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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Dong, Tian Dai, Tianxiang Chen, Ye Liu, Yueyang Liu, Hancheng Wang, Yiting Ma, Anqi Hu, Haifeng Xu, Lihua Zhao, Le Chu, Weiguo Peng, Chao Wang, Jianwei Li, Yang State Key Laboratory of Precision Measurement Technology and Instrument Department of Precision Instrument Tsinghua University Beijing100084 China State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics and Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Nanofabrication Laboratory National Center for Nanoscience and Technology Beijing100190 China

We report flexible zero-index waveguides and devices whose loss is two orders of magnitude lower than the state of the art, enabling phase-error-free high-dense photonic integrated circuits for classical and quantum i... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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Low-loss Zero-index Waveguides and Devices

Low-loss Zero-index Waveguides and Devices

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Dong, Tian Dai, Tianxiang Chen, Ye Liu, Yueyang Liu, Hancheng Wang, Yiting Ma, Anqi Hu, Haifeng Xu, Lihua Zhao, Le Chu, Weiguo Peng, Chao Wang, Jianwei Li, Yang State Key Laboratory of Precision Measurement Technology and Instrument Department of Precision Instrument Tsinghua University Beijing100084 China State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Nanofabrication Laboratory National Center for Nanoscience and Technology Beijing100190 China

关键词： Photonic integrated circuits

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Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

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CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing 23

CurvDrop: A Ricci Curvature Based Approach to Prevent Graph ...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Liu, Yang Zhou, Chuan Pan, Shirui Wu, Jia Li, Zhao Chen, Hongyang Zhang, Peng Academy of Mathematics and Systems Science Chinese Academy of Sciences China Amss Chinese Academy of Science School of Cyber Security University of Chinese Academy of Science China School of Information and Communication Technology Griffith University Australia School of Computing Macquarie University Australia Hangzhou link2do Technology China Research Center for Graph Computing Zhejiang Lab China Cyberspace Institute of Advanced Technology Guangzhou University China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) are powerful models to handle graph data and can achieve state-of-the-art in many critical tasks including node classification and link prediction. However, existing graph neural networks still face both challenges of over-smoothing and over-squashing based on previous literature. To this end, we propose a new Curvature-based topology-aware Dropout sampling technique named CurvDrop, in which we integrate the Discrete Ricci Curvature into graph neural networks to enable more expressive graph models. Also, this work can improve graph neural networks by quantifying connections in graphs and using structural information such as community structures in graphs. As a result, our method can tackle the both challenges of over-smoothing and over-squashing with theoretical justification. Also, numerous experiments on public datasets show the effectiveness and robustness of our proposed method. The code and data are released in https://***/liu-yang-maker/Curvature-based-Dropout. © 2023 ACM.

关键词： Graph neural networks

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Improving First Responder Preparedness in Indoor Evacuation through advanced Numerical Simulation Techniques

Improving First Responder Preparedness in Indoor Evacuation ...

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Computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： Asep Id Hadiana Safiza Suhana Kamal Baharin Zahriah Othman Eddie Krishna Putra Center of Advanced Computing Technology (CACT) Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Department of Informatics Universitas Jenderal Achmad Yani Cimahi Indonesia

This research explores the use of advanced numerical simulation techniques, specifically PyroSim and Pathfinder, to enhance the preparedness of first responders in indoor evacuation scenarios. A six-floor building with two staircases was simulated during a fire emergency, providing valuable insights into the effectiveness of building design, evacuation procedures, and occupant behavior during an emergency. The analysis of the data revealed significant variations in response times between different floors and staircases, emphasizing the need for effective communication and coordination among first responders. The simulation identified potential areas for improvement, including increasing occupant awareness of evacuation procedures and improving smoke detection and evacuation measures. The article also examines the strengths and limitations of the numerical simulation approach and suggests future research directions such as the need for further validation of simulation models and the integration of real-time data into evacuation modeling techniques. Overall, the study demonstrates the potential benefits of using advanced numerical simulation techniques for improving first responder preparedness and enhancing evacuation planning. The insights gained from this study can help building managers and first responders make informed decisions to create safer and more resilient communities.

