Institution
University of Warwick
Education•Coventry, Warwickshire, United Kingdom•
About: University of Warwick is a education organization based out in Coventry, Warwickshire, United Kingdom. It is known for research contribution in the topics: Population & White dwarf. The organization has 26212 authors who have published 77127 publications receiving 2666552 citations. The organization is also known as: Warwick University & The University of Warwick.
Topics: Population, White dwarf, Politics, Health care, Poison control
Papers published on a yearly basis
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University of Southern California1, French Institute for Research in Computer Science and Automation2, University of Oulu3, Princeton University4, University of Warwick5, Georgia Institute of Technology6, Rutgers University7, University of Virginia8, University of Washington9, Carnegie Mellon University10, École Polytechnique Fédérale de Lausanne11, University of Pittsburgh12, University of Wisconsin-Madison13, University of California, San Diego14, University of Illinois at Urbana–Champaign15, Nanyang Technological University16, Australian National University17, Stanford University18, IT University of Copenhagen19, Massachusetts Institute of Technology20, University of California, Berkeley21, Cornell University22, Emory University23, Hong Kong University of Science and Technology24
TL;DR: In this article, the authors describe the state-of-the-art in the field of federated learning from the perspective of distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, and statistics.
Abstract: The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client’s raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective. Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more. This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems. Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.
2,144 citations
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Radboud University Nijmegen1, Eindhoven University of Technology2, Utrecht University3, Beth Israel Deaconess Medical Center4, Massachusetts Institute of Technology5, Harvard University6, The Chinese University of Hong Kong7, Munich Business School8, Middle East Technical University9, University of Toronto10, University of Warwick11, Coventry Health Care12, Qatar University13, HTW Berlin - University of Applied Sciences14, Tampere University of Technology15, University of Tampere16, Technische Universität München17, Osaka University18, University of South Florida19, National Academy of Sciences of Belarus20, University of Castilla–La Mancha21, Pierre-and-Marie-Curie University22, Pontifical Catholic University of Peru23
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Abstract: Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884];P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
2,116 citations
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TL;DR: Intensified research efforts and global initiatives are clearly needed to address the burden of low back pain as a public health problem, where health and other systems are often fragile and not equipped to cope with this growing burden.
2,114 citations
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TL;DR: Positive results of crowding include enhancing the collapse of polypeptide chains into functional proteins, the assembly of oligomeric structures and the efficiency of action of some molecular chaperones and metabolic pathways.
2,104 citations
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TL;DR: An overview of the literature concerning successful school leadership can be found in this article, where the main findings from the wealth of empirical studies undertaken in the leadership field are summarised and discussed.
Abstract: This article provides an overview of the literature concerning successful school leadership. It draws on the international literature and is derived from a more extensive review of the literature completed in the early stage of the authors’ project. The prime purpose of this review is to summarise the main findings from the wealth of empirical studies undertaken in the leadership field.
2,071 citations
Authors
Showing all 26659 results
Name | H-index | Papers | Citations |
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David Miller | 203 | 2573 | 204840 |
Daniel R. Weinberger | 177 | 879 | 128450 |
Kay-Tee Khaw | 174 | 1389 | 138782 |
Joseph E. Stiglitz | 164 | 1142 | 152469 |
Edmund T. Rolls | 153 | 612 | 77928 |
Thomas J. Smith | 140 | 1775 | 113919 |
Tim Jones | 135 | 1314 | 91422 |
Ian Ford | 134 | 678 | 85769 |
Paul Harrison | 133 | 1400 | 80539 |
Sinead Farrington | 133 | 1422 | 91099 |
Peter Hall | 132 | 1640 | 85019 |
Paul Brennan | 132 | 1221 | 72748 |
G. T. Jones | 131 | 864 | 75491 |
Peter Simmonds | 131 | 823 | 62953 |
Tim Martin | 129 | 878 | 82390 |