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Neuromorphic Computing (ICNC), International Conference on

作者： Hao Deng Jingxin Liu Jun Peng Yingxu Wang Fei Chang School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning China School of Mathematics and Physics Sciences Chongqing University of Science and Technology Chongqing China Chongqing Research Institute of Intelligent Mathematics and Autonomous AI (RI-IM·AI*) Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

This paper studies stability conditions for neural networks with stochastic impulsive intensity and impulsive density. Firstly, the pth moment exponential stability conditions are given, where the impulsive intensities of impulsive neural networks are independent and identically distributed, and the impulsive sequence is defined by impulsive density. Then, taking a linear time-varying impulsive system as an example, it is shown that impulsive sequence defined by average impulsive interval is sometime insufficient in terms of stabilizing such linear time-varying systems. Finally, two examples are presented to verify that impulsive density method is more effective and universal than average impulsive interval method when dealing with time-varying systems.

关键词： Sufficient conditions Stability Neuromorphic engineering Neural networks Stability criteria Control theory Time-varying systems Numerical stability

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QCA based design of cost-efficient code converter with temperature stability and energy efficiency analysis

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Materials Today: Proceedings 2022年 49卷 3585-3594页

作者： Dhrubajyoti Bhowmik Jayanta Pal Munesh Chandra Apu Kumar Saha Nitendra Kumar Department of Computer Science and Engineering NIT Agartala India Department of Information Technology Tripura University India Department of Mathematics NIT Agartala India Department of Applied Sciences and Humanities IIMT College of Engineering Greater Noida India

Quantum dot Cellular Automata (QCA) are a prominent nanotechnology that is widely employed in digital circuits and systems. In comparison to complementary metal–oxide semiconductor (CMOS) technology, QCA is a remarkable and challenging alternative with many attractive aspects such as fast execution and low power use. To accomplish all arithmetic processes, code converters are the fundamental unit of information change. Most of the converters, out of many designed for far in QCA, did not considered temperature stability, energy dissipation which may contribute to propagate as a non-operational circuit in various temperatures. This paper proposes a novel concept for a binary-to-gray code and a BCD-to-Excess 3 code converter based on QCA. The promised structure diminishes the number of cells, area utilized to make the design cost effective. The fundamental purpose of this research is to give a temperature stable and energy efficient converter design. The operability of the introduced design is verified with the QCADesigner2.0.3 tool and the temperature stability factor and energy waste are evaluated with QCADesignerE2.0.3.

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Incremental Reduction Methods Based on Granular Ball Neighborhood Rough Sets and Attribute Grouping

SSRN

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SSRN 2023年

作者： Li, Yan Wu, Xiaoxue Wang, Xizhao Research Center for Applied Mathematics and Interdisciplinary Sciences Beijing Normal University Zhuhai Campus Zhuhai519087 China College of Mathematics and Information Science Hebei University Baoding071002 China Big Data Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China

In the framework of rough sets, incremental algorithms can effectively reduce repetitive computations for dynamic datasets by discovering the update principles of relevant *** the variations in attribute sets, incremental methods based on neighborhood granulation provide fast updates to attribute reduction in continuous-valued information systems. However, most of these methods assume that the sample distribution and neighborhood radius remain fixed with changes in the attribute set, which may mislead the granulation process and affect the model’s classification ability. In this paper, a new incremental reduction method based on granular balls and attribute grouping is proposed for dynamic information systems with multiple attribute additions. Different neighborhood radii are adaptively determined when the attribute set changes, and the number of neighborhood granules can also be effectively ***, the attributes are grouped based on the k-means algorithm, and only attributes from different groups or with small relevance to those in the current reduction set are considered to be incorporated as a reduction attribute, thus reducing the computation time and simultaneously preserving informative *** and nonlinear correlation coefficients are used to measure attribute relevance based on which three corresponding incremental reduction algorithms are ***, extensive experimental results on 12 benchmark datasets are shown to compare the proposed method with non-incremental and typical incremental methods in terms of time cost, classification accuracy, the size of attribute reduction, and coverage of the reduction. © 2023, The Authors. All rights reserved.

