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Exploring the tidal effect of urban business district with large-scale human mobility data

Frontiers of Computer Science 2023年第3期17卷 77-90页

作者： Hongting NIU Ying SUN Hengshu ZHU Cong GENG Jiuchun YANG Hui XIONG Bo LANG School of Computer Science and Engineering Beihang UniversityBeijing 100191China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100086China baidu talent intelligence center Baidu Inc.Beijing 100085China Business School Imperial College LondonLondon SW72AZUK Artificial intelligence Thrust The Hong Kong University of Science and TechnologyGuangzhou 510030China

Business districts are urban areas that have various functions for gathering people,such as work,consumption,leisure and *** to the dynamic nature of business activities,there exists significant tidal effect on the boundary and functionality of business ***,effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a ***,with the implicit and complex nature of business district evolution,it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business *** this end,we propose a data-driven and multi-dimensional framework for dynamic business district ***,we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time ***,we detect and forecast the functional changes in business *** results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business ***,the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business *** example,the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

关键词： business district trajectory functionality detection tidal effect boundary detection visiting score

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Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

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Neural Regeneration Research 2025年第5期20卷 1495-1504页

作者： Yunting Xiang Xiangxin Xing Xuyun Hua Yuwen Zhang Xin Xue Jiajia Wu Mouxiong Zheng He Wang Jianguang Xu School of Rehabilitation Science Shanghai University of Traditional Chinese MedicineShanghaiChina Engineering Research center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of EducationShanghaiChina Department of Rehabilitation Medicine Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina Department of Traumatology and Orthopedics Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina Institute of Science and Technology for Brain-Inspired intelligence Fudan UniversityShanghaiChina Key Laboratory of Computational Neuroscience and Brain-Inspired intelligence(Fudan University) Ministry of EducationShanghaiChina Human Phenome Institute Fudan UniversityShanghaiChina

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected ***,differences among reconstruction strategies at the brain network level have not been *** study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve ***–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper ***-state brain functional magnetic resonance imaging was obtained 7 months after *** independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the *** in intra-network resting-state functional connectivity were compared among the *** muscle reinnervation was assessed by behavioral observation(elbow flexion)and *** results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural *** repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor *** thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve *** areas related to cognition and emotion were enhanced after end-to-side nerve *** study revealed important brain networks related to different nerve *** networks may be potential targets for enhancing motor recovery.

关键词： brain functional networks end-to-end nerve transfer end-to-side nerve transfer independent component analysis nerve repair peripheral plexus injury resting-state functional connectivity

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Bigflow: A General Optimization Layer for Distributed Computing Frameworks

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Journal of Computer Science & Technology 2020年第2期35卷 453-467页

作者： Yun-Cong Zhang Xiao-Yang Wang Cong Wang Yao Xu Jian-Wei Zhang Xiao-Dong Lin Guang-Yu Sun Gong-Lin Zheng Shan-Hui Yin Xian-Jin Ye Li Li Zhan Song Dong-Dong Miao baidu Inc. Beijing 100193China center for Energy-Efficient Computing and Applications Peking UniversityBeijing 100871China

As data volumes grow rapidly, distributed computations are widely employed in data-centers to provide cheap and efficient methods to process large-scale parallel datasets. Various computation models have been proposed to improve the abstraction of distributed datasets and hide the details of parallelism. However, most of them follow the single-layer partitioning method, which limits developers to express a multi-level partitioning operation succinctly. To overcome the problem, we present the NDD (Nested Distributed Dataset) data model. It is a more compact and expressive extension of Spark RDD (Resilient Distributed Dataset), in order to remove the burden on developers to manually write the logic for multi-level partitioning cases. Base on the NDD model, we develop an open-source framework called Bigflow, which serves as an optimization layer over computation engines from most widely used processing frameworks. With the help of Bigflow, some advanced optimization techniques, which may only be applied by experienced programmers manually, are enabled automatically in a distributed data processing job. Currently, Bigflow is processing about 3 PB data volumes daily in the data-centers of baidu. According to customer experience, it can significantly save code length and improve performance over the intuitive programming style.

