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On Large-Scale Multiple Testing Over Networks: An Asymptotic Approach

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2023年 9卷 442-457页

作者： Pournaderi, Mehrdad Xiang, Yu Univ Utah Elect & Comp Engn Dept Salt Lake City UT 84112 USA

This work concerns developing communication- and computation-efficient methods for large-scale multiple testing over networks, which is of interest to many practical applications. We take an asymptotic approach and propose two methods, proportion-matching and greedy aggregation, tailored to distributed settings. The proportion-matching method achieves the global BH performance yet only requires a one-shot communication of the (estimated) proportion of true null hypotheses as well as the number of p-values at each node. By focusing on the asymptotic optimal power, we go beyond the BH procedure by providing an explicit characterization of the asymptotic optimal solution. This leads to the greedy aggregation method that effectively approximates the optimal rejection regions at each node, while computation efficiency comes from the greedy-type approach naturally. Moreover, for both methods, we provide the rate of convergence for both the FDR and power. Extensive numerical results over a variety of challenging settings are provided to support our theoretical findings.

关键词： Distributed multiple testing asymptotic FDR control communication-efficient methods heterogeneity greedy algorithm asymptotic optimality

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Coded Multicasting for Content Delivery over Predictable Time-Varying Satellite Communication Networks

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China Communications 2023年第6期20卷 339-367页

作者： Fan Xu Shuo Shao Meixia Tao Qin Huang Qifa Yan Xiaohu Tang Peng Cheng Laboratory ShenzhenGuangdong 518000China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Electronics and Information Engineering BeiHang UniversityBeijing 100191China School of Information Science and Technology Southwest Jiaotong UniversityChengduSichuan 611756China

With the development of astronautic technology, communication satellites now have a tremendous gain in both quantity and quality, and have already shown their capability on multi-functional converged communication other than telecommunication. Under this circumstance, increasing the transmission efficiency of satellite communication network becomes a top priority. In this paper, we focus on content delivery service on satellite networks, where each ground station may have prefetched some file fragments. We cast this problem into a coded caching framework so as to exploit the coded multicast gain for minimizing the satellite communication load. We first propose an optimization-based coded multicast scheme by considering the special property that the satellite network topology is predictable and timevariant. Then, a greedy based fast algorithm is proposed, which can tremendously reduce the computation complexity with a small loss in optimality. Simulation experiments conducted on two Walker constellation satellite networks show that our proposed coded multicast method can efficiently reduce the communication load of satellite networks.

关键词： satellite communication content delivery coded multicast greedy algorithm

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Shape optimization method for axisymmetric disks based on mesh deformation and smoothing approaches

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2023年第12期30卷 2532-2555页

作者： Huang, Lei Li, Hongqing Zheng, Kaiwei Tian, Kuo Wang, Bo Dalian Univ Technol Dept Engn Mech Key Lab Digital Twin Ind Equipment State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

Axisymmetric disk structures with complex contour curves are widely used in aero-engines. The shape optimization is generally carried out to reduce the stress level of axisymmetric disks. In this article, a shape optimization method for axisymmetric disks based on radial basis function (RBF) mesh deformation and Laplace smoothing approaches is proposed. This method can obtain the optimized reduced control points selection of mesh deformation under the influence of design space based on greedy algorithm. RBF mesh deformation is used to change the axisymmetric contour shape. And after deformation, the local mesh quality is monitored and improved by Laplace smoothing. In this article, two illustrative examples used in aero-engines are carried out to validate the effectiveness of the proposed method, including an independent optimization of a turbine disk and a collaborative optimization of a turbine disk with a deflector for minimizing the maximum equivalent stress. To improve the computational efficiency, a two-dimensional (2D) axisymmetric FE model is established. Compared with initial results, optimized results in two examples obtained by the proposed optimization method reduce the maximum von Mises stress by 8.02% and 9.25%, respectively. It can be concluded that the proposed method has significant potential in the shape optimization design of axisymmetric disks.

关键词： Shape optimization mesh deformation Laplace smoothing greedy algorithm axisymmetric disks

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DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text

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IEEE ACCESS 2023年 11卷 32826-32841页

作者： Bewong, Michael Wondoh, John Kwashie, Selasi Liu, Jixue Liu, Lin Li, Jiuyong Islam, Md. Zahidul Kernot, David Charles Sturt Univ Sch Comp Math & Engn Wagga Wagga NSW 2650 Australia Univ South Australia Sch Informat Technol & Math Sci Adelaide SA 5095 Australia Charles Sturt Univ Artificial Intelligence & Cyber Futures Inst Bathurst NSW 2795 Australia Dept Def Def Sci Technol Grp Edinburgh SA 5111 Australia

Topic modelling is important for tackling several data mining tasks in information retrieval. While seminal topic modelling techniques such as Latent Dirichlet Allocation (LDA) have been proposed, the ubiquity of social media and the brevity of its texts pose unique challenges for such traditional topic modelling techniques. Several extensions including auxiliary aggregation, self aggregation and direct learning have been proposed to mitigate these challenges, however some still remain. These include a lack of consistency in the topics generated and the decline in model performance in applications involving disparate document lengths. There is a recent paradigm shift towards neural topic models, which are not suited for resource-constrained environments. This paper revisits LDA-style techniques, taking a theoretical approach to analyse the relationship between word co-occurrence and topic models. Our analysis shows that by altering the word co-occurrences within the corpus, topic discovery can be enhanced. Thus we propose a novel data transformation approach dubbed DATM to improve the topic discovery within a corpus. A rigorous empirical evaluation shows that DATM is not only powerful, but it can also be used in conjunction with existing benchmark techniques to significantly improve their effectiveness and their consistency by up to 2 fold.

