7924期608卷 E36-E36页

作者： Locke, Devin P. Hillier, LaDeana W. Warren, Wesley C. Worley, Kim C. Nazareth, Lynne V. Muzny, Donna M. Yang, Shiaw-Pyng Wang, Zhengyuan Chinwalla, Asif T. Minx, Pat Mitreva, Makedonka Cook, Lisa Delehaunty, Kim D. Fronick, Catrina Schmidt, Heather Fulton, Lucinda A. Fulton, Robert S. Nelson, Joanne O. Magrini, Vincent Pohl, Craig Graves, Tina A. Markovic, Chris Cree, Andy Dinh, Huyen H. Hume, Jennifer Kovar, Christie L. Fowler, Gerald R. Lunter, Gerton Meader, Stephen Heger, Andreas Ponting, Chris P. Marques-Bonet, Tomas Alkan, Can Chen, Lin Cheng, Ze Kidd, Jeffrey M. Eichler, Evan E. White, Simon Searle, Stephen Vilella, Albert J. Chen, Yuan Flicek, Paul Ma, Jian Raney, Brian Suh, Bernard Burhans, Richard Herrero, Javier Haussler, David Faria, Rui Fernando, Olga Darre, Fleur Farre, Domenec Gazave, Elodie Oliva, Meritxell Navarro, Arcadi Roberto, Roberta Capozzi, Oronzo Archidiacono, Nicoletta Della Valle, Giuliano Purgato, Stefania Rocchi, Mariano Konkel, Miriam K. Walker, Jerilyn A. Ullmer, Brygg Batzer, Mark A. Smit, Arian F. A. Hubley, Robert Casola, Claudio Schrider, Daniel R. Hahn, Matthew W. Quesada, Victor Puente, Xose S. Ordonez, Gonzalo R. Lopez-Otin, Carlos Vinar, Tomas Brejova, Brona Ratan, Aakrosh Harris, Robert S. Miller, Webb Kosiol, Carolin Lawson, Heather A. Taliwal, Vikas Martins, Andre L. Siepel, Adam RoyChoudhury, Arindam Ma, Xin Degenhardt, Jeremiah Bustamante, Carlos D. Gutenkunst, Ryan N. Mailund, Thomas Dutheil, Julien Y. Hobolth, Asger Schierup, Mikkel H. Ryder, Oliver A. Yoshinaga, Yuko de Jong, Pieter J. Weinstock, George M. Rogers, Jeffrey Mardis, Elaine R. Gibbs, Richard A. Wilson, Richard K. The Genome Center at Washington University Washington University School of Medicine Saint Louis Missouri USA Human Genome Sequencing Center Department of Molecular and Human Genetics Baylor College of Medicine One Baylor Plaza Houston Texas USA MRC Functional Genomics Unit and Department of Physiology Anatomy and Genetics University of Oxford Le Gros Clark Building Oxford UK Wellcome Trust Centre for Human Genetics Oxford UK Department of Genome Sciences University of Washington School of Medicine Seattle Washington USA IBE Institut de Biologia Evolutiva (UPF-CSIC) Universitat Pompeu Fabra PRBB Doctor Aiguader 88 Barcelona Spain Howard Hughes Medical Institute Seattle Washington USA CIBIO Centro de Investigação em Biodiversidade e Recursos Genéticos Universidade do Porto Campus Agrário de Vairão Vairão Portugal Instituto de Tecnologia Química e Biológica Universidade Nova de Lisboa Oeiras Portugal ICREA (Institució Catalana de Recerca i Estudis Avançats) and INB (Instituto Nacional de Bioinformática) PRBB Doctor Aiguader 88 Barcelona Spain Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Cambridge UK European Bioinformatics Institute Wellcome Trust Genome Campus Hinxton Cambridge UK Center for Biomolecular Science and Engineering University of California Santa Cruz California USA Center for Comparative Genomics and Bioinformatics Penn State University University Park Pennsylvania USA Department of Biology University of Bari Bari Italy Department of Biology University of Bologna Bologna Italy Department of Biological Sciences Louisiana State University Baton Rouge Louisiana USA Center for Computation and Technology Department of Computer Sciences Louisiana State University Baton Rouge Louisiana USA Institute for Systems Biology Seattle Washington USA Department of Biology and School of Informatics and Computing Indiana University Bloomington Indiana USA Instituto Universitario de Oncologia Departamento de Bioquimica y Biol

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Parallel and scalable heat methods for geodesic distance computation

arXiv

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arXiv 2018年

作者： Tao, Jiong Zhang, Juyong Deng, Bailin Fang, Zheng Peng, Yue He, Ying School of Mathematical Sciences University of Science and Technology of China School of Computer Science and Informatics Cardiff University School of Computer Science and Engineering Nanyang Technological University

In this paper, we propose a parallel and scalable approach for geodesic distance computation on triangle meshes. Our key observation is that the recovery of geodesic distance with the heat method [1] can be reformulated as optimization of its gradients subject to integrability, which can be solved using an efficient first-order method that requires no linear system solving and converges quickly. Afterward, the geodesic distance is efficiently recovered by parallel integration of the optimized gradients in breadth-first order. Moreover, we employ a similar breadth-first strategy to derive a parallel Gauss-Seidel solver for the diffusion step in the heat method. To further lower the memory consumption from gradient optimization on faces, we also propose a formulation that optimizes the projected gradients on edges, which reduces the memory footprint by about 50%. Our approach is trivially parallelizable, with a low memory footprint that grows linearly with respect to the model size. This makes it particularly suitable for handling large models. Experimental results show that it can efficiently compute geodesic distance on meshes with more than 200 million vertices on a desktop PC with 128GB RAM, outperforming the original heat method and other state-of-the-art geodesic distance solvers. Copyright © 2018, The Authors. All rights reserved.

关键词： Poisson equation

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