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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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MatterChat: A Multi-Modal LLM for Material science

arXiv

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arXiv 2025年

作者： Tang, Yingheng Xu, Wenbin Cao, Jie Gao, Weilu Farrell, Steven Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human–AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. © 2025, CC BY.

关键词： Structure (composition)

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Surface Defect Segmentation with Multi-column Patch-Wise U-net 5

Surface Defect Segmentation with Multi-column Patch-Wise U-n...

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5th IEEE International Conference on computer and Communications, ICCC 2019

作者： Dong, Zihao Shao, Xiuli Zhang, Ruixun Nankai University Computational Intelligence Data Engineering Lab College of Computer Science Tianjin China MIT Laboratory for Financial Engineering CambridgeMA United States

ISBN: (纸本)9781728147437

Surface defect segmentation of the production plays an important role on the performance in industry. However, training the model by deep learning methods on industrial dataset with high resolution and few images is computationally impossible. Therefore, in this situation, we train a patch-wise fully convolutional neural network to perform similar to related image-wise methods. On this object segmentation task, we propose a method in inferring object segmentation by leveraging only ellipses annotation roughly indicating the defective area. This problem is viewed as a kind of weakly-supervised segmentation task, it can be solved by the approach that combines deep convolutional neural networks with the Multiple Instance Learning (MIL) framework. In view of this, we add the global pooling layer that called MIL-layer behind the last convolutional layer to compute the average class probability of all pixels in every patch. Finally, we combine three of this fully convolutional neural network with MIL-layer to become MIL-layer, which can utilize filters with receptive fields of different sizes. To address defect/non-defect class imbalance problem, we weight the loss contribution of false negative and false positive examples by our Weighted SoftMax loss function. The proposed network is evaluated on the detection of industrial inspection in the dataset of DAGM2007. Compared with some typical fully-supervised segmentation methods, our method achieves competitive accuracy (99.2%) but only requires easily ellipse annotations. © 2019 IEEE.

关键词： Surface defects

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A progressive prediction model towards home-based stroke rehabilitation programs

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Smart Health 2022年 23卷

作者： Bo, Wei Cavuoto, Lora Langan, Jeanne Subryan, Heamchand Bhattacharjya, Sutanuka Huang, Ming-Chun Xu, Wenyao Department of Computer Science and Engineering University at Buffalo (SUNY) Buffalo 14260 NY United States Department of Industrial and Systems Engineering University at Buffalo (SUNY) Buffalo 14260 NY United States Department of Rehabilitation Science University at Buffalo (SUNY) Buffalo 14214 NY United States School of Architecture and Planning University at Buffalo (SUNY) Buffalo 14214 NY United States Department of Occupational Therapy Georgia State University Atlanta 30303 GA United States Department of Data and Computational Science Duke Kunshan University Jiangsu 215316 China

More than 795,000 people suffer from stroke each year in the United States. Rehabilitation is crucial to help restore mobility function in activities of daily life. Many research works manifest that effective prediction of rehabilitation outcomes can significantly improve personalized rehabilitation strategies and maximize health outcomes. Existing studies on predicting stroke rehabilitation outcomes are primarily based on clinical or inpatient data, and there are few in-depth investigations in home-based rehabilitation prediction. In this work, we perform a retrospective study on mRehab (mobile rehabilitation), a six-week home-based rehabilitation program, where we investigated the in-program user data across 12 different rehabilitation activities performed by 16 stroke survivors. We proposed a new progressive prediction framework that constructs the multiple linear regression (MLR) and the random forest (RF) models, and found that combining the clinical and demographic data of stroke survivors with movement phenotyping data will significantly improve the prediction model's performance. Specifically, our proposed model can accurately predict the outcome only with the first two weeks’ data with the root mean square error (RMSE) of 0.1050 in the MLR model and 0.0011 in the RF model. Moreover, we identify the features that significantly contribute to the prediction outcomes in home-based rehabilitation programs through feature correlation analyses. To the best of our knowledge, this work is the first study towards the rehabilitation outcome prediction in a home-based program. © 2021

关键词： Mobile health Movement phenotyping Prediction Stroke rehabilitation

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Diagrammatic Sentence Pattern Structure System and Its Treebank Construction 23

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23rd Chinese National Conference on computational Linguistics, CCL 2024

作者： Peng, Weiming Zhao, Min Song, Yuchen Hu, Jiajia Song, Tianbao Sui, Zhifang Song, Jihua Laboratory for Chinese Character and Language Research and Social Application Beijing Normal University Beijing100875 China Linguistic Data Consortium University of Pennsylvania PhiladelphiaPA19104-2653 United States Beijing Han-sky Education Technology Co. Ltd Beijing100084 China Department of Linguistics University of Michigan Ann ArborMI48109-1220 United States School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Key Laboratory of Computational Linguistics Ministry of Education Peking University Beijing100871 China

ISBN: (纸本)9780000000002

The Sentence Pattern Structure (SPS) is a formalized syntactic structure based on sentence-based grammar, which presents the structure of sentences in a customized diagram form. This paper proposes an SPS system covering three aspects: clause structure, lexical structure, and inter-sentence structure, elucidating its design philosophy and parsing principles of sentence-based grammar. Finally, an overview of the engineering progress of building a Chinese treebank based on this system is provided. © 2024 China National Conference on computational Linguistics.