关键词： Data models Reliability Australia Task analysis Social networking (online) Context modeling Benchmark testing Document handling Document transformation greedy algorithm information retrieval latent dirichlet allocation multi-set multi-cover problem probabilistic generative topic modelling

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Performance enhancement of vision based fall detection using ensemble of machine learning model

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第6期26卷 4119-4132页

作者： Rastogi, Shikha Singh, Jaspreet Gd Goenka Univ Dept Comp Sci Sohna 122103 Haryana India

An automatic fall detection system (FDS) provides timely medical assistance to the elderly by predicting falls and non-falls, thereby preventing serious injuries and death. This article proposes an ensemble-based FDS focuses on improving the fall detection accuracy by selecting optimal classifiers with a greedy algorithm-based majority voting approach. The proposed ensemble learning is better than any of the individual models because it involves multiple machine learning models along with deep learning model, namely Support Vector Machine, K-Nearest Neighbour, Decision Tree, and Deep LSTM that are combined in majority voting fashion. The greedy algorithm-based majority voting uses three searching criteria, namely forward search, backward search, and recovery search to select the optimal classifiers. The forward search fuses the classifiers based on majority voting error (MVE) and goodness of classifiers, while the backward search removes the classifier based on the majority voting improvement selection criteria. Thus, the classifiers with the lowest MVE and processing time are fused, and the others are removed. Also, the recovery search is introduced to prevent any loss of optimal classifiers by backward search. Finally, classifiers with the best performance are combined to produce accurate fall detection. Then simulation is carried out for the individual ML model, and the proposed ensemble model. According to the evaluation outcome, the proposed ensemble-based FDS achieves high accuracy (99.98%), sensitivity (99.8%), specificity (99.9%), and precision (96.23%) than the conventional approaches. Hence, the proposed ensemble-based FDS proved to enhance fall detection accuracy.

关键词： Ensemble Vision based fall detection greedy algorithm Majority voting Selection criteria

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On approximation classes for adaptive time-stepping finite element methods

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IMA JOURNAL OF NUMERICAL ANALYSIS 2023年第5期43卷 2817-2855页

作者： Actis, Marcelo Morin, Pedro Schneider, Cornelia Univ Nacl Litoral Dept Matemat Fac Ingn Quim S3000AOM Santa Fe Argentina Consejo Nacl Invest Cient & Tecn S3000AOM Santa Fe Argentina Friedrich Alexander Univ Erlangen Nuremberg Appl Math 3 Cauerstr 11 D-91058 Erlangen Germany

We study approximation classes for adaptive time-stepping finite element methods for time-dependent partial differential equations. We measure the approximation error in L-2([0,T) x Omega ) and consider the approximation with discontinuous finite elements in time and continuous finite elements in space, of any degree. As a by-product we define anisotropic Besov spaces for Banach-space-valued functions on an interval and derive some embeddings, as well as Jackson- and Whitney-type estimates.

关键词： approximation classes Besov spaces Whitney estimate adaptive time-stepping finite element methods greedy algorithm

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Learning to Sample: Data-Driven Sampling and Reconstruction of FRI Signals

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IEEE ACCESS 2023年 11卷 71048-71062页

作者： Mulleti, Satish Zhang, Haiyang Eldar, Yonina C. C. Indian Inst Technol Dept Elect Engn Mumbai 400076 India Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210049 Peoples R China Weizmann Inst Sci Fac Math & Comp Sci IL-210049 Rehovot Israel

Finite-rate-of-innovation (FRI) signal model is well suited for time-of-flight imaging applications such as ultrasound, lidar, sonar, radar, and more. Due to their finite degrees of freedom, the sub-Nyquist sampling framework is used for FRI signals. In this framework, sampling is achieved by using appropriate sampling kernels. Reconstruction is performed by first computing Fourier samples of the FRI signal and then applying sparse-recovery algorithms. The choice of the Fourier samples and reconstruction method plays a crucial role in the reconstruction quality. In this paper, we consider jointly optimizing the choice of Fourier samples and reconstruction parameters. Our framework combines a greedy subsampling algorithm and a learning-based sparse recovery method. The combination has three distinct advantages. First, the network does not require knowledge of the FRI pulse shape, which is not the case with existing approaches. Second, the network does not suffer from differentiability issues during training which is common in sampling networks. Further, the proposed algorithm can flexibly handle changes in the sampling rate. Numerical results show that, for a given number of samples, the proposed joint design leads to lower reconstruction error for FRI signals than independent data-driven design methods for noisy and clean samples. We also propose a way to extend the approach for large-scale problems. Our learning-to-sample approach can be readily applied to other sampling setups, including compressed sensing problems.

