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arXiv 2024年

作者： Hemberg, Martin Marini, Federico Ghazanfar, Shila Ajami, Ahmad Al Abassi, Najla Anchang, Benedict Benayoun, Bérénice A. Cao, Yue Chen, Ken Cuesta-Astroz, Yesid DeBruine, Zach Dendrou, Calliope A. De Vlaminck, Iwijn Imkeller, Katharina Korsunsky, Ilya Lederer, Alex R. Meysman, Pieter Miller, Clint Mullan, Kerry Ohler, Uwe Patikas, Nikolaos Schuck, Jonas Siu, Jacqueline H.Y. Triche, Timothy J. Tsankov, Alex van der Laan, Sander W. Yajima, Masanao Yang, Jean Zanini, Fabio Jelic, Ivana The Gene Lay Institute of Immunology and Inflammation Brigham and Women's Hospital Massachusetts General Hospital United States Harvard Medical School BostonMA United States University Medical Center Mainz Mainz Germany Mainz Germany School of Mathematics and Statistics Faculty of Science University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney SydneyNSW2006 Australia Charles Perkins Centre University of Sydney SydneyNSW2006 Australia Goethe University Neurological Institute Edinger Institute University Hospital Frankfurt Frankfurt am Main Germany Goethe University Frankfurt Cancer Institute Frankfurt am Main Germany Frankfurt am Main Germany National Institute of Environmental Health Sciences United States Leonard Davis School of Gerontology University of Southern California Los AngelesCA90089 United States Molecular and Computational Biology Department USC Dornsife College of Letters Arts and Sciences Los AngelesCA90089 United States Biochemistry and Molecular Medicine Department USC Keck School of Medicine Los AngelesCA90089 United States Science Park Hong Kong The University of Texas MD Anderson Cancer Center United States Escuela de Microbiología Universidad de Antioquia Ciudad Universitaria Calle 67 No 12 53-108 Medellín Colombia Instituto Colombiano de Medicina Tropical Universidad CES Sabaneta055413 Colombia School of Computing Grand Valley State University AllendaleMI49401 United States Kennedy Institute of Rheumatology Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford United Kingdom Cornell University United States Brigham and Women’s Hospital Divisions of Genetics and Rheumatology BostonMA United States and Harvard CambridgeMA United States Lausanne1015 Switzerland Adrem data lab Department of Computer Science University of Antwerp Antwerp Belgium The Rector and Visitors The University of Virginia United States M

The field of single-cell biology is growing rapidly and is generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO, allow researchers to access large volumes of curated datasets. Although the majority of the data is from scRNAseq experiments, a wide range of other modalities are represented as well. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse, and for extracting novel biological insights. Here, we highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome. © 2024, CC BY-NC-ND.

关键词： Biotic

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Fame and Ultrafame: Measuring and comparing daily levels of 'being talked about' for United States' presidents, their rivals, God, countries, and K-pop

arXiv

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arXiv 2019年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States

When building a global brand of any kind-a political actor, clothing style, or belief system-developing widespread awareness is a primary goal. Short of knowing any of the stories or products of a brand, being talked about in whatever fashion-raw fame-is, as Oscar Wilde would have it, better than not being talked about at all. Here, we measure, examine, and contrast the day-to-day raw fame dynamics on Twitter for US Presidents and major US Presidential candidates from 2008 to 2020: Barack Obama, John McCain, Mitt Romney, Hillary Clinton, Donald Trump, and Joe Biden. We assign "lexical fame" to be the number and (Zipfian) rank of the (lowercased) mentions made for each individual across all languages. We show that all five political figures have at some point reached extraordinary volume levels of what we define to be "lexical ultrafame": An overall rank of approximately 300 or less which is largely the realm of function words and demarcated by the highly stable rank of 'god'. By this measure, 'trump' has become enduringly ultrafamous, from the 2016 election on. We use typical ranks for country names and function words as standards to improve perception of scale. We quantify relative fame rates and find that in the eight weeks leading up the 2008 and 2012 elections, 'obama' held a 1000:757 volume ratio over 'mccain' and 1000:892 over 'romney', well short of the 1000:544 and 1000:504 volumes favoring 'trump' over 'hillary' and 'biden' in the 8 weeks leading up to the 2016 and 2020 elections. Finally, we track how only one other entity has more sustained ultrafame than 'trump' on Twitter: The K-pop (Korean pop) band BTS. We chart the dramatic rise of BTS, finding their Twitter handle '@bts twt' has been able to compete with 'a' and 'the', reaching a rank of three at the day scale and a rank of one at the quarter-hour scale. Our findings for BTS more generally point to K-pop's growing economic, social, and political power. Copyright © 2019, The Authors. All rights reser

关键词： Social networking (online)

