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arXiv 2022年

作者： Zhang, Nick Gupta, Abhishek Chen, Zefeng Ong, Yew-Soon The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore 138634 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Artificial Intelligence Sun Yatsen University China The School of Computer Science and Engineering NTU Singapore The Data Science and Artificial Intelligence Research Centre School of Computer Science and Engineering NTU Singapore The A*Star Center for Frontier AI Research Singapore

Studies have shown evolution strategies (ES) to be a promising approach for reinforcement learning (RL) with deep neural networks. However, the issue of high sample complexity persists in applications of ES to deep RL over long horizons. This paper is the first to address the shortcoming of today's methods via a novel neuroevolutionary multitasking (NuEMT) algorithm, designed to transfer information from a set of auxiliary tasks (of short episode length) to the target (full length) RL task at hand. The auxiliary tasks, extracted from the target, allow an agent to update and quickly evaluate policies on shorter time horizons. The evolved skills are then transferred to guide the longer and harder task towards an optimal policy. We demonstrate that the NuEMT algorithm achieves data-efficient evolutionary RL, reducing expensive agent-environment interaction data requirements. Our key algorithmic contribution in this setting is to introduce, for the first time, a multitask skills transfer mechanism based on the statistical importance sampling technique. In addition, an adaptive resource allocation strategy is utilized to assign computational resources to auxiliary tasks based on their gleaned usefulness. Experiments on a range of continuous control tasks from the OpenAI Gym confirm that our proposed algorithm is efficient compared to recent ES baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Dynamic and Distributed Optimization for the Allocation of Aerial Swarm Vehicles

arXiv

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arXiv 2022年

作者： Hughes, Jason Larkin, Dominic O’Donnell, Charles Korpela, Christopher The Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States

Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. This paper applies discrete OT to a swarm of UAVs in a novel way to achieve appropriate task allocation and execution. Drone swarm deployments already operate in multiple domains where sensors are used to gain knowledge of an environment [1]. Use cases such as, chemical and radiation detection, and thermal and RGB imaging create a specific need for an algorithm that considers parameters on both the UAV and waypoint side and allows for updating the matching scheme as the swarm gains information from the environment. Additionally, the need for a centralized planner can be removed by using a distributed algorithm that can dynamically update based on changes in the swarm network or parameters. To this end, we develop a dynamic and distributed OT algorithm that matches a UAV to the optimal waypoint based on one parameter at the UAV and another parameter at the waypoint. We show the convergence and allocation of the algorithm through a case study and test the algorithm’s effectiveness against a greedy assignment algorithm in simulation. © 2022, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Eye Drop Instillation Success and Hand Function in Adults with Glaucoma: A Pilot Study

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Ophthalmology Glaucoma 2025年第3期8卷 275-284页

作者： Weber, Madeline K. Kabil, Gül G. Niziol, Leslie M. Urrego, Daniel Duque Haire, Cameron Cain, Stephen M. Sample, Alanson P. Burke, David T. Brown, Susan H. Newman-Casey, Paula Anne University of Michigan Medical School Ann Arbor MI United States University of Michigan School of Kinesiology Ann Arbor MI United States Department of Ophthalmology and Visual Sciences University of Michigan W.K. Kellogg Eye Center Ann Arbor MI United States Department of Chemical and Biomedical Engineering West Virginia University Morgantown WV United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Department of Human Genetics University of Michigan Ann Arbor MI United States

Purpose: To investigate hand function and eye drop instillation success in adults with and without glaucoma. Design: Cross-sectional pilot study. Subjects: Adults aged ≥ 65 years with glaucoma who use eye drops daily and adults aged 65+ without glaucoma who do not regularly use eye drops. Methods: Hand function was evaluated using the Saehan Hydraulic Pinch Gauge, Jamar Hand Dynamometer, Grooved Pegboard Test (GPT), Arthritis Hand Function Tests (AHFT), Semmes-Weinstein Monofilament Test, and a tactile pattern recognition device. Eye drop instillation success was analyzed through videography. Main Outcome Measures: Proportion of successful eye drop instillation trials assessed by (1) overall success in getting at least one drop in the eye;(2) average number of drops dispensed;and (3) bottle tip contact with the eye, eyelashes, or skin. Hand function measures: mean pinch and grip strength, GPT and AHFT completion times, smallest detected monofilament, tactile pattern identification time, and accuracy. Results: A total of 25 participants with glaucoma and 79 participants without glaucoma were included. There was no difference in the proportion of trials where at least one drop was successfully instilled, disregarding bottle tip contact (glaucoma: 0.95, nonglaucoma: 0.91;P = 0.88). Participants with glaucoma were more likely to make contact between the bottle tip and the eye, eyelashes, or skin compared with those without (glaucoma: 0.49, nonglaucoma: 0.28;P = 0.01). Participants with glaucoma dispensed a similar number of drops as those without (glaucoma: 1.37, nonglaucoma: 1.46;P = 0.47). Participants with glaucoma had significantly reduced pinch strength (glaucoma: 4.8 kg, nonglaucoma 6.1 kg, P = 0.01), grip strength (glaucoma: 23.4 kg, nonglaucoma: 27.7 kg, P = 0.02), longer completion times for the GPT (glaucoma: 113.5 seconds, nonglaucoma: 85.5 seconds, P = 0.02), and specific AHFT tasks of fastening/unfastening buttons (glaucoma: 36.6 seconds, nonglaucoma: 27.7

