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arXiv 2024年

作者： Fudolig, Mikaela Irene Bloomfield, Laura S.P. Price, Matthew Bird, Yoshi M. Hidalgo, Johanna E. Kim, Julia Llorin, Jordan Lovato, Juniper McGinnis, Ellen W. McGinnis, Ryan S. Ricketts, Taylor Stanton, Kathryn Dodds, Peter Sheridan Danforth, Christopher M. Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Gund Institute for Environment University of Vermont BurlingtonVT05405 United States Department of Psychological Science University of Vermont BurlingtonVT05405 United States Project LEMURS University of Vermont BurlingtonVT05405 United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT05405 United States Wake Forest University School of Medicine Winston-SalemNC27101 United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute New Mexico87501 United States

To optimize interventions for improving wellness, it is essential to understand habits, which wearable devices can measure with greater precision. Using high temporal resolution biometric data taken from the Oura Gen3 ring, we examine daily and weekly sleep and activity patterns of a cohort of young adults (N = 582) in their first semester of college. A high compliance rate is observed for both daily and nightly wear, with slight dips in wear compliance observed shortly after waking up and also in the evening. Most students have a late-night chronotype with a median midpoint of sleep at 5AM, with males and those with mental health impairment having more delayed sleep periods. Social jetlag, or the difference in sleep times between free days and school days, is prevalent in our sample. While sleep periods generally shift earlier on weekdays and later on weekends, sleep duration on both weekdays and weekends is shorter than during prolonged school breaks, suggesting chronic sleep debt when school is in session. Synchronized spikes in activity consistent with class schedules are also observed, suggesting that walking in between classes is a widespread behavior in our sample that substantially contributes to physical activity. Lower active calorie expenditure is associated with weekends and a delayed but longer sleep period the night before, suggesting that for our cohort, active calorie expenditure is affected less by deviations from natural circadian rhythms and more by the timing associated with activities. Our study shows that regular sleep and activity routines may be inferred from consumer wearable devices if high temporal resolution and long data collection periods are available. Copyright © 2024, The Authors. All rights reserved.

关键词： Sleep research

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Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats (vol 16, 585, 2025)

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NATURE COMMUNICATIONS 2025年第1期16卷 1-15页

作者： Steinfeldt, Jakob Wild, Benjamin Buergel, Thore Pietzner, Maik Upmeier zu Belzen, Julius Vauvelle, Andre Hegselmann, Stefan Denaxas, Spiros Hemingway, Harry Langenberg, Claudia Landmesser, Ulf Deanfield, John Eils, Roland Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany Institute of Cardiovascular Sciences University College London London UK Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany Institute of Health Informatics University College London London UK British Heart Foundation Data Science Centre London UK Health Data Research UK London UK National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA Pattern Recognition and Image Analysis Lab University of Münster Münster Germany

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

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Active learning in computational pathology with noise detection empowered by loss-based prior and feature analysis

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Biomedical Signal Processing and Control 2025年 108卷

作者： Huang, Yujian Li, Jinyang An, Hui Luo, Tong Ji, Zheyi Song, Yi Liu, Huijun U, Kaicheng Zhang, Sophia Meixuan Zhao, Junhan Ge, Yongxin School of Big Data and Software Engineering Chongqing University Chongqing401331 China College of Computer Science Chongqing University Chongqing401331 China Neurosurgery Department Chongqing University Three Gorges Hospital Chongqing404100 China Harvard College Harvard University Cambridge02138 United States College of Agriculture and Life Sciences Cornell University Ithaca14850 United States Department of Computational Oncology Memorial Sloan Kettering Cancer Center New York City10065 United States Tri-Institutional Computational Biology & Medicine Weill Cornell Medicine New York City10065 United States Department of Biomedical Informatics Harvard Medical School Boston02115 United States Department of Biostatistics Harvard T.H.Chan School of Public Health Boston02115 United States

