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computer science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Noppasorn Sutthibutr Kunihiko Hiraishi Navee Chiadamrong Suttipong Thajchayapong School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology (SIIT) Thammasat University Pathum Thani Thailand School of Information Science Japan Advanced Institute of Science and Technology (JAIST) Ishikawa Japan Data Science and Analytics Research National Electronics and Computer Technology Center Pathum Thani Thailand

This study proposes an integrated fuzzy optimization approach for sustainable supply chain planning subjected to sustainability and uncertain environments. The proposed approach combines the concept of Chance-Constrained Programming (CCP) with Intuitionistic Fuzzy Linear Programming (IFLP) that decision makers can control risk violation of fuzzy constraints as well as consider satisfaction and non-satisfaction levels simultaneously. The effectiveness of the proposed approach is demonstrated through a case study by minimizing total costs and maximizing supplier's social and environmental scores where costs and customer demands are imprecise. Our comparative result clearly shows that the obtained solution outperforms the result from the traditional integrated fuzzy optimization approach. The advantages of the proposed approach can assist decision-makers to prepare and take necessary actions for future uncertainty, leading to sustainability.

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T cell epitopes in SARS-CoV-2 proteins are substantially conserved in the Omicron variant

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Cellular & Molecular Immunology 2022年第3期19卷 447-448页

作者： Seong Jin Choi Dong-Uk Kim Ji Yun Noh Sangwoo Kim Su-Hyung Park Hye Won Jeong Eui-Cheol Shin Division of Infectious Disease Department of Internal MedicineSeoul National University HospitalSeoulRepublic of Korea Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology(KAIST)DaejeonRepublic of Korea Division of Infectious Diseases Department of Internal MedicineKorea University College of MedicineSeoulRepublic of Korea Department of Biomedical Systems Informatics Graduate School of Medical ScienceBrain Korea 21 ProjectYonsei University College of MedicineSeoulRepublic of Korea Department of Internal Medicine Chungbuk National University College of MedicineCheongjuRepublic of Korea The Center for Viral Immunology Korea Virus Research InstituteInstitute for Basic Science(IBS)DaejeonRepublic of Korea

In November 2021,the Omicron variant(B.1.1.529)emerged and was designated a variant of concern(VOC)by the World Health ***,Omicron was reported to extensively escape neutralizing antibodies elicited by COVID-19 vaccination or natural infection[1,2,3].However,whether Omicron evades the T cell immunity elicited by COVID-19 vaccination or natural infection remains to be *** address this issue,we analyzed the amino acid sequences of T cell epitopes identified from the original SARS-CoV-2 strain(Wuhan-Hu-1)in the Omicron variant(hCoV-19/South Africa/CERI-KRISP-K032284/2021).

关键词： neutral conserved elucidated

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Superconducting neutron transmission imaging for investigating a sequential change in phase separations of low-melting Wood's metal 35

Superconducting neutron transmission imaging for investigati...

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35th International Symposium on Superconductivity, ISS 2022

作者： Dang Vu, The Shishido, Hiroaki Aizawa, Kazuya Oku, Takayuki Oikawa, Kenichi Harada, Masahide Kojima, Kenji M. Miyajima, Shigeyuki Soyama, Kazuhiko Koyama, Tomio Hidaka, Mutsuo Suzuki, Soh Y. Tanaka, Manobu M. Machida, Masahiko Kawamata, Shuichi Ishida, Takekazu Division of Quantum and Radiation Engineering Osaka Metropolitan University Sakai Osaka599-8570 Japan Materials and Life Science Division J-PARC Center Japan Atomic Energy Agency Tokai Ibaraki319-1195 Japan Department of Physics and Electronics Graduate School of Engineering Osaka Metropolitan University Sakai Osaka599-8531 Japan Centre for Molecular and Materials Science TRIUMF 4004 Wesbrook Mall VancouverBCV6T 2A3 Canada Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Advanced Industrial Science and Technology Ibaraki Tsukuba305-8568 Japan Ibaraki Tsukuba305-0801 Japan Ibaraki Tsukuba305-0801 Japan Center for Computational Science & E-Systems Japan Atomic Energy Agency 178-4-4 Wakashiba Chiba Kashiwa277-0871 Japan

We built a new cryostat system for a current-biased kinetic inductance detector (CB-KID) to be able to place a sample at room temperature for conducting the neutron transmission imaging experiments with pulsed neutrons. A Wood's metal alloy sample (Bi 50wt.%, Pb 25wt.%, Sn 12.5wt.%, Cd 12.5wt.%) of melting temperature 75.2°C was used to check a practical test by observing fine microstructures under beam power of 812 kW at beamline BL10 of J-PARC (MLF). The Wood's metal is composed of four phases, of which one is a Cd-rich needle-like phase of an average width of 25 μm and length of 5 mm. Since Cd is a strong neutron absorber, it is suitable for observing the fine mosaic structure contrastingly in neutron transmission imaging. After neutron-transmission imaging with the Wood's metal in the initial state, we subsequently melted the Wood's metal sample and solidified it again by slow cooling during the same beam time. We were successful in observing an impressive change in morphology of the phases by neutron-transmission imaging. Room-temperature sample imaging is very convenient for possible users, and is expected to be very useful for applying our CB-KID system to versatile different materials of interests. © Published under licence by IOP Publishing Ltd.

