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International Conference on Information, Communication and Networks (ICICN)

作者： Xi Han Peiyang Li Yichen Liu Jiahao Zhang Yongmei Zhang Li Ma Department of Information and Communication Engineering North China University of Technology Beijing China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

This paper explores downlink communication in a multiple-input multiple-output Orthogonal Time Frequency Space (MIMO-OTFS) system. By leveraging the prior information of the beam space channel (BC), we derive a novel Laplace shrinkage function. We propose a BC estimation scheme, Laplace Distribution Enhanced LAMP (LDE-LAMP), by integrating the Learned Approximate Message Passing (LAMP) network with this new shrinkage function. Simulation results demonstrate that the proposed LDE-LAMP network algorithm achieves superior channel estimation accuracy compared to traditional LAMP networks.

关键词： Time-frequency analysis Accuracy Simulation Message passing Estimation Channel estimation approximation algorithms Downlink Millimeter wave communication MIMO communication

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Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control

Physics-Informed Kernel Embeddings: Integrating Prior System...

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American Control Conference (ACC)

作者： Adam J. Thorpe Cyrus Neary Franck Djeumou Meeko M. K. Oishi Ufuk Topcu Electrical & Computer Eng. University of New Mexico Abq. NM Department of Electrical and Computer Engineering Oden Institute for Computational Engineering and Sciences and the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin Austin TX USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Data-driven control algorithms use observations of system dynamics to construct an implicit model for the purpose of control. However, in practice, data-driven techniques often require excessive sample sizes, which may be infeasible in real-world scenarios where only limited observations of the system are available. Furthermore, purely data-driven methods often neglect useful a priori knowledge, such as approximate models of the system dynamics. We present a method to incorporate such prior knowledge into data-driven control algorithms using kernel embeddings, a nonparametric machine learning technique based in the theory of reproducing kernel Hilbert spaces. Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem. We formulate the biased learning problem as a least-squares problem with a regularization term that is informed by the dynamics, that has an efficiently computable, closed-form solution. Through numerical experiments, we empirically demonstrate the improved sample efficiency and out-of-sample generalization of our approach over a purely data-driven baseline. We demonstrate an application of our method to control through a target tracking problem with nonholonomic dynamics, and on spring-mass-damper and F-16 aircraft state prediction tasks.

关键词： Target tracking Machine learning algorithms Closed-form solutions System dynamics Heuristic algorithms Machine learning approximation algorithms

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Fully Dynamic $k-\text{Clustering}$ with Fast Update Time and Small Recourse

Fully Dynamic $k-\text{Clustering}$ with Fast Update Time an...

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Annual IEEE Symposium on Foundations of Computer Science

作者： Sayan Bhattacharya Martín Costa Naveen Garg Silvio Lattanzi Nikos Parotsidis University of Warwick United Kingdom IIT Delhi India Google Research Switzerland

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

In the dynamic metric $k-\mathbf{median}$ problem, we wish to maintain a set of $k$ centers $S\subseteq V$ in an input metric space $(V, d)$ that gets updated via point insertions/deletions, so as to minimize the objective $\sum\nolimits_{x\in V}\min\nolimits_{y\in S}d(x, y)$ . The quality of a dynamic algorithm is measured in terms of its approximation ratio, “recourse” (the number of changes in $S$ per update) and “update time” (the time it takes to handle an update). The ultimate goal in this line of research is to obtain a dynamic $O(1)$ approximation algorithm with $\tilde{O}(1)$ recourse and $\tilde{O}(k)$ update time. Dynamic $k-\mathbf{median}$ is a canonical example of a class of problems known as dynamic $k-\mathbf{clustering}$ , that has received significant attention in recent years [Fichtenberger et al, SODA'21], [Bateni et al, SODA'23], [Lacki et al, SODA'24]. To the best of our knowledge, however, all these previous papers either attempt to minimize the algorithm's recourse while ignoring its update time, or minimize the algorithm's update time while ignoring its recourse. For dynamic $k-\mathbf{median}$ in particular, the state-of-the-art results get $\tilde{O}(k^{2})$ update time and $O(k)$ recourse [Cohen-Addad et al, ICML'19], [Henzinger and Kale, ESA'20], [Bhattacharya et al, NeurIPS'23]. But, this recourse bound of $O(k)$ can be trivially obtained by recomputing an optimal solution from scratch after every update, provided we ignore the update time. In addition, the update time of $\tilde{O}(k^{2})$ is polynomially far away from the desired bound of $\tilde{O}(k)$ . We come arbitrarily close to resolving the main open question on this topic, with the following results. (I) We develop a new framework of randomized local search that is suitable for adaptation in a dynamic setting. For every $\epsilon > 0$ , this gives us a dynamic $k-\mathbf{median}$ algorithm with $O(k^{\ep

