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International Joint Conference on Neural Networks (IJCNN)

作者： Lidia Ghosh Dipanjan Konar Amit Konar Atulya K. Nagar Dept. of Computer Science and Engineering Institute of Engineering and Management Kolkata Dept. of Electronics and Communication Engineering Institute of Radio Physics and Electronics Calcutta University Kolkata India ETCE Department Artificial Intelligence Laboratory Jadavpur University Kolkata India Dept. of Math and Computer Science Liverpool Hope University UK

This paper proposes a generic architecture of a recurrent fuzzy inferential neural network realized with Petri Nets. The proposed recurrent topology allows revision or updates of fuzzy singleton memberships of inferences, which may lead to limit cycles (sustained periodic oscillations) or stability of fuzzy inferences. Determining stability in such recurrent fuzzy neural network requires adaptation of fuzzy memberships for all propositions mapped at places of the Petri Net in parallel. The network is said to have attained stability, if after $k$ updates of memberships of the propositions, the steady-state values are reached for all the propositions of the network, where $k$ denotes the number of transitions in the Petri net. If fuzzy memberships of the propositions do not converge after $k$ updates, then the network yields sustained oscillations in memberships (called Limit Cycles). Detection of stability or limit cycles in such network requires users to wait for $k$ steps of fuzzy membership updates at all places of the network. To avoid the computational overhead for $k$ updating of memberships in the entire network, this paper makes an attempt to determine the condition of stability or limit cycles using Lyapunov stability theorem before the network is invoked for fuzzy membership updating and inference generation. The results of the analysis envisage that the condition of stability depends on the topological architecture and initial assignment of memberships at the places. The condition derived can be checked to test possible stability in the network. In case the condition for stability is not attainable, the network is expected to have limit cycles, which too can be detected without updating of memberships.

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A Supervised Approach For Superpixel Segmentation

A Supervised Approach For Superpixel Segmentation

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Machine Learning, computer Systems and Security (MLCSS), International Conference on

作者： Pubali Chatterjee Kaushik Das Sharma Amlan Chakrabarti Department of Computer Science and Engineering SOA University Department of Applied Physics University of Calcutta A. K. Choudhury School of I.T. University of Calcutta

Automatic detection of brain lesion in magnetic resonance imaging (MRI) plays an important role in clinical assistance. In this paper, we propose an enhanced brain lesion detection scheme based on the Dense Convolutional Neural Network (DCNN) where the MR-image is input and the brain lesion is the output. The proposed methodology includes two stages: (a) super-pixel segmentation and (b) classification of super-pixel. In the initial stage, applying the SLIC algorithm separates the lesion and non-lesion area. In the next stage, This segmented lesion and non-lesion regions are used for DCNN training and classification. An modified classifier is used to handle this complex task. The experimental results show that the proposed segmentation scheme achieves a better accuracy than other existing schemes for the detection of brain lesion in MR images.

关键词： Training Deep learning Image segmentation Magnetic resonance imaging Network architecture Classification algorithms Lesions

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TENSOR SHAPE SEARCH FOR OPTIMUM DATA COMPRESSION

arXiv

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arXiv 2022年

作者： Solgi, Ryan He, Zichang Liang, William Jiahua Zhang, Zheng Department of Electrical and Computer Engineering UC Santa Barbara United States Department of Geography UC Santa Barbara United States School of Engineering & Applied Science University of Pennsylvania United States

Various tensor decomposition methods have been proposed for data compression. In real world applications of the tensor decomposition, selecting the tensor shape for the given data poses a challenge and the shape of the tensor may affect the error and the compression ratio. In this work, we study the effect of the tensor shape on the tensor decomposition and propose an optimization model to find an optimum shape for the tensor train (TT) decomposition. The proposed optimization model maximizes the compression ratio of the TT decomposition given an error bound. We implement a genetic algorithm (GA) linked with the TT-SVD algorithm to solve the optimization model. We apply the proposed method for the compression of RGB images. The results demonstrate the effectiveness of the proposed evolutionary tensor shape search for the TT decomposition. Copyright © 2022, The Authors. All rights reserved.

