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The Conditional Cauchy-Schwarz Divergence With Applications to Time-Series Data and Sequential Decision Making

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5901-5917页

作者： Shujian Yu Hongming Li Sigurd Løkse Robert Jenssen José C. Príncipe Machine Learning Group UiT - The Arctic University of Norway Tromsø Norway Quantitative Data Analytics Group Vrije Universiteit Amsterdam Amsterdam The Netherlands Department of Electrical and Computer Engineering University of Florida Gainesville FL USA Drones and Autonomous Systems Group NORCE Norwegian Research Centre Tromsø Norway Pioneer AI Centre Copenhagen University København Denmark Norwegian Computing Center Oslo Norway

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be elegantly estimated by a kernel density estimator from given samples. We illustrate the advantages (e.g., rigorous faithfulness guarantee, lower computational complexity, higher statistical power, and much more flexibility in a wide range of applications) of our conditional CS divergence over previous proposals, such as the conditional Kullback-Leibler divergence and the conditional maximum mean discrepancy. We also demonstrate the compelling performance of conditional CS divergence in two machine learning tasks related to time series data and sequential inference, namely time series clustering and uncertainty-guided exploration for sequential decision making.

关键词： Covariance matrices Kernel Machine learning Time series analysis Vectors Symmetric matrices Q measurement Matrix decomposition Estimation Decision making

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Modeling and performance analysis of an industrial transport platform manufacturing process

Modeling and performance analysis of an industrial transport...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： César Arzola Daniel Latorre Germán Sacramento Cristian Mahulea Jorge Júlvez Dept. of Computer Science and Systems Engineering University of Zaragoza Spain Alimak Group La Muela Spain Aragon Institute for Engineering Research (I3A) University of Zaragoza Spain

This contribution focuses on the modeling and performance analysis of the manufacturing process for transport platforms at the Alimak group facility in La Muela, Spain. The objective is to leverage formal tools to gain insights into this complex manufacturing process and explore potential applications such as control and optimization. This work presents a preliminary study, where a Generalized Stochastic Petri net model of the manufacturing process is proposed to be used for simulation, performance analysis, and optimization of the system.

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Black Box-Based Incremental Reduced-Order Modeling Framework of Inverter-Based Power systems

TechRxiv

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TechRxiv 2023年

作者： Zhou, Weihua Beerten, Jef The ELECTA Group Department of Electrical Engineering KU Leuven Leuven3000 Belgium EnergyVille Genk3600 Belgium The Department of Electrical and Computer Systems Engineering Monash University Victoria3800 Australia

Due to the capability to perform participation factor analysis and oscillation origin location, the state-space model (SSM)-based eigenvalue method has been widely used for stability assessment of inverter-penetrated power systems. However, possible internal confidentiality of inverters impedes the derivation of their SSMs. In addition, conventional derivation procedure of system SSM can be tedious when complicated transmission network topology and various transmission cables are involved, which may result in a high-order system SSM. To this end, this article presents a black box-based incremental reduced-order modeling framework. The reduced-order SSMs of the inverters and transmission cables are extracted from their dq-domain admittance frequency responses and abc-domain impedance frequency responses, respectively, by the matrix fitting algorithm. Then, the SSM operators proposed in this article recursively assemble the fitted components’ SSMs in the similar manner as the impedance model operator-based recursive components’ impedance aggregation, while preserving the dynamics of individual components. Simulation results show that the presented state-space modeling framework can properly identify the state-space models of black-box devices at component modeling stage, simplify assembling procedure at subsystems/components integration stage, and release computational burden at system participation factor analysis stage. © 2023, CC BY.

关键词： Factor analysis

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Semi/Fully-Automated Segmentation of Gastric-Polyp Using Aquila-Optimization-Algorithm Enhanced Images

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computers, Materials & Continua 2022年第2期70卷 4087-4105页

作者： Venkatesan Rajinikanth Shabnam Mohamed Aslam Seifedine Kadry Orawit Thinnukool Department of Electronics and Instrumentation Engineering St.Joseph’s College of Engineering Chennai600119India Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved *** and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its *** proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp(GP)with better *** proposed GP detection system consist;(i)Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy(Fuzzy/Shannon/Kapur)and between-class-variance(Otsu)technique,(ii)Automated(Watershed/Markov-Random-Field)and semi-automated(Chan-Vese/Level-Set/Active-Contour)segmentation of GPfragment,and(iii)Performance evaluation and validation of the proposed *** experimental investigation was performed using four benchmark EI dataset(CVC-ClinicDB,ETIS-Larib,EndoCV2020 and Kvasir).The similarity measures,such as Jaccard,Dice,accuracy,precision,sensitivity and specificity are computed to confirm the clinical significance of the proposed *** outcome of this research confirms that the fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.

