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Radiative energy bandgap of nanostructures coupled with quantum emitters around the epsilon-near-zero (ENZ) frequency

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arXiv 2022年

作者： Gong, Tao Liberal, Inigo Camacho, Miguel Spreng, Benjamin Engheta, Nader Munday, Jeremy N. Department of Electrical and Computer Engineering University of California DavisCA United States Department of Materials Science and Engineering University of California DavisCA United States Institute of Smart Cities Public University of Navarre Pamplona Spain Department of Electronics and Electromagnetism University of Seville Seville Spain Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States

Epsilon-near-zero (ENZ) materials have been demonstrated to exhibit unique electromagnetic properties. Here we propose the concept of radiative energy bandgap for an ENZ nanoparticle coupled with a quantum emitter (QE). The radiative emission of the coupled QE-nanoparticle can be significantly suppressed around the ENZ frequency and substantially enhanced otherwise, yielding an effective energy bandgap for radiation. This suppression is effectively invariant with respect to the particle size and is therefore an intrinsic property of the ENZ material. Our concept also heralds an alternative pathway to quench the emission from a QE, which may find potential application in quantum information storage. © 2022, CC BY.

关键词： Nanoparticles

Renewable Energy Resources Connected Utility Microgrid for the Tulalip Tribes

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Renewable Energy Resources Connected Utility Microgrid for t...

Annual North-American Power Symposium

作者： Charlotte Wertz Aidan Smit Isobel Baetz Sheng Qu Brenden Rappazzo Sanjeev Pannala Noel Schulz Phillip North William Carson Alex Chorey School of Electrical Engineering and Computer Science Washington State University Pullman WA USA Tulalip Tribes Tulalip WA USA Snohomish County Public Utility District Everett WA USA

ISBN: (纸本)9781665499224

Microgrids increase resilience, sovereignty, and reduce carbon emissions by utilizing renewable energy resources. This paper explores the feasibility of installing a new microgrid at the Tulalip Tribes’ Administration Building. The building will act as a "resiliency zone" for the Tribes, for use as a shelter or command post during natural disasters. This microgrid will supply sufficient power generation and storage for the building while meeting the needs of the client for emergency event coverage and environmental impact. Solar photovoltaic (PV) panels and a Battery Energy Storage System (BESS) are proposed as alternate resources, along with a pre-existing diesel generator for emergency power generation to build a sustainable microgrid. However, PV and BESS placement and sizing must be assessed properly before delivering it as an effective solution. This use case is not typical, as system constraints and operating conditions are unique from a Tribal community perspective. An extensive study has been conducted using Hybrid Optimization of Multiple Energy Resources (HOMER) software to find a feasible case where all requirements were reached for the Tribal community and associated utility. All field equivalent datasets are being used to constitute the model in HOMER, and utility outage information is excised to perform as a backup during normal and extreme scenarios.

关键词： Resistance Photovoltaic systems Renewable energy sources Buildings Microgrids Software Generators

Towards Designing Shared Digital Forensics Instructional Materials

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Towards Designing Shared Digital Forensics Instructional Mat...

IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Weifeng Xu Lin Deng Dianxiang Xu School of Criminal Justice College of Public Affairs University of Baltimore Maryland USA Department of Computer and Information Sciences Towson University Maryland USA Department of Computer Science and Electrical Engineering University of Missouri at Kansas City Missouri USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

This paper presents a systematic approach to designing a series of digital forensics instructional materials to address the severe shortage of active learning materials in the digital forensics community. The materials include real-world scenario-based case studies, a set of hands-on problem-driven labs for each case study, and an integrated forensic investigation environment. In this paper, we first clarify some fundamental concepts related to digital forensics, such as digital forensic artifacts, artifact generators, and evidence. We then re-categorize knowledge units of digital forensics based on the artifact generators for measuring the coverage of learning outcomes and topics. Finally, we utilize a real-world cybercrime scenario to demonstrate how knowledge units, digital forensics topics, concepts, artifacts, and investigation tools can be infused into each lab through active learning. The repository of the instructional materials is publicly available on GitHub. It has gained nearly 600 stars and 22k views within several months.

关键词： Systematics Conferences Digital forensics Stars Generators Software computer crime

A Conceptual Exploration of Generative AI-Induced Cognitive Dissonance and its Emergence in University-Level Academic Writing

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arXiv 2025年

作者： Seran, Carl Errol Tan, Myles Joshua Toledo Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Sciences University of St. La Salle Bacolod Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Bacolod Philippines Faculty of Education University of the Philippines Open University Los Baños Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Yo-Vivo Corporation Bacolod Philippines Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates

The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores howGenAI serves as both a trigger and amplifier of CD, as students navigateethical concerns, academic integrity, and self-efficacy in their writingpractices. By synthesizing empirical evidence and theoretical insights, weintroduce a hypothetical construct of GenAI-induced CD, illustrating thepsychological tension between AI-driven efficiency and the principles oforiginality, effort, and intellectual ownership. We further discuss strategiesto mitigate this dissonance, including reflective pedagogy, AI literacyprograms, transparency in GenAI use, and discipline-specific task *** approaches reinforce critical engagement with AI, fostering a balancedperspective that integrates technological advancements while safeguarding humancreativity and learning. Our findings contribute to ongoing discussions on AIin education, self-regulated learning, and ethical AI use, offering aconceptual framework for institutions to develop guidelines that align AIadoption with academic values. © 2025, CC BY.