关键词： Eye drop instillation success Glaucoma Hand function Sensorimotor deficits

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User Feedback Module for Women Self Help Group Chatbot for Increased Usability

User Feedback Module for Women Self Help Group Chatbot for I...

引用

Emerging Technology (INCET), International Conference for

作者： Moturi Karthikeya Sruthy Anand Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The recent advancement in AI and deep learning is revolutionizing various sectors, such as healthcare, education, and entertainment, to name a few. In this work, we explore the use of AI chatbots in rural digital empowerment and how AI chatbots can be utilized for supporting the small-scale businesses of rural self-help group members. The work explores the need for a user feedback module for a multi-lingual chatbot designed for a rural women's self-help group (SHGs) in India. Integration of the user feedback module will further enhance the usability and usefulness of the multi-lingual SHG chatbot. The work also examines the need for increased usefulness of the multi-lingual chatbot for improved adoption in SHGs. The work also emphasizes the need to design software based on the context of use considering the needs and objectives of the user group.

关键词： User-generated content Entertainment industry Oral communication Medical services Chatbots Software User experience

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UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion

arXiv

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arXiv 2025年

作者： Zhang, Gongbo Li, Yanting Luo, Renqian Hu, Pipi Zhao, Zeru Li, Lingbo Liu, Guoqing Wang, Zun Bi, Ran Gao, Kaiyuan Guo, Liya Xie, Yu Liu, Chang Zhang, Jia Xie, Tian Pinsler, Robert Zeni, Claudio Lu, Ziheng Xia, Yingce Segler, Marwin Riechert, Maik Yuan, Li Chen, Lei Liu, Haiguang Qin, Tao Microsoft Research AI for Science United States School of Electronic and Computer Engineering Peking University China DSA The Hong Kong University of Science and Technology Guangzhou China School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Department of Automation Tsinghua University China School of Computer Science and Technology Huazhong University of Science and Technology China Yau Mathematical Sciences Center Department of Mathematical Sciences Tsinghua University China

Unified generation of sequence and structure for scientific data (e.g., materials, molecules, proteins) is a critical task. Existing approaches primarily rely on either autoregressive sequence models or diffusion models, each offering distinct advantages and facing notable limitations. Autoregressive models, such as GPT, Llama, and Phi-4, have demonstrated remarkable success in natural language generation and have been extended to multimodal tasks (e.g., image, video, and audio) using advanced encoders like VQ-VAE to represent complex modalities as discrete sequences. However, their direct application to scientific domains is challenging due to the high precision requirements and the diverse nature of scientific data. On the other hand, diffusion models excel at generating high-dimensional scientific data, such as protein, molecule, and material structures, with remarkable accuracy. Yet, their inability to effectively model sequences limits their potential as general-purpose multimodal foundation models. To address these challenges, we propose UniGenX, a unified framework that combines autoregressive next-token prediction with conditional diffusion models. This integration leverages the strengths of autoregressive models to ease the training of conditional diffusion models, while diffusion-based generative heads enhance the precision of autoregressive predictions. We validate the effectiveness of UniGenX on material and small molecule generation tasks, achieving a significant leap in state-of-the-art performance for material crystal structure prediction and establishing new state-of-the-art results for small molecule structure prediction, de novo design, and conditional generation. Notably, UniGenX demonstrates significant improvements, especially in handling long sequences for complex structures, showcasing its efficacy as a versatile tool for scientific data generation. © 2025, CC BY.

