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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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Artificial intelligence-assisted niacin skin flush screening in early psychosis identification and prediction

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General Psychiatry 2022年第2期35卷 76-78页

作者： Tao Chen Haichun Liu Renfang Tian Ranpiao Gan Wenzuo Xu Tianhong Zhang Jijun Wang Department of Economics University of WaterlooWaterlooOntarioCanada Depatment of Statistics and Actuarial Science(Cross Appointed) University of Waterloo WaterlooOntarioCanada Big Data Research Lab Universty of WaterlooWaterlooOntarioCanada Shanghai Niacin Tech Co. LtdShanghal China Labor and Worklife Program Harvard UniversityCambridgeMassachusettsUSA Department of Automation Shanghai Jao Tong UniversityShanghaiChina Ningbo Artificial Intelligent Institute Shanghai Jiao Tong UniversityNingboChina School of Management Economicsand MathematicsKing's University CollegeLondon0ntarioCanada Shanghai Intelligent Psychological Evaluaton and Intervention Engineering Technology Research Center Shanghai Key Laboratory of Psychotic DisordersShanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)ShanghaiChina CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghaiChina Institute of Psychology and Behavioral Science Shanghai Jiao Tong UniversiyShanghaiChina

Schizophrenia is a devastating mental disorder affecting 20 million people *** diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the *** on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.

关键词： diagnosis prognosis prediction

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Effects of sodium alginate-poly(acrylic acid) cross-linked hydrogel beads on soil conditioner in the absence and presence of phosphate and carbonate ions

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Case Studies in Chemical and Environmental engineering 2024年 9卷

作者： Hidayat, Endar Sarbani, Nur Maisarah Mohamad Lahiri, Sudip Kumar Samitsu, Sadaki Yonemura, Seiichiro Mitoma, Yoshiharu Harada, Hiroyuki Graduate School of Comprehensive Scientific Research Program in Biological System Sciences Prefectural University of Hiroshima Shobara 727-0023 Japan Department of Life System Science Faculty of Bioresources Science Prefectural University of Hiroshima Shobara 727-0023 Japan Data-driven Polymer Design Group Research Center for Macromolecules and Biomaterials National Institute for Materials Science 1−2−1 Sengen Tsukuba 305-0047 Japan Department of Mechanical & Industrial Engineering University of Toronto Toronto M5S 3G8 ON Canada Faculty of Allied Health Sciences Chettinad Hospital and Research Institute Chettinad Academy of Research and Education Tamil Nadu Kelambakkam 603103 India

Hydrogel beads, composed of polymers, possess the ability to absorb substantial quantities of water simultaneously and gradually release it in dry conditions. In this work, hydrogel beads were synthesized using sodium alginate (S-Alg) and poly (acrylic acid) (P-Acc), either in the absence or presence of phosphate (PO₄ ³⁻) (S-Alg/P-Acc@PO₄ ³⁻) and carbonate (CO₃ ²⁻) anionic ions (S-Alg/P-Acc@CO₃ ²⁻). Subsequently, these beads were crosslinked with Ca²⁺ ions. The scanning electron microscopy (SEM) images demonstrated that the presence of anionic ions has an impact on the structure of the hydrogel beads and enhances porosity expansion while concurrently reducing the concentration of carboxyl (COOH) groups. The hydrogel beads exhibited a great swelling behavior, with S-Alg/P-Acc@CO₃ ²⁻ capable of absorbing up to 73.5 % of water in acidic conditions, but decreased under neutral and basic conditions. Soil water loss (SWL) experiments confirmed the ability of hydrogel beads to retain water in the soil. Additionally, observations of soil characteristics after a 14-day treatment revealed no significant difference in pH and C/N ratio (P-value <0.05). However, soil's available Fe and N–NO₃ ^- significantly reduced (P-value <0.05) while substantially promoting soil N–NH₄ ⁺, available phosphorus (P), cation exchange capacity (CEC), soil organic carbon (SOC), and exchangeable cation (Ca²⁺, K⁺ and Mg²⁺). The presence of hydroxyl (-OH) and –COOH groups was found to be crucial for enhancing soil properties. © 2024 The Authors

关键词： Anionic ions Hydrogel beads Poly (acrylic acid) Sodium alginate Soil characteristics Soil water retention Swelling

