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arXiv 2024年

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University 915 Partners Way RaleighNC27607 United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University 915 Partners Way RaleighNC27607 United States

Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods. © 2024, CC BY.

关键词： Failure modes

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic Computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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AN END-TO-END CHINESE TEXT NORMALIZATION MODEL BASED ON RULE-GUIDED FLAT-LATTICE TRANSFORMER

arXiv

引用

arXiv 2022年

作者： Dai, Wenlin Song, Changhe Li, Xiang Wu, Zhiyong Pan, Huashan Li, Xiulin Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong Technology Co. Ltd Beijing China

Text normalization, defined as a procedure transforming nonstandard words to spoken-form words, is crucial to the intelligibility of synthesized speech in text-to-speech system. Rule-based methods without considering context can not eliminate ambiguation, whereas sequence-to-sequence neural network based methods suffer from the unexpected and uninterpretable errors problem. Recently proposed hybrid system treats rule-based model and neural model as two cascaded sub-modules, where limited interaction capability makes neural network model cannot fully utilize expert knowledge contained in the rules. Inspired by Flat-LAttice Transformer (FLAT), we propose an end-to-end Chinese text normalization model, which accepts Chinese characters as direct input and integrates expert knowledge contained in rules into the neural network, both contribute to the superior performance of proposed model for the text normalization task. We also release a first publicly accessible large-scale dataset for Chinese text normalization. Our proposed model has achieved excellent results on this dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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Observation of transverse Thomson effect

arXiv

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arXiv 2025年

作者： Takahagi, Atsushi Hirai, Takamasa Alasli, Abdulkareem Park, Sang Jun Nagano, Hosei Uchida, Ken-Ichi Department of Mechanical Systems Engineering Nagoya University Nagoya464-8601 Japan National Institute for Materials Science Tsukuba305-0047 Japan Department of Advanced Materials Science Graduate School of Frontier Sciences The University of Tokyo Kashiwa277-8561 Japan

The thermoelectric Thomson effect, predicted in the 1850s by William Thomson, produces volumetric heating/cooling in a conductor due to the concerted action of the Seebeck and Peltier effects. Recently, transverse thermoelectrics studies on the Nernst and Ettingshausen effects have progressed rapidly to enable versatile thermal management technologies and to explore topological transport properties. However, a transverse Thomson effect, arising from the concerted action of the Nernst and Ettingshausen effects, has not yet been observed. Here, we report the observation of the transverse Thomson effect in a conductor. We observed volumetric heating/cooling in a semimetallic Bi88Sb12 alloy induced by a charge current, temperature gradient, and magnetic field applied orthogonally to each other using thermoelectric imaging techniques. We found that the heating/cooling can be switched by the field direction. Our experiments and analyses reveal the essential difference between the conventional and transverse Thomson effects;the former depends sorely on the temperature derivative of the Seebeck coefficient, while the latter depends not only on the temperature derivative of the Nernst coefficient but also on its magnitude. The observation of the transverse Thomson effect fills a missing piece in the history of thermoelectrics and provides a new principle for active thermal management technologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Seebeck coefficient

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Blockchain: A Review from the Perspective of Operations Researchers

Blockchain: A Review from the Perspective of Operations Rese...

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Simulation Winter Conference

作者： Hong Wan Kejun Li Yining Huang Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Operations Research Graduate Program North Carolina State University Raleigh NC USA

ISBN: (纸本)9781665476621

Blockchain is a distributed append-only digital ledger. The technology has caught much attention since the emergence of cryptocurrency, and there is an increasing number of blockchain applications in various businesses. The concept, however, is still novel to many members of the simulation and operations research community. In this tutorial, we introduce the blockchain technology and review its frontier related research. There are exciting opportunities for researchers in simulation, system analysis, and data science.