关键词： Prediction models

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A Multimodal Myanmar Emotion Dataset for Emotion Recognition

A Multimodal Myanmar Emotion Dataset for Emotion Recognition

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Khin Pa Pa Aung Hao-Long Yin Tian-Fang Ma Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Shanghai Jiao Tong University RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Effective emotion recognition is vital for human interaction and has an impact on several fields such as psychology, social sciences, human-computer interaction, and emotional artificial intelligence. This study centers on the innovative contribution of a novel Myanmar emotion dataset to enhance emotion recognition technology in diverse cultural contexts. Our unique dataset is derived from a carefully designed emotion elicitation paradigm, using 15 video clips per session for three emotions (positive, neutral, and negative), with five clips per emotion. We collected electroencephalogram (EEG) signals and eye-tracking data from 20 subjects, and each subject took three sessions spaced over several days. Notably, all video clips used in experiments have been well rated by Myanmar citizens through the Self-Assessment Manikin scale. We validated the proposed dataset’s uniqueness using three baseline unimodal classification methods, alongside two traditional multimodal approaches and a deep multimodal approach (DCCA-AM) under subject-dependent and subject-independent settings. Unimodal classification achieved accuracies ranging from 62.57% to 77.05%, while multimodal fusion techniques achieved accuracies ranging from 75.43% to 87.91%. These results underscore the effectiveness of the models, and highlighting the value of our unique dataset for cross-cultural applications.

关键词： Emotion recognition Accuracy Biological system modeling Psychology Gaze tracking Brain modeling Distance measurement Electroencephalography Cultural differences engineering in medicine and biology

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Optimization of Memory March X Test Algorithm Based on Nuclear Safety Level DCS System Platform 1

引用

9th International Conference on Energy engineering and Environmental engineering, ICEEEE 2022

作者： Bai, Yongfei Wu, Zhiqiang Liu, Jie Jiang, Wei Zhong, Ke Han, Wenxing Chen, Zhi School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (数字)9783031302336

ISBN: (纸本)9783031302329

At present, the testing algorithm of nuclear power safety level DCS system for memory is GALPAT algorithm. This algorithm has a high time complexity and low fault coverage for memory. During the testing process, additional storage space is required, and the security of additional memory cannot be guaranteed. Based on the memory fault testing theory, this paper analyzes and studies the March X algorithm. It is found that the algorithm can not meet the coverage requirements for memory faults when applied to the nuclear power safety level DCS system. A March X+ algorithm is proposed. The algorithm can meet the index requirements of the nuclear power safety level DCS system for memory testing algorithms, and the fault detection rate reaches more than 95%, And it innovatively proposes to save the data to the register during the detection process, which does not require additional memory and improves the security. To verify the feasibility of the algorithm designed in this paper, the algorithm test time and functionality were verified in the main control PBIST module under a nuclear power safety level DCS system platform. The results show that the proposed test algorithm has certain advantages in test time and memory fault coverage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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A 3D semantic segmentation network for accurate neuronal soma segmentation

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Journal of Innovative Optical Health sciences 2025年第1期18卷 67-83页

作者： Li Ma Qi Zhong Yezi Wang Xiaoquan Yang Qian Du School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan 430074P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074 P.R.China Department of Electrical and Computer Engineering Mississippi State University Mississippi StateMS 39762USA

Neuronal soma segmentation plays a crucial role in neuroscience ***,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate *** this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this *** an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding *** MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale ***,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different *** proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed *** achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.

关键词： Neuronal soma segmentation semantic segmentation network multi-scale feature extraction adaptive weighting fusion

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Robin: A Novel Method to Produce Robust Interpreters for Dee...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zhen Li Ruqian Zhang Deqing Zou Ning Wang Yating Li Shouhuai Xu Chen Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security Department of Computer Science University of Colorado Colorado Springs USA Center for Research in Computer Vision University of Central Florida USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.

关键词：

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QoS-aware Dynamic Service Caching and Updating in Cost-efficient Multi-Access Edge Computing

QoS-aware Dynamic Service Caching and Updating in Cost-effic...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Shuaibing Lu Xin Jin Jie Wu Shuyang Zhou Shen Wu Jackson Yang Ran Yan College of Computer Science Beijing University of Technology Beijing China Center for Networked Computing Temple University USA School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost.

关键词： Costs Multi-access edge computing Q-learning Heuristic algorithms Memory management Quality of service Dynamic scheduling Dynamic programming Servers Optimization

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