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Cas-FNE:Cascaded Face Normal Estimation

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2423-2434页

作者： Meng Wang Jiawan Zhang Jiayi Ma Xiaojie Guo the Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin 300350 the School of Computer Science and Technology Tiangong UniversityTianjin 300387China IEEE the College of Intelligence and Computing Tianjin UniversityTianjin 300365China the Electronic Information School Wuhan UniversityWuhan 430072China

Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graphics *** significant progress has been made in recent years,how to effectively and efficiently explore normal priors remains *** existing approaches depend on the development of intricate network architectures and complex calculations for in-the-wild face *** overcome the above issue,we propose a simple yet effective cascaded neural network,called Cas-FNE,which progressively boosts the quality of predicted normals with marginal model parameters and computational ***,it can mitigate the imbalance issue between training data and real-world face images due to the progressive refinement mechanism,and thus boost the generalization ability of the ***,in the training phase,our model relies solely on a small amount of labeled *** earlier prediction serves as guidance for following *** addition,our shared-parameter cascaded block employs a recurrent mechanism,allowing it to be applied multiple times for optimization without increasing network *** and qualitative evaluations on benchmark datasets are conducted to show that our Cas-FNE can faithfully maintain facial details and reveal its superiority over *** code is available at https://***/AutoHDR/***.

关键词： Cascaded learning face normal progressive refinement shared-parameter

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A benchmark test of boson sampling on Tianhe-2 supercomputer

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National Science Review 2018年第5期5卷 715-720页

作者： Junjie Wu Yong Liu Baida Zhang Xianmin Jin Yang Wang Huiquan Wang Xuejun Yang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and AstronomyShanghai Jiao Tong University Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China

Boson sampling, thought to be intractable classically, can be solved by a quantum machine composed of merely generation, linear evolution and detection of single photons. Such an analog quantum computer for this specific problem provides a shortcut to boost the absolute computing power of quantum computers to beat classical ones. However, the capacity bound of classical computers for simulating boson sampling has not yet been identified. Here we simulate boson sampling on the Tianhe-2 supercomputer, which occupied the first place in the world ranking six times from 2013 to 2016. We computed the permanent of the largest matrix using up to 312 000 CPU cores of Tianhe-2, and inferred from the current most efficient permanent-computing algorithms that an upper bound on the performance of Tianhe-2 is one 50-photon sample per ～100 min. In addition, we found a precision issue with one of two permanent-computing algorithms.

关键词： boson sampling permanent quantum supremacy quantum computing supercomputer

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A distributed heuristic for decentralized workflow scheduling in global grids

A distributed heuristic for decentralized workflow schedulin...

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2009 10th IEEE/ACM International Conference on Grid computing, Grid 2009

作者： Rahman, Mustafizur Ranjan, Rajiv Buyya, Rajkumar Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Service Oriented Computing Research Group School of Computer Science and Engineering University of New South Wales Australia

ISBN: (纸本)9781424451494

Efficient scheduling is a key concern for the effectual execution of performance driven Grid applications, such as workflows. Many list heuristics have been developed for scheduling workflows in centralized Grid environment. However, in this paper, we present a distributed list heuristic for decentralized scheduling of workflow applications in global Grids. The simulation results show that the proposed scheduling approach is scalable with respect to increased workload on the system. © 2009 IEEE.

关键词： Scheduling

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Dependable workflow scheduling in global grids

Dependable workflow scheduling in global grids

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2009 10th IEEE/ACM International Conference on Grid computing, Grid 2009

ISBN: (纸本)9781424451494

In this paper, a reputation-based Grid workflow scheduling algorithm is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Grid overlays. The proposed approach builds upon structured peer-to-peer indexing and overlay networking techniques to create a scalable wide-area networking of Grid sites for supporting dependable scheduling of applications. The scheduling algorithm considers reliability of a Grid resource as a statistical property, which is globally computed in the decentralized Grid overlay based on dynamic feedbacks or reputation scores assigned by individual service consumers (Grid Resource Brokers). The proposed algorithm can dynamically adapt to changing resource conditions and offer significant performance gains as compared to traditional approaches in the event of unsuccessful job execution or resource failure. We evaluate and demonstrate the feasibility of our approach through an extensive trace driven simulation. The results show that our scheduling technique can reduce the makespan up to 50% and successfully isolate the failure-prone resources from the system. © 2009 IEEE.

关键词： Scheduling algorithms

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Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine

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Science China(information Sciences) 2013年第5期56卷 238-250页

作者： SHAO Huan LI GuoZheng LIU GuoPing WANG YiQin School of Computer Engineering and Science Shanghai University Department of Control Science and Engineering Key Laboratory of Ministry of Education for Service Computing and Embedded SystemsTongji University Laboratory of Information Access and Synthesis of TCM Four Diagnosis Shanghai University of Traditional Chinese Medicine

In traditional Chinese medicine(TCM) diagnosis,a patient may be associated with more than one syndrome tags,and its computer-aided diagnosis is a typical application in the domain of multi-label learning of high-dimensional *** is common that a great deal of symptoms can occur in traditional Chinese medical diagnosis,which affects the modeling of diagnostic *** selection entails choosing the smallest feature subset of relevant symptoms,and maximizing the generalization performance of the *** present there are rare researches on feature selection on multi-label data.A hybrid optimization technique is introduced to symptom selection for multi-label data in TCM diagnosis in this paper,and modeling is made by means of four multi-label learning algorithms like k nearest neighbors,*** compare the performance of the algorithm with the current popular dimension reduction algorithms like MEFS(embedded feature selection for multi-Label learning),MDDM(multi-label dimensionality reduction via dependence maximization) on the UCI Yeast gene functional data set and an inquiry diagnosis dataset of coronary heart disease(CHD).Experimental results show that the algorithm we present has significantly improved the *** particular,the improvement on the average precision for the classifier is up to 10.62% and 14.54%.Syndrome inquiry modeling of CHD in TCM is realized in this paper,providing effective reference for the diagnosis of CHD and analysis of other multi-label data.

