检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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A large annotated medical image dataset for the development and evaluation of segmentation algorithms

arXiv

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arXiv 2019年

作者： Simpson, Amber L. Antonelli, Michela Bakas, Spyridon Bilello, Michel Farahani, Keyvan Van Ginneken, Bram Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc Golia-Pernicka, Jennifer Heckers, Stephan H. Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Vorontsov, Eugene Maier-Hein, Lena Jorge Cardoso, M. Department of Surgery Memorial Sloan Kettering Cancer Center Centre for Medical Image Computing University College London Center for Biomedical Image Computing and Analytics University of Pennsylvania Division of Cancer Treatment and Diagnosis National Cancer Institute Department of Pathology Radboud University Medical Center Division of Biostatistics German Cancer Research Center Department of Electrical Engineering and Computer Science Vanderbilt University Department of Informatics Technische Universität München Google DeepMind Imaging Biomarkers and Computer-aided Diagnosis Lab Radiology and Imaging Sciences National Institutes of Health Clinical Center Department of Radiology Memorial Sloan Kettering Cancer Center Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center Department of Radiology Stanford University Department of Computer Science and Software Engineering École Polytechnique de Montréal Division of Computer Assisted Medical Interventions German Cancer Research Center Department of Imaging and Biomedical Engineering King's College London

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Visibility testing and counting for uncertain segments 29

Visibility testing and counting for uncertain segments

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29th Canadian Conference on Computational Geometry, CCCG 2017

作者： Abam, Mohammad Ali Alipour, Sharareh Ghodsi, Mohammad Mahdian, Mohammad Computer Engineering Department Sharif University of Technology Iran School of Computer Science Iran School of Computer Science Iran Google Research United States

We study two well-known planar visibility problems, namely visibility testing and visibility counting, in a model where there is uncertainty about the input data. The standard versions of these problems are defined as follows: we are given a set S of n segments in R2, and we would like to preprocess S so that we can quickly answer queries of the form: is the given query segment s ∈ S visible from the given query point q ∈ R2 (for visibility testing) and how many segments in S are visible from the given query point q ∈ R2 (for visibility counting). In our model of uncertainty, each segment may or may not exist, and if it does, it is located in one of finitely many possible locations, given by a discrete probability distribution. In this setting, the probabilistic visibility testing problem (PVTP, for short) is to compute the probability that a given segment s ∈ S is visible from a given query point q and the probabilistic visibility counting problem (PVCP, for short) is to compute the expected number of segments in S that are visible from a query point q. We first show that PVTP is #Pcomplete. In the special case where uncertainty is only about whether segments exist and not about their location, we show that the problem is solvable in O(nlog n) time. Using this, together with a few old tricks, we can show that one can preprocess S in O(n5 log n) time into a data structure of size O(n4), so that PVTP queries can be answered in O(log n) time. Our algorithm for PVTP combined with linearity of expectation gives an O(n2 log n) time algorithm for PVCP. We also give a faster 2-approximation algorithm for this problem. Compilation copyright © 2017 Michiel Smid Copyright of individual papers retained by *** right reserved.

关键词： Visibility

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Visual dynamics: Stochastic future generation via layered cross convolutional networks

arXiv

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arXiv 2018年

作者： Xue, Tianfan Wu, Jiajun Bouman, Katherine L. Freeman, William T. Google Inc. Mountain ViewCA94043 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Google Inc. CambridgeMA02139 United States

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network;this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, and on real-world video frames. We present analyses of the learned network representations, showing it is implicitly learning a compact encoding of object appearance and motion. We also demonstrate a few of its applications, including visual analogy-making and video extrapolation. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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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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Quantum computer systems for scientific discovery

