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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Luo, Haohan Wang, Feng Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University School of Computer Science and Technology 3663 North Zhongshan Rd. Shanghai China

ISBN: (纸本)9781728163277

Urban traffic accident detection in surveillance videos is an essential task in intelligent traffic monitoring. The current deep-learning based approaches suffer from the lack of large-scale traffic accident video datasets due to the privacy concerns. In this paper, we propose a framework to synthesize traffic videos containing both normal traffic and accident events by simulating the real urban traffic scenarios. By introducing various weather conditions, viewpoints of surveillance cameras, and traffic statuses, we can simulate the complex real-world traffic scenarios. The vehicles are automatically controlled to behave normally or trigger accidents randomly. Based on our framework, we automatically generate a synthetic dataset containing 1,100 distinct traffic videos with video-level annotations. For accident detection, we observe that the existing reconstruction-based models produce many false alarms by predicting the pseudo accidents (non-accident anomalous traffic events) as real accidents. To address this ambiguity problem, we propose an improved approach by employing multiple instance learning to refine the detection results. Our experiments demonstrate the great potentials of our proposed framework in boosting the performance of accident detection on real-world datasets. We make our code and dataset publicly available at https://***/hankluo2/UrbanTrafficAccidentDetection. © 2023 IEEE.

关键词： Accident detection simulation surveillance videos urban traffic

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Collaborative List-and-Pairwise Filtering from Implicit Feedback : Extended Abstract 39

Collaborative List-and-Pairwise Filtering from Implicit Feed...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Yu, Runlong Liu, Qi Ye, Yuyang Cheng, Mingyue Chen, Enhong Ma, Jianhui University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology Hefei China Rutgers University Management Science and Information Systems Rutgers Business School Newark United States

ISBN: (纸本)9798350322279

Collaborative filtering (CF) from implicit datasets has attracted much attention in recent years. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proven to be helpful to exploit implicit feedback, but lead to not addressing the rank-biased scenarios where positive items are supposed to be placed on the top-k positions. Although there exist listwise methods, they have low efficiency and are not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we first smooth a well-known rank-biased measure called Mean Average Precision (MAP) as a low-bound version to make it can be optimized. After that, we combined the objective functions of optimizing the MAP with pairwise comparisons. The CLAPF framework is a new hybrid model that provides the idea of utilizing a listwise measure in a pairwise way on implicit feedback. © 2023 IEEE.

关键词： Collaborative filtering

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An Autoencoder-based Transceiver for UAV-to-Ground Free Space Optical Communication

An Autoencoder-based Transceiver for UAV-to-Ground Free Spac...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Zhang, Qianwu Chen, Guanwen Liu, Boyang Zhi, Xuzhuang Zhan, Shucheng Zhang, Jing Jiang, Ning Cao, Bingyao Li, Zhengxuan Shanghai Institute for Advanced Communication and Data Science Key Laboratory of Specialty Optics and Optical Access Networks Shanghai University Shanghai200072 China University of Electronic Science and Technology of China School of Information and Communication Engineering Sichuan Chengdu611731 China

An end-to-end autoencoder-based transceiver for UAV-to-ground free space optical communication is proposed. Simulation results show ∼ 12dB improvement compared to the PPM transmitter and ∼ 51.3% reduction of decoder... 详细信息

ISBN: (纸本)9781665462136

关键词： Unmanned aerial vehicles (UAV)

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An Improved Hybrid Switching Scheme for UAV-to-Ground ACM FSO System

An Improved Hybrid Switching Scheme for UAV-to-Ground ACM FS...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Zhang, Qianwu Liu, Boyang Chen, Guanwen Zhan, Shucheng Li, Zhiyu Zhang, Jing Jiang, Ning Cao, Bingyao Li, Zhengxuan Shanghai Institute for Advanced Communication and Data Science Shanghai University Key Laboratory of Specialty Optics and Optical Access Networks Shanghai200072 China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9798350312614

关键词： ACM FSO UAV

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Electronics based on two-dimensional materials:Status and outlook

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Nano Research 2021年第6期14卷 1752-1767页

作者： Senfeng Zeng Zhaowu Tang Chunsen Liu Peng Zhou State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityShanghai200433China School of Computer Science Fudan UniversityShanghai200433China

Since Moore’s law in the traditional semiconductor industry is facing shocks,More Moore and More than Moore are proposed as two paths to maintain the development of the semiconductor industry by adopting new architectures or new materials,in which the former is committed to the continued scaling of transistors for performance enhancement,and the latter pursues the realization of functional diversification of electronic ***-dimensional(2D)materials are supposed to play an important role in these two *** More Moore,the ultimate thin thickness and the dangling-bond-free surface of 2D channels offer excellent gate electrostatics while avoiding the degradation of carrier mobility at the same time,so that the transistors can be further scaled down for higher *** More than Moore,devices based on 2D materials can well meet the requirements of electronic systems for functional diversity,like that they can operate at high frequency,exhibit excellent sensitivity to the changes in the surroundings at room temperature,have good mechanical flexibility,and so *** this review,we present the application of 2D materials in More Moore and More than Moore domains of electronics,outlining their potential as a technological option for logic electronics,memory electronics,radio-frequency electronics,sensing electronics,and flexible electronics.