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Dynamic PET cardiac and parametric image reconstruction: A fixed-point proximity gradient approach using patch-based DCT and tensor SVD regularization

arXiv

引用

arXiv 2019年

作者： Häggström, Ida Lin, Yizun Li, Si Krol, Andrzej Xu, Yuesheng Schmidtlein, C. Ross Department of Medical Physics Memorial Sloan Kettering Cancer Center New YorkNY10065 United States School of Mathematics Guangdong Provincial Key Lab of Computational Science Sun Yat-sen University Guangzhou510275 China School of Data and Computer Science Guangdong Provincial Key Lab of Computational Science Sun Yat-sen University Guangzhou510275 China Department of Radiology Department of Pharmacology SUNY Upstate Medical University SyracuseNY13210 United States Department of Mathematics and Statistics Old Dominion University NorfolkVA23529 United States

Our aim was to enhance visual quality and quantitative accuracy of dynamic positron emission tomography (PET) uptake images by improved image reconstruction, using sophisticated sparse penalty models that incorporate both 2D spatial+1D temporal (3DT) information. We developed two new 3DT PET reconstruction algorithms, incorporating different temporal and spatial penalties based on discrete cosine transform (DCT) w/ patches, and tensor nuclear norm (TNN) w/ patches, and compared to frame-by-frame methods;conventional 2D ordered subsets expectation maximization (OSEM) w/ post-filtering and 2D-DCT and 2D-TNN. A 3DT brain phantom with kinetic uptake (2-tissue model), and a moving 3DT cardiac/lung phantom was simulated and reconstructed. For the cardiac/lung phantom, an additional cardiac gated 2D-OSEM set was reconstructed. The structural similarity index (SSIM) and relative root mean squared error (rRMSE) relative ground truth was investigated. The image derived left ventricular (LV) volume for the cardiac/lung images was found by region growing. Parametric images of the brain phantom were calculated by nonlinear least squares fitting. For the cardiac/lung phantom, 3DT-TNN yielded optimal images with an rRMSE / SSIM of 45.4±0.4% / 0.652±0.007, compared to 65.4±0.1% / 0.4439±8E-4for conventional 2DOSEM. The optimal LV volume from the 3DT-TNN images was on average 79±4% of the true value, cardiac gated 2DOSEM 68±9%, and 2D-OSEM 24±7%. 3DT-DCT had minimum rRMSE and maximum SSIM for the brain phantom at 59.5±0.3 and 0.593±0.003% respectively, whereas 2D-OSEM had an rRMSE / SSIM of 75.6±0.4% / 0.478±0.005. Compared to 2D-OSEM, parametric images based on 3DT-DCT images had smaller bias for all six parameters, and higher SSIM for all but one. Our novel methods that incorporate both 2D spatial and 1D temporal penalties produced dynamic PET images of higher quality than conventional 2D methods, w/o need for post-filtering. Breathing and cardiac motion were simultaneously captur

关键词： Image reconstruction

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A Convergent Fixed-Point Proximity Algorithm Accelerated by FISTA for the 0 Sparse Recovery Problem

A Convergent Fixed-Point Proximity Algorithm Accelerated by ...

引用

International conference on Imaging, Vision and Learning Based on Optimization and PDEs, IVLOPDE 2016

作者： Zeng, Xueying Shen, Lixin Xu, Yuesheng School of Mathematical Sciences Ocean University of China Qingdao China Department of Mathematics Syracuse University SyracuseNY United States School of Data and Computer Science Guangdong Province Key Lab of Computational Science Sun Yat-sen University Guangzhou China Department of Mathematics and Statistics Old Dominion University NorfolkVA United States

ISBN: (纸本)9783642543005

We propose an approximation model of the original 0 minimization model arising from various sparse signal recovery problems. The objective function of the proposed model uses the Moreau envelope of the 0 norm to promote the sparsity of the signal in a tight framelet system. This leads to a non-convex optimization problem involved the 0 norm. We identify a local minimizer of the proposed non-convex optimization problem with a global minimizer of a related convex optimization problem. Based on this identification, we develop a two stage algorithm for solving the proposed non-convex optimization problem and study its convergence. Moreover, we show that FISTA can be employed to speed up the convergence rate of the proposed algorithm to reach the optimal convergence rate of O(1 ∕ k2). We present numerical results to confirm the theoretical estimate. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Convex optimization

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Gauging the happiness benefit of US urban parks through Twitter

arXiv

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arXiv 2020年

作者： Schwartz, Aaron J. Dodds, Peter Sheridan O’Neil-Dunne, Jarlath P.M. Ricketts, Taylor H. Danforth, Christopher M. Gund Institute for Environment University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT05401 United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