关键词： distributed computing programming model optimization technique

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Quantum self-attention neural networks for text classification

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Science China(Information Sciences) 2024年第4期67卷 301-313页

作者： Guangxi LI Xuanqiang ZHAO Xin WANG Institute for Quantum Computing Baidu Research Centre for Quantum Software and Information University of Technology Sydney QICI Quantum Information and Computation Initiative Department of Computer ScienceThe University of Hong Kong Thrust of Artificial intelligence Information HubHong Kong University of Science and Technology(Guangzhou)

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing(NLP). Although some efforts based on syntactic analysis have opened the door to research in quantum NLP(QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum selfattention neural network(QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

关键词： quantum neural networks self-attention natural language processing text classification parameterized quantum circuits

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In-memory computing with emerging nonvolatile memory devices

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Science China(Information Sciences) 2021年第12期64卷 23-68页

作者： Caidie CHENG Pek Jun TIW Yimao CAI Xiaoqin YAN Yuchao YANG Ru HUANG State Key Laboratory for Advanced Metals and Materials School of Materials Science and EngineeringUniversity of Science and Technology Beijing Key Laboratory of Microelectronic Devices and Circuits (MOE) Department of Micro/nanoelectronicsPeking University center for Brain Inspired Chips Institute for Artificial IntelligencePeking University center for Brain Inspired intelligence Chinese Institute for Brain Research (CIBR)

The von Neumann bottleneck and memory wall have posed fundamental limitations in latency and energy consumption of modern computers based on von Neumann architecture. In-memory computing represents a radical shift in the computer architecture that can address such problems by merging computing functions within the memory itself. In this article, we review the emerging nonvolatile memory devices,such as resistance-based and charge-based memory devices, that are explored for in-memory computing applications. We will provide an overview of the materials, mechanisms, and integration of these devices,and discuss the optimizations at the device and array levels that are required to better support in-memory computing. Recent progress in the application of in-memory computing in artificial neural networks, spiking neural networks, digital logic in memory as well as hardware security will also be discussed. Finally, we will discuss the remaining challenges in this field and potential pathways to address them.

关键词： in-memory computing von Neumann bottleneck nonvolatile memory energy efficiency neural network

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Zero-bit watermarking resisting geometric attacks based on composite-chaos optimized SVR model

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The Journal of China Universities of Posts and Telecommunications 2011年第2期18卷 94-101页

作者： GAO Guang-yong JIANG Guo-ping center for Control & intelligence Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China School of Information Science & Technology Jiujiang University Jiujiang 332005 China

The problem to improve the performance of resisting geometric attacks in digital watermarking is addressed in this *** on the optimized support vector regression（SVR）,a zero-bit watermarking algorithm is *** proposed algorithm encrypts the watermarking image by using composite chaos with large key space and capacity against prediction,which can strengthen the safety of the proposed *** using the relationship between Tchebichef moment invariants of detected image and watermarking characteristics,the SVR training model optimized by composite chaos enhances the ability of resisting geometric *** analysis and simulations demonstrate that the proposed algorithm herein possesses better security and stronger robustness than some similar methods.

关键词： Tchebicbef moment invariants composite chaos SVR zero-bit watermarking

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Robust Object Tracking via Information Theoretic Measures

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International Journal of Automation and computing 2020年第5期17卷 652-666页

作者： Wei-Ning Wang Qi Li Liang Wang center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial intelligence University of Chinese Academy of SciencesBeijing 100190China Artificial intelligence Research Chinese Academy of SciencesQingdao 266300China

Object tracking is a very important topic in the field of computer *** sophisticated appearance models have been *** them,the trackers based on holistic appearance information provide a compact notion of the tracked object and thus are robust to appearance variations under a small amount of ***,in practice,the tracked objects are often corrupted by complex noises(e.g.,partial occlusions,illumination variations)so that the original appearance-based trackers become less *** paper presents a correntropy-based robust holistic tracking algorithm to deal with various ***,a half-quadratic algorithm is carefully employed to minimize the correntropy-based objective *** on the proposed information theoretic algorithm,we design a simple and effective template update scheme for object *** results on publicly available videos demonstrate that the proposed tracker outperforms other popular tracking algorithms.