关键词： More Moore More Than Moore two-dimensional materials transition metal dichalcogenides(TMDs)

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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

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Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

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Deep nonparametric quantile regression under covariate shift

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 18851-18900页

作者： Xingdong Feng Xin He Yuling Jiao Lican Kang Caixing Wang School of Statistics and Data Science & Institute of Data Science and Statistics Shanghai University of Finance and Economics Shanghai China School of Statistics and Data Science Shanghai University of Finance and Economics Shanghai China School of Artificial Intelligence Hubei Key Laboratory of Computational Science Wuhan University Wuhan China Institute for Math and AI School of Mathematics and Statistics Wuhan University Wuhan China

This work focuses on addressing the challenges posed by covariate shift in nonparametric quantile regression using deep neural networks. We propose a two-stage pretraining reweighted method that leverages importance weighting to mitigate the effects of distribution shift. In the first stage, density ratios are estimated with a neural network by minimizing least squares. In the second stage, a deep neural network estimator is obtained using pre-training weights. Theoretical analysis is provided, offering non-asymptotic error bounds for the unweighted, reweighted, and pre-training reweighted estimators. We consider scenarios with both bounded and unbounded density ratios. Notably, we employ a novel proof technique to bound the generalization error, characterized by the size and weights bound of ReLU neural networks. This enables us to establish fast rates of convergence under the adaptive self-calibration condition, distinguishing our approach from those relying on local Rademacher complexity techniques. Additionally, we derive the approximation error with weight bounds for ReLU neural networks approximating the Hölder class. Our theoretical findings provide valuable insights for the pre-training process and highlight the efficacy of reweighted techniques. Numerical experiments are conducted to further validate the theoretical findings and demonstrate the effectiveness of our proposed method.

关键词： deep neural networks distribution shift non-asymptotic error bounds robust estimation

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Artificial intelligence empowering research on loneliness, depression and anxiety-Using Covid-19 as an opportunity

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Journal of Safety science and Resilience 2023年第4期4卷 396-409页

作者： Qijian Zheng Feng Liu Shuya Xu Jingyi Hu Haixing Lu Tingting Liu School of Computer Science and Technology East China Normal University3663 Zhongshan North RoadShanghai200062China New York University Shanghai No.567Yangsi West RoadPudong DistrictShanghai200126China School of Psychology and Cognitive Science East China Normal University3663 Zhongshan North RoadShanghai200062China Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention School of Psychology and Cognitive ScienceEast China Normal University3663 Zhongshan North RoadShanghai200062China Faculty of Economics and Management East China Normal University3663 Zhongshan North RoadShanghai200062China

The COVID-19 pandemic has had a profound impact on public mental health,leading to a surge in loneliness,depression,and *** these public psychological issues increasingly become a factor affecting social *** researchers explore ways to address these issues,artificial intelligence(AI)has emerged as a powerful tool for understanding and supporting mental *** this paper,we provide a thorough literature review on the emotions(EMO)of loneliness,depression,and anxiety(EMO-LDA)before and during the COVID-19 ***,we evaluate the application of AI in EMO-LDA research from 2018 to 2023(AI-LDA)using Latent Dirichlet Allocation(LDA)topic *** analysis reveals a significant increase in the proportion of literature on EMO-LDA and AI-LDA before and during the COVID-19 *** also observe changes in research hotspots and trends in both ***,our results suggest that the collaborative research of EMO-LDA and AI-LDA is a promising direction for future *** conclusion,our review highlights the urgent need for effective interventions to address the mental health challenges posed by the COVID-19 *** findings suggest that the integration of AI in EMO-LDA research has the potential to provide new insights and solutions to support individuals facing loneliness,depression,and *** we hope that our study will inspire further research in this vital and revelant domin.

关键词： COVID-19 Loneliness Depression Anxiety EMO-LDA AI-LDA Computational affection

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Harry: A Scalable SIMD-based Multi-literal Pattern Matching Engine for Deep Packet Inspection 42

Harry: A Scalable SIMD-based Multi-literal Pattern Matching ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Xu, Hao Chang, Harry Zhu, Wenjun Hong, Yang Langdale, Geoff Qiu, Kun Zhao, Jin Fudan University School of Computer Science China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Intel Asia-Pacific Research & Development Ltd. Shanghai China

ISBN: (纸本)9798350334142

Deep Packet Inspection (DPI) is a significant network security technique. It examines traffic workloads by searching for specific rules. Since every byte of packets needs to be examined by many literal rules, multi-literal matching becomes the performance bottleneck of DPI. FDR, the fastest multi-literal matching engine on CPUs, takes advantage of Single-Instruction-Multiple-data (SIMD) to alleviate this bottleneck and achieves a performance boost over the widely-used Aho-Corasick (AC) algorithm. However, FDR does not deeply exploit the data-level parallelism of SIMD and its SIMD vector utilization is only 50%. Besides, limited by certain SIMD shift instructions, it cannot benefit from advanced SIMD instruction sets. To overcome these issues, we propose Harry, a scalable and SIMD-based multi-literal matching engine. Harry adopts a column-vector-based matching algorithm to improve the data-level parallelism and SIMD vector utilization. To support the algorithm, it takes two encoding methods to compress the mask table. Also, it utilizes shuffle instruction to implement shift. We implement Harry on commodity CPU and evaluate it with real network traffic and DPI rules. Our evaluation shows that Harry reaches a throughput of 30∼70Gbit/s, up to 52x that of AC and 2.09x of FDR. It has been successfully deployed in Hyperscan. © 2023 IEEE.

关键词： Network security

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