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Enhanced thermal rectification in coupled qutrit–qubit quantum thermal diode

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APL Quantum

APL Quantum 2024年第4期1卷

作者： Anuradhi Rajapaksha Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

We present a quantum thermal diode model based on a coupled qutrit–qubit system designed to control heat flow between two thermal baths with unprecedented efficiency. This differs from previous models in terms of the asymmetry introduced by spin particles and their interaction. By exploiting the interactions between degenerate states within the coupled qutrit–qubit system, our model demonstrates diode-like behavior that is both robust and energy-efficient. Utilizing the frameworks of open quantum systems and the quantum Markovian master equation, with the Born and rotating wave approximations, we comprehensively analyze the system’s behavior. Numerical simulations reveal significant thermal rectification across a wide temperature range, positioning our model as a groundbreaking solution for nanoscale heat management. In addition, we employ state transition diagrams to elucidate the transition rate characteristics that underpin the diode behavior. Finally, we explore the potential for physical implementation using superconducting circuits, highlighting the practical relevance of our design.

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Deep Learning for Instance Retrieval: A Survey

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arXiv 2021年

作者： Chen, Wei Liu, Yu Wang, Weiping Bakker, Erwin M. Georgiou, Theodoros Fieguth, Paul Liu, Li Lew, Michael S. Academy of Advanced Technology Research of Hunan Changsha China DUTRU International School of Information Science and Engineering Dalian University of Technology China Leiden Institute of Advanced Computer Science Leiden University Netherlands The Department of Systems Design Engineering University of Waterloo Canada Center for Machine Vision and Signal Analysis University of Oulu Finland

In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, particularly that of searching databases for similar content - Content Based Image Retrieval (CBIR) - a long-established research area in which improved efficiency and accuracy are needed for real-time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of instance search. In this survey we review recent instance retrieval works that are developed based on deep learning algorithms and techniques, with the survey organized by deep feature extraction, feature embedding and aggregation methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, whereby we identify milestone work, reveal connections among various methods and present the commonly used benchmarks, evaluation results, common challenges, and propose promising future directions. © 2021, CC BY-NC-SA.

关键词： Learning algorithms

Single Exposure Quantitative Phase Imaging with a Conventional Microscope Using Diffusion Models 39

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Single Exposure Quantitative Phase Imaging with a Convention...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： della Maggiora, Gabriel Croquevielle, Luis Alberto Horsley, Harry Heinis, Thomas Yakimovich, Artur Center for Advanced Systems Understanding (CASUS) Görlitz Germany Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR) Dresden Germany Department of Computing Imperial College London London United Kingdom Bladder Infection and Immunity Group (BIIG) UCL Centre for Kidney and Bladder Health Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Institute of Computer Science University of Wrocław Wrocław Poland School of Computation Information and Technology Technical University of Munich Germany

ISBN: (纸本)157735897X

Phase imaging is gaining importance due to its applications in fields like biomedical imaging and material characterization. In biomedical applications, it can provide quantitative information missing in label-free microscopy modalities. One of the most prominent methods in phase quantification is the Transport-of-Intensity Equation (TIE). TIE often requires multiple acquisitions at different defocus distances, which is not always feasible in a clinical setting, due to hardware constraints. To address this issue, we propose the use of chromatic aberrations to induce the required through-focus images with a single exposure, effectively generating a through-focus stack. Since the defocus distance induced by the aberrations is small, conventional TIE solvers are insufficient to address the resulting artifacts. We propose Zero-Mean Diffusion, a modified version of diffusion models designed for quantitative image prediction, and train it with synthetic data to ensure robust phase retrieval. Our contributions offer an alternative TIE approach that leverages chromatic aberrations, achieving accurate single-exposure phase measurement with white light and thus improving the efficiency of phase imaging. Additionally, we present a new class of diffusion models that are well-suited for quantitative data and have a sound theoretical basis. To validate our approach, we employ a widespread brightfield microscope equipped with a commercially available color camera. We apply our model to clinical microscopy of patients’ urine, obtaining accurate phase measurements. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer’s Disease

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arXiv 2023年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China The Department of Systems and Enterprises Stevens Institute of Technology United States School of Science and Technology Hong Kong Metropolitan University Hong Kong College of Engineering and Computer Science VinUni-Illinois Smart Health Center VinUniversity Viet Nam Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Game Design Faculty of Arts Uppsala University Sweden The School of Software Tiangong University Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer’s disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

Improvement of Industrial Applications with AI-Powered Power Quality Monitoring in Electric Vehicles: Maximizing Smart Energy Management and Predictive Analytics

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Improvement of Industrial Applications with AI-Powered Power...

IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Chandramohan Devarajan Sujatha S J. Sumithra V.R. Kavitha Jeyanthi P V. Samuthira Pandi Department of Mechanical Engineering St. Peter's Institute of Higher Education and Research Chennai Tamil Nadu India Department of EEE Sri Sairam College of Engineering Bangalore Karnataka India Department of ECE RMD Engineering College Kavarapettai Tamil Nadu India Department of Computer Science and Business Systems R.M.K. Engineering College Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

A revolution is underway in the field of electric vehicle (EV) power quality monitoring thanks to advancements in artificial intelligence (AI), which holds great promise for the enhancement of industrial applications. This research delves into the potential of AI to enhance electric car systems' energy management and predictive analytics. Power quality measures, which are critical for electric vehicle performance, may be effectively monitored and evaluated by artificial intelligence. These metrics include frequency fluctuations, harmonic distortion, and voltage stability. Using algorithms for machine learning and analyzing data in real-time makes this possible. In order to create predictive models that can foresee potential power quality issues, this study's results have prompted research into several AI techniques, including support vector machines and neural networks. As a result, operational downtime will be reduced and preventative maintenance can be carried out. Also, power quality monitoring systems are able to collect and analyze data more efficiently with the help of artificial intelligence (AI) and Internet of Things (IoT) devices. The goal of this research is to highlight the benefits of electric vehicle (EV) power quality monitoring powered by artificial intelligence (AI), such as enhanced user experience, longer battery life, and greater energy efficiency. Electric vehicles in industrial settings may become more dependable and environmentally friendly if artificial intelligence is used to improve power quality control. This could lead to smarter energy management solutions.

关键词： Electric potential Protocols Power quality Electric vehicles Real-time systems Internet of Things Artificial intelligence Energy management Predictive analytics Monitoring

Improving quantum thermal transistors through feedback-controlled baths

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APL Quantum

APL Quantum 2024年第3期1卷

作者： Uthpala N. Ekanayake Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

In recent years, integrating quantum feedback mechanisms into thermal machines has gained attention due to its benefits in manipulating the system states and energy flows. This is particularly advantageous for quantum thermal transistors in preserving their inherent quantum properties as they lose the purity of the system states due to decoherence and relaxation from interactions with thermal baths, within the subsystems, and monitoring. In the literature, studies have demonstrated that preserving quantum coherence can enhance the performance of quantum thermal machines, improving their efficiency. In our paper, we present a model that proposes engineering baths to be equipped with detectors and a controller to enable feedback in a quantum thermal transistor that emulates a role played by a feedback resistor in an electronic transistor. We use the framework of quantum feedback control via weak monitoring. We modify the system evolution trajectories by using a weak monitoring record from a detector. By taking the ensemble average of these trajectories, we unveil the evolution of the system density matrix that corresponds to the Markovian dynamics of the transistor. This type of feedback introduces minimal perturbation to the system and, once tuned, enhances the system coherence that would otherwise degrade due to bath interactions. Furthermore, there will be no change in the relaxation times. The probabilities of population terms remain unchanged. We treat this an enhancement in the operational characteristics of the quantum thermal transistor as it maintains its quantum features with an added benefit of improved amplification capabilities.

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Empowering Patient Care: A Novel Patient Drug Recommendation System Using Artificial Intelligence with Modified Learning Strategy

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Empowering Patient Care: A Novel Patient Drug Recommendation...

