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Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data

Nature communications 2021年第1期12卷 4103页

作者： Jeongbin Park Wonyl Choi Sebastian Tiesmeyer Brian Long Lars E Borm Emma Garren Thuc Nghi Nguyen Bosiljka Tasic Simone Codeluppi Tobias Graf Matthias Schlesner Oliver Stegle Roland Eils Naveed Ishaque Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. Faculty of Biosciences Heidelberg University Heidelberg Germany. Division of Computational Genomics and System Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science Boston University Boston MA USA. Allen Institute for Brain Science Seattle WA USA. Division of molecular neurobiology Department of medical biochemistry and biophysics Karolinska Institutet Stockholm Sweden. Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany. Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany. Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. roland.eils@bih-charite.de. Health Data Science Unit Heidelberg University Hospital Heidelberg Germany. roland.eils@bih-charite.de. Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. naveed.ishaque@bih-charite.de.

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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A Grouping Genetic Algorithm for Joint Stratification and Sample Allocation Designs

arXiv

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arXiv 2017年

作者： O’Luing, Mervyn Prestwich, Steven Tarim, S. Armagan Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland Department of Management Cankaya University Ankara Turkey

Predicting the cheapest sample size for the optimal stratification in multivariate survey design is a problem in cases where the population frame is large. A solution exists that iteratively searches for the minimum sample size necessary to meet accuracy constraints in partitions of atomic strata created by the Cartesian product of auxiliary variables into larger strata. The optimal stratification can be found by testing all possible partitions. However the number of possible partitions grows exponentially with the number of initial strata. There are alternative ways of modelling this problem, one of the most natural is using Genetic Algorithms (GA). These evolutionary algorithms use recombination, mutation and selection to search for optimal solutions. They often converge on optimal or near-optimal solution more quickly than exact methods. We propose a new GA approach to this problem using grouping genetic operators instead of traditional operators. The results show a significant improvement in solution quality for similar computational effort, corresponding to large monetary savings. Copyright © 2017, The Authors. All rights reserved.

关键词： Iterative methods

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Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation 26

Geo-teaser: Geo-temporal sequential embedding rank for point...

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26th International World Wide Web Conference, WWW 2017 Companion

作者： Zhao, Shenglin Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics Application Shenzhen Research Institute Chinese University of Hong Kong Nanshan District Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9781450349147

Point-of-interest (POI) recommendation is an important application for location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. Previous studies show that modeling the sequential pattern of user check-ins is necessary for POI recommendation. Markov chain model, recurrent neural network, and the word2vec framework are used to model check-in sequences in previous work. However, all previous sequential models ignore the fact that check-in sequences on different days naturally exhibit the various temporal characteristics, for instance, "work" on weekday and "entertainment" on weekend. In this paper, we take this challenge and propose a Geo-Temporal sequential embedding rank (Geo-Teaser) model for POI recommendation. Inspired by the success of the word2vec framework to model the sequential contexts, we propose a temporal POI embedding model to learn POI representations under some particular temporal state. The temporal POI embedding model captures the contextual check-in information in sequences and the various temporal characteristics on different days as well. Furthermore, We propose a new way to incorporate the geographical influence into the pairwise preference ranking method through discriminating the unvisited POIs according to geographical information. Then we develop a geographically hierarchical pairwise preference ranking model. Finally, we propose a unified framework to recommend POIs combining these two models. To verify the effectiveness of our proposed method, we conduct experiments on two real-life datasets. Experimental results show that the Geo-Teaser model outperforms state-of-the-art models. Compared with the best baseline competitor, the Geo-Teaser model improves at least 20% on both datasets for all metrics. © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.