关键词： Analytical models Operations research Tutorials Data science Data models Blockchains Cryptocurrency

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Low-symmetry vacancy-related spin qubit in hexagonal boron nitride

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npj Computational Materials 2024年第1期10卷 1318-1326页

作者： Rohit Babar Gergely Barcza Anton Pershin Hyoju Park Oscar Bulancea Lindvall GergőThiering Örs Legeza Jamie H.Warner Igor A.Abrikosov Adam Gali Viktor Ivády HUN-REN Wigner Research Centre for Physics PO Box 49H-1525 BudapestHungary MTA-ELTE Lendület“Momentum”NewQubit Research Group Pázmány PéterSétány 1/A1117 BudapestHungary Department of Atomic Physics Insitute of PhysicsBudapest University of Technology and EconomicsMűegyetem rakpart 3.H-1111 BudapestHungary Walker Department of Mechanical Engineering and Materials Graduate Program Texas Materials InstituteThe University of Texas at AustinAustinTX78712USA Department of Physics Chemistry and BiologyLinköping UniversitySE-58183 LinköpingSweden Institute for Advanced Study Technical University of MunichLichtenbergstrasse 2a85748 GarchingGermany MTA-WFK Lendület“Momentum”Semiconductor Nanostructures Research Group PO Box 49H-1525 BudapestHungary Department of Physics of Complex Systems Eötvös Loránd UniversityEgyetem tér 1-3H-1053 BudapestHungary

Point defect qubits in semiconductors have demonstrated their outstanding capabilities for high spatial resolution sensing generating broad multidisciplinary *** boron nitride(hBN)hosting point defect qubits have recently opened up new horizons for quantum sensing by implementing sensing *** sensitivity of point defect sensors in hBN is currently limited by the linewidth of the magnetic resonance signal,which is broadened due to strong hyperfine couplings.

关键词： boron defect hexagonal

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Janus MoSSe Nanotubes on 1D SWCNT-BNNT van der Waals Heterostructure

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Small 2025年 e2412454页

作者： Yang, Chunxia Lin, Qingyun Sato, Yuta Gao, Yanlin Zheng, Yongjia Wang, Tianyu Ma, Yicheng Dai, Wanyu Li, Wenbin Maruyama, Mina Okada, Susumu Suenaga, Kazu Maruyama, Shigeo Xiang, Rong Department of Mechanical Engineering The University of Tokyo Tokyo113-8656 Japan Center of Electron Microscopy State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Material Science and Engineering Zhejiang University Hangzhou310027 China Tsukuba305-8565 Japan Department of Physics Graduate School of Science and Technology University of Tsukuba Tsukuba305-8571 Japan State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China School of Engineering Westlake University Hangzhou310030 China The University of Osaka Osaka567-0047 Japan

Two-dimensional (2D) Janus transition metal dichalcogenide (TMDC) layers with broken mirror symmetry exhibit giant Rashba splitting and unique excitonic behavior. For their one-dimensional (1D) counterparts, the Janus nanotubes possess curvature, which introduces an additional degree of freedom to break the structural symmetry. This can potentially enhance these effects or even give rise to novel properties. Moreover, Janus MSSe nanotubes (M = W, Mo), with diameters surpassing 40 Å and Se positioned externally consistently demonstrate lower energy states compared to their Janus monolayer counterparts. However, there are limited studies on the preparation of Janus nanotubes, due to the synthesis challenge and limited sample quality. In this study, we first synthesized MoS2 nanotubes on single-walled carbon nanotube (SWCNT) and boron nitride nanotube (BNNT) heterostructures and then explored the growth of Janus MoSSe nanotubes from MoS2 nanotubes at room temperature with the assistance of H2 plasma. The successful formation of the Janus structure is confirmed by Raman spectroscopy, and atomic structure and elemental distribution of the grown samples are further characterized by advanced electronic microscopy. The synthesis of Janus MoSSe nanotubes based on SWCNT-BNNT heterostructures paves the way for further exploration of novel properties in Janus TMDC nanotubes. © 2025 The Author(s). Small published by Wiley-VCH GmbH.

关键词： Van der Waals forces

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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Development of Machine Learning-Based Assessment System for Laparoscopic Surgical Skills Using Motion-Capture

Development of Machine Learning-Based Assessment System for ...