AI-based histopathological image analysis has significantly advanced the field of computer-aided diagnosis. While labeled data can enhance model performance, manual annotation by pathologists is labor-intensive and time-consuming, with variability and reliance on coarse slide-level annotations often introducing noise. To address these challenges, we propose introduces BPAL (Beta Mixture Model and Penalized Regression for Active Learning), a novel active learning framework for histopathological whole-slide image analysis. BPAL aims to reduce expert annotation costs and mitigate the impact of noisy samples during training by autonomously managing highly informative samples in each active learning iteration. Our approach integrates two noise detection modules into active learning frameworks. By incorporating Penalized Regression (PR) with parallel computation capabilities into our framework, we enhance the efficiency of noisy sample detection. Leveraging a Beta Mixture Model (BMM) with prior loss knowledge further augments this process by enabling a comprehensive analysis from various angles within the merged feature and label spaces. This approach maximizes the utilization of information extracted from pathological image samples, ensuring a robust and thorough assessment of data quality. We propose a heuristic sampling strategy based on these enhancements. High-information samples identified by the module are categorized into three types: typical samples with high confidence levels that can receive pseudo labels for training, difficult samples requiring expert re-annotation due to complex features, and mislabeled noisy samples. The iterative addition of training sets retains high-information samples while mitigating the impact of noisy samples. Comparative evaluations demonstrate the superior performance of our approach on breast cancer and prostate cancer classification tasks. © 2025 Elsevier Ltd

关键词： Active learning

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Immersive Biography: Supporting Intercultural Empathy and Understanding for Displaced Cultural Objects in Virtual Reality 25

Immersive Biography: Supporting Intercultural Empathy and Un...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Ke Zhao Ruiqi Chen Xiaziyu Zhang Chenxi Wang Siling Chen Xiaoguang Wang Yujue Wang Xin Tong School of Information Management Wuhan University Wuhan China and Duke Kunshan University Kunshan China Human Centered Design & Engineering University of Washington Seattle Washington USA and Duke Kunshan University Kunshan China School of Information Management Wuhan University Wuhan China School of Design Politecnico di Milano Milan Italy School of Information Management Wuhan University Wuhan China and Intelligent Computing Laboratory for Cultural Heritage Wuhan University Wuhan China Thrust of Computational Media and Arts Hong Kong University of Science and Technology (Guangzhou) Guangzhou China and Data Science Research Center Duke Kunshan University Kunshan Suzhou China

ISBN: (纸本)9798400713941

Displaced cultural objects often act as mediators of intercultural understanding due to their connection between the original and host communities. This study explores how immersive embodied VR biography enhances intercultural empathy and understanding of displaced cultural objects. We took the famous Chinese painting, the Admonitions Scroll, housed at the British Museum as an example to design an Immersive Biography in VR. We conducted an empirical study with 24 participants from source and non-source communities. Findings suggested that interacting with biographical narratives of displaced cultural objects in a personified embodied way can effectively promote intercultural empathy and understanding. Additionally, simulated intercultural scenarios and dialogues with personified cultural objects fostered intercultural empathy in both groups, with a stronger effect observed in non-source communities due to differences in cultural identity and personal connections. Our study provided the potential and practical insights of immersive technologies to inspire intercultural communication for displaced cultural objects. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Intercultural empathy

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Higher-order rich-club phenomenon in collaborative research grant networks

arXiv

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arXiv 2022年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more productive in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs. Copyright © 2022, The Authors. All rights reserved.

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Tensor completion via gaussian process based initialization

arXiv

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arXiv 2019年

作者： Kapushev, Yermek Oseledets, Ivan Burnaev, Evgeny Center for Computational and Data-Intensive Science Engineering of Skolkovo Institute of Science and Technology Moscow Russia

In this paper, we consider the tensor completion problem representing the solution in the tensor train (TT) format. It is assumed that tensor is high-dimensional, and tensor values are generated by an unknown smooth function. The assumption allows us to develop an efficient initialization scheme based on Gaussian Process Regression and TT-cross approximation technique. The proposed approach can be used in conjunction with any optimization algorithm that is usually utilized in tensor completion problems. We empirically justify that in this case the reconstruction error improves compared to the tensor completion with random initialization. As an additional benefit, our technique automatically selects rank thanks to using the TT-cross approximation *** Codes 68W25, 65F99, 60G15 Copyright © 2019, The Authors. All rights reserved.