关键词： Location based services

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Preface

OpenAccess Series in Informatics

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OpenAccess Series in Informatics 2019年 70卷 IX-X页

作者： Eskevich, Maria de Melo, Gerard Fäth, Christian McCrae, John P. Buitelaar, Paul Chiarcos, Christian Klimek, Bettina Dojchinovski, Milan CLARIN ERIC Utrecht Netherlands Department of Computer Science Rutgers University New BrunswickNJ United States Applied Computational Linguistics Goethe University Frankfurt Germany Insight Centre for Data Analytics National University of Ireland Galway Ireland Agile Knowledge Engineering and Semantic Web University of Leipzig Germany

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Parle: Parallelizing stochastic gradient descent

arXiv

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arXiv 2017年

作者： Chaudhari, Pratik Baldassi, Carlo Zecchina, Riccardo Soatto, Stefano Talwalkar, Ameet Oberman, Adam Computer Science Department University of California Los Angeles Institute for Data Science and Analytics Bocconi University Milano Italy Department of Mathematics and Statistics McGill University Montreal Canada

We propose a new algorithm called Parle for parallel training of deep networks that converges 2-4× faster than a data-parallel implementation of SGD, while achieving significantly improved error rates that are nearly state-of-the-art on several benchmarks including CIFAR-10 and CIFAR-100, without introducing any additional hyper-parameters. We exploit the phenomenon of flat minima that has been shown to lead to improved generalization error for deep networks. Parle requires very infrequent communication with the parameter server and instead performs more computation on each client, which makes it well-suited to both single-machine, multi-GPU settings and distributed implementations. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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NL-Augmenter A Framework for Task-Sensitive Natural Language Augmentation

arXiv

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arXiv 2021年

作者： Dhole, Kaustubh D. Gangal, Varun Gehrmann, Sebastian Gupta, Aadesh Li, Zhenhao Mahamood, Saad Mahendiran, Abinaya Mille, Simon Shrivastava, Ashish Tan, Samson Wu, Tongshuang Sohl-Dickstein, Jascha Choi, Jinho D. Hovy, Eduard Dušek, Ondřej Ruder, Sebastian Anand, Sajant Aneja, Nagender Banjade, Rabin Barthe, Lisa Behnke, Hanna Berlot-Attwell, Ian Boyle, Connor Brun, Caroline Sobrevilla Cabezudo, Marco Antonio Cahyawijaya, Samuel Chapuis, Emile Che, Wanxiang Choudhary, Mukund Clauss, Christian Colombo, Pierre Cornell, Filip Dagan, Gautier Das, Mayukh Dixit, Tanay Dopierre, Thomas Dray, Paul-Alexis Dubey, Suchitra Ekeinhor, Tatiana Giovanni, Marco Di Goyal, Tanya Gupta, Rishabh Hamla, Louanes Han, Sang Harel-Canada, Fabrice Honoré, Antoine Jindal, Ishan Joniak, Przemyslaw K. Kleyko, Denis Kovatchev, Venelin Krishna, Kalpesh Kumar, Ashutosh Langer, Stefan Lee, Seungjae Ryan Levinson, Corey James Liang, Hualou Liang, Kaizhao Liu, Zhexiong Lukyanenko, Andrey Marivate, Vukosi de Melo, Gerard Meoni, Simon Meyer, Maxime Mir, Afnan Moosavi, Nafise Sadat Muennighoff, Niklas Hon Mun, Timothy Sum Murray, Kenton Namysl, Marcin Obedkova, Maria Oli, Priti Pasricha, Nivranshu Pfister, Jan Plant, Richard Prabhu, Vinay Pais, Vasile Qin, Libo Raji, Shahab Rajpoot, Pawan Kumar Raunak, Vikas Rinberg, Roy Roberts, Nicholas Rodriguez, Juan Diego Roux, Claude Vasconcellos, P.H.S. Sai, Ananya B. Schmidt, Robin M. Scialom, Thomas Sefara, Tshephisho Shamsi, Saqib N. Shen, Xudong Shi, Yiwen Shi, Haoyue Shvets, Anna Siegel, Nick Sileo, Damien Simon, Jamie Singh, Chandan Sitelew, Roman Soni, Priyank Sorensen, Taylor Soto, William Srivastava, Aman Aditya Srivatsa, K.V. Sun, Tony Mukund Varma, T. Tabassum, A. Tan, Fiona Anting Teehan, Ryan Tiwari, Mo Tolkiehn, Marie Wang, Athena Wang, Zijian Wang, Zijie J. Wang, Gloria Wei, Fuxuan Wilie, Bryan Winata, Genta Indra Wu, Xinyi Wydmanski, Witold Xie, Tianbao Yaseen, Usama Yee, Michael A. Zhang, Jing Zhang, Yue ACKO Agara Amelia R&D New York United States Applied Research Laboratories The University of Texas at Austin United States Bloomberg Brigham Young University United States Carnegie Mellon University United States Center for Data and Computing in Natural Sciences Universität Hamburg Germany Charles River Analytics Charles University Prague Czech Republic Columbia University United States Council for Scientific and Industrial Research DeepMind United Kingdom Department of Computer Science University of Pretoria South Africa Drexel University United States Eberhard Karls University of Tübingen Germany Edinburgh Napier University United Kingdom Emory University United States Fablab by Inetum in Paris France Fraunhofer IAIS Germany Georgia Tech United States Google Brain United States Google Research United States Harbin Institute of Technology China Hasso Plattner Institute University of Potsdam Germany Hong Kong University of Science and Technology Hong Kong IBM Research IIT Delhi India IIT Madras India Illinois Mathematics and Science Academy United States Imperial College London United Kingdom Independent Indian Institute of Science Bangalore India Institut Teknologi Bandung Indonesia Institute of Data Science National University of Singapore Singapore International Institute of Information Technology Hyderabad India Jagiellonian University Poland Jean Monnet University France Johns Hopkins United States KTH Royal Institute of Technology Sweden KU Leuven Belgium MTS AI France Microsoft RedmondWA United States Mphasis NEXT Labs National University of Ireland Galway Ireland National University of Science and Technology Pakistan National University of Singapore Singapore Naver Labs Europe France Peking University China Politecnico di Milano University of Bologna Italy Polytechnic Institute of Paris France Pompeu Fabra University Spain Pontifical Catholic University of Minas Gerais Brazil Princeton University United States Rakuten India India Research Institu