关键词： Computer science Heuristic algorithms approximation algorithms Extraterrestrial measurements Time measurement

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GAIPPTSC: An Efficient Container Tasks Orchestration and Scheduling Algorithm

GAIPPTSC: An Efficient Container Tasks Orchestration and Sch...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Guofeng Zheng Bo Jiang Jinhui Xie Chuxuan Shi The 32nd Research Institute of China Electronics Technology Group Corporation Shanghai China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Containers are a very promising virtualization technology because of their lightweight, portable, and scalable advantages. In the case of mixed integer nolinear programming (MINP) problems such as orchestration and scheduling for container tasks, it is particularly important to combine heuristic methods to solve the problem. In order to solve the problem quickly and improve from a number of performance indicators, this paper firstly establishes a mixed integer nonlinear model for container tasks orchestration and scheduling scenarios, and then proposes a GAIPPTSC algorithm to obtain advantageous solutions in various indicators. In the experimental results, in terms of load balancing, the GAIPPTSC algorithm is 4.2% of the average of the other algorithms. Makespan also shortened by an average of 24.7%. In terms of energy consumption, it was reduced by an average of 53.6%. Overall communication overhead is also minimized. Correspondingly, the overall time cost of the algorithm is also increased by approximately 5 times, which is acceptable in the absolute time dimension. Therefore, the experiment proves the efficiency of the GAIPPTSC algorithm.

关键词： Measurement Scheduling algorithms Containers Programming approximation algorithms Load management Scheduling

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Multiple Greedy Quasi-Newton Methods for Saddle Point Problems

Multiple Greedy Quasi-Newton Methods for Saddle Point Proble...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Minheng Xiao Shi Bo Zhizhong Wu Dept. Integrated System Engineering Ohio State University Columbus USA Dept. Mathematics and Statistics Boston University Boston USA College of Engineering UC Berkeley Berkeley USA

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

This paper introduces the Multiple Greedy Quasi-Newton (MGSR1-SP) method, a novel approach to solving strongly-convex-strongly-concave (SCSC) saddle point problems. Our method enhances the approximation of the squared indefinite Hessian matrix inherent in these problems, significantly improving both stability and efficiency through iterative greedy updates. We provide a thorough theoretical analysis of MGSR1-SP, demonstrating its linear-quadratic convergence rate. Numerical experiments conducted on AUC maximization and adversarial debiasing problems, compared with state-of-the-art algorithms, underscore our method's enhanced convergence rate. These results affirm the potential of MGSR1-SP to improve performance across a broad spectrum of machine learning applications where efficient and accurate Hessian approximations are crucial.

关键词： Machine learning algorithms Accuracy Estimation Machine learning approximation algorithms Stability analysis Iterative algorithms Optimization Numerical stability Convergence

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A dynamic optimization approach to steady-state input allocation for nonlinear redundant systems

A dynamic optimization approach to steady-state input alloca...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Roberto Masocco Lorenzo Tarantino Sergio Galeani Mario Sassano Department of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Rome Italy

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper addresses the problem of dynamic input allocation for nonlinear systems that exhibit a redundant input configuration and admit a periodic steady-state behavior. The control task is formulated in a general framework that encompasses a wide variety of practical scenarios, providing greater flexibility in the definition of the operators involved. The peculiar challenges posed by nonlinear control allocation problems are then examined from a different perspective, offered by a formulation in the context of optimal control theory. The similarities and differences between the two formulations are highlighted and discussed, with the aim of establishing a link between the two subjects in order to share their respective tools and results. Finally, an algorithm to determine approximate solutions to the control allocation problem is developed and refined using dynamic optimization tools.