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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Template-free try-on image synthesis via semantic-guided optimization

arXiv

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arXiv 2021年

作者： Chou, Chien-Lung Chen, Chieh-Yun Hsieh, Chia-Wei Shuai, Hong-Han Liu, Jiaying Cheng, Wen-Huang Department of Electrical and Computer Engineering National Chiao Tung University Taiwan Institute of Electronics National Chiao Tung University Taiwan Artificial Intelligence and Data Science Program National Chung Hsing University Taiwan Wangxuan Institute of Computer Technology Peking University China

The virtual try-on task is so attractive that it has drawn considerable attention in the field of computer vision. However, presenting the three-dimensional (3D) physical characteristic (e.g., pleat and shadow) based on a 2D image is very challenging. Although there have been several previous studies on 2D-based virtual try-on work, most 1) required user-specified target poses that are not user-friendly and may not be the best for the target clothing, and 2) failed to address some problematic cases, including facial details, clothing wrinkles and body occlusions. To address these two challenges, in this paper, we propose an innovative template-free try-on image synthesis (TF-TIS) network. The TF-TIS first synthesizes the target pose according to the user-specified in-shop clothing. Afterward, given an in-shop clothing image, a user image, and a synthesized pose, we propose a novel model for synthesizing a human try-on image with the target clothing in the best fitting pose. The qualitative and quantitative experiments both indicate that the proposed TF-TIS outperforms the state-of-the-art methods, especially for difficult cases. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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ANTARCTICA AND THE SOUTHERN OCEAN

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2024年第8期105卷 S331-S370页

作者： Clem, K. R. Raphael, M. N. School of Geography Environment and Earth Sciences Victoria University of Wellington Wellington New Zealand Department of Geography University of California Los Angeles Los Angeles California Scripps Institution of Oceanography University of California San Diego La Jolla California Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Department of Atmospheric and Oceanic Sciences University of Colorado Boulder Boulder Colorado Earth Science and Observation Center Cooperative Institute for Research in Environmental Sciences (ESOC/CIRES) University of Colorado Boulder Boulder Colorado Argentine Naval Hydrographic Service Buenos Aires Argentina Department of Earth and Environmental Science Temple University Philadelphia Pennsylvania Department of Meteorology University of Valparaíso Valparaíso Chile Center for Climate and Resilience Research (CR)2 Santiago Chile British Antarctic Survey Cambridge United Kingdom Science Systems and Applications Inc. Lanham Maryland NASA Goddard Space Flight Center Greenbelt Maryland Centre for Ocean River Atmosphere and Land Sciences (CORAL) Indian Institute of Technology Kharagpur Kharagpur India Royal Netherlands Meteorological Institute (KNMI) DeBilt The Netherlands Department of Marine Sciences University of Gothenburg Gothenburg Sweden National Institute of Water and Atmospheric Research Ltd. (NIWA) Auckland New Zealand Department of Geography Ohio University Athens Ohio NOAA/OAR Earth System Research Laboratory Global Monitoring Laboratory Boulder Colorado University of Colorado Boulder Boulder Colorado Marine Systems Modelling National Oceanography Center Southampton United Kingdom Department of Atmospheric and Oceanic Sciences University of Wisconsin-Madison Madison Wisconsin Antarctic Meteorological Research and Data Center Space Science and Engineering Center University of Wisconsin-Madison Madison Wisconsin Department of Geography Pennsylvania Sta

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Secure decentralized decisions to enhance coordination in consolidated hospital systems

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IISE Transactions on Healthcare Systems engineering 2020年第2期10卷 99-112页

作者： Badré, Adrien Mohebbi, Shima Soltanisehat, Leili Data Science and Analytics Program University of Oklahoma Norman OK United States School of Industrial and Systems Engineering and Data Science and Analytics Program University of Oklahoma Norman OK United States School of Industrial and Systems Engineering University of Oklahoma Norman OK United States

Shared decision making has become a crucial solution to build a consolidated healthcare system. While there is some research in the healthcare literature discussing the advantages and disadvantages of shared decision making, its efficiency has not been addressed quantitatively. In this paper, we propose a Decentralized Patients Assignment System (DPAS) as a universal decentralized decision making architecture. It utilizes the blockchain technology, machine learning, and integer programing to enhance coordination among healthcare providers and patients in consolidated hospital systems. To test the efficiency of the proposed DPAS, a prototype system is developed using an Agent-based model and Ethereum and is compared to the current practice of central referral systems in consolidated hospital systems. The agent-based model consists of four agents including patients, physicians, hospitals, and miners interacting within a decentralized system. The proposed system highlights the importance of interoperability and consensus among healthcare agents in the decision making process. The results demonstrate the DPAS efficiency in decreasing computational time and rejection rates for patients transfer. © 2019, © 2019 “IISE”.