关键词： Generative adversarial networks

Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study

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arXiv 2024年

作者： Du, Hongru Zhao, Jianan Zhao, Yang Xu, Shaochong Lin, Xihong Chen, Yiran Gardner, Lauren M. Yang, Hao Center for Systems Science and Engineering Johns Hopkins University BaltimoreMD United States Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD United States Mila - Quebec AI Institute MontréalQC Canada Department of Computer Science Universite de Montréal MontréalQC Canada Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Department of Statistics Harvard University CambridgeMA United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Epidemiology Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States

Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information—such as textual policies and genomic surveillance data—previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. By redefining the forecasting process as an ordinal classification task, PandemicLLM yields more robust and trustworthy predictions, facilitating public health decision-making. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. for a duration of 16 weeks. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enh

关键词： Genome

Recursive Feature Elimination with Cross Validation for Alzheimer’s Disease Classification using Cognitive Exam Scores

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Recursive Feature Elimination with Cross Validation for Alzh...

Intelligent Methods, Systems, and Applications (IMSA)

作者： Christian Yaphet Freytes Robin Perry Mayrand Luana Okino Sawada Thony Yan Liang Rosie E. Curiel Cid Shanna Burke David Loewenstein Ranjan Duara Malek Adjouadi Electrical & Computer Engineering Florida International University Miami FL USA Computing & Information Sciences Florida International University Miami FL USA Department of Neuropsychology University of Miami Miami FL USA Public Health and Social Work Florida International University Miami United States Cognitive Neurosciences and Aging University of Miami Miami FL USA Wien Center for AD & MD Mt Sinai Medical Center Miami FL USA Electrical & Computer Engineering Florida International University Miami United States

Prodromal detection of Alzheimer’s Disease(AD) is a substantial challenge in the research community. Among the tools used in AD diagnosis, cognitive exams are standard in most procedures. However, the barrage of cognitive examinations is both time and resource consuming. With the use of Machine Learning, Feature Elimination (FE) can be combined with classification algorithms to determine which cognitive exams are best suited for diagnosis. Using the results of FE, it can be determined if subsections of different composite scores can be combined to create a new enhanced and exhaustive exam. This paper implements a Recursive Feature Elimination with Cross Validation (RFECV) machine learning algorithm to determine which cognitive exams perform best for AD classification tasks. Out of 119 features, an average of 16 features were selected as optimal. These optimal features average 75% Accuracy, 70% Precision, and 75% Recall and an F1 Weighted score of 71% in classification.

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RELD: Regularization by Latent Diffusion Models for Image Restoration

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arXiv 2025年

作者： Cascarano, Pasquale Stacchio, Lorenzo Sebastiani, Andrea Benfenati, Alessandro Kamilov, Ulugbek S. Marfia, Gustavo Department of the Arts University of Bologna Bologna40123 Italy Department of Political Sciences Communication and International Relations University of Macerata Macerata62100 Italy Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia Modena41125 Italy Department of Environmental and Science Policy University of Milan Milan20133 Italy Department of Computer Science & Engineering Department of Electrical & System Engineering Washington University in St. Louis St. LouisMO63130 United States

In recent years, Diffusion Models have become the new state-of-the-art in deep generative modeling, ending the long-time dominance of Generative Adversarial Networks. Inspired by the Regularization by Denoising principle, we introduce an approach that integrates a Latent Diffusion Model, trained for the denoising task, into a variational framework using Half-Quadratic Splitting, exploiting its regularization properties. This approach, under appropriate conditions that can be easily met in various imaging applications, allows for reduced computational cost while achieving high-quality results. The proposed strategy, called Regularization by Latent Denoising (RELD), is then tested on a dataset of natural images, for image denoising, deblurring, and super-resolution tasks. The numerical experiments show that RELD is competitive with other state-of-the-art methods, particularly achieving remarkable results when evaluated using perceptual quality metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

Meaningful human control: actionable properties for AI system development

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AI and Ethics 2022年第1期3卷 241-255页

作者： Cavalcante Siebert, Luciano Lupetti, Maria Luce Aizenberg, Evgeni Beckers, Niek Zgonnikov, Arkady Veluwenkamp, Herman Abbink, David Giaccardi, Elisa Houben, Geert-Jan Jonker, Catholijn M. van den Hoven, Jeroen Forster, Deborah Lagendijk, Reginald L. AiTech Interdisciplinary Research Program on Meaningful Human Control Delft University of Technology Delft The Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands Faculty of Industrial Design Engineering Delft University of Technology Delft The Netherlands Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Delft The Netherlands Faculty of Technology Policy and Management Delft University of Technology Delft The Netherlands

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.

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Weight Decay Regularized Adversarial Training for Attacking Angle Imbalance

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SSRN

SSRN 2024年

作者： Wang, Guorong Tang, Jinchuan Ding, Zehua Dang, Shuping Chen, Gaojie State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang China Department of Electrical & Electronic Engineering University of Bristol Bristol United Kingdom School of Flexible Electronics the State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangdong Guangzhou510275 China

In this paper, we explore the latent distribution of adversarial examples in terms of the angle between the adversarial perturbation and the image manifold to optimize adversarial training (AT). Firstly, we study the norm ratio of a random vector to its projection on the image manifold to demonstrate that adversarial perturbations favor dimensions outside the manifold. Second, we explore the effect of the angle between the adversarial perturbation and the image manifold. By studying this distribution and establishing its relation to the loss between adversarial and real samples, we find that the angle distribution is approximately normal. Consequently, this unbalanced training in terms of attacking angles causes the loss to fluctuate heavily for angles located in the tails of the distribution. To address this problem, we propose a novel weight decay regularized AT, termed WDR-AT. Specifically, WDR-AT assesses the attack fluctuation of each adversarial example, compares it with the scaled mean, and down-weights anomalous adversarial examples. Finally, experimental results obtained on four typical datasets, MNIST, CIFAR10, CIFAR100, and TinyImageNet demonstrate the superiority of WDR-AT over state-of-the-art AT methods. © 2024, The Authors. All rights reserved.

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