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arXiv 2022年

作者： Marynets, Kateryna Pantova, Dona Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

We use a numerical-analytic technique to construct a sequence of successive approximations to the solution of a system of fractional differential equations, subject to Dirichlet boundary conditions. We prove the uniform convergence of the sequence of approximations to a limit function, which is the unique solution to the boundary value problem under consideration, and give necessary and sufficient conditions for the existence of solutions. The obtained theoretical results are confirmed by a model example. © 2022, CC BY.

关键词： Boundary conditions

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DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning

arXiv

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arXiv 2025年

作者： Bakir, Volkan Goktas, Polat Akyuz, Sureyya Graduate School of Natural and Applied Sciences Bahçeşehir University Turkey School of Computer Science & CeADAR University College Dublin Ireland Department of Mathematics Faculty of Engineering and Natural Sciences Bahçeşehir University Turkey

Explainable artificial intelligence (XAI) has become increasingly important in decision-critical domains such as healthcare, finance, and law. Counterfactual (CF) explanations, a key approach in XAI, provide users with actionable insights by suggesting minimal modifications to input features that lead to different model outcomes. Despite significant advancements, existing CF generation methods often struggle to balance proximity, diversity, and robustness, limiting their real-world applicability. A widely adopted framework, Diverse Counterfactual Explanations (DiCE), emphasizes diversity but lacks robustness, making CF explanations sensitive to perturbations and domain constraints. To address these challenges, we introduce DiCE-Extended, an enhanced CF explanation framework that integrates multi-objective optimization techniques to improve robustness while maintaining interpretability. Our approach introduces a novel robustness metric based on the Dice-Sørensen coefficient, ensuring stability under small input variations. Additionally, we refine CF generation using weighted loss components (λp, λd, λr) to balance proximity, diversity, and robustness. We empirically validate DiCE-Extended on benchmark datasets (COMPAS, Lending Club, German Credit, Adult Income) across multiple ML backends (Scikit-learn, PyTorch, TensorFlow). Results demonstrate improved CF validity, stability, and alignment with decision boundaries compared to standard DiCE-generated explanations. Our findings highlight the potential of DiCE-Extended in generating more reliable and interpretable CFs for high-stakes applications. Future work will explore adaptive optimization techniques and domain-specific constraints to further enhance CF generation in real-world scenarios. © 2025, CC BY.

关键词： Backpropagation

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Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces

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Microsystems & Nanoengineering 2022年第5期8卷 183-196页

作者： Nasim Bakhshaee Babaroud Merlin Palmar Andrada Iulia Velea Chiara Coletti Sebastian Weingärtner Frans Vos Wouter A.Serdijn Sten Vollebregt Vasiliki Giagka Department of Microelectronics Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyMekelweg 4Delft 2628 CDThe Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection TechnologiesFraunhofer Institute for Reliability and Micro-integration IZMGustav-Meyer-Allee 25Berlin 13355Germany Department of Imaging Physics Faculty of Applied ScienceDelft University of TechnologyLorentzweg 1Delft 2628 CJThe Netherlands Erasmus University Medical Center(Erasmus MC) dr.Molewaterplein 40Rotterdam 3015 GDThe Netherlands

Multimodal platforms combining electrical neural recording and stimulation,optogenetics,optical imaging,and magnetic resonance(MRI)imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific *** conductive,optically transparent,and MRI-compatible electrodes can optimally combine all *** as a suitable electrode candidate material can be grown via chemical vapor deposition(CVD)processes and sandwiched between transparent biocompatible ***,due to the high graphene growth temperature(≥900℃)and the presence of polymers,fabrication is commonly based on a manual transfer process of pre-grown graphene sheets,which causes reliability *** this paper,we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural *** fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and *** also exhibit the highest charge storage capacity(CSC)reported to date among all previously fabricated CVD graphene *** graphene electrodes did not reveal any photo-induced artifact during 10-Hz light pulse ***,we show here,for the first time,that CVD graphene electrodes do not cause any image artifact in a 3T MRI *** results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal *** fabricated graphene electrodes enable multimodal neural recording,electrical and optogenetic stimulation,while allowing for optical imaging,as well as,artifact-free MRI studies.

