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2010 IEEE computer Society Conference on computer Vision and Pattern Recognition - Workshops, CVPRW 2010

作者： Zhang, Xiaoqin Hu, Weiming Wang, Xiangyang Kong, Yu Xie, Nianhua Wang, Hanzi Ling, Haibin Maybank, Steve College of Mathematics and Information Science Wenzhou University Zhejiang China National Laboratory of Pattern Recognition Institute of Automation Beijing China School of Communication and Information Engineering Shanghai University Shanghai China School of Computer Science Beijing Institute of Technology Beijing China School of Computer Science University of Adelaide Australia Department of Computer and Information Science Temple University Philadelphia United States School of Computer Science and Information Systems Birkbeck College London United Kingdom

ISBN: (纸本)9781424470297

This paper proposes an annealed particle swarm optimization based particle filter algorithm for articulated 3D human body tracking. In our algorithm, a sampling covariance and an annealing factor are incorporated into the velocity updating equation of particle swarm optimization (PSO). The sampling covariance and the annealing factor are initiated with appropriate values at the beginning of the PSO iteration, and 'annealing' is carried out at reasonable steps. Experiments with multi-camera walking sequences from the Brown dataset show that: 1) the proposed tracker can effectively alleviate the problem of inconsistency between the image likelihood and the true model;2) the tracker is also robust to noise and body self-occlusion. © 2010 IEEE.

关键词： Swarm intelligence

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Roadmap on Neuromorphic Photonics

arXiv

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arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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Dominic II: Meta-Level Control in Iterative Redesign 7

Dominic II: Meta-Level Control in Iterative Redesign

引用

7th National Conference on Artificial Intelligence, AAAI 1988

作者： Orelup, Mark F. Dixon, John R. Cohen, Paul R. Simmons, Melvin K. Mechanical Design Automation Laboratory Department of Mechanical Engineering University of Massachusetts AmherstMA01003 United States Experimental Knowledge Systems Laboratory Department of Computer and Information Science University of Massachusetts United States Artificial Intelligence Branch General Electric Corporate Research and Development Schenectady New York12301 United States

ISBN: (纸本)0262510553

This paper describes the meta-level control system of a program (Dominic) for parametric design of mechanical components by iterative redesign. We view parametric design as search, and thus Dominic is a hill climbing algorithm. However, from experience with Dominic we concluded that modeling engineering design as hill climbing has several limitations. Therefore, a need for meta-level control knowledge exists. To implement meta-level control, we have taken the approach of dynamically modifying the way hill climbing is performed for this task, rather than requiring the addition of domain-specific control knowledge. We have identified the limitations of hill climbing, constructed various generic hill climbing strategies, and developed a me&t-strategy to guide the application of the strategies. The program monitors its own performance for unproductive efforts and selects among different strategies to improve its performance as it designs. This met&level control significantly improves the performance of the program over the performance of an earlier version. © 1988, AAAI (***). All rights reserved.

关键词： Level control

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sCompile: Critical Path Identification and Analysis for Smart Contracts 21st

sCompile: Critical Path Identification and Analysis for Smar...

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21st International Conference on Formal Engineering Methods, ICFEM 2019

作者： Chang, Jialiang Gao, Bo Xiao, Hao Sun, Jun Cai, Yan Yang, Zijiang Department of Computer Science Western Michigan University KalamazooMI49009 United States Pillar of Information System Technology and Design Singapore University of Technology and Design Singapore Singapore School of Information Systems Singapore Management University Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030324087

Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise present them with user friendly warnings for user inspection. We identify paths which involve monetary transaction as critical paths, and prioritize those which potentially violate important properties. For scalability, symbolic execution techniques are only applied to top ranked critical paths. Our approach has been implemented in a tool called sCompile, which has been applied to 36,099 smart contracts. The experiment results show that sCompile is efficient, i.e., 5 s on average for one smart contract. Furthermore, we show that many known vulnerabilities can be captured if user inspects as few as 10 program paths generated by sCompile. Lastly, sCompile discovered 224 unknown vulnerabilities with a false positive rate of 15.4% before user inspection. © 2019, Springer Nature Switzerland AG.

