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Entropic and Operational Characterizations of Dynamic Quantum Resources

IEEE Transactions on Information Theory 2025年

作者： Ji, Kaiyuan Chitambar, Eric Cornell University School of Electrical and Computer Engineering IthacaNY14850 United States University of Illinois at Urbana-Champaign Coordinated Science Laboratory Department of Electrical and Computer Engineering UrbanaIL61801 United States

We offer new methods for characterizing general closed and convex quantum resource theories, including dynamic ones, based on entropic concepts and operational tasks. We propose a resource-theoretic generalization of the quantum conditional min-entropy, termed the free conditional min-entropy (FCME), in the sense that it quantifies an observer's "subjective"degree of uncertainty about a quantum system given that the observer's information processing is limited to free operations of the resource theory. Using this generalized concept, we provide a complete set of entropic conditions for free convertibility between quantum states or channels in any closed and convex quantum resource theory. We also derive an information-theoretic interpretation for the resource global robustness of a state or a channel in terms of a mutual-information-like quantity based on the FCME. Apart from this entropic approach, we characterize dynamic resources by also analyzing their performance in operational tasks. We construct operationally meaningful and complete sets of resource monotones with these tasks, which enable faithful tests of free convertibility between quantum channels. Finally, we show that every well-defined robustness-based measure of a channel can be interpreted as an operational advantage of the channel over free channels in a communication task. © 1963-2012 IEEE.

关键词： Quantum channel

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management System Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年第4期16卷 3282-3294页

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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Two-timescale Optimization for E2E Network Slicing-aided Cloud-edge Collaborative Networks

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9777-9789页

作者： Wang, Yunfeng Zhao, Liqiang Chu, Xiaoli Song, Shenghui Deng, Yansha Nallanathan, Arumugam Zhou, Guorong Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510100 China University of Sheffield Department of Electronic and Electrical Engineering SheffieldS10 2TN United Kingdom Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong King's College London Department of Engineering LondonWC2R 2LS United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

To leverage the synergy between cloud computing (CC) and edge computing (EC) to support various services while reducing the CC/EC switching overhead, the two-timescale utility maximization problem for an end-to-end network slicing-aided cloud-edge collaborative network (E2E-CECN) is formulated covering both the high throughput and low delay service requirements. To solve the utility maximization problem while dynamically adjusting the weights of the E2E-CECN utility to accommodate the variation of users' service requests, we proposed a reward comparison double deep Q network algorithm to optimize the large timescale joint virtual base station activation and CC-EC scheduling, and a reward comparison deep deterministic policy gradient algorithm to optimize the small timescale allocation of backhaul link capacity (BLC), CC/EC capability and transmission power. Numerical results show that both the high-throughput and low-delay service requirements can be satisfied simultaneously under the reasonable BLC usage and power consumption. © 1967-2012 IEEE.

关键词： Cloud platforms

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computer Code Representation through Natural Language Processing for fMRI Data Analysis 4

Computer Code Representation through Natural Language Proces...

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4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

作者： Kim, Jaeyoon O'Reilly, Una-May Seok, Junhee Korea University Department of Electrical and Computer Engineering Seoul Korea Republic of Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory Cambridge United States

ISBN: (纸本)9781665458184

There are many attempts to analyze the relationship between functional magnetic resonance imaging (fMRI) data and text stimuli representation in cognitive neuroscience research. Because programming codes are exemplary text stimuli, appropriate code representation for neuroscience research has been actively studied. In this paper, we focus on representing python code for fMRI research through natural language processing (NLP) techniques. We collect 7, 893 python codes of 23 question types from a code competition website and build three different models based on sequence-to-sequence, bag-of-words, and bigram representation. The model is evaluated to classify the types of questions. Finally, the model is applied to classify 108 python codes which were used for a cognitive neuroscience study of fMRI. We are looking forward to analyzing fMRI data with the proposed code representation for understanding how the human brain is active. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

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Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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science China(Information sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG Chunjie YANG Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence

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Catalysts for electrochemical ammonia oxidation:Trend,challenge,and promise

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science China Materials 2022年第12期65卷 3334-3352页

作者： Sol A Lee Mi Gyoung Lee Ho Won Jang Department of Materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Department of Electrical and Computer Engineering University of TorontoToronto M5S 3G4OntarioCanada Advanced Institute of Convergence Technology Seoul National UniversitySuwon 16229Republic of Korea

Developing sustainable and clean energy technology is critial to address the current energy and environmental *** an energy carrier,hydrogen has been getting attention due to its abundance,high energy density,and zero carbon *** to the difficulty of hydrogen storage and transport,the electro-oxidation of ammonia shows great potential for renewable energy since it satisfies both energy supply and environmental protection as a carbon-free fuel with high hydrogen ***,ammonia electro-oxidation is a sluggish reaction and shows low *** the complicated mechanism and intermediates is conducive to developing efficient electrocatalysts with high catalytic activity and *** review begins with the ammonia oxidation technologies and their *** two typical mechanisms for ammonia electro-oxidation,Oswin-Salomon mechanism and Gerischer-Mauerer mechanism,are discussed,and attention to the poisoning species is ***,the electrocatalysts for ammonia reaction are classified into Pt-based catalysts and non-noble metal-based catalysts,and their performances are reviewed with various *** on the progress on the electrochemical ammonia oxidation catalysts,the challenges and future insight on ammonia oxidation reaction are discussed and proposed.