关键词： INDEX TERMS FRI signal sub-Nyquist sampling sum-of-sincs filter model-based deep learning unrolling greedy algorithm LISTA joint sampling and recovery learn to sample

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The Practical Significance of Bradford's Informetric Model to Predict the Scattering of Articles and Optimize Journal Selection

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AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2024年第1期58卷 32-42页

作者： Fedorets, O. V. Soloshenko, N. S. Russian Acad Sci All Russian Inst Sci & Tech Informat Moscow Russia

Articles scattered in the VINITI abstract database dated 2020-2022 are studied using the example of three thematic fragments: chemistry and chemical technologies, mechanical engineering, and metallurgy and welding. The mathematical formulas of Bradford and Vickery were used to calculate the number of journals in the third scattering zone. It is argued that the Bradford-Vickery law of publication scattering allows us to predict the minimum number of journals that cover more than 90% of articles quantity on a certain topic. The optimization of journal selection is reduced to the solution of the well-known knapsack problem. It is proposed to rank journals by thematic relevance. Unlike the Bradford model, intended to select the most productive journals, the proposed model is designed to select the most relevant journals.

关键词： scientific journals informetry Bradford-Vickery scattering law scattering prediction optimization of information center acquisition knapsack problem greedy algorithm journal thematic relevance State Rubricator of Scientific and Technical Information VINITI RAS

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Resource Allocation in an Open RAN System Using Network Slicing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第1期20卷 471-485页

作者： Motalleb, Mojdeh Karbalaee Shah-Mansouri, Vahid Parsaeefard, Saeedeh Lopez, Onel Luis Alcaraz Univ Tehran Sch ECE Tehran *** Iran Univ Toronto Dept Elect Engn Toronto ON M5S 3G4 Canada Univ Oulu Ctr Wireless Commun Oulu 90570 Finland

The next radio access network (RAN) generation, open RAN (O-RAN), aims to enable more flexibility and openness, including efficient service slicing, and to lower the operational costs in 5G and beyond wireless networks. Nevertheless, strictly satisfying quality-of-service requirements while establishing priorities and promoting balance between the significantly heterogeneous services remains a key research problem. In this paper, we use network slicing to study the service-aware baseband resource allocation and virtual network function (VNF) activation in O-RAN systems. The limited fronthaul capacity and end-to-end delay constraints are simultaneously considered. Optimizing baseband resources includes O-RAN radio unit (O-RU), physical resource block (PRB) assignment, and power allocation. The main problem is a mixed-integer non-linear programming problem that is non-trivial to solve. Consequently, we break it down into two different steps and propose an iterative algorithm that finds a near-optimal solution. In the first step, we reformulate and simplify the problem to find the power allocation, PRB assignment, and the number of VNFs. In the second step, the O-RU association is resolved. The proposed method is validated via simulations, which achieve a higher data rate and lower end-to-end delay than existing methods.

关键词： Resource management Radio access networks Ultra reliable low latency communication Quality of service Network slicing Delays Baseband Open radio access network (O-RAN) virtual network function (VNF) Index Terms network slicing knapsack problem greedy algorithm Karush-Kuhn-Tucker (KKT) conditions

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Improving access to urban parks through public transit optimization

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Frontiers of Architectural Research 2024年第3期13卷 575-592页

作者： Ning Xu Kaidan Guan Pu Wang School of Architecture Southeast UniversityNanjing 210096China Jiangsu Provincial Urban and Rural Digital Technology Engineering Center Nanjing 210096China

This study establishes an evaluation and optimization framework for the public transit network based on social network analysis and a greedy algorithm,aiming to explore a quantitative approach to improving access to urban parks through public transit *** network analysis and the ArcGIS platform are used to build a public transit network model within Nanjing Old City and analyze its overall network structure *** study also focuses on a method to improve the convenience of reaching regional and citylevel parks by public transit by increasing access and connecting points accordingly.A greedy algorithm is introduced to generate an optimized solution for improving public transit accessibility to regional and city-level parks,consequently enhancing their *** major findings include:(1)The greedy algorithm effectively enhances the performance of the public transit network,but its benefits gradually diminish as more stations are added.(2)Strategically adding stations enhances the performance of most public transit access points,creating efficient pathways for other stations to directly reach these access points and enter regional and city-level parks.(3)The optimized public transit network model offers guidance for the planning and layout of regional and city-level *** site selection for new parks should prioritize establishing connections with the“hubs”in the public transit *** proposed optimization of the public transit network in this study is specific to a single type of urban park,but subsequent research could be conducted to extend the optimization of public transit accessibility around more urban public resources.

关键词： Urban parks Social network analysis Accessibility optimization Public transit network greedy algorithm

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