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arXiv 2022年

作者： Sirshar, Mehreen Hassan, Taimur Akram, Muhammad Usman Khan, Shoab Ahmed Department of Computer and Software Engineering National University of Sciences and Technology Islamabad44000 Pakistan Center of Cyber-Physical Systems Department of Electrical Engineering and Computer Sciences Khalifa University Abu Dhabi127788 United Arab Emirates

The human respiratory network is a vital system that provides oxygen supply and nourishment to the whole body. Pulmonary diseases can cause severe respiratory problems, leading to sudden death if not treated timely. Many researchers have utilized deep learning systems (in both transfer learning and fine-tuning modes) to diagnose pulmonary disorders using chest X-rays (CXRs). However, such systems require exhaustive training efforts on large-scale (and well-annotated) data to effectively diagnose chest abnormalities (at the inference stage). Furthermore, procuring such large-scale data (in a clinical setting) is often infeasible and impractical, especially for rare diseases. With the recent advances in incremental learning, researchers have periodically tuned deep neural networks to learn different classification tasks with few training examples. Although, such systems can resist catastrophic forgetting, they treat the knowledge representations (which the network learns periodically) independently of each other, and this limits their classification performance. Also, to the best of our knowledge, there is no incremental learning-driven image diagnostic framework (to date) that is specifically designed to screen pulmonary disorders from the CXRs. To address this, we present a novel framework that can learn to screen different chest abnormalities incrementally (via few-shot training). In addition to this, the proposed framework is penalized through an incremental learning loss function that infers Bayesian theory to recognize structural and semantic inter-dependencies between incrementally learned knowledge representations to diagnose the pulmonary diseases effectively (at the inference stage), regardless of the scanner specifications. We tested the proposed framework on five public CXR datasets containing different chest abnormalities, where it achieved an accuracy of 0.8405 and the F1 score of 0.8303, outperforming various state-of-the-art incremental learning scheme

关键词： Diagnosis

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A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring

arXiv

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arXiv 2022年

作者： Papatheodoulou, Dimitris Pavlou, Pavlos Vrachimis, Stelios G. Malialis, Kleanthis Eliades, Demetrios G. Theocharides, Theocharis Kios Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies. We focus on a specific type of time series classification which we refer to as aggregated time series classification. We consider an aggregated sequence of a multi-variate time series, and propose a methodology to make predictions based solely on the aggregated information. As a case study, we apply our methodology to the challenging problem of household water end-use dissagregation when using non-intrusive water monitoring. Our methodology does not require a-priori identification of events, and to our knowledge, it is considered for the first time. We conduct an extensive experimental study using a residential water-use simulator, involving different machine learning classifiers, multi-label classification methods, and successfully demonstrate the effectiveness of our methodology. © 2022, CC BY-NC-SA.

关键词： Time series

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A Model Selection criterion for the Mixture Reduction problem based on the Kullback - Leibler Divergence

A Model Selection criterion for the Mixture Reduction proble...

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International Conference on Information Fusion

作者： Alessandro D' Ortenzio Costanzo Manes Umut Orguner Department of Information Engineering Computer Science and Mathematics University of L&#x0027 Aquila Center of Excellence EX-EMERGE University of L&#x0027 Department of Electrical and Electronics Engineering Middle East Technical University Turkey

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

In order to be properly addressed, many practical problems require an accurate stochastic characterization of the involved uncertainties. In this regard, a common approach is the use of mixtures of parametric densities which allow, in general, to arbitrarily approximate complex distributions by a sum of simpler elements. Nonetheless, in contexts like target tracking in clutter, where mixtures of densities are commonly used to approximate the posterior distribution, the optimal Bayesian recursion leads to a combinatorial explosion in the number of mixture components. For this reason, many mixture reduction algorithms have been proposed in the literature to keep limited the number of hypotheses, but very few of them have addressed the problem of finding a suitable model order for the resulting approximation. The commonly followed approach in those algorithms is to reduce the mixture to a fixed number of components, disregarding its features which may vary over time. In general, finding an optimal number of mixture components is a very difficult task: once a meaningful optimality criterion is identified, potentially burdensome computational procedures must be devised to reach the optimum. In this work, by exploiting the optimal transport theory, an efficient and intuitive model selection criterion for the mixture reduction problem is proposed.

