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Correction: Causal analysis for multivariate integrated clinical and environmental exposures data

BMC medical informatics and decision making 2025年第1期25卷 78页

作者： Meghamala Sinha Perry Haaland Ashok Krishnamurthy Bo Lan Stephen A Ramsey Patrick L Schmitt Priya Sharma Hao Xu Karamarie Fecho School of Electrical Engineering and Computer Science Oregon State University Corvallis OR 97331 USA. meghamala.sinha@***. Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill NC USA. Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Computer Science University of North Carolina Chapel Hill NC USA. UNC Highway Safety Research Center University of North Carolina at Chapel Hill Chapel Hill NC USA. School of Electrical Engineering and Computer Science Oregon State University Corvallis OR 97331 USA.

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Susceptibility of Large Language Models to User-Driven Factors in Medical Queries

arXiv

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arXiv 2025年

作者： Lim, Kyung Ho Kang, Ujin Li, Xiang Kim, Jin Sung Jung, Young-Chul Park, Sangjoon Kim, Byung-Hoon Department of Psychiatry Yonsei University College of Medicine 50-1 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Institute of Behavioral Science in Medicine Yonsei University College of Medicine 50-1 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Department of Computer Science and Engineering Yonsei University 50-1 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School 55 Fruit Street BostonMA02114 United States Department of Radiation Oncology Yonsei University College of Medicine 50-1 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Institute for Innovation in Digital Healthcare Yonsei University 50-1 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Oncosoft Inc. Teheran-ro 26-gil Gangnam-gu Seoul06236 Korea Republic of Department of Biomedical Systems Informatics Yonsei University College of Medicine 50-1 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of

Background Large language models (LLMs) are increasingly used in healthcare;however, their reliability is influenced by query phrasing and the completeness of provided information. Via this study, we aim to assess how user-driven factors, including misinformation framing, source authority, model personas, and omission of critical clinical details, influence the diagnostic accuracy and reliability of LLM-generated medical responses. Method This study incorporates two tests: (1) perturbation—evaluating LLM persona (assistant vs. expert AI), misinformation source authority (inexperienced vs. expert), and tone (assertive vs. hedged);and (2) ablation—omission of key clinical data, utilizing publicly available medical datasets, including MedQA, and Medbullets. Proprietary LLMs (GPT-4o (OpenAI), Claude-3·5 Sonnet(Anthropic), Claude-3·5 Haiku(Anthropic), Gemini-1·5 Pro(Google), Gemini-1·5 Flash(Google)) and open-source LLMs (LLaMA-3 8B, LLaMA-3 Med42 8B, DeepSeek-R1 8B) was used for evaluation. Findings All LLMs were susceptible to user-driven misinformation;however, proprietary models were more influenced by definitive, authoritative misinformation, and assertive tone had the most significant impact. In the ablation test, omitting physical examination findings and laboratory results caused the largest accuracy decline. Proprietary models showed higher baseline accuracy;however, their performance declined with misinformation exposure. Interpretation Structured prompts and complete clinical context are essential for accurate responses. Users should avoid authoritative misinformation framing and provide a complete clinical context, especially for complex and challenging queries. Funding Bio&Medical Technology Development Program of the National Research Foundation (NRF), Korean government (MSIT;RS-2024-00509289) Basic science Research Program through the National Research Foundation of Korea(NRF), Ministry of Education(NRF-2022R1I1A1A01069589 and RS-2023-0024216). © 2025, CC

关键词： Diagnosis

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SpEx+: A Complete Time Domain Speaker Extraction Network

arXiv

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arXiv 2020年

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker’s reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Extraction

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Dynamic Management of Traffic Signals through Social IoT

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Procedia computer science 2020年 171卷 1908-1916页

作者： Roopa M S Ayesha Siddiq S Rajkumar Buyya Venugopal K R S S Iyengar L M Patnaik IoT Lab University Visvesvaraya College of Engineering Bangalore University Bengaluru India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University USA National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru India

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed scheme along with the various social relationship types allocates green signal to maximize the traffic flow passing through an intersection. Simulation results show that the D-TMSA outperforms the existing work by achieving high throughput, lowering the total traveling time and reducing the average waiting time to better the flow of traffic based on their social attributes with each other.

