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arXiv 2024年

作者： Miao, Brenda Y. Williams, Christopher Y.K. Chinedu-Eneh, Ebenezer Zack, Travis Alsentzer, Emily Butte, Atul J. Chen, Irene Y. Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Department of Medicine University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Division of General Internal Medicine Brigham and Women's Hospital BostonMA United States Harvard Medical School BostonMA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States

Background: Understanding why patients switch contraceptives is of significant interest but these factors are often only captured in unstructured clinical notes and can be difficult to extract. We evaluate the zero-shot abilities of a recently developed large language model, GPT-4, to identify reasons for switching between classes of contraceptives from clinical notes. Methods: Clinical notes associated with contraceptive switches, defined as a difference in prescribed contraceptive modality between consecutive encounters, were selected from the UCSF Information Commons dataset. GPT-4 (HIPAA-compliant Microsoft Azure API) was used to extract contraceptive class started, stopped, and reasons for switching from these clinical notes. We evaluated six prompts on a subset of human annotated notes, and extracted reasons for contraceptive switching from the full dataset using the best prompt based on microF1 scores. BERTopic topic modeling was used to identify key reasons for contraceptive switching and enrichment of topics was assessed in patient subgroups stratified by race and ethnicity. Results: We extracted 1,964 contraceptive switches from 1,515 patients. Younger patients and patients with self-reported race/ethnicities listed as "Black/African American" and "Latinx" had significantly higher rates of switching. GPT-4 demonstrated extraction of contraceptives started and stopped had MicroF1 scores of 0.849 and 0.881, respectively. Accuracy of GPT-4 extracted reasons for switching was 91.4% and 2.2% contained hallucinations. Key reasons for switching included patient preference, adverse events, and insurance. "Weight gain/mood change" and "insurance coverage" topics were enriched in patients self-reporting as being "Black or African American", "Latinx", or "Multi-Race/Ethnicity". Conclusion: We demonstrate that GPT-4 can accurately extract reasons for contraceptive switching and identify certain reasons that are disproportionately found in specific demographic populati

关键词： Extraction

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Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

arXiv

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arXiv 2023年

作者： Kalinin, Sergei V. Mukherjee, Debangshu Roccapriore, Kevin Blaiszik, Ben Ghosh, Ayana Ziatdinov, Maxim Al-Najjar, A. Doty, Christina Akers, Sarah Rao, Nageswara S. Agar, Josh Spurgeon, Steven R. Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37831 United States Computational Sciences & Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Argonne National Laboratory Data Science and Learning Division ChicagoIL60439 United States University of Chicago Globus ChicagoIL60637 United States National Security Pacific Northwest National Laboratory RichlandWA99352 United States Department of Materials Science and Engineering Drexel University PhiladelphiaPA19104 United States Energy and Environment Pacific Northwest National Laboratory RichlandWA99352 United States Department of Physics University of Washington SeattleWA98195 United States

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centered experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for the edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows and the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation. © 2023, CC BY-NC-SA.

关键词： data handling

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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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Giant Isolated Attosecond Pulses from Two-Color Laser-Plasma Interactions

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Physical Review Letters 2020年第11期124卷 114802-114802页

作者： Y. X. Zhang S. Rykovanov Mingyuan Shi C. L. Zhong X. T. He B. Qiao M. Zepf Center for Applied Physics and Technology HEDPS SKLNPT and School of Physics Peking University Beijing 100871 China Helmholtz Institute Jena 07743 Jena Germany Laser Fusion Research Center China Academy of Engineering Physics Mianyang 621900 China Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology 121205 Moscow Russia Collaborative Innovation Center of IFSA Shanghai Jiao Tong University Shanghai 200240 China Institute of Applied Physics and Computational Mathematics Beijing 100094 China Institute of Optics and Quantum Electronics Friedrich Schiller University 07743 Jena Germany

A new regime in the interaction of a two-color (ω,2ω) laser with a nanometer-scale foil is identified, resulting in the emission of extremely intense, isolated attosecond pulses—even in the case of multicycle lasers. For foils irradiated by lasers exceeding the blow-out field strength (i.e., capable of fully separating electrons from the ion background), the addition of a second harmonic field results in the stabilization of the foil up to the blow-out intensity. This is then followed by a sharp transition to transparency that essentially occurs in a single optical cycle. During the transition cycle, a dense, nanometer-scale electron bunch is accelerated to relativistic velocities and emits a single, strong attosecond pulse with a peak intensity approaching that of the laser field.

关键词： Fast particle effects in plasmas High-energy-density plasmas High-order harmonic generation Laser-plasma interactions Radiation & particle generation in plasmas Radiation pressure acceleration X-ray generation in plasmas

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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A DECOUPLING TWO-GRID METHOD FOR THE STEADY-STATE POISSON-NERNST-PLANCK EQUATIONS

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Journal of computational Mathematics 2019年第4期37卷 556-578页

作者： Ying Yang Benzhuo Lu Yan Xie School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin 541004 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing the National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

Poisson-Nernst-Planck equations are widely used to describe the electrodiffusion of ions in a solvated biomolecular system. Two kinds of two-grid finite element algorithms are proposed to decouple the steady-state Poisson-Nernst-Planck equations by coarse grid finite element approximations. Both theoretical analysis and numerical experiments show the efficiency and effectiveness of the two-grid algorithms for solving Poisson-Nernst-Planck equations.

关键词： Poisson-Nernst-Planck equations Two-grid finite element method Decoupling method Error analysis Gummel iteration

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Financial Instruments Generation via Tokenization into Commodity

Financial Instruments Generation via Tokenization into Commo...