关键词： multi-label learning feature selection high-dimensionality inquiry of traditional Chinese medicine coronary heart disease

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Parallel matrix-matrix multiplication based on HPL with a GPU-accelerated PC cluster

Parallel matrix-matrix multiplication based on HPL with a GP...

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International Conference on Networking and computing

作者： Wang, Qin Ohmura, Junichi Axida, Shan Miyoshi, Takefumi Irie, Hidetsugu Yoshinaga, Tsutomu University of Electro-Communications Graduate School of Information Systems Department of Information Network Systems Laboratory for Network Computing 1-5-1 Chofugaoka Chofu-shi 182-8585 Tokyo Japan

ISBN: (纸本)9780769542775

In this paper, we propose an approach for significantly improving the performance of parallel matrix-matrix multiplication using a GPU-accelerated cluster. For one node, we implement a CPUs-GPU parallel double-precision general matrix-matrix multiplication (dgemm) operation and achieve a performance improvement of 32% as compared to the GPU-only case and 56% as compared to the CPUs-only case. For the entire cluster, we use the overlap GPU acceleration solution to high-performance Linpack (HPL), which eliminates the close dependency between the LU decomposition and the dgemm operation, and achieve a performance improvement of 17% as compared to the flat GPU acceleration case. © 2010 IEEE.

关键词： Program processors

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The Future of cloud computing: Opportunities, Challenges and Research Trends

The Future of Cloud Computing: Opportunities, Challenges and...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and cloud) (I-SMAC)

作者： Amanpreet Kaur V.P. Singh Sukhpal Singh Gill Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala Punjab India Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (纸本)9781538614433;9781538614426

In a cloud computing environment, datacenter consists of number of servers, cooling and power delivery equipment's that require enormous measure of computational energy to drive complex frameworks. Due to the rising demand of the computation power, datacenter has become the hub for significant increase in the power consumption, heat dissipation and rise in temperature of the servers. cloud datacenter's energy consumption has increased tremendously due to increase in the computation requirements of the user workload. Thus, saving energy has become an important concern to address. Researchers proposed different techniques to optimize the energy consumption. In this paper, we focus on different aspects of cloud computing for holistic management of cloud resources in an energy-efficient, reliable and sustainable manner. We recognized different opportunities, identified research challenges and propose possible future research directions for cloud computing.

关键词： Servers cloud computing Energy efficiency Energy consumption Data centers Processor scheduling Dynamic scheduling

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Selecting good measurements via 1 relaxation: A convex approach for robust estimation over graphs

Selecting good measurements via 1 relaxation: A convex appro...

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2014 IEEE/RSJ International Conference on Intelligent Robots and systems, IROS 2014

作者： Carlone, Luca Censi, Andrea Dellaert, Frank School of Interactive Computing College of Computing Georgia Institute of Technology AtlantaGA United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781479969340

Pose graph optimization is an elegant and efficient formulation for robot localization and mapping. Experimental evidence suggests that, in real problems, the set of measurements used to estimate robot poses is prone to contain outliers, due to perceptual aliasing and incorrect data association. While several related works deal with the rejection of outliers during pose estimation, the goal of this paper is to propose a grounded strategy for measurements selection, i.e., the output of our approach is a set of 'reliable' measurements, rather than pose estimates. Because the classification in inliers/outliers is not observable in general, we pose the problem as finding the maximal subset of the measurements that is internally coherent. In the linear case, we show that the selection of the maximal coherent set can be (conservatively) relaxed to obtain a linear programming problem with 1 objective. We show that this approach can be extended to (nonlinear) planar pose graph optimization using similar ideas as our previous work on linear approaches to pose graph optimization. We evaluate our method on standard datasets, and we show that it is robust to a large number of outliers and different outlier generation models, while entailing the advantages of linear programming (fast computation, scalability). © 2014 IEEE.

关键词： Linear programming

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EdgeLens: Deep Learning based Object Detection in Integrated IoT, Fog and cloud computing Environments 4

EdgeLens: Deep Learning based Object Detection in Integrated...

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4th International Conference on information systems and Computer Networks, ISCON 2019

作者： Tuli, Shreshth Basumatary, Nipam Buyya, Rajkumar CLOUDS Laboratory School of Computing and Information Systems University of Melbourne Australia Department of Computer Science and Engineering Delhi India Madras India

ISBN: (纸本)9781728136516

Data-intensive applications are growing at an increasing rate and there is a growing need to solve scalability and high-performance issues in them. By the advent of cloud computing paradigm, it became possible to harness remote resources to build and deploy these applications. In recent years, new set of applications and services based on Internet of Things (IoT) paradigm, require to process large amount of data in very less time. Among them surveillance and object detection have gained prime importance, but cloud is unable to bring down the network latencies to meet the response time requirements. This problem is solved by Fog computing which harnesses resources in the edge of the network along with remote cloud resources as required. However, there is still a lack of frameworks that are successfully able to integrate sophisticated software and applications, especially deep learning, with fog and cloud computing environments. In this work, we propose a framework to deploy deep learning-based applications in fog-cloud environments to harness edge and cloud resources to provide better service quality for such applications. Our proposed framework, called EdgeLens, adapts to the application or user requirements to provide high accuracy or low latency modes of services. We also tested the performance of the software in terms of accuracy, response time, jitter, network bandwidth and power consumption and show how EdgeLens adapts to different service requirements. © 2019 IEEE.

关键词： Object detection

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