关键词： Wood

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MetaUE: Model-based Meta-learning for Underwater Image Enhancement

arXiv

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arXiv 2023年

作者： Zhang, Zhenwei Yan, Haorui Tang, Ke Duan, Yuping Center for Applied Mathematics Tianjin University Tianjin300072 China The Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

The challenges in recovering underwater images are the presence of diverse degradation factors and the lack of ground truth images. Although synthetic underwater image pairs can be used to overcome the problem of inadequately observing data, it may result in over-fitting and enhancement degradation. This paper proposes a model-based deep learning method for restoring clean images under various underwater scenarios, which exhibits good interpretability and generalization ability. More specifically, we build up a multi-variable convolutional neural network model to estimate the clean image, background light and transmission map, respectively. An efficient loss function is also designed to closely integrate the variables based on the underwater image model. The meta-learning strategy is used to obtain a pre-trained model on the synthetic underwater dataset, which contains different types of degradation to cover the various underwater environments. The pre-trained model is then fine-tuned on real underwater datasets to obtain a reliable underwater image enhancement model, called MetaUE. Numerical experiments demonstrate that the pre-trained model has good generalization ability, allowing it to remove the color degradation for various underwater attenuation images such as blue, green and yellow, etc. The fine-tuning makes the model able to adapt to different underwater datasets, the enhancement results of which outperform the state-of-the-art underwater image restoration methods. All our codes and data are available at https://***/Duanlab123/MetaUE. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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IoT Based Health Monitoring System with AI-Powered Disease Prediction

IoT Based Health Monitoring System with AI-Powered Disease P...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： S. Radha Mahendran Mohammad Shahbaz Khan Hari Priya M P Ahmed J. Obaid Karrar Shareef Mohsen Ashraf Mohammed Shareef Department of Bioinformatics School of Life Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India operations and regulatory affairs Children's National Hospital Washington DC USA Department of Computer Science Kristu Jayanti College (Autonomous) Bangalore Karnataka India Najaf Iraq Department of Computer Technical Engineering Faculty of Computer Science and Mathematics University of Kufa Technical Engineering College Al-Ayen University Thi-Qar Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq National University of Science and Technology DhiQar Iraq

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Healthcare monitoring systems have advanced significantly in emergency rooms and many other health settings. Today, many nations are deeply concerned about the rise of small healthcare monitoring systems. In-person advising and telemedicine are two different ways that the Internet of Things (IoT) is further developing healthcare. The Internet of Things makes things and individuals reliably available, and its relationship with the cloud improves our personal satisfaction. Proactive clinical exploration can help change a responsive healthcare approach into a proactive one, as man-made reasoning (AI) and high-level human insight become increasingly more coordinated into the healthcare area. Within the field of machine learning, deep learning holds significant potential in precisely and swiftly analysing vast quantities of data, producing perceptive experiences, and resolving intricate issues. Preventive treatment and early intervention for persons who are at risk depend on accurate and useful illness prediction. The constant inflow of electronic medical information necessitates the need for more accurate prediction models in recurrent neural network versions of deep learning that handle successive time-series data. Devices connected to the Internet of Things provide data to the proposed system, and future analysis will be conducted on cloud-stored electronic clinical records pertaining to patient histories.

关键词： Deep learning Cloud computing Accuracy Telemedicine Medical services Internet of Things Reliability

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LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers

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Journal of Modern Power systems and Clean Energy 2021年第5期9卷 1205-1216页

作者： Huayanran Zhou Yihong Zhou Junjie Hu Guangya Yang Dongliang Xie Yusheng Xue Lars Nordström State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Center for Electric Power and Energy Technical University of DenmarkLyngbyDenmark State Grid Electric Power Research Institute NanjingChina Division of Electric Power and Energy Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden

As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management ***,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online *** address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building *** the proposed system control structure,the LSTM algorithm can be separated into offline and online *** the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network *** the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM *** simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

关键词： Building energy management system(BEMS) electric vehicle(EV) long short-term memory(LSTM) recurrent neural network machine learning prosumer

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Axion optical induction of antiferromagnetic order

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Nature Materials 2023年第5期22卷 583-590页