关键词： Endoscopic/colonoscopic-image gastric polyp aquila-optimizationalgorithm fuzzy entropy chan-vese segmentation

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Evolutionary Computing based Analysis of Diversity in Grammatical Evolution

Evolutionary Computing based Analysis of Diversity in Gramma...

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2021 International Conference on Artificial Intelligence and Smart systems, ICAIS 2021

作者： Youssef, Ayman Gupt, Krishn Kumar Raja, Muhammad Adil Murphy, Aidan Ryan, Conor Research Group Department of Computer Science and Information Systems Limerick Ireland Limerick Institute of Technology Bds and Department of Electrical and Electronic Engineering Limerick Ireland

ISBN: (纸本)9781728195377

Diversity is a much sought after aspect of any evolutionary system. More diversity means a cornucopia of diverse behaviors and traits among the individuals of a population. Lack of diversity, on the other hand, leads to a stagnant population whose individuals are more or less similar to each other. Subsequently, they fail to produce a variety of offspring. Grammatical Evolution (GE), being an Evolutionary Algorithm (EA), is also an aspirant of diversity. It allows a GE system to maintain a dynamic population over multiple *** this paper, we present our reflections about diversity estimates in a (large) number of experiments. We performed evolutionary experiments to estimate a bunch of well-known benchmark polynomials. We also employed hybrid optimization in our experiments. Our results are insightful. In this paper, we also test the effect of hybrid optimization algorithms integrated with GE on the diversity of the population. © 2021 IEEE.

关键词： Computational grammars

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Multilayer perceptron ensembles in a truly sparse training context

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Neural Computing and Applications 2025年

作者： van der Wal, Peter R. D. Strisciuglio, Nicola Azzopardi, George Mocanu, Decebal Constatin Information Systems Group Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 Groningen9747 AG Netherlands Data Management and Biometrics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Hallenweg 19 Enschede7522 NH Netherlands Department of Computer Science Faculty of Science Technology and Medicine University of Luxembourg Maison du Nombre 6 avenue de la Fonte Esch4364 Luxembourg

Ensemble learning for artificial neural networks (ANNs) is an effective method to enhance predictive performance. However, ANNs are computationally and memory intensive, and naively training multiple networks can lead to excessive training times and costs. An effective tool for improving ensemble efficiency is introducing topological sparsity. Even though several implementations of efficient ensembles have been proposed, none of them can provide actual benefits in terms of computational overhead as the sparsity is simulated using binary masks. In this paper, we address this issue by introducing a Truly Sparse Ensemble without binary masks and directly incorporate native sparsity. We also propose two algorithms for initializing new subnetworks within the ensemble, leveraging this native topological sparsity to enhance subnetwork diversity. We demonstrate the performance of the resulting models at high levels of sparsity on several datasets in terms of classification accuracy, floating point operations (FLOPs), and actual running time. The proposed methods outperform all baseline dense and truly sparse models on tabular data, successfully diversify the training trajectory of the subnetworks, and increase the topological distance between subnetworks after re-initialization. © The Author(s) 2025.

关键词： Digital arithmetic

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Proactive and Automatic Underfrequency Load Shedding via PMUs and Particle Filters

arXiv

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arXiv 2023年

作者： Paramo, Gian Bretas, Arturo Bretas, Newton Electrical and Computer Engineering University of Florida GainesvilleFL United States Distributed Systems Group Pacifc Northwest National Laboratory RichlandWA United States Electrical and Computer Engineering University of Sao Paulo SP Sao Carlos Brazil

Underfrequency (UF) load shedding schemes are traditionally implemented in two ways: One approach is based on manual load shedding, with system operators requesting loads to be shed ahead of anticipated stressful operating conditions. Manual load shedding is usually done through phone calls. The second method is automatic load shedding via underfrequency relays. Using static settings, these schemes can be designed to operate in stages and drop previously identified loads. The main limitation of traditional load shedding schemes is that they are reactive and leave little room for optimized corrective actions. This work presents a proactive and automatic underfrequency load shedding solution for power systems. Measurements are captured via phasor measurement units (PMUs) at relatively low sampling rates of 30 Hz. These measurements are then processed by particle filters who predict the future state of the system’s frequency. Based on these predictions excess load is determined and shed. Comparative case studies are performed in simulated environments. Easy-to-implement models, without hard-to-derive parameters, highlight potential aspects for real-life implementation. © 2023, CC BY.