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IEEE/SICE International Symposium on System Integration

作者： Koki Ebina Takashige Abe Lingbo Yan Kiyohiko Hotta Madoka Highuchi Naoya Iwahara Jun Furumido Masafumi Kon Sachiyo Murai Yo Kurashima Shunsuke Komizunai Teppei Tsujita Kazuya Sase Xiaoshuai Chen Taku Senoo Nobuo Shinohara Atsushi Konno Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Department of Renal and Genitourinary surgery Faculty of Medicine Hokkaido University Sapporo Japan Clinical simulation center Faculty of Medicine Hokkaido University Sapporo Japan Faculty of Engineering and Design Kagawa University Takamatsu Japan Department of Mechanical Engineering National Defense Academy of Japan Yokosuka Japan Department of Mechanical Engineering and Intelligent Systems Tohoku Gakuin University Sendai Japan Graduate School of Science and Technology Hirosaki University Hi-rosaki Japan

Laparoscopic surgery is a widely used surgical technique, on the other hand, its high degree of difficulty makes it difficult for beginners to learn the technique efficiently. In addition, recent working hour restrictions and shortages of surgeons have resulted in insufficient training time, and establishing the efficiently training methods is becoming urgent needs. Therefore, to promote the skill proficiency of novice surgeons, machine learning-based assessment system for laparo-scopic surgical skills was developed. A measurement system with a simple configuration was introduced so that trainees can easily use it alone. Alternatively, the indices related to the opening ratio and the rotation angle of surgical instruments, which were measured in the authors' previous study were no longer available. Therefore, comparative experiments were conducted to verify the effect of the lack of indices related to these data on the accuracy of skill evaluation. Based on the measurement data of 104 wet lab trainings measured in the previous study, a machine learning model that evaluates surgeon's skill at 3 levels based on the number of surgical experiences, and global operative assessment of laparoscopic skills (GOALS) which is a type of surgical skill evaluation index were established. By using the explainable AI method, this system can present the skill evaluation result including its basis to the trainee. Since the developed system can be easily operated by a GUI-based program, trainees can confirm the quantitative evaluation result on-site immediately after the training.

关键词：

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Non-Negative Tensor Factorization of Infant Spontaneous Movements: A Pilot Study for ASD Risk Evaluation of Newborn Infants

Non-Negative Tensor Factorization of Infant Spontaneous Move...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Yonei, Rikuya Furui, Akira Doi, Hirokazu Shimatani, Koji Hayashi, Hideaki Yamamoto, Midori Sakurai, Kenichi Mori, Chisato Tsuji, Toshio Hiroshima University Graduate School of Advanced Science and Engineering 1-4-1 Kagamiyama Hiroshima Higashi-Hiroshima739-8527 Japan Nagaoka University of Technology Department of Information and Management Systems Engineering 1603-1 Kamitomioka Niigata Nagaoka940-2188 Japan Prefectural University of Hiroshima Faculty of Health and Welfare 1-1 Gakuen-machi Hiroshima Mihara723-0053 Japan Osaka University Institute for Datability Science 2-8 Yamadaoka Osaka Suita565-0871 Japan Chiba University Center for Preventive Medical Sciences Department of Nutrition and Metabolic Medicine 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Chiba University Center for Preventive Medical Sciences Department of Sustainable Health Science 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Chiba University Graduate School of Medicine Department of Bioenvironmental Medicine 1-8-1 Inohana Chuo-ku Chiba260-8670 Japan

ISBN: (纸本)9798331531614

Early detection of infants with autism spectrum disorder (ASD) can lead to effective developmental support. In clinical practice, early screening of 18-month-old infants is implemented using a parent-completed questionnaire. However, research has suggested that signs of ASD may also appear in movement characteristics during the first few months of age. In this paper, we propose a method to evaluate infant movement from videos based on non-negative tensor factorization (NTF) and apply it to ASD risk assessment in the neonatal period. The proposed method applies NTF to pose data estimated from videos, and decomposes the infant's movements into multiple components while considering the linkage between body parts. In the experiment, we evaluated the effectiveness of the proposed method using 36 low-risk infants and 13 high-risk infants for ASD, with the aim of applying the method to ASD risk assessment. The results showed that the proposed method captured the tendency for infants to perform different movements depending on their risk level. Machine learning analysis revealed that the proposed method identified ASD risk with an accuracy exceeding 70%, which was comparable or superior to the existing video evaluation method based on heuristically designed indicators. © 2025 IEEE.

关键词： Non-negative matrix factorization

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