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Learning and Meshing From Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第12期44卷 10068-10086页

作者： Lei, Jiabao Jia, Kui Ma, Yi South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Reconstruction of object or scene surfaces has tremendous applications in computer vision, computer graphics, and robotics. The topic attracts increased attention with the emerging pipeline of deep learning surface reconstruction, where implicit field functions constructed from deep networks (e.g., multi-layer perceptrons or MLPs) are proposed for generative shape modeling. In this paper, we study a fundamental problem in this context about recovering a surface mesh from an implicit field function whose zero-level set captures the underlying surface. To achieve the goal, existing methods rely on traditional meshing algorithms (e.g., the de-facto standard marching cubes);while promising, they suffer from loss of precision learned in the implicit surface networks, due to the use of discrete space sampling in marching cubes. Given that an MLP with activations of Rectified Linear Unit (ReLU) partitions its input space into a number of linear regions, we are motivated to connect this local linearity with a same property owned by the desired result of polygon mesh. More specifically, we identify from the linear regions, partitioned by an MLP based implicit function, the analytic cells and analytic facesthat are associated with the function's zero-level isosurface. We prove that under mild conditions, the identified analytic faces are guaranteed to connect and form a closed, piecewise planar surface. Based on the theorem, we propose an algorithm of analytic marching, which marches among analytic cells to exactly recover the mesh captured by an implicit surface network. We also show that our theory and algorithm are equally applicable to advanced MLPs with shortcut connections and max pooling. Given the parallel nature of analytic marching, we contribute AnalyticMesh, a software package that supports efficient meshing of implicit surface networks via CUDA parallel computing, and mesh simplification for efficient downstream processing. We apply our method to different settin

关键词： Surface reconstruction Shape Faces Isosurfaces Surface treatment partitioning algorithms Three-dimensional displays Generative shape modeling implicit surface representation polygon mesh deep learning multi-layer perceptron

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Data-Plane-Assisted State Replication With Network Function Virtualization

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 2934-2945页

作者： Lotfimahyari, Iman Sviridov, German Giaccone, Paolo Bianco, Andrea Politecn Torino Dipartimento Elettron & Telecomunicaz I-10129 Turin Italy

Modern 5G networks are capable of providing ultralow-latency and highly scalable network services by employing modern networking paradigms, such as software-defined networking (SDN) and network function virtualization. The latter enables performance-critical network applications to be run in a distributed fashion directly inside the infrastructure. Being distributed, those applications rely on sophisticated state replication algorithms to synchronize states among each other. Nevertheless, current implementations of such algorithms do not fully exploit the potential of the modern infrastructures, thus leading to suboptimal performance. In this article, we propose STARE, a novel state replication system tailored for 5G networks. At its core, STARE exploits stateful SDN to offload replication-related processes to the data plane, ultimately leading to reduced communication delays and processing overhead for virtual network functions. We provide a detailed description of the STARE architecture alongside a publicly-available P4-based implementation. Furthermore, our evaluation shows that STARE is capable of scaling to big networks while introducing low overhead in the network.

关键词： Protocols Network function virtualization partitioning algorithms 5G mobile communication Wireless fidelity Scalability Real-time systems 5G network function virtualization (NFV) programmable data planes publish– subscribe model state sharing

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Optimal Weighted Load Balancing in TCAMs

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第3期30卷 985-998页

作者： Sadeh, Yaniv Rottenstreich, Ori Kaplan, Haim Tel Aviv Univ Dept Comp Sci IL-69978 Tel Aviv Israel Technion Israel Inst Technol IL-3200003 Haifa Israel

Traffic splitting is a required functionality in networks, for example for load balancing over multiple paths or among different servers. The capacities of the servers determine the partition by which traffic should be split. A recent approach implements traffic splitting within the ternary content addressable memory (TCAM), which is often available in switches. It is important to reduce the amount of memory allocated for this task since TCAMs are power consuming and are often also required for other tasks such as classification and routing. Previous work showed how to compute the smallest prefix-matching TCAM necessary to implement a given partition exactly. In this paper we solve the more practical case, where at most n prefix-matching TCAM rules are available, restricting the ability to implement exactly the desired partition. We give simple and efficient algorithms to find n rules that generate a partition closest in $L_{infinity}$ to the desired one. We do the same for a one-sided version of $L_{infinity}$ which equals to the maximum overload on a server and for a relative version of it. We use our algorithms to evaluate how the expected error changes as a function of the number of rules, the number of servers, and the width of the TCAM.