关键词： Phasor measurement units

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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SHELTER: extending arm CCA with isolation in user space 23

SHELTER: extending arm CCA with isolation in user space

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Proceedings of the 32nd USENIX Conference on Security Symposium

作者： Yiming Zhang Yuxin Hu Zhenyu Ning Fengwei Zhang Xiapu Luo Haoyang Huang Shoumeng Yan Zhengyu He Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology and Department of Computing The Hong Kong Polytechnic University Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology College of Computer Science and Electronic Engineering Hunan University and Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering and Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computing The Hong Kong Polytechnic University Ant Group

ISBN: (纸本)9781939133373

The increasing adoption of confidential computing is providing individual users with a more seamless interaction with numerous mobile and server devices. TrustZone is a promising security technology for the use of partitioning sensitive private data into a trusted execution environment (TEE). Unfortunately, third-party developers have limited accessibility to TrustZone. This is because TEE vendors need to validate such security applications to preserve their security rigorously. Moreover, TrustZone-based systems suffer from vulnerabilities affecting Trusted App and trusted OS, possibly causing the entire system to be *** virtualization-based TEE introduced in the recently new concept of Confidential Compute Architecture (CCA) creates a new physical address space called Realm world for confidential computing to protect the data confidentiality and integrity. The current version of CCA primarily targets the VM level in the Realm world and does not provide user-level isolated environments. To fill up this gap, we present SHELTER, which is a complement to CCA's primary Realm VM-style architecture. SHELTER allows thirdparty developers to deploy their applications with isolation in userspace. SHELTER is designed by cooperating with Arm CCA hardware primitive available in Armv9.2 to provide hardware-based isolation while removing the need for software workloads to trust their data to a Host OS, hypervisor, or privileged software (e.g., trusted OS, Secure/Realm hypervisor). We have implemented and evaluated SHELTER, and the results demonstrated that SHELTER guarantees the security of applications with a modest performance overhead (<15%) on real-world workloads.

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A Deep Learning Framework for Real-time Oil Spill Detection and Classification 2

A Deep Learning Framework for Real-time Oil Spill Detection ...

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2nd Workshop on Artificial Intelligence for Perception and Artificial Consciousness, AIxPAC 2024

作者： Patel, Pranshu Desai, Kedar Jain, Rinkal Bhatt, Chintan Vanlanduit, Steve Bruno, Alessandro Mazzeo, Pier Luigi Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India InViLab Research Group Universiteit Antwerpen Groenenborgerlaan Antwerpen171 2020 Belgium Department of Business Law Economics and Consumer Behaviour "Carlo A. Ricciardi" IULM AI Laboratory IULM University Milan20143 Italy ISASI Institute of Applied Sciences Intelligent Systems-CNR Lecce73100 Italy

Efficient detection of oil spills is critical for minimizing environmental damage. This study introduces a novel approach utilizing deep learning, specifically the YOLOv8 architecture, augmented with advanced computer vision techniques for oil spill detection. Through meticulous dataset curation and model training, the YOLOv8 model achieved an impressive overall accuracy (R-score) of 0.531 and a Mean Average Precision (mAP) of 0.549. Performance varied across different spill types, with the model demonstrating notable accuracy in distinguishing between oil spills and natural features, achieving precision and recall rates of up to 0.75 and 0.68, respectively, for sheen detection. Visualizations such as box loss, class loss, and confusion matrices provide insights into the model’s performance dynamics, revealing a steady decrease in losses and an improvement in accuracy over epochs. In this dataset, the measurements are drone measurements performed by Port of Antwerp Bruges. Furthermore, practical applications showcase the model’s versatility in detecting various oil spill types in both image and video data, affirming its potential for real-world deployment in environmental monitoring and disaster response scenarios. This research represents a significant stride towards more effective oil spill detection, contributing to environmental sustainability and resilience efforts. © 2024 Copyright for this paper by its authors.

关键词： Oil spills

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