关键词： Smart contract

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Adaptive nonlinear auto-associative modeling through manifold learning

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9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005

作者： Zhang, Junping Li, Stan Z. Intelligent Information Processing Laboratory Department of Computer Science and Engineering Fudan University Shanghai 200433 China Key Laboratory of Complex Systems and Intelligence Science Chinese Academy of Sciences China National Laboratory of Pattern Recognition Center for Biometrics and Security Research Institute of Automation Beijing 100080 China

ISBN: (纸本)3540260765

We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separately and recognition thereby. Unlike traditional supervised manifold learning algorithm, the proposed ANAM algorithm has several advantages: 1) it implicitly embodies discriminant information because the suboptimal parameters of ANAM are determined based on error rate of the validation set. 2) it avoids the curse of dimensionality without loss accuracy because recognition is completed in the original space. Experiments on character and digit databases show that the advantages of the proposed ANAM algorithm. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Learning systems

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The Quantum Internet (Technical Version)

arXiv

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

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Classification with the hybrid of manifold learning and gabor wavelet

Classification with the hybrid of manifold learning and gabo...

引用

3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Zhang, Junping Shen, Chao Feng, Jufu Shanghai Key Laboratory of Intelligent Information Processing Department of Computer Science and Engineering Fudan University Shanghai 200433 China Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China National Key Laboratory for Machine Perception School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

ISBN: (纸本)354034439X

While manifold learning algorithms can discover intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space, the discriminant ability of the low-dimensional subspaces obtained by the algorithms is often lower than those obtained by the conventional dimensionality reduction approaches. Furthermore, the original feature vectors may include redundancy such as high-order correlation which cannot be removed by manifold learning algorithms. To address the two problems, we first employ Gabor wavelet to remove intrinsic redundancies of images and obtain a set of over-completed feature vectors. Then a supervised manifold learning algorithm (ULLELDA) is applied to project Gabor-based data and out-of-the-samples into a common low-dimensional subspace. Experiments in two FERET face databases indicate that Gabors indeed help supervised manifold learning to remarkably improve the discriminant ability of low-dimensional subspaces. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Wavelet transforms

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Link Prediction in a Modified Heterogeneous Bibliographic Network

Link Prediction in a Modified Heterogeneous Bibliographic Ne...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： John Boaz Lee Henry Adorna Department of Information Systems and Computer Science Ateneo de Manila University Philippines Algorithms and Complexity Laboratory Department of Computer Science University of the Philippines Diliman Philippines

Researchers have discovered, in recent years, the advantages of modeling complex systems using heterogeneous information networks. These networks are comprised of heterogeneous sets of nodes and edges that better represent the different entities and relationships often found in the real world. Although heterogeneous networks provide a richer semantic view of the data, the added complexity makes it difficult to directly apply existing techniques that work well on homogeneous networks. In this paper, we propose a graph modification process that alters an existing heterogeneous bibliographic network into another network, with the purpose of highlighting the important relations in the bibliographic network. Several importance scores, some adopted from existing work and others defined in this work, are then used to measure the importance of links in the modified network. The link prediction problem is studied on the modified network by implementing a random walk-based algorithm on the network. The importance scores and the structure of the modified graph are used to guide a random walker towards relevant parts of the graph, i.e. towards nodes to which new links will be created in the future. The different properties of the proposed algorithm are evaluated experimentally on a real world bibliographic network, the DBLP. Results show that the proposed method outperforms the state-of-the-art supervised technique as well as various approaches based on topology and node attributes.

关键词： Prediction algorithms Frequency measurement Weight measurement Joining processes computer science Buildings Social network services

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Recent trends in chip-level design automation for digital microfluidic biochips

Recent trends in chip-level design automation for digital mi...

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International Symposium on Integrated Circuits (ISIC)

作者： Sudip Roy Chi-Ruo Wu Tsung-Yi Ho Department of Computer Science & Information Engineering National Cheng Kung University Electronic Design Automation Laboratory Taiwan

In recent years, digital microfluidic (DMF) biochips have become the most popular and emerging technology for implementing a wide range of biochemical laboratory protocols on a small hand-held device. Due to its high-throughput, high-sensitivity, automatic control, and low cost, DMF biochips are now being considered for several real-life applications like on-chip diagnosis of various cardiovascular diseases, cancer, diabetes, HIV/AIDS and others. As the complexity of biochemical assay increases, powerful computer-aided-design tools are needed for developing DMF biochips. This paper presents an overview of recent research trends in the chip-level design of DMF biochips.

关键词： Electrodes Routing Wires design automation Pins Optimization Reliability

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