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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

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

关键词： Photonic integrated circuits

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of Computer Science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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data Acquisition and Monitoring Scheme for Satellite Network with Time-Varying Topology

Data Acquisition and Monitoring Scheme for Satellite Network...

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Satellite computing (Satellite), IEEE International Conference on

作者： Ying Zhang Tianyu Wo Guangjian Wang Tianyu Ye Jiwei Zhang Xinye Liu Xiao Feng Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University China Electronics Technology Taiji Group Corporation Limited Beijing China

As human beings further explore space, satellite systems have become an indispensable tool for human society. However, managing satellite networks, especially in edge cloud computing scenarios, presents unique challenges due to its time-varying nature. To address these challenges, we propose a novel data acquisition and monitoring scheme specifically designed for satellite network environments. Our scheme leverages eBPF (extended Berkeley Packet Filter) technology for real-time monitoring data collection and utilizes a spatial-temporal graph representation to capture the dynamic topology of the satellite network. The Max-Min Fair (MMF) algorithm is employed to compute efficient routing paths, while data preprocessing techniques are applied to alleviate data transmission pressure. The scheme is designed to support user-defined monitoring targets and enables active detection and monitoring of application processes based on specific requirements. To evaluate the effectiveness of our proposed scheme, we conduct a comparative analysis against the Shortest Path on the Spatial-Temporal graph (SPST) algorithm and the Energy-Aware Muitipath (EAMP) algorithm. Experimental results demonstrate the superiority of the MMF algorithm, exhibiting a significant 32.6% and 21.5% improvement in data transmission volume compared to the SPST and EAMP algorithms, respectively. Moreover, the utilization of the MMF algorithm reduces the average satellite bandwidth utilization by 37.1 % and 41.2% throughout the transmission cycle, surpassing the performance of the SPST and EAMP alogrithms.

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Driving Capability, a Unified Driver Model for ADAS

Driving Capability, a Unified Driver Model for ADAS

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2021 International Conference on advanced Technologies and Applications of Modern Industry, ATAMI 2021

作者： Sun, Bohua Zhai, Yang Li, Yaxin Deng, Weiwen Zhao, Shuai State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China Co. Ltd. Tianjin China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

To allocate driving privilege in a reasonable way in shared control for intelligent vehicle, the study on driving capability, the unified driver model for ADAS in the longitudinal and lateral scenarios was proposed, which can improve the safety and comfort for intelligent vehicles as well. Driving capability is defined and analyzed and car-following stimulate in longitudinal scenario and moving double lane change stimulate in lateral scenario were designed. data collection was conducted in Driver-In-the-Loop Intelligent Simulation Platform (DILISP). Driving capability identification model was established basing on Hammerstein process and Principal Component Analysis (PCA) was used to decouple and reduce the dimension for the key parameters in Hammerstein identification model. The classification is done basing on the particle clustering algorithm and the evaluation equation for driving capability was calculated by Multiple Linear Regression (MLR). Results show that the proposed evaluation method for driving capability in the longitudinal and lateral scenarios can achieve accurate and reliable evaluation results. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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Optimization Strategies for data Placement in Satellite Cloud-oriented Distributed File Systems

Optimization Strategies for Data Placement in Satellite Clou...

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Ying Zhang Tianyu Wo Kun Fan Tianyu Ye Jiwei Zhang Junhua Zhang Xiao Feng Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University China Electronics Technology Taiji Group Corporation Limited Beijing China

Forming a collaborative computing network among the deployed satellites in space can process data rapidly and reduce data transmission delay by leveraging the communication, storage, and computing capacities of the satellites. Due to the dynamics of satellite orbits, the distance between satellites varies over time, bringing new challenges to the designing of dedicated distributed file systems for collaborative satellites. Traditional distributed file systems did not consider the network topology and dynamics of satellite clouds and the limited computing resources of satellite node cores. In view of the torus network topology of satellite clouds, we design a highly available distributed metadata management mechanism for satellite clouds to ensure efficient metadata reading and reliability. On this basis, a topology-aware replica placement strategy is proposed to minimize communication costs and energy consumption for replica placement. Based on the metadata strategy and replica placement strategy mentioned above, we design and implement a distributed file system for satellite clouds. Experimental results demonstrate that our proposed placement strategy can improve the data transmission performance of the replica placement by more than double compared to the random placement strategy.