关键词： Trees (mathematics)

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Recommendation for Cardiac Magnetic Resonance Imaging-Based Phenotypic Study:Imaging Part

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Phenomics 2021年第4期1卷 151-170页

作者： Chengyan Wang Yan Li Jun Lv Jianhua Jin Xumei Hu Xutong Kuang Weibo Chen He Wang Human Phenome Institute Fudan University825 Zhangheng RoadPudong New DistrictShanghai 201203China Department of Radiology Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina School of Computer and Control Engineering Yantai UniversityYantaiChina School of Data Science Fudan UniversityShanghaiChina Philips Healthcare.Co. ShanghaiChina Institute of Science and Technology for Brain-Inspired Intelligence Fudan University220 Handan RoadYangpu DistrictShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of EducationShanghaiChina

Cardiac magnetic resonance(CMR)imaging provides important biomarkers for the early diagnosis of many cardiovascular diseases and has been reported to reveal phenome-wide associations of cardiac/aortic structure and functionality in popula-tion ***,due to the complexity of operation and variations among manufactural vendors,magnetic field strengths,coils,sequences,scan parameters,and image analysis approaches,CMR is rarely used in large cohort *** guidelines mainly focused on the diagnosis of cardiovascular diseases,which did not aim to basic *** purpose of this study was to propose a recommendation for CMR based phenotype measurements for cohort *** classify the imaging sequences of CMR into three categories according to the importance and universality of correspond-ing measurable *** acquisition time and repeatability of the phenotypic measurement were also taken into consideration during the *** other guidelines,this recommendation focused on quantitative measurement of large amount of phenotypes from CMR.

关键词： Cardiac imaging Magnetic resonance imaging Phenotype Standardization

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Closed-looped sensing and stimulation system for Parkinson's disease early diagnosis and rehabilitation

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Smart Health 2022年 26卷

作者： Cai, Yi Qian, Xiaoye Li, Qin Lin, Feng Huang, Ming-Chun Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland OH United States Department of Data and Computational Science Duke Kunshan University Jiangsu 215316 China ZJU-Hangzhou Global Scientific and Technological Innovation Center School of Cyber Science and Technology Zhejiang University Zhejiang 310058 China Suzhou Huanmu Intelligence Technology Co. Ltd. Jiangsu China

Parkinson's disease (PD) patients are involved in motor dysfunctions and gait issues. The absence of long-term reliable gait rehabilitations could result in poor gait function, gait deficits, and locomotion problems. Meanwhile, PD patients usually face significant challenges to complete the rehabilitation programs effectively without a specialized gait laboratory or after discharge from the hospital. This paper presents a closed-loop sensing and computing system to facilitate long-term medical care for PD patients. The proposed system consists of wearable sensing, data streaming, online data processing, real-time auditory stimulation for PD gait rehabilitation, and data services for medical providers. To address the potential issues of user-friendly and comforts, we implemented a wrapped shoe based plantar pressure sensing device for under-feet pressure data collection. Afterward, quantitative measurements of gait are fulfilled through online data streaming and processing architecture. Physical therapy is generally preferred for PD rehabilitation because it provides a more significant benefit superior to the drugs. Hence, applications of gait activity recognition and closed-loop stimulation are proposed to build a reliable closed-loop system and support self-contained training. To evaluate the proposed system, we conduct experiments with three gait and PD related datasets. It broads Parkinson's gait health care prospects that make long-term PD rehabilitation possible in community-living environment. © 2022 Elsevier Inc.

关键词： data streaming processing Gait rehabilitation Rhythmic auditory stimulation Wearable sensing

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Incorruptible Auditing: Blockchain-Powered Graph database Management

Incorruptible Auditing: Blockchain-Powered Graph Database Ma...

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Victor Ermolaev Indrek Klangberg Yash Madhwal Silver Vapper Sjoerd Wels Yury Yanovich Software Department Bitfury Amsterdam The Netherlands Electrical Engineering Mathematics and Computer Science Department University of Twente Enschede The Netherlands Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology (Skoltech) Moscow Russia

ISBN: (数字)9781728166803

ISBN: (纸本)9781728166810

In modern and interconnected world, information is accumulatively stored digitally, making the process of exchanging, gathering and querying the information much easier. Continuously, it has introduced new challenges about how to ensure its consistency and reliability due to the sheer volume of data. A blockchain-based information system can provide an incorruptible record of history, enabling better auditing and data management practices. The paper describes how to combine an Exonum blockchain and a Neo4j graph database into a system that can provide a verifiable audit trail of data integrity and its modifications for information stored in a graph database.

关键词： History Bitcoin Peer-to-peer computing Contracts

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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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