关键词： Dynamic Traffic Control Traffic Congestion Management Social Internet of Vehicles (SIoV) Social Relationships

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Revolutionizing Genomics with Reinforcement Learning Techniques

arXiv

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arXiv 2023年

作者： Karami, Mohsen Jahanian, Khadijeh Alizadehsani, Roohallah Argha, Ahmadreza Dehzangi, Iman Gorriz, Juan M. Zhang, Yu-Dong Hajati, Farshid Yang, Min Alinejad-Rokny, Hamid Amirkabir University of Technology Tehran Iran Faculty of Engineering and IT University of Technology Sydney Sydney Australia Deakin University VIC Australia School of Biomedical Engineering UNSW SYDNEY SydneyNSW2052 Australia Department of Computer Science Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States Department of Signal Theory Networking and Communications Universidad de Granada Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2350 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering UNSW SYDNEY SydneyNSW2052 Australia

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics. © 2023, CC BY-SA.

关键词： Adversarial machine learning

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CLAWER: Context-aware Cloud-Fog based Workflow Management Framework for Health Emergency Services

CLAWER: Context-aware Cloud-Fog based Workflow Management Fr...

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IEEE/ACM International Symposium on Cluster computing and the Grid (CCGRID)

作者： Shreya Ghosh Jaydeep Das Soumya K Ghosh Rajkumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

With the major development of sensor technologies and advancements of communication network infrastructures, there is a growing interest to add more intelligence in the e-health monitoring for facilitating an effective healthcare system. While IoT devices are capable of continuous health-parameter sensing and providing notifications to the user, an effective business process management (BPM) facilitates effective system integration and data processing workflow. This paper proposes an efficient framework for managing emergency situations (specifically, health-related) through the analysis of heterogeneous data sources. The proposed framework, named CLAWER (CLoud-Fog bAsed Workflow for Emergency seRvice) aims to bridge the gap between process management and data analytics by providing an automated workflow for personalized health-monitoring and efficient recommendation system. Here, the IoT devices are used for collecting the movement and health data. The smart phone can act as an edge device to acquire data with user movement information. The accumulated data is initially processed inside the fog device, and finally the analysis and recommendations are generated by the cloud. In this paper the indoor health-status of the users are analysed in small cell cloud enhanced eNode B, which is used as fog device. The generated recommendations are stored in the fog device to provide the recommendations to the users with low latency and in timely manner. The experimental analysis of CLAWER yields better precision and recall values than the existing methods.

关键词： Cloud computing Roads Medical services Markov processes Monitoring Delays Biomedical monitoring

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Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations

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SIAM Journal on Scientific computing 2025年第3期47卷 C680-C705页

作者： Zheyuan Hu Zhongqiang Zhang George E. Karniadakis Kenji Kawaguchi Department of Computer Science National University of Singapore Singapore119077 Singapore Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Division of Applied Mathematics Brown University USA 02912 ProvidenceRI United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA99352 United States

The Fokker–Planck (FP) equation is a foundational partial differential equation (PDE) in stochastic processes involving Brownian motions. However, the curse of dimensionality (CoD) poses a formidable challenge when dealing with high-dimensional FP equations. Although Monte Carlo simulation and (vanilla) Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit significant numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The pointwise PDF values tend to decrease exponentially as dimensionality increases, surpassing the precision of numerical simulations and resulting in substantial errors. Moreover, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a notoriously difficult Hamilton–Jacobi–Bellman (HJB) equation, which is impractical for PINN learning, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in stochastic differential equations (SDEs). The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling, offering an effective means to overcome the CoD. Three fitting methods, Score Matching (SM), Sliced Score Matching (SSM), and Score-PINN, are introduced, each contributing unique advantages in computational complexity, accuracy, and generality. The proposed score-based SDE solver operates in two stages: first, employing SM or Score-PINN to acquire the score function; and second, solving the LL via an ordinary differential equation (ODE) using the obtained score function. Comparative evaluations across these methods showcase varying tradeoffs. The proposed methodology is evaluated across diverse SDEs, including anisotropic Ornstein–Uhlenbeck processes, geometr

关键词： Fokker Planck equation

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Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

arXiv

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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

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Double JPEG Compression Detection Based on Markov Model 18th

Double JPEG Compression Detection Based on Markov Model

引用

18th International Workshop on Digital Forensics and Watermarking, IWDW 2019

作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yung-Qing Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Information Security Institute Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030435745

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (ψ, φ, and θ). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with (Formula Presented) is a challenging task, and when the images with small size (i.e., 64 × 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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