关键词： NH3 oxidation reaction electrocatalysts green hydrogen

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Defect Recognition based on Radon Transform in Pentacene organic thin-film Archimedean interdigitated spirals transistor

Defect Recognition based on Radon Transform in Pentacene org...

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2023 International Conference on Clean electrical Power, ICCEP 2023

作者： Sciuto, Grazia Lo Linde, Sivan Shikler, Rafi Kowol, Pawel Coco, Salvo Capizzi, Giacomo Silesian University of Technology Department of Mechatronics Gliwice Poland Ben-Gurion University of the Negev School of Electrical and Computer Engineering Optoelectronic Organic Semiconductor Devices Laboratory Department of Electrical and Computer Engineering Beer-Sheva Israel Ben-Gurion University of the Negev Ilse Katz Institute for Nanoscale Science and Technology Beer-Sheva Israel University of Catania Department of Electrical Electronics and Informatics Engineering Catania Italy

ISBN: (纸本)9798350348378

In this paper the problem of automatic defects detection in thin film transistor is considered in order to increase the efficiency of electronic devices. An effective approach based on defect pattern recognition in organic transistor is developed using the Radon transform. The adopted design and technology in Pentacene organic thin-film transistor exhibits two interdigitated Archimedean spirals pattern. In the conducted experiments, the images acquisition is performed with a microscope camera to capture the various defects of surface morphology on the top of the examined transistors. The defects detection is obtained by using the Radon transform on each channel (R,G,B) of these images. © 2023 IEEE.

关键词： Thin film transistors

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A Detailed Analysis of the Critical Role of artificial intelligence in Enabling High-Performance Cloud Computing Systems 2

A Detailed Analysis of the Critical Role of artificial intel...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Ahamad, Shahanawaj Mohseni, Milad Shekher, Vineet Smaisim, Ghassan Fadhil Tripathi, Amrendra Alanya-Beltran, Joel College of Computer Science and Engineering University of Hail Saudi Arabia Degree in Computer Architecture Computer Tabriz Iran Bit Sindri Electrical Engineering Department Dhanbad India University of Kufa Faculty of Engineering Department of Mechanical Engineering Iraq Faculty of Engineering Iraq School of Computer Science Upes Dehradun India Universidad Tecnológica del Perú Electronic Department Peru

ISBN: (数字)9781665437899

ISBN: (纸本)9781665437899

Cloud computing reflects effective delivery of computing services such as software, storage purposes, analytics, intelligence and many others. Besides that, it also offers faster innovation and at the same time flexible resources. In an organisation, a cloud computing system provides tight security of data with a low maintenance cost. A huge collaboration has been seen between various industries through cloud systems. Those all processes have been boosted up by the implementation of AI. Incorporation of AI by those industries hugely influences the efficiency rate of cloud computing techniques and due to this reason, the combined effect of those two technologies brings up huge profitability to those organisations. Implementation of machine learning models, as well as cognitive computing, increases the performance of cloud systems. In addition to this, a lot of benefits have been seen when organisations incorporate AI in cloud computing techniques. This paper contains a detailed introduction part along with a well informative literature review. A research methodology also has been provided where three research questions are mentioned. The data analysis part comprises a multi regression analysis where the independent variables are years and percentage of usage of AI in various organisations. The dependent variable is profitability. A critical discussion part also has been presented and a conclusion has also been provided with a few recommendations. © 2022 IEEE.

关键词： Cost effectiveness

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A dual-mode image sensor using an all-inorganic perovskite nanowire array for standard and neuromorphic imaging

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Journal of Semiconductors 2023年第9期44卷 75-82页

作者： Zhenghao Long Yucheng Ding Xiao Qiu Yu Zhou Shivam Kumar Zhiyong Fan Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina State Key Laboratory of Advanced Displays and Optoelectronics Technologies HKUSTClear Water BayKowloonHong Kong SARChina Department of Chemical and Biological Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina Shanghai Artificial Intelligence Laboratory Shanghai 200232China

The high-density,vertically aligned retinal neuron array provides effective vision,a feature we aim to replicate with electronic ***,the conventional complementary metal-oxide-semiconductor(CMOS)image sensor,based on separate designs for sensing,memory,and processing units,limits its integration ***,redundant signal communication significantly increases energy *** neuromorphic devices integrating sensing and signal processing show promise in various computer vision applications,but there is still a need for frame-based imaging with good *** this study,we developed a dual-mode image sensor based on a high-density all-inorganic perovskite nanowire *** device can switch between frame-based standard imaging mode and neuromorphic imaging mode by applying different *** unique bias-dependent photo response is based on a well-designed energy band *** biomimetic alignment of nanowires ensures the potential for high-resolution *** further demonstrate the imaging ability,we conducted pattern reconstruction in both modes with a 10×10 crossbar *** study introduces a novel image sensor with high compatibility and efficiency,suitable for various applications including computer vision,surveillance,and robotics.

关键词： mode image inorganic

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