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IEEE International Conference on data Mining (ICDM)

作者： Fei Jie Chunpai Wang Feng Chen Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University at Albany – SUNY Albany NY USA Erik Jonsson School of Engineering & Computer Science The University of Texas at Dallas Dallas TX USA Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Knowledge Science Hefei University of Technology Hefei China

We propose a generalized optimization framework for detecting anomalous patterns (subgraphs that are interesting or unexpected) in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. We frame the problem as a non-convex optimization that has a general nonlinear score function and a set of block-structured and non-convex constraints. We develop an effective, efficient, and parallelizable projection-based algorithm, namely Graph Block-structured Gradient Projection (GBGP), to solve the problem. It is proved that our algorithm 1) runs in nearly-linear time on the network size, and 2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications, and we conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm.

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PD-L1 Aptamer-Functionalized Metal–Organic Framework Nanoparticles for Robust Photo-Immunotherapy against Cancer with Enhanced Safety

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Angewandte Chemie 2022年第5期135卷

作者： Dr. Jingfang Zhang Dr. Wenzhe Li Dr. Yafei Qi Dr. Guorong Wang Prof. Lele Li Prof. Zhengyu Jin Prof. Jie Tian Prof. Yang Du Beijing Key Laboratory for Bioengineering and Sensing Technology School of Chemistry and Biological Engineering University of Science and Technology Beijing Beijing 100083 China CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing 100190 China State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Sciences Peking University Beijing 100191 China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Radiology Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical Sciences Beijing 100730 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing 100191 China

Immune checkpoint blockade has become a paradigm-shifting treatment modality to combat cancer, while conventional administration of immune checkpoint inhibitors, such as anti-PD-L1 antibody (α-PD-L1), often shows unsatisfactory immune responses and lead to severe immune-related adverse effects (irAEs). Herein, we develop a PD-L1 aptamer-based spherical nucleic acids (SNAs), which consists of oxaliplatin (OXA) encapsulated in a metal–organic framework nanoparticle core and a dense shell of aptPD-L1 (denoted as M@O-A). Upon light irradiation, this nanosystem enables concurrent photodynamic therapy (PDT), chemotherapy, and enhanced immunotherapy in one shot to inhibit both primary colorectal tumors and untreated distant tumors in mice. Notably, M@O-A shows scarcely any systemic immunotoxicity in a clinical irAEs-mimic transgenic mouse model. Collectively, this study presents a novel strategy for priming robust photo-immunotherapy against cancer with enhanced safety.

关键词： DNA Nanotechnology Immunotherapy Metal–Organic Frameworks PD-L1 Aptamer Spherical Nucleic Acids (SNAs)

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Can stock markets lead the macroeconomic variables? Dynamic analysis based on TOP method

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Procedia Computer Science 2019年 162卷 39-45页

作者： Zhenni Jin Yijing Wang Xiao Cui Kun Guo Sino-Danish College University of Chinese Academy of Science Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Science Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science Beijing China School of Economics and Management University of Chinese Academy of Science Beijing China CHINALCO Finance Company Limited Beijing China

Fictitious economy connects with real economy in many aspects. The interaction between stock markets and macro-economy is becoming more and more crucial for different parties in the whole economic society. On the basis of the Thermal Optimal Path (TOP) method, this paper used the quarterly and monthly data of seven pairs of time series to analyse the dynamic lead-lag connections between the macro-economic fluctuation and the stock market change. The results suggest that the data of stock market can be used to predict the macro-economic variables for most time periods. Investors as an important agent in the stock markets, their expectations can be reflected by stock prices. The irrationality of investors can affect the” barometer” function of stock markets.

关键词： Stock market Macro-economy Thermal optimal path Lead-lag analysis

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ALOR: Adaptive layout optimization of raft groups for heterogeneous distributed key-value stores 1

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15th IFIP International Conference on Network and Parallel Computing, NPC 2018

作者： Wang, Yangyang Chai, Yunpeng Wang, Xin Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9783030056773

ISBN: (纸本)9783030056766

Many distributed key-value storage systems employ the simple and effective Raft protocol to ensure data consistency. They usually assume a homogeneous node hardware configuration for the underlying cluster and thus adopt even data distribution schemes. However, today’s distributed systems tend to be heterogeneous in nodes’ I/O devices due to the regular worn I/O device replacement and the emergence of expensive new storage media (e.g., non-volatile memory). In this paper, we propose a new data layout scheme called Adaptive Layout Optimization of Raft groups (ALOR), considering the hardware heterogeneity of the cluster. ALOR aims to optimize the data layout of Raft groups to achieve a better practical load balance, which leads to higher performance. ALOR consists of two components: leader migration in Raft groups and skewed data layout based on cold data migration. We conducted experiments on a practical heterogeneous cluster, and the results indicate that, on average, ALOR improves throughput by 36.89%, reduces latency and 99th percentile tail latency by 24.54% and 21.32%, respectively. © IFIP International Federation for Information Processing 2018.