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Progress and challenges of cerebrovascular endothelial cells research promoted by single-cell transcriptome sequencing technology

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Journal of Translational Neuroscience 2024年第2期9卷 32-41页

作者： Yakun Gu Jia Liu Xunming Ji Beijing Institute of Brain Disorders Laboratory of Brain DisordersLaboratory for Clinical MedicineMinistry of Science and TechnologyCollaborative Innovation Center for Brain DisordersBeijing Advanced Innovation Center for Big Data-based Precision MedicineCapital Medical UniversityBeijing 100069China Department of Neurosurgery Xuanwu HospitalCapital Medical UniversityBeijing 100053China

Single-cell transcriptome sequencing has been a rapidly developing and powerful biological tool in recent years,and it plays a vital role in describing tissue development,cell heterogeneity,stress response,*** disease is one of the leading causes affecting human health in the ***,it is important to understand the characteristics of cerebrovascular structure,function,and environmental ***,single-cell transcriptome sequencing provides deeper insights into cerebrovascular research in health and disease *** article will briefly introduce the basic structure and function of cerebrovascular endothelial cells(ECs),summarize the current research and new findings on cerebrovascular ECs at the single-cell transcriptome level,and discuss the challenges in this field.

关键词： blood-brain barrier cerebrovascular endothelial cells single-cell transcriptome sequencing cerebrovascular diseases

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data-Driven Intersection Safety Evaluation Methodology and Analysis 23

Data-Driven Intersection Safety Evaluation Methodology and A...

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23rd COTA International Conference of Transportation Professionals: Emerging data-Driven Sustainable Technological innovation in Transportation, CICTP 2023

作者： Wu, Pingping Lu, Guangquan Liu, Miaomiao Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Southeast Univ. Nanjing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

With the development of information and sensing technologies, a wide variety of intersection traffic data is available. The purpose of this paper is to make full use of video and commercial vehicle trajectory data to deeply explore the intersection safety status. In this paper, based on different sample sizes, different sampling frequencies, and different accuracies of data, the safety analysis of intersections is carried out from micro and macro aspects based on post-encroachment time (PET) and vehicle deceleration behavior, respectively. On the one hand, we use the full-sample and high-precision trajectory data extracted from video to analyze the safety of each region of intersection based on PET. On the other hand, using the trajectory data with low penetration rate and low sampling frequency extracted from the positioning data of the online car-hailing, a distribution fit, and non-parametric test are conducted for the vehicle deceleration behavior, and then the overall safety state of the intersection is evaluated. The results can provide ideas for the identification of intersection risk points and the application of different kinds of data on intersection safety evaluation. © ASCE.

关键词： Commercial vehicles

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Reusing Deep Neural Network Models through Model Re-engineering

Reusing Deep Neural Network Models through Model Re-engineer...

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International Conference on Software Engineering (ICSE)

作者： Binhang Qi Hailong Sun Xiang Gao Hongyu Zhang Zhaotian Li Xudong Liu SKLSDE Lab Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Chongqing University Chongqing China

Training deep neural network (DNN) models, which has become an important task in today's software development, is often costly in terms of computational resources and time. With the inspiration of software reuse, building DNN models through reusing existing ones has gained increasing attention recently. Prior approaches to DNN model reuse have two main limitations: 1) reusing the entire model, while only a small part of the model's functionalities (labels) are required, would cause much overhead (e.g., computational and time costs for inference), and 2) model reuse would inherit the defects and weaknesses of the reused model, and hence put the new system under threats of security attack. To solve the above problem, we propose SeaM, a tool that re-engineers a trained DNN model to improve its reusability. Specifically, given a target problem and a trained model, SeaM utilizes a gradient-based search method to search for the model's weights that are relevant to the target problem. The re-engineered model that only retains the relevant weights is then reused to solve the target problem. Evaluation results on widely-used models show that the re-engineered models produced by SeaM only contain 10.11% weights of the original models, resulting 42.41% reduction in terms of inference time. For the target problem, the re-engineered models even outperform the original models in classification accuracy by 5.85%. Moreover, reusing the re-engineered models inherits an average of 57% fewer defects than reusing the entire model. We believe our approach to reducing reuse overhead and defect inheritance is one important step forward for practical model reuse.

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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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