关键词： Tensors

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An Approach to Prevent Weight Manipulation by Minimum Adjustment and Maximum Entropy Method in Large-Scale Group Decision Making

SSRN

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SSRN 2022年

作者： Sun, Qi Wu, Jian Chiclana, Francisco Wang, Sha Herrera-Viedma, Enrique School of Economics and Management Shanghai Maritime University Shanghai201306 China Center for Artificial Intelligence and Decision Sciences Shanghai Maritime University Shanghai201306 China Institute of Artificial Intelligence Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

This paper proposes a theoretical framework to prevent weight manipulation in large-scale group decision making (LSGDM). Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the rights index (RI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’manipulation behavior. The minimum adjustment rule aims for ‘effificiency’ while the maximum entropy rule aims for ‘justice’. Consequently, by keeping a balance between ‘effificiency’ and ‘justice’ in the mechanism of assigning weights, the rationality of weight allocation to reach consensus effificiently can be ensured. Detailed numerical and simulation analyses are carried out to verify the validity of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Decision making

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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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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Narrowing of the Racial Disparities Gap in Survival of Patients With Mycosis Fungoides: A Longitudinal Analysis of the SEER database

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Clinical Lymphoma, Myeloma and Leukemia 2025年第7期25卷 e501-e506页

作者： Le, Britney Soror, Noha Ismail, Hamid D. Baker, Mohammed Shetayyah, Salman Abu Chung, Catherine G. William, Basem M. Ohio University Heritage College of Osteopathic Medicine Dublin OH The University of Oklahoma College of Medicine Oklahoma City OK United States Department of Computational Data Science and Engineering North Carolina Agricultural and Technical State University Greensboro NC United States Faculty of Medicine Jordan University of Science and Technology Irbid Jordan The Ohio State University Comprehensive Cancer Center: Arthur G. James Cancer Hospital and Richard J. Solove Research Institute Division of Dermatology Columbus OH Ohio Health Blood and Marrow Transplant Program Columbus OH

Background: Mycosis fungoides (MF) is the commonest subtype of cutaneous T-cell lymphoma. In the United States, prior studies reported that African Americans (AA) with MF had poor outcomes. data characterizing differences in racial disparity outcomes over time are limited. Patients and Methods: We collected data from the United States Surveillance, Epidemiology, and End Results (SEER) database to investigate the survival patterns of patients with MF from 1988 to 2011. Cases were divided into 3 cohorts based on the year of diagnosis. Univariable and multivariable analysis were conducted to assess for factors associated with overall survival (OS). Results: 2896 cases of MF were detected, with a median follow-up duration of 60 months. The disparity in survival between the years 1988-1995 and 2004-2011 was significant (P =.05). The parameter estimates of the Cox proportional hazards model for the 1988-1995 period (using the 2004-2011 period as a reference) was also significant (P =.024). Patients diagnosed between 1988 and 1995 were 1.4 times more likely to die from the disease than those diagnosed between 2004 and 2011. The survival gap between AA and white patients narrowed in 1996-2003 and 2004-2011 in comparison to 1988-1995. This indicates improvements in the survival of AA patients over time. Conversely, the survival rates of white patients remained relatively stable over time. Conclusions: Our study demonstrates that AA with MF have reduced survival. Despite the persistent pattern of lower survival across all periods, the gap in survival between white and AA seems to be narrowing over time. © 2025 Elsevier Inc.

关键词： Cutaneous lymphoma Mycosis fungoides Outcome research Racial disparity SEER database

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Gaussian polymer chains in a harmonic potential: The path integral approach

arXiv

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arXiv 2020年

作者： Paradezhenko, G.V. Gascoigne, C. Brilliantov, N.V. Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow121205 Russia Department of Mathematical Sciences University of Bath Claverton Down BathBA2 7AY United Kingdom

We study conformations of the Gaussian polymer chains in d-dimensional space in the presence of an external field with the harmonic potential. We apply a path integral approach to derive an explicit expression for the probability distribution function of the gyration radius. We calculate this function using Monte Carlo simulations and show that our numerical and theoretical results are in a good agreement for different values of the external *** Codes 82D60, 82M31 Copyright © 2020, The Authors. All rights reserved.

关键词： Monte Carlo methods

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