关键词： Tensors

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On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI

On a Survival Gradient Boosting, Neural Network and Cox PH B...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Henry Musto Daniel Stamate Doina Logofatu Lahcen Ouarbya Department of Computing Goldsmiths College University of London United Kingdom Department of Computing Goldsmiths College University of London and School of Health Sciences The University of Manchester United Kingdom Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Germany

ISBN: (数字)9798350337488

ISBN: (纸本)9798350337495

In recent years, attention within the clinical prediction community has turned to the use of survival machine learning as a tool for predicting the risk of developing a disease as a function of time. The current work seeks to contribute to existing literature which demonstrates the utility of these methods when applied to a dementia prediction context. We use the Alzheimer's Disease Neuroimaging Initiative ADNI dataset and model deterioration within two distinct groups, those deemed cognitively normal and those with a formal diagnosis of Mild Cognitive Impairment. In agreement with existing literature we find that survival machine learning outperforms standard survival analysis methods such as Cox PH model, and has very good predictive ability. We propose an innovative approach to predicting dementia diagnosis risk on ADNI, which explores the use of survival neural network and survival extreme gradient boosting techniques that have hitherto seldom been applied to this context. The stability of our models was investigated within a Monte Carlo simulation framework.

关键词： Analytical models Biological system modeling Neural networks Predictive models Boosting Stability analysis Alzheimer's disease

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Two-dimensional computer Modelling of Contaminant Migration in Unsaturated Porous Media with Nanoparticles

Two-dimensional Computer Modelling of Contaminant Migration ...

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International computer sciences and Information Technologies (CSIT)

作者： Viktor Zhukovskyy Nataliia Zhukovska Jan Perhac Department of Computer Science and Applied Mathematics The National University of Water and Environmental Engineering Rivne Ukraine Department of Computers and Informatics Technical University of Kosice Kosice Slovak Republic

ISBN: (纸本)9798350334326

The complicated physical problem of computer modeling pollutant migration in the zone of suspended water with nanoparticles to the catcher filter is discussed in this study. The brand-new mathematical model of nonlinearity in two dimensions is offered. The generalized Darcy-Clute laws for mass and moisture movement in various subregions of soil are taken into careful consideration in the model. The finite difference method was used to find a numerical solution to the boundary value problem and to provide an algorithm for computer implementation. Utilizing a newly created Nanosurface program solution, computer modeling was carried out along with the analysis of numerical experiments. The nanoparticle influence was demonstrated.

关键词： Nanoparticles Cloud computing Boundary value problems Computational modeling Soil Predictive models mathematical models

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Significance of slip velocity and viscosity variation on squeezed film couple-stress properties between a rough plate and a cylinder

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Partial Differential Equations in applied mathematics 2025年 14卷

作者： K, Arshiya Kousar A, Salma Althobaiti, Saja Abdulrahman BN, Hanumagowda Tawade, Jagadish V Abduvalieva, Dilsora Waqas, M. Saeed, Mohammed Azeez Gupta, Manish Department of Mathematics School of Applied Science REVA University India Department of Chemistry College of Sciences & Humanities Prince Sattam Bin Abdulaziz University Saudi Arabia Department of Mathematics Vishwakarma University Pune 411048 India Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent 100070 Uzbekistan Department of Computer Science National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Environmental Engineering College of Engineering Knowlwdge University Erbil 44001 Iraq Division of Research and Development Lovely Professional University Phagwara India