data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its tranformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robutstness analysis results are available publicly on the NL-Augmenter repository (https://***/GEM-benchmark/NL-Augmenter). © 2021, CC BY.

关键词： Python

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An improved metaheuristic algorithm for maximizing demand satisfaction in the population harvest cutting stock problem 9

An improved metaheuristic algorithm for maximizing demand sa...

引用

9th Annual Symposium on Combinatorial Search, SoCS 2016

作者： Climent, Laura O’Sullivan, Barry Wallace, Richard J Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland

ISBN: (纸本)9781577357698

We present a greedy version of an existing metaheuristic algorithm for a special version of the Cutting Stock Problem (CSP). For this version, it is only possible to have indirect control over the patterns via a vector of continuous values which we refer to as a weights vector. Our algorithm iteratively generates new weights vectors by making local changes over the best weights vector computed so far. This allows us to achieve better solutions much faster than is possible with the original metaheuristic. Copyright © 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Iterative methods

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Revisiting two-sided stability constraints

Revisiting two-sided stability constraints

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Douziemes Journees Francophones de Programmation par Contraintes, JFPC 2016 - 12th French-Speaking Conference on Constraint Programming, JFPC 2016

作者： Siala, Mohamed O'Sullivan, Barry Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland

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Causal discovery by randomness test

Causal discovery by randomness test

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2016 International Symposium on Artificial Intelligence and Mathematics, ISAIM 2016

作者： Prestwich, S. Tarim, S.A. Ozkan, I. Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland

Probabilistic methods for causal discovery are based on the detection of patterns of correlation between variables. They are based on statistical theory and have revolutionised the study of causality. However, when correlation itself is unreliable, so are probabilistic methods: nonsense correlations can lead to spurious causal links, while nonmonotonic functional relationships between variables can prevent the detection of causal links. We describe a new heuristic method for inferring causality between two continuous or integer variables, based on a nonparametric randomness test. We evaluate the accuracy of the method by comparing it to published algorithms on real and artificial datasets, and show that it largely avoids these false positives and negatives. © 2016 University of Virginia. All rights reserved.

关键词： Heuristic methods

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