关键词： Servers partitioning algorithms Approximation algorithms Task analysis Complexity theory Load management Memory management Computer network management packet switching data structures algorithms

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WL-WD: Wear-Leveling Solution to Mitigate Write Disturbance Errors for Phase-Change Memory

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IEEE ACCESS 2022年 10卷 11420-11431页

作者： Kim, Moonsoo Lee, Hyokeun Kim, Hyun Lee, Hyuk-Jae Samsung Elect Hwasung 18448 South Korea Seoul Natl Univ Dept Elect & Comp Engn Interuniv Semicond Res Ctr Seoul 08826 South Korea Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Res Ctr Elect & Informat Technol Seoul 01811 South Korea

Phase-change memory (PCM) is a promising non-volatile memory device due to its attractive properties such as fast access time and byte-addressability. However, PCM is still difficult to be used as a main memory because of its weakness in endurance and write disturbance. Conventional wear-leveling algorithms have attempted to handle short endurance, but they have not addressed the issue of write disturbance even though both issues are caused by write operations to PCM. This paper proposes a wear-leveling algorithm that addresses not only the endurance, but also the write disturbance. From the observation that the write disturbance errors and short endurance issues mostly take place in hot addresses, the proposed algorithm first detects hot addresses and maps them to 'hot' regions, which are customized memory regions designed to be robust to write disturbance errors and to support effective wear-leveling. The 'hot' region is not fixed to a specific part, but it moves over an entire memory space to make every cell belong to 'hot' regions in a uniform manner. On the other hand, cold addresses are mapped in 'normal' memory regions by simple linear mapping to reduce the hardware overhead. The proposed algorithm can reduce the write disturbance errors by more than 70% with only a slight instruction per cycle (IPC) degradation. Moreover, the wear-leveling performance is also enhanced by more than 3% compared to other wear-leveling algorithms.

关键词： Phase change materials partitioning algorithms Interference Probabilistic logic Memory management Phase change memory Heuristic algorithms Phase-change memory write disturbance error wear-leveling memory reliability memory endurance

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Co-Clustering on Bipartite Graphs for Robust Model Fitting

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 6605-6620页

作者： Lin, Shuyuan Luo, Hailing Yan, Yan Xiao, Guobao Wang, Hanzi Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Xiamen Univ Fujian Key Lab Sensing & Comp Smart City Sch Informat Xiamen 361005 Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou 350108 Peoples R China

Recently, graph-based methods have been widely applied to model fitting. However, in these methods, association information is invariably lost when data points and model hypotheses are mapped to the graph domain. In this paper, we propose a novel model fitting method based on co-clustering on bipartite graphs (CBG) to estimate multiple model instances in data contaminated with outliers and noise. Model fitting is reformulated as a bipartite graph partition behavior. Specifically, we use a bipartite graph reduction technique to eliminate some insignificant vertices (outliers and invalid model hypotheses), thereby improving the reliability of the constructed bipartite graph and reducing the computational complexity. We then use a co-clustering algorithm to learn a structured optimal bipartite graph with exact connected components for partitioning that can directly estimate the model instances (i.e., post-processing steps are not required). The proposed method fully utilizes the duality of data points and model hypotheses on bipartite graphs, leading to superior fitting performance. Exhaustive experiments show that the proposed CBG method performs favorably when compared with several state-of-the-art fitting methods.

