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arXiv 2020年

作者： Weng, Jiasi Weng, Jian Cai, Chengjun Huang, Hongwei Wang, Cong The College of Information Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this paper, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design. © 2020, CC BY.

关键词： Blockchain

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Flow-based sampling for multimodal and extended-mode distributions in lattice field theory

arXiv

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arXiv 2021年

作者： Hackett, Daniel C. Hsieh, Chung-Chun Pontula, Sahil Albergo, Michael S. Boyda, Denis Chen, Jiunn-Wei Chen, Kai-Feng Cranmer, Kyle Kanwar, Gurtej Shanahan, Phiala E. Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics Center for Theoretical Physics National Taiwan University Taipei106 Taiwan Department of Physics University of Maryland College ParkMD20742 United States Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Society of Fellows Harvard University CambridgeMA02138 United States Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei106 Taiwan Department of Physics Data Science Institute University of Wisconsin–Madison MadisonWI53706 United States Higgs Centre for Theoretical Physics University of Edinburgh EdinburghEH9 3FD United Kingdom

Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of training- and architecture-based methods to construct flow models for targets with multiple separated modes (i.e. vacua) as well as targets with extended/continuous modes. We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories in their symmetry-broken phases. In this context we investigate different flow-based sampling algorithms, including a composite sampling algorithm where flow-based proposals are occasionally augmented by applying updates using traditional algorithms like HMC. Copyright © 2021, The Authors. All rights reserved.

关键词： Lattice theory

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Resting-state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Mohammad S.E. Sendi Elaheh Zendehrouh Robyn L Miller David H Salat Vince D Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Georgia State University Atlanta GA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Jamaica Plain MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Charlestown MA USA

Background Age-related cognitive decline after 65 is a well-known phenomenon, but little is known about how brain functional changes are related to cognitive decline. To this end, previous studies explored the link between functional network connectivity (FNC) estimated from resting-state functional MRI (rs-fMRI) and cognitive scores in healthy adults. FNC is often assumed to be static over time. However, this assumption runs contrary to the dynamic nature of brain FNC, and dynamic FNC (dFNC) has been recently introduced to overcome this limitation. The current study investigated the relationship between dFNC estimated from 36,263 individuals from UK Biobank and cognition. Method We used the resting-state fMRI (duration: 5min) data of 37,784 (20,157 females) adults’ brains, demographic information (age:64.06± 7.51), and cognitive scores from the UK Biobank in which the cognitive scores include fluid intelligence (FI), reaction time (RT), and pairs matching (Pairs). We adapted group independent component analysis to extract 53 data-driven components for the whole brain using a fully automated approach (Fig.1: Step1). Next, we used the sliding window and Pearson correlation to estimate the dFNC among 53 components (Fig.1: Step2). We used k-means clustering to put dFNCs of all individuals into three separated states and calculated individual state vectors (Fig.1: Step3). Then, we estimated 32 dFNC features based on three estimated states and state vectors (Fig.1: Step4). Finally, we trained a two-fold cross-validation support vector regression to predict the cognitive scores (Fig.1: Step5). Result The estimated dFNC feature predicted FI score with high accuracy (the correlation between measure and predicted score is R = 0.043, p = 6.6e −17 , Fig.2A), mean time to correctly identify match in RT task (R = 0.065, p = 1.9 −34 , Fig.2B), and time to complete Pairs task (R = 0.049, p = 1.3e −20 , Fig.2C). Conclusion Here we explored the link between dFNC features and cogniti

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In-silico structural and functional analysis of nonsynonymous single nucleotide polymorphisms in human FOLH1 gene

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In silico pharmacology 2025年第1期13卷 32页

作者： Abtin Tondar Muhammad Irfan Sergio Sánchez-Herrero Hafsa Athar Aleena Haqqi Asim Kumar Bepari Laura Calvet Liñán David Hervás Marin Department of Computer Science Multimedia and Telecommunication Interuniversity Doctoral Program in Bioinformatics Universitat Oberta de Catalunya Barcelona (UOC) Spain. Stanford Deep Data Research Center Stanford University Stanford USA. Atta-Ur-Rahman School of Applied Biosciences (ASAB) National University of Sciences and Technology (NUST) Islamabad Punjab Pakistan. School of Medical Laboratory Technology Minhaj University Lahore (MUL) Lahore Punjab Pakistan. Department of Pharmaceutical Sciences North South University (NSU) Dhaka Bangladesh. Telecommunications and Systems Engineering Department Universitat Autònoma de Barcelona (UAB) Sabadell Spain. Department of Applied Statistics and Operational Research and Quality Alcoy Universitat Politècnica de València (UPV) Alcoy Spain.

Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual’s vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehensive in-silico analysis to examine the structural and functional implications of nsSNPs within the Folate Hydrolase 1(FOLH1) gene, which encodes the Prostate-Specific Membrane Antigen (PSMA). A total of 504 SNPs were retrieved, and after filtering, 15 pathogenic nsSNPs were identified using five different in-silico tools. Three of these SNPs—R255H (rs375565491), R255C (rs201789325), and G168E (rs267602926)—were consistently predicted to be pathogenic across all in-silico tools. MutPred2 was used to predict the structural and functional consequences of the identified mutations. The analysis revealed multiple alterations in the PSMA protein, including changes in helical conformations, glycosylation patterns, transmembrane properties, and solvent accessibility. Furthermore, I-Mutant 2.0 analysis demonstrated a decrease in protein stability for most nsSNPs, except for rs267602926 (G168E), which was predicted to increase stability. Conservation analysis using ConSurf revealed varying degrees of amino acid conservation, with R255H and R255C identified as highly conserved residues, indicating their potential functional and structural significance. Additionally, post-translational modification (PTM) analysis indicated that while phosphorylation and methylation sites remained unchanged, specific glycosylation sites were lost in two pathogenic mutant variants (R255H and R255C), potentially affecting PSMA function and adversely impacting prostate cancer. Our findings highlight the importance of in silico studies to investigate the structural and functional impacts of FOLH1 nsSNPs on the PSMA protein. Such in silico studies can deepen our understanding of the roles of nsSNPs in prostate cancer onset, progression, and drug resistance.

关键词： FOLH1 In-silico structural and functional analyses PSMA PTM nsSNPs

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Searching for gravitational wave optical counterparts with the Zwicky Transient Facility: summary of O4a

arXiv

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arXiv 2024年

作者： Ahumada, Tomás Anand, Shreya Coughlin, Michael W. Gupta, Vaidehi Kasliwal, Mansi M. Karambelkar, Viraj R. Stein, Robert D. Waratkar, Gaurav Swain, Vishwajeet Jegou du Laz, Theophile Anumarlapudi, Akash Andreoni, Igor Bulla, Mattia Srinivasaragavan, Gokul P. Toivonen, Andrew Wold, Avery Bellm, Eric C. Cenko, S. Bradley Kaplan, David L. Sollerman, Jesper Bhalerao, Varun Perley, Daniel Salgundi, Anirudh Suresh, Aswin Hinds, K.-Ryan Reusch, Simeon Necker, Jannis Cook, David O. Pletskova, Natalya Singer, Leo P. Banerjee, Smaranika Barna, Tyler Copperwheat, Christopher M. Healy, Brian Kiendrebeogo, R. Weizmann Kumar, Harsh Kumar, Ravi Pezzella, Marianna Sagués-Carracedo, Ana Sravan, Niharika Bloom, Joshua S. Chen, Tracy X. Graham, Matthew Helou, George Laher, Russ R. Mahabal, Ashish A. Purdum, Josiah Anupama, G.C. Barway, Sudhanshu Basu, Judhajeet Raman, Dhananjay Roychowdhury, Tamojeet Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics Indian Institute of Technology West Bengal Kharagpur721302 India Department of Physics Indian Institute of Technology Bombay Powai 400 076 India Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Joint Space-Science Institute University of Maryland College ParkMD20742 United States Department of Astronomy University of Maryland College ParkMD20742 United States Astrophysics Science Division NASA Goddard Space Flight Center MC 661 GreenbeltMD20771 United States Department of Physics and Earth Science University of Ferrara via Saragat 1 FerraraI-44122 Italy INFN Sezione di Ferrara via Saragat 1 FerraraI-44122 Italy INAF Osservatorio Astronomico d’Abruzzo via Mentore Maggini snc Teramo64100 Italy IPAC California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States DIRAC Institute Department of Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States The Oskar Klein Centre Department of Astronomy Albanova Stockholm University StockholmSE-106 91 Sweden Astrophysics Research Institute Liverpool John Moores University IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany Department of Physics Drexel University PhiladelphiaPA19104 United States Astrophysics Research Institute IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Laboratoire de Physique et de Chimie de l’Environnement Université Joseph KI-ZERBO Ouagadougou Burkina Faso Artemis Observatoire de la Côte d’Azur Université Côte d’Azur Boulevard de l’Observatoire NiceF-06304 France Department of Physics Indian Institute o