作者： Qiu, Jian-Xiang Tzschaschel, Christian Ahn, Junyeong Gao, Anyuan Li, Houchen Zhang, Xin-Yue Ghosh, Barun Hu, Chaowei Wang, Yu-Xuan Liu, Yu-Fei Bérubé, Damien Dinh, Thao Gong, Zhenhao Lien, Shang-Wei Ho, Sheng-Chin Singh, Bahadur Watanabe, Kenji Taniguchi, Takashi Bell, David C. Lu, Hai-Zhou Bansil, Arun Lin, Hsin Chang, Tay-Rong Zhou, Brian B. Ma, Qiong Vishwanath, Ashvin Ni, Ni Xu, Su-Yang Department of Chemistry and Chemical Biology Harvard University CambridgeMA United States Department of Physics Harvard University CambridgeMA United States Department of Physics Boston College Chestnut HillMA United States Department of Physics Northeastern University BostonMA United States Department of Physics and Astronomy and California NanoSystems Institute University of California Los AngelesCA United States Shenzhen China Shenzhen China Shenzhen Key Laboratory of Quantum Science and Engineering Shenzhen China International Quantum Academy Shenzhen China Department of Physics National Cheng Kung University Tainan Taiwan Tainan Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan Department of Condensed Matter Physics and Materials Science Tata Institute of Fundamental Research Mumbai India Research Center for Functional Materials National Institute for Materials Science Tsukuba Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba Japan Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Center for Nanoscale Systems Harvard University CambridgeMA United States Institute of Physics Academia Sinica Taipei Taiwan Canadian Institute for Advanced Research Toronto Canada

Using circularly polarized light to control quantum matter is a highly intriguing topic in physics, chemistry and biology. Previous studies have demonstrated helicity-dependent optical control of chirality and magnetization, with important implications in asymmetric synthesis in chemistry;homochirality in biomolecules;and ferromagnetic spintronics. We report the surprising observation of helicity-dependent optical control of fully compensated antiferromagnetic order in two-dimensional even-layered MnBi2Te4, a topological axion insulator with neither chirality nor magnetization. To understand this control, we study an antiferromagnetic circular dichroism, which appears only in reflection but is absent in transmission. We show that the optical control and circular dichroism both arise from the optical axion electrodynamics. Our axion induction provides the possibility to optically control a family of PT-symmetric antiferromagnets (P, inversion;T, time-reversal) such as Cr2O3, even-layered CrI3 and possibly the pseudo-gap state in cuprates. In MnBi2Te4, this further opens the door for optical writing of a dissipationless circuit formed by topological edge states. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Dichroism

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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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Context-aware Emotion Recognition Based on Vision-Language Pre-trained Model

Context-aware Emotion Recognition Based on Vision-Language P...

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International Conference on advanced Robotics and Mechatronics (ICARM)

作者： XingLin Li Xinde Li Chuanfei Hu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Given the difficulty of recognizing ambiguous emotions in facial expression recognition tasks, we propose a visual-language model named CAER-CLIP to address this challenge. The proposed CAER-CLIP standed for Context-Aware Emotion Recognition (CAER), and were incorporated structure of the Contrastive Language–Image Pre-training (CLIP) model as promising alternative to classifier. There are two parts in CAER-CLIP model. In the visual part, facial expressions and contextual information of the image are simultaneously extracted to obtain the final feature embeddings, which are then used as a learnable “class” token for text-image pairing with desired module. In the textual part, we use text labels for emotion recognition classes as input. The outputs were merged to participate the comparative study to generated parameters of the model. The experiments demonstrate the effectiveness of the proposed method and show that our CAER-CLIP outperforms the state-of-the-art results on the CAER benchmark. The ablation experiment verified the effectiveness of both the classifier-based and text-based (ours without classifier) models, demonstrating that our method with the CAER-CLIP structure performed better, and the incorporation of a text encoder in the deep network model architecture effectively enhancing recognition accuracy.

关键词： Training Emotion recognition Visualization Accuracy Text recognition Face recognition Benchmark testing Feature extraction Data mining Context modeling

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Deep Reinforcement Learning Based Scheduling Strategy in Blockchain Payment Channel Networks

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IEEE/ACM Transactions on Networking 2024年

作者： Ren, Zhe Wang, Zihao Li, Xinghua Miao, Yinbin Li, Zhuowen Liu, Ximeng Han, Lei Deng, Robert H. Xidian University State Key Laboratory of Integrated Services Networks The School of Cyber Engineering Xi'an710071 China Ministry of Education Engineering Research Center of Big Data Security Xi'an710071 China Fuzhou University College of Mathematics and Computer Science Fuzhou350108 China Fuzhou University Fujian Provincial Key Laboratory of Information Security of Network Systems Fuzhou350116 China Beijing Institute of Computer Technology and Application Beijing100000 China Singapore Management University School of Information Systems Singapore178902 Singapore

With the popularity of blockchains, low transaction throughput has become a significant bottleneck in applications such as cryptocurrencies. Payment channel networks (PCNs) have received attention as a way to improve throughput. However, due to the difficulty of predicting future transactions for nodes, the transactions are prone to failure when the channel balances do not meet required conditions. It has been shown that increasing buffers (queues) in PCNs can increase the success rate of transactions and throughput. Nevertheless, there is no effective transaction scheduling strategy in buffers when transaction values are flexible and variable. To solve this problem, we first formulate the Scheduling Problem in PCNs (named PSP), and then prove it is NP-hard. We design a neural network solver based on the Sequence to Sequence (Seq2Seq) architecture and train the solver using the reinforcement learning method. With the solver, we first give two scheduling strategies to maximize transaction throughput, and then design a PCN simulator for performance evaluation. Extensive experiments are conducted to show the superiority and various performances of our proposal and illustrate that our proposal can get a significant advantage in terms of the transaction throughput compared to the existing works. © 2024 IEEE.

关键词： Deep reinforcement learning

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