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Cover Image, Volume 5, Issue 4

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WIrEs Water 2018年第4期5卷

作者： remko Uijlenhoet Aart Overeem Hidde Leijnse Hydrology and Quantitative Water Management Group Wageningen University & Research Wageningen The Netherlands R&D Observations and Data Technology Royal Netherlands Meteorological Institute (KNMI) De Bilt The Netherlands

The cover image, by remko Uijlenhoet et al, is based on Primer Opportunistic remote sensing of rainfall using microwave links from cellular communication networks , dOI: 10.1002/wat2.1289 . Image courtesy of identim/S...

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Targeted enzyme gene re-positioning: A computational approach for discovering alternative bacterial enzymes for the synthesis of plant-specific secondary metabolites

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Metabolic Engineering Communications 2019年 9卷 e00102页

作者： Nakamura, Yuya Hirose, Shuichi Taniguchi, Yuko Moriya, Yuki Yamada, Takuji School of Life Science and Technology Tokyo Institute of Technology 2-12-1 Ookayama Meguro Tokyo 152-8550 Japan NAGASE R&D Center Nagase & Co. Ltd Kobe High Tech Park 2-2-3 Murotani Nishi- ku Kobe 651-2241 Hyogo Japan Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Kashiwa 277-0871 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Metabologenomics Inc 246-2 Kakuganji Tsuruoka 997-0052 Yamagata Japan

Plant-biosynthesised secondary metabolites are unique sources of pharmaceuticals, food additives, and flavourings, among other industrial uses. However, industrial production of these metabolites is difficult because of their structural complexity, dangerousness and unfriendliness to natural environment, so the development of new methods to synthesise them is required. In this study, we developed a novel approach to identifying alternative bacterial enzyme to produce plant-biosynthesised secondary metabolites. Based on the similarity of enzymatic reactions, we searched for candidate bacterial genes encoding enzymes that could potentially replace the enzymes in plant-specific secondary metabolism reactions that are contained in the KEGG database (enzyme re-positioning). As a result, we discovered candidate bacterial alternative enzyme genes for 447 plant-specific secondary metabolic reaction. To validate our approach, we focused on the ability of an enzyme from Streptomyces coelicolor strain A3(2) strain to convert valencene to the grapefruit metabolite nootkatone, and confirmed its enzymatic activity by gas chromatography-mass spectrometry. This enzyme re-positioning approach may offer an entirely new way of screening enzymes that cannot be achieved by most of other conventional methods, and it is applicable to various other metabolites and may enable microbial production of compounds that are currently difficult to produce industrially. © 2019 The Authors

关键词： Enzyme repositioning Genomics Nootkatone Plant secondary metabolism reaction similarity

data Harmonisation for Information Fusion in digital Healthcare: A State-of-the-Art Systematic review, Meta-Analysis and Future research directions

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arXiv 2022年

作者： Nan, Yang del Ser, Javier Walsh, Simon Schönlieb, Carola roberts, Michael Selby, Ian Howard, Kit Owen, John Neville, Jon Guiot, Julien Ernst, Benoit Pastor, Ana Alberich-Bayarri, Angel Menzel, Marion I. Walsh, Sean Vos, Wim Flerin, Nina Charbonnier, Jean-Paul van rikxoort, Eva Chatterjee, Avishek Woodruff, Henry Lambin, Philippe Cerdá-Alberich, Leonor Martí-Bonmatí, Luis Herrera, Francisco Yang, Guang Xing, Xiaodan Li, Ming Wagers, Scott Baker, rebecca Nardi, Cosimo van Eeckhout, Brice Skipp, Paul Powell, Pippa Carroll, Miles ruggiero, Alessandro Thillai, Muhunthan Babar, Judith Sala, Evis Murch, William Hiscox, Julian Baralle, diana Sverzellati, Nicola Blanco, Ana Miguel Bataller, Fuensanta Bellvís Aznar, Mario Suarez, Amelia Figueiras, Sergio Krischak, Katharina Hierath, Monika Mirsky, Yisroel Elovici, Yuval Beregi, Jean Paul Fournier, Laure Sardanelli, Francesco Penzkofer, Tobias Seymour, Karine Blanquer, Nacho Neri, Emanuele Laghi, Andrea França, Manuela Martinez, ricard National Heart and Lung Institute Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom Department of Communications Engineering University of the Basque Country UPV/EHU Bilbao48013 Spain Derio48160 Spain Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Oncology R&D AstraZeneca Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Data Interchange Standards Consortium AustinTX United States Respiratory medicine department Liège Belgium University of Liege Department of Clinical Sciences Pneumology-Allergology Liège Belgium QUIBIM Valencia Spain Technische Hochschule Ingolstadt Ingolstadt Germany GE Healthcare GmbH Munich Germany Liège Belgium Thirona Nijmegen Netherlands Department of Precision Medicine Maastricht University Maastricht Netherlands Medical Imaging Department Hospital Universitari i Politècnic La Fe Valencia Spain University of Granada Granada Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia National Heart and Lung Institute Imperial College London London United Kingdom BioSci Consulting Maasmechelen Belgium Clinical Data Interchange Standards Consortium AustinTX United States University of Florence Firenze Italy Medical Cloud Company Liège Belgium TopMD Southampton United Kingdom European Lung Foundation Sheffield United Kingdom Department of Health Public Health England London United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Owlstone Medical Cambridge United Kingdom University of Liverpool Liverpool United Kingdom University of Southampton Southampton United Kingdom University of Parma Parma Italy Medical Imaging Department Hospital Universi

removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research. © 2022, CC BY-NC-Nd.