During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star–black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance (False Alarm Rate less than 1 per year) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations (ToO) and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up observations through a user-friendly interface. ZTF covered a total of 2841 deg2 within the skymaps of the high-significance GW events, reaching a median depth of g ≈ 20.2 mag. We circulated 15 candidates, but found no viable KN counterpart to any of the GW events. Based on the ZTF non-detections of the high-significance events in O4a, we used a Bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than -16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 BNS and 4 NSBH events), along with our O4a follow-ups. Following Kasliwal et al. (2020), we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag day-1 can peak at a magnitude brighter than -17.5 mag. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravity waves

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The 2D Materials Roadmap

arXiv

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arXiv 2025年

作者： Ren, Wencai Bøggild, Peter Redwing, Joan Marie Novoselov, Kostya Sun, Luzhao Qi, Yue Jia, Kaicheng Liu, Zhongfan Burton, Oliver Alexander-Webber, Jack Hofmann, Stephan Cao, Yang Long, Yu Yang, Quan-Hong Li, Dan Choi, Soo Ho Kim, Ki Kang Lee, Young Hee Li, Mian Huang, Qing Gogotsi, Yury Clark, Nicholas Carl, Amy Gorbachev, Roman Olsen, Thomas Rosen, Johanna Thygesen, Kristian Sommer Efetov, Dmitri K. Jessen, Bjarke S. Yankowitz, Matthew Barrier, Julien Kumar, Roshan Krishna Koppens, Frank H.L. Deng, Hui Li, Xiaoqin Dai, Siyuan Basov, D.N. Wang, Xinran Das, Saptarshi Duan, Xiangfeng Yu, Zhihao Borsch, Markus Ferrari, Andrea C. Huber, Rupert Kira, Mackillo Xia, Fengnian Wang, Xiao Wu, Zhong-Shuai Feng, Xinliang Simon, Patrice Cheng, Hui-Ming Liu, Bilu Xie, Yi Jin, Wanqin Nair, Rahul Raveendran Xu, Yan Zhang, Qing Katiyar, Ajit K. Ahn, Jong-Hyun Aharonovich, Igor Hersam, Mark C. Roche, Stephan Hua, Qilin Shen, Guozhen Ren, Tianling Zhang, Hao-Bin Koo, Chong Min Koratkar, Nikhil Pellegrini, Vittorio Young, Robert J. Qu, Bill Lemme, Max Pollard, Andrew J. Shenyang National Laboratory for Materials Science Institute of Metal Research Chinese Academy of Sciences 72 Wenhua Road Shenyang110016 China Technical University of Denmark Denmark The Pennsylvania State University United States University of Manchester United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Beijing Graphene Institute China University of Cambridge United Kingdom Department of Chemical Engineering The University of Melbourne Victoria Australia Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology Tianjin University Tianjin300072 China The Hong Kong University of Science and Technology Hong Kong Center for Integrated Nanostructure Physics Institute for Basic Science Suwon16419 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Drexel University United States Linköping University Sweden Ludwig-Maximilians-Universität München Germany University of Washington United States ICFO The Institute of Photonic Sciences Castelldefels Barcelona08860 Spain University of Michigan United States University of Texas Austin United States Auburn University United States Columbia University United States State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of California Los Angeles United States Suzhou Laboratory Suzhou China School of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States University of Regensburg Germany Department of Electrical and Computer Engineering Yale University New Have

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials. © 2025, CC BY.

关键词： Integrated optoelectronics

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SHREC’20 Track: Retrieval of digital surfaces with similar geometric reliefs

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computers and Graphics (Pergamon) 2020年 91卷 199-218页

作者： Moscoso Thompson, Elia Biasotti, Silvia Giachetti, Andrea Tortorici, Claudio Werghi, Naoufel Obeid, Ahmad Shaker Berretti, Stefano Nguyen-Dinh, Hoang-Phuc Le, Minh-Quan Nguyen, Hai-Dang Tran, Minh-Triet Gigli, Leonardo Velasco-Forero, Santiago Marcotegui, Beatriz Sipiran, Ivan Bustos, Benjamin Romanelis, Ioannis Fotis, Vlassis Arvanitis, Gerasimos Moustakas, Konstantinos Otu, Ekpo Zwiggelaar, Reyer Hunter, David Liu, Yonghuai Arteaga, Yoko Luxman, Ramamoorthy Istituto di Matematica Applicata e Tecnologie Informatiche ‘E. Magenes’ - CNR Italy Department of Computer Science University of Verona Italy University of Science Ho Chi Minh city Viet Nam Center for Mathematical Morphology - Mines ParisTech - PSL France Department of Engineering Pontifical Catholic University of Peru PUCP Peru Millennium Institute Foundational Research on Data Department of Computer Science University of Chile Chile Electrical and Computer Engineering Department University of Patras Rion-Patras Greece Department of Computer Science Aberystwyth University Aberystwyth SY23 3DB United Kingdom Department of Computer Science Edge Hill University Ormskirk L39 4QP United Kingdom Technology Innovation Institute Abu Dhabi United Arab Emirates KUCARS Department of Electrical Engineering and Computer Sciences Khalifa University United Arab Emirates University of Florence Florence Italy Centre de Recherche et Restauration des Musees de France Paris France Vietnam National University Ho Chi Minh city Viet Nam Université Bourgogne Franche-Comté Dijon France Norwegian University of Science and Technology Gjovik Norway