关键词： transparent transfer process

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Developing a Robotic Solar-Powered Fire Detection and Avoidance System With Machine Learning-Based Sensing 2

Developing a Robotic Solar-Powered Fire Detection and Avoida...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Verma, Preeti Gangwar, Arun Singh, Bharat Singh, Shubhendra Pratap Vats, Pooja Ganesh, Reddy School of Electronic and Electrical Engineering Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Invertis University Department of Electrical Engineering Uttar Pradesh Bareilly243123 India School of Computer Science and Applications Iimt University Meerut India Tmu Department of Ee Moradabad India School of Basic & Applied Sciences K. R. Mangalam University Department of Mathematics Haryana Gurugram122103 India UTAS-IBRI Engineering Department Oman

ISBN: (纸本)9798350369960

This research addresses the challenge of ensuring a consistent energy supply for forest fire surveillance systems, which is critical in the field of computer science, AI, ML, Data Mining, NLP, computer Vision, and Robotics. Traditional solar-powered solutions rely on fixed-mounted rooftop panels, which are often inadequate due to their limited electricity production capacity, especially under variable weather conditions. This study introduces an innovative, all-weather, autonomous solar tracking system that adapts to the changing position of the sun throughout the seasons, ensuring optimal energy collection for the surveillance equipment. To properly navigate the sun motion, the cautioned gadget merges sun's trajectory monitoring modalities with pv sensing. It begins via determining the season with a sensor that measures the volume of sunshine. The sun function detector & a customized PV itinerary algorithms then estimate the solar's course. The gadget makes use of specific tracking algorithms for sunny, gloomy, and moist instances, using artificial intelligence algorithms to forecast versions in the weather & adjust the panels as it should be. The approach has been proved in simulated experiments to be able to mitigating the influence of versions in the climate at the same time as keeping a steady and specific alignment facing the sun in all climates. The technique will increase the ability of photovoltaic cells utilized for wooded area fire tracking to produce electricity, improving the dependability and effectiveness of such vital device. This precise use of AI and gadget getting to know in environmental surveillance represents a step towards stronger and longer-lasting fire caution systems, which might be important for woodland preservation and management. © 2023 IEEE.

关键词： Autonomous Fire Detection Fire Avoidance Robotics Integrated Sensing Solutions Safety Automation Technology Solar-Powered Systems

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Parametric activation functions modelling fuzzy connectives for better explainability of neural models 20

Parametric activation functions modelling fuzzy connectives ...

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20th IEEE Jubilee International Symposium on Intelligent Systems and Informatics, SISY 2022

作者： Pusztahazi, Luca Sara Csiszar, Gabor Gashler, Michael S. Csiszar, Orsolya Physiological Control Research Center Obuda University Budapest Hungary Applied Informatics and Applied Mathematics Doctoral School Obuda University Budapest Hungary Institute of Materials Physics University of Stuttgart Stuttgart Germany SupplyPike Fayetteville Arkansas72703 United States Faculty of Electrical Engineering and Computer Science Aalen University Aalen Germany

ISBN: (纸本)9781665489881

In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-readable relationships between input features with the help of nilpotent fuzzy logic and multi-criteria decision making (MCDM). In the neural network a parameterized, differentiable activation function is used, where the parameter is determined by gradient descent. The goal is to find the optimal regularization value by applying the deep learning model to classification problems from the UCI Machine Learning Repository. © 2022 IEEE.

关键词： Fuzzy inference

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Data Freshness Optimization Under CAA in the UAV-Aided MECN: A Potential Game Perspective

Data Freshness Optimization Under CAA in the UAV-Aided MECN:...

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作者： Wang, Weizheng Srivastava, Gautam Lin, Jerry Chun-Wei Yang, Yaoqi Alazab, Mamoun Gadekallu, Thippa Reddy City University of Hong Kong Department of Computer Science Hong Kong Brandon University Department of Mathematics and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung406040 Taiwan Western Norway University of Applied Sciences Department of Computer Science Bergen5063 Norway Army Engineering University of Pla Graduate School Nanjing210000 China Melbourne Polytechnic Prahran Campus Department of Information Technology MelbourneVIC3181 Australia Vellore Institute of Technology School of Information Technology Vellore632014 India

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAV) have become more and more important in Mobile Edge Computing Networks (MECN), such as communication, computation, collection and control service supply. Although Age of Information (AoI) minimization is indispensable for fresh information collection and computation in the UAV-aided MECN, some attackers can launch attacks to deteriorate the availability of precious channel resources, such as revealed channel access attacks (CAAs). Moreover, recent research has not considered the system's active probability and security issues concurrently, e.g., CAA, in the average AoI minimization process. In this paper, to deal with this problem, we consider an AoI-oriented channel access problem under CAA with a game theory viewpoint. Firstly, to obtain a MECN-based AoI indicator under CAA, the system model with active probability consideration is established. Next, the channel access-based AoI minimization problem is formulated from the viewpoint of the Ordinary Potential Game (OPG). Furthermore, two algorithms called AACSD and DCASD are proposed to determine channel access strategies, by which the Nash Equilibrium (NE) solution of the OPG could be reached. Finally, experiments are conducted under homogeneous and heterogeneous parameter settings, and the simulation results evaluate the correctness and effectiveness of our proposals. © 2000-2011 IEEE.