This study examines the impact of surface roughness and viscosity variation on the couple stress squeeze film characteristics between a cylinder and a rough plate with slip velocity. Two distinct one-dimensional roughness patterns—longitudinal and transverse—are considered. Using Christensen's theory, the stochastic modified Reynolds equation is derived for Stokes couple stress fluid, incorporating viscosity variation with pressure. The standard perturbation technique is applied to solve the average Reynolds equation, yielding closed-form expressions for the mean fluid film pressure, load-carrying capacity, and squeeze film time. Various parameters are varied, and the results are discussed through graphical representations in 2D and 3D. This study highlights the importance of viscosity variation, couple stresses, and surface roughness in optimizing squeeze film performance. While increased viscosity and couple stresses enhance load-bearing capacity and film time, slip velocity detracts from these properties. Additionally, surface roughness has a significant impact, with transverse roughness improving, and longitudinal roughness reducing, the squeeze film characteristics. Applications of this study include improving the design and performance of bearing systems, lubrication in mechanical seals, and hydraulic systems where surface roughness and viscosity variation play a significant role in operational efficiency. © 2025

关键词： Couple stress fluid Rough surface Slip velocity Squeeze-film Viscosity variation

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Hybrid Optimization for NOMA-Based Transmissive-RIS Mounted UAV Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Zain Asif, Muhammad Khan, Wali Ullah Elfikky, Abdelrahman Ihsan, Asim Ahmed, Manzoor Ranjha, Ali Srivastava, Gautam Electrical and Computer Engineering Department Baskin School of Engineering University of California Santa Cruz United States School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China University of Luxembourg Luxembourg City1855 Luxembourg Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom School of Computer and Information Science Hubei Engineering University Xiaogan City432000 China Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan City432000 China Ecole de Technologie Supérieure MontréalQCH3C 1K3 Canada Department of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung40402 Taiwan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

In this work, we introduce a novel hybrid joint optimization framework specifically designed for enhancing the performance of consumer electronics in vehicular networks using a transmissive reconfigurable intelligent surface (T-RIS)-mounted unmanned aerial vehicle (UAV) system. The UAV employs the non-orthogonal multiple access (NOMA) protocol to broadcast data to multiple ground devices, ensuring efficient communication. Our primary objective is to maximize the overall system sum rate while adhering to key constraints such as the rate requirements of ground devices, UAV battery capacity, and UAV coordinate boundaries. The optimization challenge of maximizing the system's sum rate is inherently non-convex and complex. To address this, we decompose the problem into manageable subproblems. The beamforming optimization problem is tackled using successive convex approximation and semi-definite programming techniques, allowing for effective handling of non-convexity. For power allocation, we employ the Lagrangian dual method along with the sub-gradient technique, ensuring optimal power distribution among devices. To optimize the UAV's location, we propose a dueling-based double deep reinforcement learning (D3RL) framework. This approach effectively combines all computed solutions, resulting in a comprehensive joint optimization strategy. Simulation results highlight the exceptional performance of the proposed framework. Specifically, optimizing the UAV's location leads to a substantial performance gain of up to 65.9% compared to a system where only beamforming and power allocation are optimized with the UAV positioned at the center of the service area. These findings underscore the potential of our framework in advancing consumer electronics connectivity in vehicular networks. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Pseudomagic Quantum States

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Physical Review Letters 2024年第21期132卷 210602-210602页

作者： Andi Gu Lorenzo Leone Soumik Ghosh Jens Eisert Susanne F. Yelin Yihui Quek Department of Physics Harvard University 17 Oxford Street Cambridge Massachusetts 02138 USA Department of Physics University of Massachusetts Boston 100 Morrissey Boulevard Boston Massachusetts 02125 USA Dahlem Center for Complex Quantum Systems Freie Universität Berlin 14195 Berlin Germany Department of Computer Science University of Chicago Chicago Illinois 60637 USA Helmholtz-Zentrum Berlin für Materialien und Energie 14109 Berlin Germany Fraunhofer Heinrich Hertz Institute 10587 Berlin Germany Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences 150 Western Ave Boston Massachusetts 02134 USA