关键词： Data models Bipartite graph Computational modeling partitioning algorithms Clustering methods Analytical models Fitting Robust model fitting multiple models bipartite graph partitioning co-clustering multiple-structure data

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Clustering Big Data Based on Distributed Fuzzy K-Medoids: An Application to Geospatial Informatics

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IEEE ACCESS 2022年 10卷 20926-20936页

作者： Madbouly, Magda M. Darwish, Saad M. Bagi, Noha A. Osman, Mohamed A. Alexandria Univ Inst Grad Studies & Res Dept Informat Technol Alexandria 21526 Egypt Alexandria Water Co Alexandria 21581 Egypt Higher Inst Management Informat Technol Management Informat Syst Dept Kafr Al Sheikh 33511 Egypt

The advent of big data related to spatial position knowledge, called geospatial big data, provides us with opportunities to recognize the urban environment. Existing database processing methods are inadequate to rapidly provide reliable results in a geospatial big data context due to the need for defining approximation "measures" and the increasing execution time for the queries. The clustering method yields the functional effects. How to scale and accelerate clustering algorithms while maintaining high clustering efficiency, on the other hand, remains a significant challenge. The paper's primary contribution is the introduction of a modified hierarchical distributed k-medoid clustering method that is specific to spatial query analysis for big data. To improve the efficiency of the k-medoid algorithm and obtain more precise clusters, the suggested model utilizes the Fuzzy k-Medoids method to overcome outliers in the spatial data set and to deal with data uncertainty. The method is complex in nature since it is not predicated on the number of right clusters. The proposed model is divided into two phases: the first step creates local clusters based on a portion of the entire dataset;this stage makes extensive use of the parallelism paradigm provided by the Apache Spark framework;and the second phase aggregates the local clusters to produce compact and reliable final clusters. The proposed model greatly reduces the amount of knowledge shared during the aggregation process and automatically produces the appropriate number of clusters based on the dataset characteristics. The results show that the proposed model outperforms the traditional K-medoids in terms of accuracy of obtained centers in big data applications.

关键词： Clustering algorithms Big Data Geospatial analysis Heuristic algorithms partitioning algorithms Distributed databases Scalability Geospatial informatics big data clustering dynamic clustering Apache Spark fuzzy K-medoids

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Understanding Communities From a New Functional Perspective in Power Grids

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 3072-3083页

作者： Wang, Xiaoliang Xue, Fei Lu, Shaofeng Jiang, Lin Bompard, Ettore Masera, Marcelo Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China Univ Liverpool Liverpool L69 3BX Merseyside England South China Univ Technol Sch Intelligent Engn Guangzhou Int Campus Guangzhou 511436 Peoples R China Univ Liverpool Dept Elect Engn & Electron Liverpool L69 3BX Merseyside England Politecn Torino I-10129 Turin Italy Joint Res Ctr European Commiss I-21027 Ispra Italy

The contributions of this article include a new concept, a new question, and a new method. This article proposes the concept of functional community, which is different from conventional topological community. Functional community is defined with two meanings: (1) tighter internal coupling for better efficiency within the same community;(2) more internal transmission from source to sink nodes within the same community. Then a new question about how to detect functional communities in power grids is analyzed, and most existing algorithms are not applicable. Therefore, a new method is put forward. Corresponding to meaning 1, electrical coupling strength (ECS) is defined to replace conventional adjacency matrix;corresponding to meaning 2, power supply strength is defined and integrated with ECS to form the newly defined electrical functional strength. Based on these two changes, we apply the proposed power supply modularity as a benchmark to evaluate any partitioning of power grids. Moreover, the Newman fast algorithm is modified with power supply modularity maximization to detect functional communities in power grids. The capability of the proposed partitioning method is demonstrated via the IEEE-118, IEEE-300 bus systems, and an Italian power grid. We argue that the conventional topological modularity may exaggerate the community characteristics of power grids and is inferior to the power supply modularity in detecting some functional features in communities. The work can also give inspiration to other engineering networks for functional communities.