关键词： Game theory

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Transferable polychromatic optical encoder for neural networks

arXiv

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arXiv 2024年

作者： Choi, Minho Xiang, Jinlin Wirth-Singh, Anna Baek, Seung-Hwan Shlizerman, Eli Majumdar, Arka Department of Electrical and Computer Engineering University of Washington SeattleWA98103 United States Department of Physics University of Washington SeattleWA98103 United States Department of Computer Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang37673 Korea Republic of Department of Applied Mathematics University of Washington SeattleWA98103 United States

Artificial neural networks (ANNs) have fundamentally transformed the field of computer vision, providing unprecedented performance. However, these ANNs for image processing demand substantial computational resources, often hindering real-time operation. In this paper, we demonstrate an optical encoder that can perform convolution simultaneously in three color channels during the image capture, effectively implementing several initial convolutional layers of a ANN. Such an optical encoding results in 24, 000× reduction in computational operations, with a state-of-the art classification accuracy (∼ 73.2%) in free-space optical system. In addition, our analog optical encoder, trained for CIFAR-10 data, can be transferred to the ImageNet subset, High-10, without any modifications, and still exhibits moderate accuracy. Our results evidence the potential of hybrid optical/digital computer vision system in which the optical frontend can pre-process an ambient scene to reduce the energy and latency of the whole computer vision system. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Optimizing Fog Computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Ceres Dbritto Joel Lopes Sanjay Oli Maulik S Joshi Aniket Singh Ashish Rayal Reseacher St Louis USA Reseacher Redmond USA Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Department of Humanity and Science Gujarat Power Engineering and Research Institute GTU Gujarat India Department of Computer Science and Engineering Veer Madho Singh Bhandari Uttarakhand Technical University Pithoragarh Campus India Department of Mathematics School of Applied and Life Sciences Uttaranchal University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework.

关键词： Fuzzy logic Schedules Processor scheduling Clustering algorithms Machine learning Dynamic scheduling Time factors Resource management Unsupervised learning Edge computing

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Optimal Experimental Design to Estimate Insulin Response in Type 2 Diabetes

Optimal Experimental Design to Estimate Insulin Response in ...

引用

Control Technology and Applications (CCTA),

作者： Sarah Ellinor Engell Henrik Bengtsson Karim Davari Benam Anders Lyngvi Fougner John Bagterp Jørgensen Novo Nordisk A/S Bagsværd Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark Department of Engineering Cybernetics Norwegian University of Science and Technology (NTNU) Trondheim Norway

In late-stage type 2 diabetes, automated titration algorithms provide a promising alternative to the current standard-of-care. Many published methods rely on personalized dose-response models to predict a safe and effective insulin dose. In this case study, we address the challenge of how to collect an informative data set to ensure practical identifiability of such models. We apply optimal experimental design to enhance the performance of a published titration algorithm. For a 24-hour experiment, we solve an optimization problem to select the size of three meals and the hourly fast-acting insulin infusion rate. In simulation, we demonstrate how the optimized protocol improves the safety of the algorithm’s dose-predictions. The results indicate that optimal experimental design has the potential to improve model-based algorithms and may be used as a qualitative tool when planning clinical experiments.

关键词：

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The nonlinear mechanism for the same responses of neuronal bursting to opposite self-feedback modulations of autapse

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science China(Technological sciences) 2021年第7期64卷 1459-1471页

作者： LI YuYe GU HuaGuang JIA Bing DING XueLi College of Mathematics and Computer Science Chifeng UniversityChifeng024000China School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai200092China Department of Physiology and Biophysics School of Life SciencesFudan UniversityShanghai200438China Department of Basic Education Fuyang Institute of TechnologyFuyang236031China

In the traditional viewpoint,inhibitory and excitatory effects always induce opposite *** the present study,the enhanced bursting activities induced by excitatory autapses,which are consistent with the recent experimental observations,and those induced by inhibitory autapses,which is a paradoxical phenomenon,were simulated using the Chay *** same bifurcations and different ionic currents for the same responses were acquired with fast-slow variable dissection and current decomposition,*** the inhibitory or excitatory autaptic conductance increased,the ending phase of the burst related to a homoclinic bifurcation of the fast subsystem changed to widen the burst duration to contain more spikes,which was induced by an elevated minimal potential(V_(min)) of spiking of the fast *** inhibitory and excitatory autaptic conductances induced smaller and larger maximal potentials(V_(max)) of spiking,*** the downstroke,a weaker potassium current induced by the smaller V_(max) played a dominant role for the inhibitory autapse,and the stronger potassium current induced by the larger V_(max) became weaker due to the opposite autaptic current of the excitatory autapse,which induced the V_(min) *** results present the nonlinear and biophysical mechanisms of the same responses to opposite effects,which extends nonlinear dynamics knowledge and provides potential modulation measures for the nervous system.

关键词： bifurcation bursting autapse inhibitory effect excitatory effect fast-slow variable dissection

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