关键词： Granulation

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An Effective Over-sampling Method for Imbalanced Data Sets Classification

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Chinese Journal of Electronics 2023年第3期20卷 489-494页

作者： Yun ZHAI Nan MA Da RUAN Bing AN School of Information Engineering University of Science and Technology Beijing Beijing China School of Computer Science Liaocheng University Liaocheng China Information College Beijing Union University Beijing China Department of Applied Mathematics and Computer Science Ghent University Ghent Belgium Belgian Nuclear Research Centre (SCK*CEN) Mol Belgium

Imbalanced data sets in real-world applications have a majority class with normal instances and a minority class with abnormal or important instances. Learning from such data sets usually generates biased classifiers that have a higher predictive accuracy over the majority class, but a rather poorer predictive accuracy over the minority class. The Synthetic minority over-sampling technique (SMOTE) is specifically designed for learning from imbalanced data sets. This paper presents a novel approach for learning from imbalanced data sets, based on an improved SMOTE algorithm. The approach deals with noise data by a hierarchical filtering mechanism, employs a selection strategy of the minority instances and makes full use of dynamic distribution density of the minority followed by the SMOTE process. This empirical analysis of the approach showed quantitatively competitive with SMOTE and series of its improved algorithm in terms of the receiver operating characteristic curve when applied to several highly and moderately imbalanced data sets.

关键词： Electric potential Filtering Heuristic algorithms Decision making Receivers Machine learning Knowledge discovery

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Wigner non-negative states that verify the Wigner entropy conjecture

arXiv

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arXiv 2024年

作者： Qian, Qipeng Gagatsos, Christos N. Program in Applied Mathematics The University of Arizona TucsonAZ85721 United States Department of Electrical and Computer Engineering The University of Arizona TucsonAZ85721 United States Wyant College of Optical Sciences The University of Arizona TucsonAZ85721 United States

We present further progress, in the form of analytical results, on the Wigner entropy conjecture set forth in [Phys. Rev. A 104, 042211 (2021)] and [J. Phys. A: Math. Theor. 50 385301]. Said conjecture asserts that the differential entropy defined for non-negative, yet physical, Wigner functions is minimized by pure Gaussian states while the minimum entropy is equal to 1 + ln π. We prove this conjecture for the qubits formed by Fock states |0) and |1) that correspond to non-negative Wigner functions. In particular, we derive an explicit form of the Wigner entropy for those states lying on the boundary of the set of Wigner non-negative qubits. We then consider general mixed states and derive a sufficient condition for the conjecture’s validity. Lastly, we elaborate on the states which are in accordance with our condition. Copyright © 2024, The Authors. All rights reserved.

关键词： Entropy

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Delay-Induced Watermarking for Detection of Replay Attacks in Linear Systems

Delay-Induced Watermarking for Detection of Replay Attacks i...

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American Control Conference (ACC)

作者： Christoforos Somarakis Raman Goyal Erfaun Noorani Shantanu Rane Data Science and Applied Mathematics Group Merck & Co West Point PA USA Palo Alto Research Center - An SRI Company Palo Alto CA USA Department of Electrical and Computer Engineering the Institute for System Research (ISR) at the University of Maryland College Park College Park MD USA A. James Clark School of Engineering

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking in a LQG controlled system. Our theoretical results are applied on a temperature process control example. While the proposed secure control scheme requires very involved analysis, it, nevertheless, holds promise of being superior to conventional, feed-forward, watermarking schemes, in both its ability to detect attacks as well as the secured system performance.

关键词： Linear systems System performance Process control Watermarking Control systems Temperature control Signal design

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A NOTE ON MULTIPLICATIVE ROOTS OF MULTIVARIABLE FORMAL POWER SERIES

arXiv

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arXiv 2025年

作者： Maćkowiak, Piotr Mokatren, Motaz Department of Nonlinear Analysis and Applied Topology Faculty of Mathematics and Computer Science Adam Mickiewicz University Poznań Uniwersytetu Poznańskiego 4 Poznań61-614 Poland Department of Electrical and Electronics Engineering Kinneret College Emek HaYarden Mobile Post Zemach 15132 Israel

Suppose that we are given a formal power series of many variables with coefficients in R (or ℂ) and we want to compute its n-th (multiplicative) root. As can be expected coefficients of the root have to satisfy a system of infinitely many equations. We present such a system of equations that strictly corresponds with the system for n-th of a formal power series of one variable. With help of an example we show that the case of formal power series of many variables is very different from the one variable case with respect to the existence of *** Codes 13F25, 13J05 © 2025, CC BY-NC-ND.