引用

Blockchain, Robotics and AI for Networking Security Conference (BRAINS)

作者： Vyacheslav Davydov Yury Yanovich Moscow Institute of Electronics and Mathematics National Research University Higher School of Economics 123458 Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology (Skoltech) 121205 Moscow Russia Laboratory of Data Mining and Predictive Modeling Institute for Information Transmission Problems (IITP RAS) 127051 Moscow Russia

ISBN: (数字)9781728170916

ISBN: (纸本)9781728170923

The diversification is a classic approach to reduce investor risks. Blockchain and smart contracts allow tokenizing heterogeneous assets into a commodity and open a new tool for it. The authors describe how to apply the approach to the various finance cases from peer-to-peer lending to real estate and leases, and theoretically estimate minimum token package size to ensure the given reliability parameter.

关键词： commodities LEASING REAL PROPERTY diversification packet size lease token peer to peer EQUIPMENT contract

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Generative AI Enables Medical Image Segmentation in Ultra Low-data Regimes

arXiv

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arXiv 2024年

作者： Zhang, Li Jindal, Basu Alaa, Ahmed Weinreb, Robert Wilson, David Segal, Eran Zou, James Xie, Pengtao Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA United States Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Hamilton Glaucoma Center Shiley Eye Institute Viterbi Family Department of Ophthalmology University of California La Jolla San DiegoCA United States Division of Pulmonary Allergy and Critical Care Medicine Department of Medicine University of Pittsburgh PittsburghPA United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Department of Medicine University of California La Jolla San DiegoCA United States

Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated segmentation masks, which are resource-intensive to produce due to the required expertise and time. This scenario often leads to ultra low-data regimes, where annotated images are extremely limited, posing significant challenges for the generalization of conventional deep learning methods on test images. To address this, we introduce a generative deep learning framework, which uniquely generates high-quality paired segmentation masks and medical images, serving as auxiliary data for training robust models in data-scarce environments. Unlike traditional generative models that treat data generation and segmentation model training as separate processes, our method employs multi-level optimization for end-to-end data generation. This approach allows segmentation performance to directly influence the data generation process, ensuring that the generated data is specifically tailored to enhance the performance of the segmentation model. Our method demonstrated strong generalization performance across 9 diverse medical image segmentation tasks and on 16 datasets, in ultra-low data regimes, spanning various diseases, organs, and imaging modalities. When applied to various segmentation models, it achieved performance improvements of 10-20% (absolute), in both same-domain and out-of-domain scenarios. Notably, it requires 8 to 20 times less training data than existing methods to achieve comparable results. This advancement significantly improves the feasibility and cost-effectiveness of applying deep learning in medical imaging, particularly in scenarios with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging

arXiv

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arXiv 2024年

作者： Pati, Sarthak Mazurek, Szymon Bakas, Spyridon Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States AGH University of Krakow Academic Computer Centre Cyfronet Krakow Poland Indiana University Melvin and Bren Simon Comprehensive Cancer Center IndianapolisIN United States Department of Radiology & Imaging Sciences Indiana University School of Medicine IndianapolisIN United States Department of Biostatistics & Health Data Science Indiana University School of Medicine IndianapolisIN United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States

Generative Artificial Intelligence (GenAI) is a field of AI that creates new data samples from existing ones. It utilizing deep learning to overcome the scarcity and regulatory constraints of healthcare data by generating new data points that integrate seamlessly with original datasets. This paper explores the background and motivation for GenAI, and introduces the Generally Nuanced Deep Learning Framework for Synthesis (GaNDLF-Synth) to address a significant gap in the literature and move towards democratizing the implementation and assessment of image synthesis tasks in healthcare. GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models. Leveraging the GANDLF-core framework, it supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing. The aim of GaNDLF-Synth is to lower the entry barrier for GenAI, and make it more accessible and extensible by the wider scientific community. © 2024, CC BY.

关键词： Generative adversarial networks

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Geometric Optimisation of Unmanned Aerial Vehicle Trajectories in Uncertain Environments

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Vehicular Communications 2025年 54卷

作者： Akshya, J. Sundarrajan, M. Amutha, S. Dhanaraj, Rajesh Kumar Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds United Kingdom Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The problem of efficient trajectory optimisation for Unmanned Aerial Vehicles (UAVS) in dynamic and constrained environments is one where energy efficiency, spatial coverage, and path smoothness need to be balanced. The existing methods, namely RRT*, A*, and Dijkstra, are popular but generally heuristic and do not provide globally optimal solutions. They face significant limitations while dealing with complex geometries, dynamic obstacles, and multi-objective requirements. These challenges call for a mathematically sound framework that seamlessly integrates convex analysis and computational geometry to provide an optimal trajectory planning framework. This research work introduces a convex optimisation framework for UAV trajectory planning which unifies multiple objectives, like minimising energy consumption, maximising spatial coverage, and ensuring the smoothness of the path, into a single convex objective function. More importantly, it indicates that obstacle dynamics and uncertain environmental conditions are handled better by it, so it is relatively easier for safe and efficient navigation. Proven to converge faster and with higher precision than RRT*, A*, and Dijkstra, the approach proposed here enjoys intrinsic convex properties, which ensure global optimality. Qualitative measurements show the efficiency of the proposed framework. The result is energy efficiency of 90%, with 92% coverage, 98% constraint satisfaction, and 95% path smoothness, which is 15-25% better on all metrics than traditional approaches can offer. By bridging between theory in convex optimisation and practice for solving multi-objective problems in a dynamic setting, this study provides a more robust solution for UAV trajectory planning. © 2025 The Author(s)

关键词： computational Geometry Convex Optimisation Delaunay Triangulations Spatial Coverage UAV Trajectory Voronoi Diagrams

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