关键词： Object tracking information theoretic measures correntropy template update robust to complex noises

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Epidemic spreading on scale-free networks with diversity of node anti-attack abilities

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The Journal of China Universities of Posts and Telecommunications 2010年第1期17卷 73-76,126页

作者： SONG Yu-rong JIANG Guo-ping center for Control and intelligence Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China College of Automation Nanjing University of Posts and Telecommunications Nanjing 210003 China

In this article, a modified susceptible-infected-removed （SIR） model is proposed to study the influence of diversity of node anti-attack abilities on the threshold of propagation in scale-free networks. In particular, a vulnerability function related to node degree is introduced into the model to describe the diversity of a node anti-attack ability. Analytical results are derived using the mean-field theory and it is observed that the diversity of anti-attack of nodes in scale-free networks can increase effectively the threshold of epidemic propagation. The simulation results agree with the analytical results. The results show that the vulnerability functions can help adopt appropriate immunization strategies.

关键词： epidemic spreading scale-free network SIR model anti-attack vulnerability function

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Development of machine learning-based models to predict 10-year risk of cardiovascular disease:a prospective cohort study

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Stroke & Vascular Neurology 2023年第6期8卷 475-485页

作者： Jia You Yu Guo Ju-Jiao Kang Hui-Fu Wang Ming Yang Jian-Feng Feng Jin-Tai Yu Wei Cheng Institute of Science and Technology for Brain-Inspired intelligence Department of NeurologyHuashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina Key Laboratory of Computational Neuroscience and Brain-Inspired intelligence Fudan UniversityMinistry of EducationShanghaiChina Zhangjiang Fudan International Innovation center Fudan UniversityShanghaiChina School of Data Science Fudan UniversityShanghaiChina Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired intelligence Zhejiang Normal UniversityZhejiangChina Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer center Fudan UniversityShanghaiChina

Background Previous prediction algorithms for cardiovascular diseases(CVD)were established using risk factors retrieved largely based on empirical clinical *** study sought to identify predictors among a comprehensive variable space,and then employ machine learning(ML)algorithms to develop a novel CVD risk prediction *** From a longitudinal population-based cohort of UK Biobank,this study included 473611 CVD-free participants aged between 37 and 73 years *** implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident *** model was validated through a leave-one center-out *** During a median follow-up of 12.2 years,31466 participants developed CVD within 10 years after baseline visits.A novel UK Biobank CVD risk prediction(UKCRP)model was established that comprised 10 predictors including age,sex,medication of cholesterol and blood pressure,cholesterol ratio(total/high-density lipoprotein),systolic blood pressure,previous angina or heart disease,number of medications taken,cystatin C,chest pain and pack-years of *** model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve(AUC)of 0.762±0.010 that outperformed multiple existing clinical models,and it was well-calibrated with a Brier Score of 0.057±***,the UKCRP can obtain comparable performance for myocardial infarction(AUC 0.774±0.011)and ischaemic stroke(AUC 0.730±0.020),but inferior performance for haemorrhagic stroke(AUC 0.644±0.026).Conclusion ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.

关键词： cardiovascular prediction learning

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Rectal Cancer Stages T2 and T3 Identification Based on Asymptotic Hybrid Feature Maps

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 923-938页

作者： Shujing Sun Jiale Wu Jian Yao Yang Cheng Xin Zhang Zhihua Lu Pengjiang Qian R&D center of Artificial intelligence Systems and Applications School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China Shanghai Enterprise Information Operation center China Telecom Group Co.Ltd.Shanghai201315China Department of Radiology Dushu Lake Hospital Affiliated to Soochow UniversitySuzhou215123China Suzhou Yunmai Software Technology Co. Ltd.Suzhou215100China

Many existing intelligent recognition technologies require huge datasets for model ***,it is not easy to collect rectal cancer images,so the performance is usually low with limited training *** addition,traditional rectal cancer staging is time-consuming,error-prone,and susceptible to physicians’subjective awareness as well as professional *** settle these deficiencies,we propose a novel deep-learning model to classify the rectal cancer stages of T2 and ***,a novel deep learning model(RectalNet)is constructed based on residual learning,which combines the squeeze-excitation with the asymptotic output layer and new cross-convolution layer links in the residual block ***,a two-stage data augmentation is designed to increase the number of images and reduce deep learning’s dependence on the volume of *** experiment results demonstrate that the proposed method is superior to many existing ones,with an overall accuracy of ***,other traditional techniques,such as VGG16,DenseNet121,EL,and DERNet,have an average accuracy of 0.6981,0.7032,0.7500,and 0.7685,respectively.

关键词： Rectal cancer staging T2WI images data expansion asymptotic output layer

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