The relationship between nature contact and mental well-being has received increasing attention in recent years. While a body of evidence has accumulated demonstrating a positive relationship between time in nature and mental well-being, there have been few studies comparing this relationship in different locations over long periods of time. In this study, we estimate a happiness benefit, the difference in expressed happiness between in- and out-of-park tweets, for the 25 largest cities in the US by population. People write happier words during park visits when compared with non-park user tweets collected around the same time. While the words people write are happier in parks on average and in most cities, we find considerable variation across cities. Tweets are happier in parks at all times of the day, week, and year, not just during the weekend or summer vacation. Across all cities, we find that the happiness benefit is highest in parks larger than 100 acres. Overall, our study suggests the happiness benefit associated with park visitation is on par with US holidays such as Thanksgiving and New Year’s Day. Copyright © 2020, The Authors. All rights reserved.

关键词： Parks

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Integrals of life: Tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao's Q index

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Ecological Complexity 2023年第1期52卷

作者： Thouverai, Elisa Marcantonio, Matteo Lenoir, Jonathan Galfré, Mariasole Marchetto, Elisa Bacaro, Giovanni Cazzolla Gatti, Roberto Da Re, Daniele Di Musciano, Michele Furrer, Reinhard Malavasi, Marco Moudrý, Vítězslav Nowosad, Jakub Pedrotti, Franco Pelorosso, Raffaele Pezzi, Giovanna Šímová, Petra Ricotta, Carlo Silvestri, Sonia Tordoni, Enrico Torresani, Michele Vacchiano, Giorgio Zannini, Piero Rocchini, Duccio BIOME Lab Department of Biological Geological and Environmental Sciences Alma Mater Studiorum University of Bologna via Irnerio 42 Bologna 40126 Italy Evolutionary Ecology and Genetics Group Earth & Life Institute UCLouvain Louvain-la-Neuve 1348 Belgium UMR CNRS 7058 Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN) Université de Picardie Jules Verne 1 rue des Louvels Amiens Cedex 1 F-80037 France Department of Life Sciences University of Trieste via Giorgieri 10 Trieste 34127 Italy Department of Life Health and Environmental Sciences University of L'Aquila Piazzale Salvatore Tommasi 1 L'Aquila 67100 Italy Department of Mathematics and Institute of Computational Science University of Zürich Zürich Switzerland Department of Chemistry Physics Mathematics and Natural Sciences University of Sassari Sassari Italy Department of Spatial Sciences Faculty of Environmental Sciences Czech University of Life Sciences Prague Praha - Suchdol Czech Republic Institute of Geoecology and Geoinformation Adam Mickiewicz University Krygowskiego 10 Poznan 61-680 Poland University of Camerino Camerino Italy DAFNE Department Tuscia University Viterbo 01100 Italy Department of Environmental Biology Sapienza University Piazzale Moro 5 Rome 00185 Italy Department of Biological Geological and Environmental Sciences Alma Mater Studiorum University of Bologna via Irnerio 42 Bologna 40126 Italy Department of Botany Institute of Ecology and Earth Sciences University of Tartu J. Liivi 2 Tartu 50409 Estonia Libera Università di Bolzano - Freie Universität Bozen Bolzano/Bozen Italy Department of Agricultural and Environmental Sciences (DiSAA) Università degli Studi di Milano Via Celoria 2 Milano 20133 Italy LifeWatch Italy Lecce Italy Plant Data – Interuniversity Research Centre for Plant Biodiversity and Big Data Department of Biological Geological and Environmental Sciences University of Bologna Bologna Italy

Spatio-ecological heterogeneity is strongly linked to many ecological processes and functions such as plant species diversity patterns and change, metapopulation dynamics, and gene flow. Remote sensing is particularly useful for measuring spatial heterogeneity of ecosystems over wide regions with repeated measurements in space and time. Besides, developing free and open source algorithms for ecological modelling from space is vital to allow to prove workflows of analysis reproducible. From this point of view, NASA developed programs like the Surface Biology and Geology (SBG) to support the development of algorithms for exploiting spaceborne remotely sensed data to provide a relatively fast but accurate estimate of ecological properties in vast areas over time. Most of the indices to measure heterogeneity from space are point descriptors: they catch only part of the whole heterogeneity spectrum. Under the SBG umbrella, in this paper we provide a new R function part of the rasterdiv R package which allows to calculate spatio-ecological heterogeneity and its variation over time by considering all its possible facets. The new function was tested on two different case studies, on multi- and hyperspectral images, proving to be an effective tool to measure heterogeneity and detect its changes over time. © 2023 Elsevier B.V.