关键词： Health care

917P OPTIMA: Improve care for patients with prostate, breast, and lung cancer through artificial intelligence

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Annals of Oncology 2022年 33卷 S966-S966页

作者： J. N’dow E.J. Smith K. Polychronopoulos A. Cannon M. roobol S. Auweter M. Thomas A. Kremer B. de Meulder d. dellamonica d.P. Alhambra A. Asiimwe M. Bussmann X. Ji P. Torremante S. Keller F. Kube H. Krueger Academic Urology Unit University of Aberdeen Aberdeen UK Guideline Office EAU - European Association of Urology Arnhem Netherlands Department of Innovation and Digitalisation in Law University of Vienna Vienna Austria Genentech Inc. Hillsboro OR USA Erasmus MC Cancer Institute Erasmus University Medical Center Rotterdam Netherlands Scientific Project Management Smart Reporting GmbH Munich Germany Global Medical Affairs F. Hoffmann-La Roche AG Basel Switzerland R&D Information Technology for Translational Medicine Luxembourg EISBM Association EISBM Vourles France Europe and Canada Oncology AstraZeneca AG Zug Switzerland CSM-NDORMS University of Oxford Oxford UK Open Innovation Private Public Partnerships Bayer AG Leverkusen Germany Center for Advanced Systems Understanding Helmholtz-Zentrum Dresden-Rossendorf Dresden Germany Data Science and Advance Analytics CoE Pfizer Digital Pfizer Inc. Collegeville PA USA ARTTIC Innovation GmbH ARTTIC Innovation GmbH Munich Germany Oncology Pfizer Pharma GmbH Berlin Germany Medical Affairs Oncology Pfizer Pharma GmbH Karlsruhe Germany Medical Affairs Oncology Pfizer Pharma GmbH Berlin Germany

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

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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

Machine Learning Based State-Space Approximate dynamic Programming Approach for Energy and reserve Management of Power Plants

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Machine Learning Based State-Space Approximate Dynamic Progr...

IEEE Innovative Smart Grid Technologies - Asia

作者： Chanaka Keerthisinghe Hongbo Sun Yusuke Takaguchi daniel Nikovski Hiroyuki Hashimoto Dept. of Electrical Engineering University of Washington Data Analytics Group Mitsubishi Electric Research Laboratories Advanced Technology R&D Center Mitsubishi Electric Corporation Headquarters Mitsubishi Electric Research Laboratories

ISBN: (纸本)9781538642924

This paper proposes a machine learning based state-space approximate dynamic programming (MSAdP) approach to solve the self-scheduling problem faced by power plants under an integrated energy and reserve market. By extending the concept of residual demand curves (rdCs) from energy to reserve, the residual reserve curves (rrCs) are proposed to model the regulation price as a function of the power plant's reserve power. Both rrCs and rdCs are obtained using a clustering based neural network approach, which resulted in better estimates than using only a non-parametric approach. The machine learning is used to make approximations to the state space, and the dynamic programming only loops over the required states. As such, the computation effort is reduced but the solution quality does not be impacted. The value functions generated during the day-ahead optimization are used to generate optimal supply offer curves for the day-ahead market, and make real-time decisions and real-time bids to stay optimal by solving Bellman optimality condition. The effectiveness of the MSAdP approach is demonstrated using empirical data obtained from the New England ISO.

关键词： Power plant self-scheduling residual demand curves residual reserve curves Machine learning State-space approximate dynamic programming

The tracking machine learning challenge: Accuracy phase

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arXiv 2019年

作者： Amrouche, Sabrina Basara, Laurent Calafiura, Paolo Estrade, Victor Farrell, Steven Ferreira, diogo r. Finnie, Liam Finnie, Nicole Germain, Cécile Gligorov, Vladimir Vava Golling, Tobias Gorbunov, Sergey Gray, Heather Guyon, Isabelle Hushchyn, Mikhail Innocente, Vincenzo Kiehn, Moritz Moyse, Edward Puget, Jean-François reina, Yuval rousseau, david Salzburger, Andreas Ustyuzhanin, Andrey Vlimant, Jean-roch Wind, Johan Sokrates Xylouris, Trian Yilmaz, Yetkin Département de Physique Nucléaire et Corpusculaire Université de Genève Geneva Switzerland LRI TAU Univ. Paris-Sud INRIA CNRS Université Paris-Saclay Gif-sur-Yvette France Physics Division Lawrence Berkeley National Laboratory University of California BerkeleyCA United States IST University of Lisbon Lisbon Portugal IBM Germany Research and Development Germany Bosch Center for Artificial Intelligence Germany LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS IN2P3 Paris France Goethe University Frankfurt am Main Germany UPSud INRIA Université Paris-Saclay Orsay France ChaLearn BerkeleyCA United States National Research University Higher School of Economics Yandex School of Data Analysis Moscow Russia CERN Geneva Switzerland Department of Physics University of Massachusetts AmherstMA United States Data and AI R&D IBM France Lab Biot France Tel-Aviv Israel LAL Univ. Paris-Sud CNRS IN2P3 Université Paris-Saclay Orsay France California Institute of Technology PasadenaCA United States Norwegian University of Science and Technology Oslo Norway Frankfurt Germany

This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100.000 points, the participants had to connect them into about 10.000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the document. © 2019, CC BY.

关键词： Machine learning

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

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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

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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