arXiv

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arXiv 2019年

作者： Alexeev, Yuri Bacon, Dave Brown, Kenneth R. Calderbank, Robert Carr, Lincoln D. Chong, Frederic T. DeMarco, Brian Englund, Dirk Farhi, Edward Fefferman, Bill Gorshkov, Alexey V. Houck, Andrew Kim, Jungsang Kimmel, Shelby Lange, Michael Lloyd, Seth Lukin, Mikhail D. Maslov, Dmitri Maunz, Peter Monroe, Christopher Preskill, John Roetteler, Martin Savage, Martin J. Thompson, Jeff Argonne National Laboratory LemontIL United States Google Inc. SeattleWA United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Chemistry Duke University DurhamNC United States Department of Physics Duke University DurhamNC United States Department of Computer Science Department of Mathematics Duke University DurhamNC United States Department of Physics Colorado School of Mines GoldenCO United States Department of Computer Science University of Chicago ChicagoIL United States Department of Physics and IQUIST University of Illinois Urbana-ChampaignIL United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Google Inc. VeniceCA United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States Joint Quantum Institute Joint Center for Quantum Information and Computer Science Department of Physics University of Maryland College ParkMD United States National Institute of Standards and Technology GaithersburgMD United States Department of Electrical Engineering Princeton University PrincetonNJ United States IonQ Inc. College ParkMD United States Department of Computer Science Middlebury College MiddleburyVT United States L3Harris Technologies MelboruneFL Australia Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Physics Harvard University CambridgeMA United States IBM T.J. Watson Research Center Yorktown HeightsNY United States Sandia National Laboratories AlbuquerqueNM United States Institute for Quantum Information and Matter Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA United States Microsoft Quantum RedmondWA United States Institute for Nuclear Theory and Department of Physics University of Washington SeattleWA United States

The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by co-designing full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2–10 years. Copyright © 2019, The Authors. All rights reserved.

关键词： Qubits

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Leveraging contact forces for learning to grasp

arXiv

引用

arXiv 2018年

作者： Merzić, Hamza Bogdanovic, Miroslav Kappler, Daniel Righetti, Ludovic Bohg, Jeannette DeepMind London United Kingdom MPI for Intelligent Systems Tübingen Germany Tandon School of Engineering New York University United States Computer Science Department Stanford University United States Google X Robotics Mountain ViewCA United States

Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used two fingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Mitigation of Cosmic Ray Effect on Microwave Kinetic Inductance Detector Arrays

arXiv

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arXiv 2019年

作者： Karatsu, K. Endo, A. Bueno, J. de Visser, P.J. Barends, R. Thoen, D.J. Murugesan, V. Tomita, N. Baselmans, J.J.A. SRON Netherlands Institute for Space Research Sorbonnelaan 2 Utrecht3584CA Netherlands Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Kavli Institute of Nanoscience Faculty of Applied Sciences Delft University of Technology Lorentzweg 1 Delft2628CJ Netherlands Google Santa BarbaraCA93117 United States Department of Physics School of Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan

For space observatories, the glitches caused by high energy phonons created by the interaction of cosmic ray particles with the detector substrate lead to dead time during observation. Mitigating the impact of cosmic rays is therefore an important requirement for detectors to be used in future space missions. In order to investigate possible solutions, we carry out a systematic study by testing four large arrays of Microwave Kinetic Inductance Detectors (MKIDs), each consisting of ∼ 960 pixels and fabricated on monolithic 55 mm × 55 mm × 0.35 mm Si substrates. We compare the response to cosmic ray interactions in our laboratory for different detector arrays: A standard array with only the MKID array as reference;an array with a low Tc superconducting film as phonon absorber on the opposite side of the substrate;and arrays with MKIDs on membranes. The idea is that the low Tc layer down-converts the phonon energy to values below the pair breaking threshold of the MKIDs, and the membranes isolate the sensitive part of the MKIDs from phonons created in the substrate. We find that the dead time can be reduced up to a factor of 40 when compared to the reference array. Simulations show that the dead time can be reduced to below 1 % for the tested detector arrays when operated in a spacecraft in an L2 or a similar far-Earth orbit. The technique described here is also applicable and important for large superconducting qubit arrays for future quantum computers. Copyright © 2019, The Authors. All rights reserved.

关键词： Substrates

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Deep graph infomax

arXiv

引用

arXiv 2018年

作者： Veličković, Petar Fedus, William Hamilton, William L. Liò, Pietro Bengioy, Yoshua Hjelm, R. Devon Department of Computer Science and Technology University of Cambridge Mila - Québec Artificial Intelligence Institute Google Brain McGill University Université de Montréal Microsoft Research

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs-both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Web science 2024: Conference Chairs' Welcome Message

Proceedings of the 16th ACM Web Science Conference, WebSci 2...