关键词： Blockchain architecture intelligent agents optimization patients referral predictive analytics smart contracts

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Jobs analysis for business intelligence skills requirements in the ASEAN region: A text mining study

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The Joint International Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2017

作者： Poonnawat, Waranya Pacharawongsakda, Eakasit Henchareonlert, Nuttaporn School of Science and Technology Sukhothai Thammathirat Open University Nonthaburi Thailand Big Data Engineering Program College of Innovative Technology and Engineering Dhurakij Pundit University Bangkok Thailand

ISBN: (纸本)9783319947020

This paper applies text mining and data mining techniques to analyse online job advertisements related to Business Intelligence (BI) in web portals around the ASEAN region. Several techniques are applied: data preprocessing, tokenization, association rules (AR) with FP-Growth and visualisation. The research goal is the profiles of BI skills required for industry and the market. The results would be useful for individual to prepare for their BI competence, for faculties to prepare for BI-related courses and curriculum, and for organizations to prepare for Human Resource (HR) and job advertisement purposes. © Springer Nature Switzerland AG 2019.

关键词： Portals

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A call to action to address escalating global threats to academic research

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The Innovation 2025年第4期6卷 17-19页

作者： Gaelle Piret Fun Man Fung Josie Fullerton Giuseppe Fico Dmitriy Ponkratov Wenxin Chen Daniela Latorre Kirsty Y.Wan Nima Aghaeepour Jules Welgryn Adeel Razi Patricia Silveyra Ahmet Altun Renata Z.Jurkowska Alice C.Hughes Joy Wolfram INSERM U1205 UGA38400 Saint Martin d’HèresFrance School of Chemistry University College DublinD04 C1P1 BelfieldIreland UCD Geary Institute for Public Policy University College DublinD04 N9Y1 DublinIreland School of Cardiovascular&Metabolic Health University of GlasgowG128TA GlasgowUK European Alliance for Medical and Biological Engineering and Science 3001 LeuvenBelgium Life Supporting Technologies Universidad Politécnica de Madrid28040 MadridSpain Siemens Digital Industries Software B908BG SolihullUK Department of Philosophy of Science and Technology University of Science and Technology of ChinaHefei 230026China Institute of Microbiology ETH Zurich8093 ZurichSwitzerland Living Systems Institute&Department of Mathematics and Statistics University of ExeterEX44QD ExeterUK Department of Anesthesiology Painand Perioperative MedicineDepartment of PediatricsDepartment of Biomedical Data ScienceStanford University School of MedicineStanfordCA 94305USA UniversitéParis Nanterre 92000 NanterreFrance Turner Institute for Brain and Mental Health School of Psychological Sciencesand Monash Biomedical ImagingMonash UniversityClaytonVIC 3800Australia Wellcome Centre for Human Neuroimaging University College LondonWC1N 3AR LondonUK CIFAR Azrieli Global Scholars Program TorontoON M5G 1M1Canada Department of Environmental and Occupational Health Indiana University School of Public Health BloomingtonBloomingtonIN 47408USA Department of Pharmacology Sivas Cumhuriyet UniversitySivas 58140Turkey Division of Biomedicine School of BiosciencesCardiff UniversityCF103AX CardiffUK School of Biological Sciences University of Hong KongHong Kong SAR 999077China Australian Institute for Bioengineering and Nanotechnology The University of QueenslandBrisbaneQLD 4072Australia School of Chemical Engineering The University of QueenslandBrisbaneQLD 4072Australia

This article is a call to action to address escalating threats to scientific progress that affect academic researchers across the *** threats include public mistrust of science,challenges in translating academic research to end-user applications,a disconnect between academics and policymakers,emerging barriers to international collaboration,and a reliance on conventional metrics to evaluate academic *** article presents various calls to action informed by exemplary approaches across the globe that serve as frameworks to drive beneficial transformation for researchers,academic institutions,and society.

关键词： academic research calls action global threats public mistrust sciencechallenges translating academic research disconnect academics policymakers call action translation research

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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