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Modeling Crowdedness of Emergency Departments Leveraging Crowdsensing Mobility Data 19

Modeling Crowdedness of Emergency Departments Leveraging Cro...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Shou, Tieqi Wang, Zhiyuan Shi, Shang Yang, Dingqi Zhou, Binbin Wang, Cheng Chen, Longbiao Xiamen University SpAtial Sensing and Computing Lab Xiamen China University of Virginia School of Engineering and Applied Science Charlottesville United States Imperial College London Department of Mathematics London United Kingdom University of Macau State Key Laboratory of Internet of Things for Smart City China Zhejiang University City College Department of Computer Science and Computing Hangzhou China

ISBN: (纸本)9798350346558

Hospital emergency departments (EDs) are crucial medical facilities providing emergency healthcare. Understanding and measuring the patient flow in EDs plays a key role in maximizing the utility of scarce hospital resources. Existing approaches either require manual reporting by staff or collect sensitive data about patients via camera or hospital registration system, causing potential privacy breaches. Against this background, we propose a data-driven modeling strategy of ED crowdedness leveraging multi-source urban crowdsensing data, which automatically provides fine-grained and timely information about the crowdedness of EDs. Specifically, our model can not only accurately extract the emergency visit demand from noisy human mobility data with minimum expert knowledge using active learning and co-training techniques, but also estimate ED crowdedness by modeling the emergency service process by integrating three queueing models for general practice, internal medicine, and surgical clinics, respectively. We evaluate our method leveraging large-scale taxi and hire vehicle trajectory datasets and hospital information system datasets from the government open data portal. Results show that our approach effectively extracts the emergency visit demand and models the emergency service process to assess the crowdedness of EDs. We have deployed the system in Xiamen City by collaborating with the municipal government to provide services for citizens and health providers. © 2022 IEEE.

关键词： Emergency services

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Energy-Efficient Transmission in Wireless Networks Using Resnet with Gravitational Search Algorithm

Energy-Efficient Transmission in Wireless Networks Using Res...

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Disruptive Technologies (ICDT), International Conference on

作者： Rajakumari S Kantharaju HC Shadab Ahmad Rupa Rani Sharma S. Sudha T Ramesh Department of Mathematics R.M.D. Engineering College Department of AI & ML Vemana Institute of Technology Bengaluru Karnataka India Department of Electrical and Computer Engineering Samara University Afar region Ethiopia Department of Applied Science G.L Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Department of ECE Sri Ranganathar Institute of Engineering and Technology Coimbatore Tamilnadu India Department Of Computer Science and Engineering R.M.K Engineering College

Although wireless networks are extremely important in modern communication, one of the most significant challenges they face is the quantity of energy that they use. Within this study, a new way of sending data over wireless networks is suggested. The gravitational search algorithm (GSA) and residue networks of the ResNet type are used together in this method. By using wireless networks, the main goal of this method is to find answers to problems that come up with using energy more efficiently. The creation and use of a hybrid ResNet-GSA model for wireless communication is one of the most important parts of our method. Because it follows the rules of gravity, GSA can quickly look through the solution space to find the right parameter values. At the same time, the network works better when the deep learning features of ResNet are used. The results of our research show that there are big improvements in how much energy is used compared to using traditional ways. If you want to cut down on the amount of energy used, the ResNet-GSA strategy that was suggested also works well. The findings demonstrate that optimisation techniques and deep learning methodologies are not only compatible with one another but also perform effectively together to make wireless networks more environmentally friendly.

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Distributed stochastic gradient descent: nonconvexity, nonsmoothness, and convergence to local minima

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 14751-14812页

作者： Brian Swenson Ryan Murray H. Vincent Poor Soummya Kar Applied Research Laboratory Pennsylvania State University State College PA Department of Mathematics North Carolina State University Raleigh NC Department of Electrical and Computer Engineering Princeton University Princeton NJ Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA

Gradient-descent (GD) based algorithms are an indispensable tool for optimizing modern machine learning models. The paper considers distributed stochastic GD (D-SGD)--a network-based variant of GD. Distributed algorithms play an important role in large-scale machine learning problems as well as the Internet of Things (IoT) and related applications. The paper considers two main issues. First, we study convergence of D-SGD to critical points when the loss function is nonconvex and nonsmooth. We consider a broad range of nonsmooth loss functions including those of practical interest in modern deep learning. It is shown that, for each fixed initialization, D-SGD converges to critical points of the loss with probability one. Next, we consider the problem of avoiding saddle points. It is well known that classical GD avoids saddle points; however, analogous results have been absent for distributed variants of GD. For this problem, we again assume that loss functions may be nonconvex and nonsmooth, but are smooth in a neighborhood of a saddle point. It is shown that, for any fixed initialization, D-SGD avoids such saddle points with probability one. Results are proved by studying the underlying (distributed) gradient flow, using the ordinary differential equation (ODE) method of stochastic approximation.

关键词： nonconvex optimization distributed optimization stochastic optimization saddle point gradient descent

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