Computing and Data Science (ICCDS), International Conference on

作者： Samuthira Pandi V V.R. Kavitha S Arumai Shinev Roopa Chandrika R A.S. Valarmathy Glory E Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Business Systems R.M.K. Engineering College Chennai Tamil Nadu India Department of CSE S A Engineering College Chennai Tamil Nadu India Department of CSE Karpagam Academy of Higher Education Coimbatore TamilNadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

In the era of personalized medicine, the demand for efficient and accurate drug recommendation systems has escalated. This paper introduces a pioneering approach to enhance patient care through a novel Patient Drug Recommendation System (PDRS). Leveraging the power of machine learning (ML), specifically Cascaded Multi-Layer Perceptrons (MLP), our system aims to provide precise and personalized drug recommendations based on an extensive Drug Review Dataset. The proposed PDRS capitalizes on the inherent complexities of drug reviews, extracting valuable insights and patterns from patient feedback. The cascaded MLP architecture is designed to sequentially process and analyze diverse aspects of drug reviews, capturing nuanced information and correlations that may be overlooked by traditional recommendation systems. Our methodology involves preprocessing the drug review dataset to address noise and ensure data quality. Feature extraction techniques are applied to distill meaningful information, and the cascaded MLP model is trained using a robust set of labeled data. The system's performance is evaluated on various metrics, with a remarkable accuracy of 97%, signifying its efficacy in predicting suitable drug recommendations for patients. The cascaded MLP architecture demonstrates superior adaptability, allowing the system to comprehend intricate relationships between patients, drugs, and their respective effects. The achieved accuracy underscores the potential of our PDRS to revolutionize clinical decision-making, fostering a more personalized and efficient approach to prescribing medications.

关键词： Drugs Accuracy Reviews Precision medicine System performance Decision making Medical services

Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration

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arXiv 2022年

作者： Liu, Peixi Zhu, Guangxu Wang, Shuai Jiang, Wei Luo, Wu Poor, H. Vincent Cui, Shuguang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Shenzhen Research Institute of Big Data Shenzhen China The Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen518060 China Department of Electrical and Computer Engineering Princeton University Princeton NJ08544 United States Shenzhen Research Institute of Big Data Shenzhen China Peng Cheng Laboratory China

With the breakthroughs in deep learning and contactless sensors, the recent years have witnessed a rise of ambient intelligence applications and services, spanning from healthcare delivery to intelligent home. Federated edge learning (FEEL), as a privacy-enhancing paradigm of collaborative learning at the network edge, is expected to be the core engine to achieve ambient intelligence. Sensing, computation, and communication (SC2) are highly coupled processes in FEEL and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at the edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC2 resource jointly affect the FEEL performance. In this paper, we address the problem of joint SC2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing in ambient intelligence. First, by analyzing the wireless sensing process in human motion recognition, we find that there exists a thresholding value for the sensing transmit power, exceeding which yields sensing data samples with approximately the same satisfactory quality. Then, the joint SC2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time, energy supply, and sensing quality of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determine the joint sensing and communication resource allocation that maximizes the total number of samples that can be sensed during the entire training process;the second one concerns the partition of the attained total number of sensed samples over all the communication rounds to determine the batch size at each round for convergence speed maximization. The first subproblem on joint sensing and communication resource allocation is converted to a single-variable optimization problem by exploiting

关键词： Motion estimation

Channel performance estimations with extended channel probing

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arXiv 2021年

作者： Kaeval, Kaida Elbers, Jörg-Peter Grieβer, Helmut Tikas, Marko Grobe, Klaus Jervan, Gert Advanced Technology ADVA Optical Networking SE Martinsried Germany Transmission Networks Tele2 Estonia AS Tallinn Estonia Global Sustainability ADVA Optical Networking SE Martinsried Germany Department of Computer Systems Tallinn University of Technology Tallinn Estonia

We test the concept of extended channel probing in a Optical Spectrum as a Service scenario in coherent optimized flex-grid long-haul and 10Gbit/s OOK optimized 100-GHz fixed-grid dispersion managed legacy DWDM networks. An estimation accuracy better than ±0.1dB in GSNR implementation margin is obtained for both networks by using flexible coherent transceivers on lightpaths up to 822km on regional haul legacy network and up to 5738km on a long-haul network. We also explain, how to detect the operation regime of the channel by analyzing the received GSNR results from constant power spectral density (PSD) probing mode and constant signal power probing mode. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical communication