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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.

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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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Digital Forensic Investigation Framework for Marine Industry

Digital Forensic Investigation Framework for Marine Industry

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Vihara Fernando Amila Senarathne Department of Computer Systems Engineering Faculty of Graduate Studies and Research Sri Lanka Institute of Information Technology Malabe Sri Lanka

A massive 400% increase of cyber-attacks was reported in the Marine industry since February 2020 up to date. Experiments and research are being conducted towards safeguarding data in various technological aspects of the marine environment with regards to navigation, transportation and system management. At present, generic investigation processes are developed for computer incidents and forensic readiness in this sector, but those do not describe the investigation process precisely. Although there are incident analysis technologies implemented for the Marine environment, it lacks reliable digital forensic tools for evidence extraction and analysis. Therefore, in this research, a comprehensive digital forensic investigation framework is proposed, answering the issues of existing frameworks, and a tool is implemented for the examination of navigational evidence. Forensic procedures of existing investigation methodologies were taken into consideration when developing the fusion framework. The Marine Geo Navigator was implemented with the technologies of Machine Learning and Visualization to evaluate geolocations and paths of vessels. Through expert analysis, the proposed investigation framework was verified as a suitable process to be utilized in the Marine industry and the tool was validated resulting an acceptable level of accuracy.

关键词： Industries Wireless communication Root cause analysis Navigation Geology Digital forensics Transportation

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DiffDVC: Accurate Event Detection for Dense Video Captioning via Diffusion Models 39

DiffDVC: Accurate Event Detection for Dense Video Captioning...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Wei Niu, Jianwei Liu, Xuefeng Wang, Zhendong Tang, Shaojie Zhu, Guogang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology Zhengzhou University Zhengzhou China Department of Management Science and Systems University at Buffalo Buffalo NY United States

ISBN: (纸本)157735897X

Dense video captioning (DVC) aims to describe multiple events within a video, and its performance is greatly affected by the accuracy of video event detection. Video event detection involves predicting the proposal boundaries (start and end times) and the classification score of each event in a video. Recently, a few methods have applied diffusion models originally designed for image object detection to detect events in DVC. These methods add noise to the ground-truth event proposal boundaries, and subsequently learn the denoising process. However, these methods often overlook the fundamental differences between videos and images. We observe that, whereas in images the important information for object classification is normally around the boundaries of the ground-truth boxes, in videos the key information for event classification is typically centered in the middle of ground-truth event proposals. As a result, the classification module in these existing diffusion models becomes insensitive to boundary changes introduced by the added noise, leading to suboptimal performance. This paper introduces DiffDVC, an innovative diffusion model for DVC. The core of DiffDVC is a boundary-sensitive detector. The detector increases the sensitivity of the classification module to boundary changes by focusing on frames within a specific range around the start and end times of noisy event proposals. Additionally, this range is dynamically adjusted to suit different event proposals. Comprehensive experiments on ActivityNet-1.3, ActivityNet Captions, and YouCook2 datasets show DiffDVC achieving superior performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 912-927页

作者： Alexandra J.Lee Dallas L.Mould Jake Crawford Dongbo Hu Rani K.Powers Georgia Doing James C.Costello Deborah A.Hogan Casey S.Greene Genomics and Computational Biology Graduate Program University of PennsylvaniaPhiladelphiaPA 19104USA Department of Microbiology and Immunology Geisel School of Medicine at DartmouthHanoverNH 03755USA Department of Systems Pharmacology and Translational Therapeutics University of PennsylvaniaPhiladelphiaPA 19104USA Wyss Institute for Biologically Inspired Engineering Harvard UniversityBostonMA 02115USA Department of Pharmacology University of Colorado School of MedicineDenverCO 80045USA Center for Health AI University of Colorado School of MedicineDenverCO 80045USA Department of Biochemistry and Molecular Genetics University of Colorado School of MedicineDenverCO 80045USA

Genome-wide transcriptome profiling identifies genes that are prone to differential expression(DE)across contexts,as well as genes with changes specific to the experimental *** genes that are specifically changed in a context of interest from common differentially expressed genes(DEGs)allows more efficient prediction of which genes are specific to a given biological process under ***,common DEGs or pathways can only be identified through the laborious manual curation of experiments,an inordinately time-consuming *** we pioneer an approach,Specific cOntext Pattern Highlighting In Expression data(SOPHIE),for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be *** apply SOPHIE to diverse datasets including those from human,human cancer,and bacterial pathogen Pseudomonas *** identifies common DEGs in concordance with previously described,manually and systematically determined common *** molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional ***’s measure of specificity can complement log2 fold change values generated from traditional DE *** example,by filtering the set of DEGs,one can identify genes that are specifically relevant to the experimental condition of ***,these results can inform future research *** scripts used in these analyses are available at https://***/greenelab/*** can access https://***/greenelab/sophie to run SOPHIE on their own data.

关键词： Neural network Transcriptomics Software Differential expression analysis Machine learning

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS

arXiv

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arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data. © 2022, CC BY-NC-SA.

关键词： Machine learning

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