关键词： Uncertainty Target tracking Computational modeling Transportation Stochastic processes Approximation algorithms Loss measurement

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Fabrication and Application of Nano-SQUID Magnetometer to Scanning Imaging of Two-Dimensional Quantum Materials

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Electromagnetic Science 2024年第2期2卷 21-35页

作者： Bingke Xiang Yihua Wang Hao Li Shane A.Cybart Shanghai Research Center for Quantum Sciences Shanghai 201315China State Key Laboratory of Surface Physics and Department of Physics Fudan UniversityShanghai 200433China Shanghai Institute of Microsystem and Information Technology National Key Laboratory of Materials for Integrated CircuitsChinese Academy of Sciences(CAS)Shanghai 200050China 2020 X-Lab Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China CAS Center for Excellence in Superconducting Electronics Shanghai 200050China Electrical and Computer Engineering University of California RiversideRiversideCA 92521USA

Superconducting quantum interference devices(SQUIDs)are directly sensitive to magnetic ***-fabricated SQUID chip with miniaturized superconducting circuits can be further utilized as scanning probes for imaging of *** SQUID microscopy(SSM)combines both high spatial resolution and high magnetic field sensitivity and is especially suitable for studying low dimensional materials with small sensing ***,we briefly review the fabrication of different types of nano-SQUIDs and the recent progress of utilizing them for scanning microscopy of quantum *** focus on but are not limited to topological states,unconventional superconductivity and exotic magnetism with a particular interest in two-dimensional *** magnet-ometry,susceptometry and current imaging modes of the SSM coupled with the external tuning of the material by magnetic field,electrical field gating and strain reveals a multitude of information beyond the scopes of charge-sensing probes.

关键词： Josephson junctions Focused ion beam Scanning SQUID microscopy Two-dimensional quantum materials

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An Optimal Transport Perspective on Gamma Gaussian Inverse-Wishart Mixture Reduction

An Optimal Transport Perspective on Gamma Gaussian Inverse-W...

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International Conference on Information Fusion

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

Recent advances in the Optimal Transport theory allow to rewrite several known problems in a neat way, while providing a more general perspective. When dealing with mixture densities, or in general with intensities, such a framework naturally induces composite dissimilarities, together with corresponding Greedy Reduction and Refinement algorithms. In applications like target tracking in clutter, it is common to deal with the Mixture Reduction problem, since the optimal Bayesian recursion leads to a combinatorial explosion of hypotheses for the posterior distribution. Moreover, in the extended target case, more complex distributions are being considered to describe the features of an object, for instance the Gamma Gaussian inverse-Wishart density, which makes the reduction problem intrinsically more difficult. For the reasons above, having theoretically sound reduction algorithms results to be important for many practical problems. In this work, we will provide an optimal transport perspective to the Gamma Gaussian inverse-Wishart mixture reduction problem, together with algorithms which are suitable for real-time applications.

关键词： Target tracking Pipelines Approximation algorithms Real-time systems Explosions Bayes methods Clutter

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Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients

Empowering Donors: How Blockchain Technology Can Help Ensure...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Sahar Ajmal Muteeb Bin Muzammil Muhammad Shoaib Muhammad Hassaan Mehmood Department of Software Engineering National University of Computer and Emmerging Sciences Chiniot-Faisalabad Pakistan College of Computing Riphah International University Faisalabad Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman Jordan

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential.

关键词： Decentralized autonomous organization Smart contracts Transforms Blockchains Consensus protocol Internet of Things Security

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Nonstationary Data Stream Classification with Online Active Learning and Siamese Neural Networks

arXiv

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arXiv 2022年

作者： Malialis, Kleanthis Panayiotou, Christos G. Polycarpou, Marios M. KIOS Research Innovation Center of Excellence University of Cyprus Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus Cyprus

We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances' labels is available. We publicly release our code to the community. © 2022, CC BY-NC-SA.

关键词： Budget control

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Data Augmentation On-the-fly and Active Learning in Data Stream Classification

Data Augmentation On-the-fly and Active Learning in Data Str...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Kleanthis Malialis Dimitris Papatheodoulou Stylianos Filippou Christos G. Panayiotou Marios M. Polycarpou KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (纸本)9781665487696

There is an emerging need for predictive models to be trained on-the-fly, since in numerous machine learning applications data are arriving in an online fashion. A critical challenge encountered is that of limited availability of ground truth information (e.g., labels in classification tasks) as new data are observed one-by-one online, while another significant challenge is that of class imbalance. This work introduces the novel Augmented Queues method, which addresses the dual-problem by combining in a synergistic manner online active learning, data augmentation, and a multi-queue memory to maintain separate and balanced queues for each class. We perform an extensive experimental study using image and time-series augmentations, in which we examine the roles of the active learning budget, memory size, imbalance level, and neural network type. We demonstrate two major advantages of Augmented Queues. First, it does not reserve additional memory space as the generation of synthetic data occurs only at training times. Second, learning models have access to more labelled data without the need to increase the active learning budget and / or the original memory size. Learning on-the-fly poses major challenges which, typically, hinder the deployment of learning models. Augmented Queues significantly improves the performance in terms of learning quality and speed. Our code is made publicly available.