关键词： Power supplies Power grids partitioning algorithms Clustering algorithms Couplings Complex networks Topology Community detection complex network functional community power grids partitioning power supply modularity

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Design and Assessment of Sweep Coverage algorithms for Multiagent Systems With Online Learning Strategies

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第9期52卷 5494-5505页

作者： Zhai, Chao Zhang, Hai-Tao Xiao, Gaoxi Chen, Michael Z. Q. China Univ Geosci Sch Automat Engn Res Ctr Intelligent Technol Geoexplorat Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Adv Control & Intelligent Automat C Engn Res Ctr Intelligent Technol Geoexplorat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipments & Technol Wuhan 430074 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

Cooperative sweep coverage of multiagent systems (MASs) has found broad applications in various fields. This article proposes a scheme to address the sweep coverage problem of MASs within uncertain environments. In the proposed formulation, the coverage region is divided into multiple stripes, of which each has the workload completed by MASs in sequence. When the workload on the current stripe is completed, all the agents switch to the next together. The temporal dependence between the switching time computation and the sweep coverage operation is taken into account, and an online learning strategy is designed to handle environmental uncertainties and balance the workload among agents on the same stripe. Thereby, the distributed sweep coverage algorithm is developed to guarantee the complete sweep coverage, which consists of three operations, i.e., communication, workload partition, and sweeping. Theoretical analysis is afterward conducted to estimate the upper bound for the error between the actual and optimal coverage time. Finally, numerical simulations are carried out to substantiate the effectiveness and superiority of the proposed scheme.

关键词： Uncertainty Upper bound Switches Task analysis Robot sensing systems partitioning algorithms Multi-agent systems Distributed control distributed estimation learning control systems

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Clustering Driven Iterated Hybrid Search for Vertex Bisection Minimization

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IEEE TRANSACTIONS ON COMPUTERS 2022年第10期71卷 2370-2380页

作者： Jin, Yan Xiong, Bowen He, Kun Hao, Jin-Kao Li, Chu-Min Fu, Zhang-Hua Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Univ Angers Dept Comp Sci LERIA F-49045 Angers France Inst Univ France F-75231 Paris France Univ Picardie Jules Verne MIS Fac Comp Sci F-80039 Amiens 01 France Chinese Univ Hong Kong Inst Robot & Intelligent Mfg Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China

The Vertex Bisection Minimization Problem (VBMP) is a relevant graph partitioning model with a variety of practical applications. This work introduces a clustering driven iterated hybrid search algorithm (CLUHS), which is the first approach that applies clustering to reinforce iterated local search for solving VBMP. The proposed CLUHS uses hierarchical clustering to build an initial solution, guide local search process and perform search diversification. Experimental studies on 137 benchmark instances show the high competitiveness of the proposed approach compared to the state-of-the-art methods. In particular, CLUHS finds new record-breaking solutions for 18 instances.

关键词： Clustering algorithms Minimization Measurement partitioning algorithms Search problems Perturbation methods Linear programming Vertex bisection graph partitioning clustering NP-hard local search

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A Block-Based Triangle Counting Algorithm on Heterogeneous Environments

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第2期33卷 444-458页

作者： Yasar, Abdurrahman Rajamanickam, Sivasankaran Berry, Jonathan W. Catalyurek, Umit V. Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA Sandia Natl Labs Ctr Res Comp POB 5800 Albuquerque NM 87185 USA

Triangle counting is a fundamental building block in graph algorithms. In this article, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition goes one step further: it partitions the set of triangles in the graph. By streaming these small tasks to compute resources, we can solve problems that do not fit on a device. We demonstrate the effectiveness of our approach by providing an implementation on a compute node with multiple sockets, cores and GPUs. The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU. Using that metric, our approach is 20 percent faster. When copy times are included, our algorithm takes 3.2 seconds. This is 5.6 times faster than the fastest published CPU-only time.

关键词： Task analysis partitioning algorithms Heuristic algorithms Kernel Hardware Graphics processing units Ear Triangle counting task-based block-based sub-graph multi-core multi-GPU

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