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Experimental demonstration of a quantum downstream access network in continuous variable quantum key distribution with a local local oscillator

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Photonics Research 2024年第6期12卷 1262-1273页

作者： DENGKE QI XIANGYU WANG ZHENGHUA LI JIAYU MA ZIYANG CHEN YUEMING LU SONG YU State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service(MoE) Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronicsand Center for Quantum Information TechnologyPeking UniversityBeijing 100871China

Quantum networks provide opportunities and challenges across a range of intellectual and technical frontiers,including quantum computation,communication,and *** traditional communication networks,quantum networks utilize quantum bits rather than classical bits to store and transmit *** key distribution(QKD)relying on the principles of quantum mechanics is a key component in quantum networks and enables two parties to produce a shared random secret key,thereby ensuring the security of data *** this work,we propose a cost-effective quantum downstream access network structure in which each user can get their corresponding key information through terminal *** on this structure,we demonstrate the first four-end-users quantum downstream access network in continuous variable QKD with a local local *** contrast to point-to-point continuous variable QKD,the network architecture reevaluates the security of each user and accounts for it accordingly,and each user has a lower tolerance for excess noise as the overall network expands with more ***,the feasibility of the experiment is based on the analysis of the theoretical model,noise analysis,and multiple techniques such as the particle filter and adaptive equalization algorithm used to suppress excess *** results show that each user can get a low level of excess noise and can achieve secret key rates of 546 kbps,535 kbps,522.5 kbps,and 512.5 kbps under a transmission distance of 10 km,respectively,with the finite-size block of 1×10~*** not only verifies the good performance but also provides the foundation for the future multi-user quantum downstream access networks.

关键词： quantum oscillator local

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Quantum and Classical communications in Shared Optical Fibers: Teleportation and Beyond

Quantum and Classical Communications in Shared Optical Fiber...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Thomas, Jordan M. Yeh, Fei I. Chen, Jim Hao Mambretti, Joe J. Kohlert, Scott J. Kanter, Gregory S. Kumar, Prem Center for Photonic Communication and Computing Department of Electrical and Computer Engineering Northwestern University 2145 Sheridan Road EvanstonIL60208 United States International Center for Advanced Internet Research Northwestern University 750 N. Lake Shore Drive ChicagoIL60611 United States Ciena Corporation 7035 Ridge Road HanoverMD21076 United States NuCrypt LLC 1460 Renaissance Drive Suite 205 Park RidgeIL60068 United States

We discuss recent experimental progress in quantum teleportation systems operating in the same fibers as high-rate classical communications. We evaluate methods for optimizing teleportation fidelity in the presence of... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical fibers Parametric down conversion Photonic entanglement Quantum communications Quantum teleportation Raman scattering

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WMRE: Enhancing Distant Supervised Relation Extraction with Word-level Multi-instance Learning and Multi-hierarchical Feature

WMRE: Enhancing Distant Supervised Relation Extraction with ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiang Ying Xiangchuan Xie Tianyi Xu Yue Zhao Zechen Meng Mankun Zhao College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of New Media and Communication Tianjin University Tianjin China Information and Network Center Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Distant supervised relation extraction (DSRE) obtains large amounts of data cost-effectively by aligning knowledge base with natural texts but also brings noisy data. Existing methods deal with noise through multi-instance learning (MIL) with attention. However, these approaches typically use attention at sentence-level and above, while ignoring word-level information. Intuitively, words in the same sentence are also of different importance. However, effective methods for distinguishing word-level importance differences are still lacking. Because multiplying with weighted attention leads word embeddings to be shifted in vector space. Therefore, we proposed WMRE. Specifically, WMRE concatenates the embeddings of multiple sentences. Then it uses filtering attention to remove low-importance words at different levels to extract multi-hierarchical features. In WMRE, words are the smallest units of mining information, and filtering attention distinguishes word-level importance differences while avoiding the embedding shifts. Extensive experiments on two widely used datasets show that WMRE fully utilizes word-level information and achieves state-of-the-art (SOTA) on both datasets.

关键词： Filtering Noise Knowledge based systems Feature extraction Vectors Acoustics Data mining Noise measurement Speech processing

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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