关键词： Training Codes Computational modeling Neural networks Machine learning Predictive models Data models

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Photon avalanche-driven multicolor emission: yttrium vanadate upconversion phosphors for optical thermometry, forensics, and healing light applications

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Chemical engineering Journal 2025年 516卷

作者： Pavitra, Eluri Antony, Lintymol Park, Jin Young Hwang, Seung Kyu Lee, Jeong-Hwan Chandra, Gogineni Rajesh Basaveswara Rao, M.V. Raju, Ganji Seeta Rama Han, Young-Kyu Huh, Yun Suk NanoBio High-Tech Materials Research Center Department of Biological Sciences and Bioengineering Inha University Incheon22212 Korea Republic of 3D Convergence Center Inha University Incheon22212 Korea Republic of Department of Energy and Materials Engineering Dongguk University-Seoul Seoul04620 Korea Republic of Department of Electrical Electronics and Software Engineering Pukyong National University Yongdang Campus Busan48547 Korea Republic of Department of Materials Science and Engineering Inha University Incheon22212 Korea Republic of Program in Semiconductor Convergence Inha University Incheon22212 Korea Republic of Department of Computer Science and Engineering Vignan's Lara Institute of Technology & Science Andhra Pradesh Guntur India Department of Chemistry Krishna University Andhra Pradesh Machillipatnam521001 India

The exploration of photon upconversion has led to the development of multifunctional lanthanide-based materials, particularly through the mechanism of photon avalanche, marking a transformative advancement in material science. In this study, yttrium vanadate upconversion nanocrystalline phosphors (UCNCPs) activated by Ho3+/Er3+/Tm3+/Yb3+ ions are reported. These UCNCPs demonstrate a remarkable ability in the photon avalanche, offering highly intense and tunable emissions across the visible spectrum at room temperature under low excitation power of 980 nm laser. The average number of photons involved in the UC process at threshold pump power, highlighting the intricate interplay of cross-relaxation and energy transfer loops in avalanche mechanism. Notably, these UCNCPs have shown exceptional temperature sensing capabilities, where non-thermally coupled levels of the Ho3+ and Tm3+ ions provide higher relative sensitivity and accuracy than thermally coupled levels of Er3+ ions. The versatility of these UCNCPs is further established by their application in solid-state lighting, where they can produce vibrant red, green, blue, and warm-white light, and in security, serving as multi-level anti-counterfeiting inks that respond to 980 nm laser illumination. These developments not only pave the way for more energy-efficient lighting solutions but also offer new avenues for enhancing the security of valuable items. The temperature sensing capabilities of these phosphors add another layer of functionality, making them ideal for high-tech applications ranging from medical diagnostics to environmental monitoring. © 2025 Elsevier B.V.

关键词： Phosphors

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Robust Frequency Regulation for Dual-Area Interconnected Grids Using Hybrid Controller: A Comparative Study

Robust Frequency Regulation for Dual-Area Interconnected Gri...

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Smart Technologies for Power, Energy and Control (STPEC), IEEE International Conference on

作者： Awadh Ba Wazir Abdullah A. Alhussainy Abdulraheem H. Alobaidi Aatif Altaf Anil Kumar Mahendra Kumar Sultan Alghamdi Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Instrumentation and Control Engineering Department Dr. B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India

This paper introduces a robust controller designed for load frequency control in a dual-area integrated power grid. Here, the suggested controller is a blend of a fuzzy logic controller with an integral controller. The parameters of the integral controller and frequency bias settings are optimized to enhance the dynamic response of the proposed controller. To attain this, three distinct optimization algorithms are employed, i.e., genetic algorithm, simulated annealing, and pattern search. A new objective function using settling time, settling-max, and settling-min with a suitable weight factor is used to improve the parameters estimation process. A comparative analysis is performed using the proposed approach and classical-I controller under two cases considering several load disturbance scenarios. Furthermore, the suggested hybrid controller is evaluated and compared with similar models in the literature. As expected, the proposed controller exhibits superior performance.

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