Notions of nonstabilizerness, or “magic,” quantify how nonclassical quantum states are in a precise sense: states exhibiting low nonstabilizerness preclude quantum advantage. We introduce “pseudomagic” ensembles of quantum states that, despite low nonstabilizerness, are computationally indistinguishable from those with high nonstabilizerness. Previously, such computational indistinguishability has been studied with respect to entanglement, introducing the concept of pseudoentanglement. However, we demonstrate that pseudomagic neither follows from pseudoentanglement nor implies it. In terms of applications, the study of pseudomagic offers fresh insights into the theory of quantum scrambling: it uncovers states that, even though they originate from nonscrambling unitaries, remain indistinguishable from scrambled states to any physical observer. Additional applications include new lower bounds on state synthesis problems, property testing protocols, and implications for quantum cryptography. Our Letter is driven by the observation that only quantities measurable by a computationally bounded observer—intrinsically limited by finite-time computational constraints—hold physical significance. Ultimately, our findings suggest that nonstabilizerness is a “hide-able” characteristic of quantum states: some states are much more magical than is apparent to a computationally bounded observer.

关键词： Quantum algorithms & computation Quantum chaos Quantum computation Quantum information processing Quantum information theory Resource theories

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Differentially private partitioned variational inference

arXiv

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arXiv 2022年

作者： Heikkilä, Mikko A. Ashman, Matthew Swaroop, Siddharth Turner, Richard E. Honkela, Antti Telefónica Research Finland Department of Engineering University of Cambridge United Kingdom School of Engineering and Applied Sciences Harvard University United States Department of Computer Science University of Helsinki Finland

Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. The problem is often formulated in the federated learning context, with the aim of learning a single global model while keeping the data distributed. Moreover, Bayesian learning is a popular approach for modelling, since it naturally supports reliable uncertainty estimates. However, Bayesian learning is generally intractable even with centralised non-private data and so approximation techniques such as variational inference are a necessity. Variational inference has recently been extended to the non-private federated learning setting via the partitioned variational inference algorithm. For privacy protection, the current gold standard is called differential privacy. Differential privacy guarantees privacy in a strong, mathematically clearly defined sense. In this paper, we present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution in the federated learning setting while minimising the number of communication rounds and providing differential privacy guarantees for data subjects. We propose three alternative implementations in the general framework, one based on perturbing local optimisation runs done by individual parties, and two based on perturbing updates to the global model (one using a version of federated averaging, the second one adding virtual parties to the protocol), and compare their properties both theoretically and empirically. We show that perturbing the local optimisation works well with simple and complex models as long as each party has enough local data. However, the privacy is always guaranteed independently by each party. In contrast, perturbing the global updates works best with relatively simple models. Given access to suitable secure primitives, such as secure aggregation or secure shuffling, th

关键词： Sensitive data

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ATGP_RISC-V: Automation of test generator using pluggy for RISC-V architecture 3

ATGP_RISC-V: Automation of test generator using pluggy for R...

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3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020

作者： Madhavan, B. Kamerish, A. Manimegalai, R. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781728158211

The reduced instruction set computing (RISC) architecture is a free and open Instruction Set Architecture (ISA), which enables a new era of processor innovation through open standard collaboration. It directly challenges several well-established processor families such as intel x-86, Motorola 68k processor. To thrive an RISC-V ecosystem, the core suppliers need an independent verification solution to ensure that their designs are compliant with the ISA specification. Verification of RISC-V designs become challenging due to their optional features, implementation flexibility, and provisions for customer extensions. Hence, a thorough verification is essential to compete successfully against the established processor families. Automation is the key for reducing the time taken for the processor verification. This paper provides a way to develop an automated tool ATGP_RISC-V, which uses the same arguments to run all instruction generators. This helps in verifying the processor in an efficient way by reducing the time taken to manually compare the test results. © 2020 IEEE.

关键词： Automation

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