关键词： Digital storage

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Synthetic High-Density Lipoprotein-Based Nanomedicine to Silence SOCS1 in Tumor Microenvironment and Trigger Antitumor Immunity against Glioma

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Angewandte Chemie 2023年第49期135卷

作者： Chunrong Yang Yujie Li Yuchen Yang Qiankun Ni Zeyu Zhang Yi Chai Prof. Jinghong Li Department of Chemistry Center for BioAnalytical Chemistry Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology Tsinghua University Beijing 100084 China Center for Bioanalytical Chemistry Hefei National Laboratory of Physical Science at Microscale University of Science and Technology of China Hefei 230026 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing 100191 China Department of Neurosurgery Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai 200025 China New Cornerstone Science Laboratory Shenzhen 518054 China Beijing Institute of Life Science and Technology Beijing 102206 China

Immunotherapies have shed light on the treatment of many cancers, but have not improved the outcomes of glioma (GBM). Here, we demonstrated that suppressor of cytokine signaling 1 (SOCS1) was associated with the GBM-associated immunosuppression and developed a multifunctional nanomedicine, which silenced SOCS1 in the tumor microenvironment (TME) of GBM and triggered strong antitumor immunity against GBM. Synthetic high-density lipoprotein (sHDL) was selected as the nanocarrier and a peptide was used to facilitate the blood-brain-barrier (BBB) penetration. The nanocarrier was loaded with a small interfering RNA (siRNA), a peptide, and an adjuvant to trigger antitumor immunity. The nanomedicine concentrated on the TME in vivo, further promoting dendritic cell maturation and T cell proliferation, triggering strong cytotoxic T lymphocyte responses, and inhibiting tumor growth. Our work provides an alternative strategy to simultaneously target and modulate the TME in GBM patients and points to an avenue for enhancing the efficacy of immunotherapeutics.

关键词： Glioma Nanomedicine SOCS1 Tumor Microenvironment

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Efficient Multi-robot Exploration via Multi-head Attention-based Cooperation Strategy

arXiv

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arXiv 2019年

作者： Liu, Shuqi Wu, Zhaoxia National Key Laboratory of Big Data Management and Analysis College of Computer and Science Northeastern University Shenyang110004 China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China

The goal of coordinated multi-robot exploration tasks is to employ a team of autonomous robots to explore an unknown environment as quickly as possible. Compared with "human-designed" methods, which began with heuristic and rule-based approaches, "learning-based" methods enable individual robots to learn sophisticated and hard-to-design cooperation strategies through deep reinforcement learning technologies. However, in decentralized multi-robot exploration tasks, learning-based algorithms are still far from being universally applicable to the continuous space due to the difficulties associated with area calculation and reward function designing;moreover, existing "learning-based" methods encounter problems when attempting to balance the "historical trajectory" issue and target area conflict problem. Furthermore, the scalability of these methods to a large number of agents is poor because of the exponential explosion problem of state space. Accordingly, this paper proposes a novel approach Multi-head Attention-based Multi-robot Exploration in Continuous Space (MAMECS) aimed at reducing the state space and automatically "learning" the cooperation strategies required for decentralized multi-robot exploration tasks in continuous space. Computational geometry knowledge is applied to describe the environment in continuous space and to design an improved reward function to ensure a superior exploration rate. Moreover, the multi-head attention mechanism employed helps to solve the "historical trajectory" issue in the decentralized multi-robot exploration task, as well as to reduce the quadratic increase of action space. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Hypothetical analysis of ground deformation and its effects on archaeological sites of the Silk roads with SBAS-DInSAR: An exploratory application in Xi'an City, China

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Journal of Applied Geophysics 2025年 241卷

作者： Meng Zhu Liang Tao Fulong Chen Xiaochen Zhou Wei Zhou Xun Yao Xiaohuan Xi Wenbo Li Jianyang Chang Hongqiang Li Sheng Gao International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO Beijing 100094 China Xi'an Academy of Conservation and Archaeology Xi'an 710068 China ICOMOS International Conservation Center-Xi'an Xi'an 710068 China School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo 454003 Henan China University of Chinese Academy of Sciences Beijing 101408 China

“Silk Roads: Routes Network of Chang'an-Tianshan Corridor” is a series of heritage sites inscribed in the World Heritage List. Heritage sites are vulnerable to local ground deformations, but corresponding measurements to the Silk Roads are lacking. Xi'an City is the eastern origin of the Silk Roads and boasts the most sites of all cities along the corridor. Moreover, differential ground deformation in Xi'an has been given more prominence. The Small BAseline Subset (SBAS) Differential Interferometric Synthetic Aperture Radar (DInSAR) is one of the time-series techniques analyzing a stack of multiple SAR image pairs to measure ground deformations, and has been well-assessed for heritage studies. Here we provide technical insights and references by investigating the ground's deformations through the use of SBAS-DInSAR, in some areas integrated with ground-based geodetic techniques to derive digital models of heritage entities. Specifically, we processed 97 Sentinel-1 SAR images from 2019 to 2022 through SBAS-DInSAR. The results revealed that the Great Wild Goose Pagoda (GWGP) and Small Wild Goose Pagoda (SWGP) were located at uplifting areas, while the Weiyang Palace (WP) of the Western Han Dynasty was located in a subsiding area. Along the Line-of-Sight direction, the uplifting rates at GWGP and SWGP exceeded 9 mm/year, and the time series showed positive correlations with groundwater recharging. Based on the fundamental assumption that injections into the aquifer resulted in uplifting, groundwater recharging nearby benefited the restoration of the GWGP, providing an insightful example for the treatment of leaning problems in other ancient buildings. The south palace wall of WP was subsiding less than 4 mm/year. The Daming Palace of the Tang Dynasty remained stable throughout the monitoring period. The Xingjiaosi Pagodas (XP) remained stable with ∼1 mm of accumulative deformation until May 2022 when a subsiding trend was observed. On-site observations and numerical mo

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Corrigendum to “Understanding the urban mobility community by taxi travel trajectory” [Commun. Nonlinear Sci. Numer. Simul. 101(2021) 105863/6]

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Communications in Nonlinear Science and Numerical Simulation 2022年 104卷

作者： Wei-Peng Nie Zhi-Dan Zhao Shi-Min Cai Tao Zhou Complex Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 610054 China Complexity Computation Laboratory Department of Computer Science School of Engineering Shantou University Shantou 515063 China Key Laboratory of Intelligent Manufacturing Technology (Ministry of Education) Shantou University Shantou 515063 China

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Containers

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Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

arXiv

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arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

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