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Proceedings of the 16th ACM Web science Conference, WebSci 2024 2024年 III-X页

作者： Staab, Steffen Aiello, Luca Maria Mejova, Yelena Seneviratne, Oshani Sindermann, Cornelia Hagendorff, Thilo Brandes, Simone Rohlmann, Juliane Spaniol, Marc Kinder-Kurlanda, Katharina Faleńska, Agnieszka Heiberger, Raphael Sun, Jun Kaiser, Sierra De Choudhury, Munmun Weber, Matthew De Francisci Morales, Gianmarco Dunn, Adam Gadiraju, Ujwal Gaito, Sabrina Hooper, Silas Javarone, Marco Alberto Kalimeri, Kyriaki Kwak, Haewoon Maity, Suman Kalyan Mashhadi, Afra Mustafaraj, Eni Purohit, Hemant Rossetti, Giulio Sastry, Nishanth Srinivasa, Srinath Vaz-De-Melo, Pedro Weal, Mark Weber, Ingmar Weninger, Tim Withing, Mark Zubiaga, Arkaitz Ahlers, Dirk Tamime, Reham Al Alam, Sawood Alhoori, Hamed Andriotis, Panagiotis Bär, Dominik Bernstein, Mark Boratto, Ludovico Camargo, Chico Capozzi, Arthur Chelmis, Charalampos Vieira, Carolina Coimbra De Meo, Pasquale Dinh, Ly Dividino, Renata Doerfel, Stephan Farahbakhsh, Reza Ferreira, Carlos H.G. Flores-Saviaga, Claudia Florio, Komal Galdeman, Alessia Guo, Cheng Hakimov, Sherzod Rad, Radin Hamidi Helic, Denis Hohmann, Marilena Horawalavithana, Sameera Horne, Benjamin Ibanez-Gonzalez, Luis Jiang, Julie Jones, Shawn Kamdar, Maulik R. Kao, Hsien-Te Kassa, Yonas Mitike Kastrin, Andrej Kazama, Kazuhiro Kelly, Mat Khanday, Akib Khatua, Aparup Khudabukhsh, Ashiqur Krüger, Frank Kumari, Neha Lai, Mirko Lamba, Hemank Lee, Roy Ka-Wei Lee, Eun Liao, Yiming Lin, Wenqing Magelinski, Thomas Magnani, Matteo Hill, Benjamin Mako Manikonda, Lydia Masud, Sarah Mekouar, Soufiana Møller, Anders Giovanni Muthiah, Sakthi Ni, Congning Nishimura, Joel Nwala, Alexander Pera, Arianna Piao, Guangyuan Piccardi, Tiziano Pierri, Francesco Porcaro, Lorenzo Ramasco, Jose J. Reelfs, Jens Rezapour, Rezvaneh Salminen, Joni Šćepanović, Sanja Shahi, Gautam Kishore Sinha, Priyanka Smaragdakis, Georgios Smirnova, Inna Song, Melodie Yun-Ju Sosnovik, Vera Truong, Bao Tran Tsugawa, Sho Vasarhelyi, Orsolya D'Aurelio, Davide Vega Vekaria, Yash Vilella, Salvatore Viviani, Marco Wang, Yichen Wu, Siqi Yang, Kaicheng Zeng, Yilei Zha University of Stuttgart Germany IT University of Copenhagen Denmark ISI Foundation Italy Rensselaer Polytechnic Institute United States Université de Caen Normandie France University of Klagenfurt Austria University of Washington United States GESIS Germany Georgia Tech United States IIIT Bangalore India Rutgers University United States CENTAI Italy University of Sydney Australia Delft University of Technology Netherlands University of Milan Italy Canada Centro Ricerche Enrico Fermi UCL CBT Italy Indiana University Bloomington United States Northwestern University United States UW United States Wellesley College United States George Mason University United States CNRS France King's College London United Kingdom International Institute of Information Technology Bangalore India UFMG Brazil University of Southampton United Kingdom Saarland University Germany University of Notre Dame United States UPenn United States Queen Mary University of London United Kingdom Norwegian University of Science and Technology Norway Internet Archive United States Northern Illinois University United States University of the West of England United Kingdom LMU Munich Germany Eastgate Systems Inc. United States University of Cagliari Italy University of Exeter United Kingdom University of Turin Italy University at Albany State University of New York United States Max Planck Institute for Demographic Research Germany Vrije Universiteit Amsterdam Netherlands University of South Florida United States Brock University Canada Kiel University of Applied Sciences Germany Institut Mines-Télécom Télécom SudParis France Universidade Federal de Ouro Preto Brazil Northeastern University United States Università di Torino Dipartimento di Informatica Italy Clemson University Google United States TIB - Leibniz Information Centre for Science and Technology Germany Ryerson University Canada Graz University of Technology Austria University of Copenhagen Denmark Pacific Northwest National La

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