关键词： Machine learning algorithms Heuristic algorithms Optimal control Machine learning Dynamic scheduling approximation algorithms Steady-state Resource management Nonlinear systems Optimization

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Implementation and Performance Measurement of the SUHDSA (Secure, Useful, and High-performance Data-Stream Anonymization)+ Algorithm

Implementation and Performance Measurement of the SUHDSA (Se...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Jiyeon Lee Soonseok Kim Dept. AI Information Security Halla University Wonju Korea

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

This paper focuses on tables in microdata format, where each tuple contains specific individual information collected in real-time and stored at the tuple level. The collected information within each tuple, being personal, necessitates privacy protection. Therefore, we need to anonymize this information, which is the main theme of this paper. In this context, we have proposed the SUHDSA algorithm, an improvement over Sopaoglu and Abul's UBDSA algorithm, and have further enhanced its security with the proposed SUHDSA+ algorithm. This paper aims to validate the results of our previously proposed SUHDSA+ algorithm through empirical experiments. The experiments show that while the existing SUHDSA+ takes between 3.94 to 7.98 seconds longer in execution time compared to the SUHDSA algorithm, it performs about 45.6% faster than the original UBDSA. In terms of information loss, there was an increase of up to 18% compared to the original SUHDSA; however, when the k-value increased from 3 to 10, the loss rate was up to 19% less, indicating a significant impact of the increased k-value.

关键词： Privacy Codes Data integrity Computational modeling approximation algorithms Real-time systems Loss measurement

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A Novel Data Mining Framework for Discovering Association Rules in Multivariate Time Series

A Novel Data Mining Framework for Discovering Association Ru...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Shengwei Wang Jianhua Lyu Baili Zhang School of Software Southeast University Nanjing China College of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

As a significant field within data mining, association rule mining plays a pivotal role in unveiling relationships between items within a dataset. Existing approaches in multivariate time series (MTS) association rule mining primarily emphasize the generation of association rules by combining features from different variables, but neglect to adequately consider the temporal relationship between these features and ignore the trend information of variables, resulting in an incomplete capture of the essential characteristics inherent in MTS data. In this paper, we introduce a novel framework for association rule mining in multivariate time series. First, we discretize the multivariate time series into the symbolic sequence using trend-based symbolic aggregate approximation(TSAX). Then, a rule mining method is introduced to discover association rules that consider the temporal relationship among symbols of different variables. The results of experiments demonstrate the discovery of significant rules within the “Weather&Traffic” dataset which gives the traffic conditions in France depending on the weather. Further, we also provide the outcomes of the sensitivity analysis conducted on our method, illustrating that significant rules can be mined by adjusting the parameters of TSAX to different settings. The experimental results highlight the utility of our proposed framework in discovering temporal association rules within multivariate time series datasets.

关键词： Control engineering Sensitivity analysis Aggregates Time series analysis Symbols Market research approximation algorithms

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Invariant Checking for SMT-based Systems with Quantifiers

arXiv

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arXiv 2024年

作者： Redondi, Gianluca Cimatti, Alessandro Griggio, Alberto McMillan, Kenneth Fondazione Bruno Kessler Universitá di Trento Italy Fondazione Bruno Kessler Italy University of Texas Austin United States

This paper addresses the problem of checking invariant properties for a large class of symbolic transition systems, defined by a combination of SMT theories and quantifiers. State variables can be functions from an uninterpreted sort (finite, but unbounded) to an interpreted sort, such as the the integers under the theory of linear arithmetic. This formalism is very expressive and can be used for modeling parameterized systems, array-manipulating programs, and more. We propose two algorithms for finding universal inductive invariants for such systems. The first algorithm combines an IC3-style loop with a form of implicit predicate abstraction to construct an invariant in an incremental manner. The second algorithm constructs an under-approximation of the original problem, and searches for a formula which is an inductive invariant for this case;then, the invariant is generalized to the original case, and checked with a portfolio of techniques. We have implemented the two algorithms and conducted an extensive experimental evaluation, considering various benchmarks and different tools from the literature. As far as we know, our method is the first capable of handling in a large class of systems in a uniform way. The experiment shows that both algorithms are competitive with the state of the art. © 2024, CC BY.

关键词： approximation algorithms

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Deep-Unfolded Joint Activity and Data Detection for Grant-Free Transmission in Cell-Free Systems

Deep-Unfolded Joint Activity and Data Detection for Grant-Fr...

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International Symposium on Wireless Communication Systems (ISWCS)

作者： Gangle Sun Wenjin Wang Wei Xu Christoph Studer National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolded joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formulate a joint activity and data detection optimization problem, which we solve approximately using forward-backward splitting (FBS). We then apply deep unfolding to FBS to optimize algorithm parameters using machine learning. In order to improve data detection (DD) performance, reduce algorithm complexity, and enhance active user detection (AUD) accuracy, we employ a momentum strategy, an approximate posterior mean estimator, and a novel soft-output AUD module, respectively. Simulation results confirm the efficacy of DU-JAD for AUD and DD.

关键词： Wireless communication Machine learning algorithms Accuracy Simulation Machine learning approximation algorithms Complexity theory

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