This paper presents the methods that have participated in the SHREC’20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local "geometric pattern" information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches. © 2020 Elsevier Ltd

关键词： Benchmarking

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Informal caregivers' attitudes and compliance towards a connected health platform for home care support: Insights from a long-term exposure

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Gerontechnology 2019年第4期18卷 231-242页

作者： Guisado-Fernández, Estefanía Giunti, Guido Mackey, Laura Silva, Paula Alexandra Blake, Catherine Caulfield, Brian Insight Centre for Data Analytics University College Dublin 3rd floor O'Brien Science Building East Dublin Ireland M3S Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland Department of Computer Science Center for Informatics and Systems of the University of Coimbra (CISUC) University of Coimbra Coimbra Portugal

Background When designing Connected Health (CH) solutions for home care, it is vital to focus on usability and user experience to ensure that technologies are easy to use and meet users' expectations and needs. Generally, the usability and user experience tests are conducted during short-term exposure, which does not allow a true understanding of how the technology can help with the home caring tasks. research aim We aim to investigate informal caregivers' feedback on the utility and usability of a CH monitoring platform for People with Dementia (PwD) during a period of extended use in the natural living context, and to understand how this was related to compliance patterns. Methods Informal caregiver's feedback about the CH platform, usability, and the impact of short-term versus long-term exposure were investigated through semi-structured individual interviews at the beginning and end of a 6-month deployment in the home care setting. Informal caregivers' compliance with the CH platform was analysed from their daily platform utilization during the deployment time. Results 11 informal caregivers agreed to participate. There was a change in the participants' opinions about the CH platform between the short-term and the long-term exposure feedback. Their initial impressions about what the CH platform could offer them to improve their delivery of home care for the PwD did not correspond with what they found that the CH platform could provide them following the long-term exposure. If at the beginning they saw the CH platform as a helpful tool to facilitate home care delivery and to improve their self-efficacy, after the deployment they expressed that because of the way the platform was designed it was mainly conceived for dementia research benefit and not to fulfill their caring needs. Compliance with the CH platform was quite low and similar between all participants. Conclusions Most contemporary CH studies are not conducted in real-life settings and without enough dur

关键词： Connected health Dementia Informal caregiver Usability User experience

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Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

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A physics-constrained deep learning framework for actual evapotranspiration estimation using ground station observations and remote sensing data

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Environmental Modelling & Software 2025年 192卷

作者： Lingyue Wang Ping Hu Hongwei Zheng Jie Bai Ying Liu Xingwen Cao Olaf Hellwich Tie Liu Anming Bao Xi Chen State Key Laboratory of Desert and Oasis Ecology Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi 830011 China University of Chinese Academy of Sciences Beijing 100049 China Research Center for Ecology and Environment of Central Asia Chinese Academy of Sciences Urumqi 830011 China Key Laboratory of RS & GIS Application Xinjiang Urumqi 830011 China School of Information Science and Engineering Xinjiang University Urumqi 830017 China Department of Computer Vision & Remote Sensing Technische Universität Berlin 10587 Berlin Germany

Evapotranspiration (ET) is a key process in the water cycle. While machine learning-based methods have been increasingly applied to ET estimation, they typically neglect physical mechanisms and ecological impacts. This study proposes a physics-constrained hybrid model (TST-PHY) that incorporates Penman-Monteith-derived physical knowledge into the time series transformer (TST). The framework integrates Bayesian optimization to automatically determine optimal weights for physical mechanisms within the data-driven modeling workflow. The arid and semi-arid regions of Northern China (ASNC) were chosen as the study area, and our results show that the proposed framework optimally balances estimation error and the physical law, with value and trend constraint weights of 0.26 and 0.41, respectively. Moreover, proper physical constraints can improve the prediction accuracy, generalization, and physical consistency of TST-PHY at various spatiotemporal scales. In conclusion, this study establishes a novel paradigm for evapotranspiration estimation in data-sparse regions through the synergistic integration of data-driven and knowledge-based models.

关键词： Evapotranspiration Hybrid model Transformer Physical mechanism Bayesian optimization

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