关键词： Biodiversity Ecological informatics Modelling Remote sensing Satellite imagery

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Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

arXiv

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arXiv 2024年

作者： Evans, Matthew L. Bergsma, Johan Merkys, Andrius Andersen, Casper W. Andersson, Oskar B. Beltrán, Daniel Blokhin, Evgeny Boland, Tara M. Balderas, Rubén Castañeda Choudhary, Kamal Díaz, Alberto Díaz García, Rodrigo Domínguez Eckert, Hagen Eimre, Kristjan Montero, María Elena Fuentes Krajewski, Adam M. Mortensen, Jens Jørgen Duarte, José Manuel Nápoles Pietryga, Jacob Qi, Ji de Jesús Trejo Carrillo, Felipe Vaitkus, Antanas Yu, Jusong Zettel, Adam de Castro, Pedro Baptista Carlsson, Johan Cerqueira, Tiago F.T. Divilov, Simon Hajiyani, Hamidreza Hanke, Felix Jose, Kevin Oses, Corey Riebesell, Janosh Schmidt, Jonathan Winston, Donald Xie, Christen Yang, Xiaoyu Bonella, Sara Botti, Silvana Curtarolo, Stefano Draxl, Claudia Cobas, Luis Edmundo Fuentes Hospital, Adam Liu, Zi-Kui Marques, Miguel A.L. Marzari, Nicola Morris, Andrew J. Ong, Shyue Ping Orozco, Modesto Persson, Kristin A. Thygesen, Kristian S. Wolverton, Chris Scheidgen, Markus Toher, Cormac Conduit, Gareth J. Pizzi, Giovanni Gražulis, Saulius Rignanese, Gian-Marco Armiento, Rickard Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Matgenix SRL 185 Rue Armand Bury Gozée6534 Belgium École Polytechnique Fédérale de Lausanne Avenue de Forel 3 Lausanne1015 Switzerland Institute of Biotechnology Life Sciences Center Vilnius University Sauletekio av. 7 VilniusLT-10257 Lithuania SINTEF P.O. Box 4760 Torgarden TrondheimNO-7465 Norway Materials Design and Informatics unit Department of Physics Chemistry and Biology Linköping University Sweden Baldiri i Reixac 10-12 Barcelona08028 Spain Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Computational Atomic-Scale Materials Design Technical University of Denmark Kgs. Lyngby Denmark Av. Miguel de Cervantes 120 Complejo Industrial Chihuahua Chihuahua Chih. 31136 Mexico Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Center for Extreme Materials Duke University DurhamNC27708 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Universidad Autónoma de Chihuahua Facultad de Ciencias Quimicas Chihuahua31125 Mexico Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of NanoEngineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0448 United States National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan Dassault Systèmes Germany GmbH Am Kabellager 11-13 Cologne51063 Germany CFisUC Department of Physics University of Coimbra Rua Larga Coimbra3004-516 Portugal Dassault Systèmes 22 Science Park CB4 0FJ United Kingdom Theory of Condensed Matter Cavendis

The Open databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development. © 2024, CC BY.

关键词： Electronic data interchange

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Generalized word shift graphs: A method for visualizing and explaining pairwise comparisons between texts

arXiv

引用

arXiv 2020年

作者： Gallagher, Ryan J. Frank, Morgan R. Mitchell, Lewis Schwartz, Aaron J. Reagan, Andrew J. Danforth, Christopher M. Dodds, Peter Sheridan Network Science Institute Northeastern University BostonMA02115 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States Connection Science Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Human-Centered Artifical Intelligence Stanford University StanfordCA94305 United States School of Mathematical Sciences The University of Adelaide SA5005 Australia Computational Story Lab Vermont Complex Systems Center Vermont Advanced Computing Core The University of Vermont BurlingtonVT05401 United States Gund Institute for Environment & Rubenstein School of Environment and Natural Resources The University of Vermont BurlingtonVT05401 United States Department of Ecology and Evolutionary Biology University of Colorado at Boulder BoulderCO80309 United States MassMutual Data Science AmherstMA01002 United States MassMutual Center of Excellence for Complex Systems and Data Science Department of Mathematics and Statistics The University of Vermont BurlingtonVT05401 United States

A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content. However, collapsing the texts' rich stories into a single number is often conceptually perilous, and it is difficult to confidently interpret interesting or unexpected textual patterns without looming concerns about data artifacts or measurement validity. To better capture fine-grained differences between texts, we introduce generalized word shift graphs, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average. We show that this framework naturally encompasses many of the most commonly used approaches for comparing texts, including relative frequencies, dictionary scores, and entropy-based measures like the Kullback-Leibler and Jensen-Shannon divergences. Through several case studies, we demonstrate how generalized word shift graphs can be flexibly applied across domains for diagnostic investigation, hypothesis generation, and substantive interpretation. By providing a detailed lens into textual shifts between corpora, generalized word shift graphs help computational social scientists, digital humanists, and other text analysis practitioners fashion more robust scientific narratives. Copyright © 2020, The Authors. All rights reserved.

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