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arXiv 2023年

作者： Hu, Zhirui Wolle, Robert Tian, Mingzhen Guan, Qiang Humble, Travis Jiang, Weiwen Department of Electrical and Computer Engineering George Mason University VA United States Quantum Science and Engineering Center George Mason University VA United States Department of Physics & Astronomy George Mason University VA United States Department of Computer Science Kent State University OH United States Oak Ridge National Laboratory TN United States

In the near-term noisy intermediate-scale quantum (NISQ) era, high noise will significantly reduce the fidelity of quantum computing. What’s worse, recent works reveal that the noise on quantum devices is not stable, that is, the noise is dynamically changing over time. This leads to an imminent challenging problem: At run-time, is there a way to efficiently achieve a consistent high-fidelity quantum system on unstable devices? To study this problem, we take quantum learning (a.k.a., variational quantum algorithm) as a vehicle, which has a wide range of applications, such as combinatorial optimization and machine learning. A straightforward approach is to optimize a variational quantum circuit (VQC) with a parameter-shift approach on the target quantum device before using it;however, the optimization has an extremely high time cost, which is not practical at run-time. To address the pressing issue, in this paper, we proposed a novel quantum pulse-based noise adaptation framework, namely QuPAD. In the proposed framework, first, we identify that the CNOT gate is the fidelity bottleneck of the conventional VQC, and we employ a more robust parameterized multi-qubit gate (i.e., Rzx gate) to replace CNOT gate. Second, by benchmarking Rzx gate with different parameters, we build a fitting function for each coupling qubit pair, such that the deviation between the theoretic output of Rzx gate and its on-device output under a given pulse amplitude and duration can be efficiently predicted. On top of this, an evolutionary algorithm is devised to identify the pulse amplitude and duration of each Rzx gate (i.e., calibration) and find the quantum circuits with high fidelity. Experiments show that the runtime on quantum devices of QuPAD with 8-10 qubits is less than 15 minutes, which is up to 270× faster than the parameter-shift approach. In addition, compared to the vanilla VQC as a baseline, QuPAD can achieve 59.33% accuracy gain on a classification task, and average 66.34% clos

关键词： Combinatorial optimization

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Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding

arXiv

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arXiv 2022年

作者： Liang, Shining Shou, Linjun Pei, Jian Gong, Ming Zuo, Wanli Zuo, Xianglin Jiang, Daxin College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering MOE China STCA Microsoft Department of Electrical & Computer Engineering Duke University United States

Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data. The recent multilingual code-switching approach achieves better alignments of model representations across languages by constructing a mixed-language context in zero-shot cross-lingual SLU. However, current code-switching methods are limited to implicit alignment and disregard the inherent semantic structure in SLU, i.e., the hierarchical inclusion of utterances, slots, and words. In this paper, we propose to model the utterance-slot-word structure by a multi-level contrastive learning framework at the utterance, slot, and word levels to facilitate explicit alignment. Novel code-switching schemes are introduced to generate hard negative examples for our contrastive learning framework. Furthermore, we develop a label-aware joint model leveraging label semantics to enhance the implicit alignment and feed to contrastive learning. Our experimental results show that our proposed methods significantly improve the performance compared with the strong baselines on two zero-shot cross-lingual SLU benchmark datasets. © 2022, CC BY-NC-ND.

关键词： Benchmarking

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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engineering Casimir interactions with epsilon-near-zero materials

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Physical Review A 2022年第6期105卷 L061501-L061501页

作者： Miguel Camacho Tao Gong Benjamin Spreng Iñigo Liberal Nader Engheta Jeremy N. Munday Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Electronics and Electromagnetism Universidad de Sevilla 41012 Seville Spain Department of Electrical and Computer Engineering University of California Davis California 95616 USA Department of Materials Science and Engineering University of California Davis California 95616 USA Department of Electrical and Electronic Engineering Public University of Navarra 31006 Pamplona Spain

In this paper, we theoretically demonstrate the tunability of the Casimir force both in sign and magnitude between parallel plates coated with dispersive materials. We show that this force, existing between uncharged plates, can be tuned by carefully choosing the value of the plasma frequency (i.e., the epsilon-near-zero frequency) of the coating in the neighborhood of the resonance frequency of the cavity. The coating layer enables a continuous variation of the force between four limiting values when a coating is placed on each plate. We explore the consequences of such variation when pairs of electric and magnetic conductors (i.e., low and high impedance surfaces) are used as substrates on either side, showing that this continuous variation results in changes in the sign of the force, leading to both stable and unstable conditions, which could find interesting potential applications in nanomechanics, including nanoparticle tweezing.

关键词： Casimir effect Electromagnetic interactions Light-matter interaction Metamaterials Optical materials & elements

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Low-Dose Pet Image Reconstruction Using Self-Supervised Deep Kernel Representations Learned from MR

Low-Dose Pet Image Reconstruction Using Self-Supervised Deep...

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IEEE International Symposium on Biomedical Imaging

作者： Cameron Dennis Pain Gary Egan Zhaolin Chen Monash Biomedical Imaging Monash University Clayton Australia Department of Electrical and Computer Systems Engineering Monash University Clayton Australia Turner Institute for Brain and Mental Health Monash University Clayton Australia Department of Data Science and AI Monash University Clayton Australia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Positron emission tomography is a modality for imaging physiological process in vivo. Image quality depends on the level of ionising radiation exposure, motivating PET image processing methods which recover standard-dose image quality from low-dose signal. This work presents DK-ADMM; a self-supervised deep learning approach for low-dose PET image reconstruction. Our method explicitly models deep latent space features using a kernel function derived from coregistered MR images for improved noise control and anatomical consistency. We compare our results with baseline methods and demonstrate improved performance, and generalisability across patients without subject-specific fine tuning.

关键词： Image quality In vivo Ionizing radiation Noise Physiology Inference algorithms Positron emission tomography

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Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications

arXiv

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arXiv 2024年

作者： Zhang, Yichi Zhang, Yuchen Zhu, Lipeng Xiao, Sa Tang, Wanbin Eldar, Yonina C. Zhang, Rui National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of subarrays, under the constraints of unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function/constraints, as well as the highly coupled variables, the formulated problem is challenging to solve. By employing fractional programming, we develop an alternating optimization framework to solve the problem via a combination of Lagrange multipliers, penalty method, and gradient descent. Numerical results reveal that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to its fixed-position antenna (FPA) counterpart. Moreover, with sufficiently large movable regions, the proposed scheme with sub-connected MA arrays even outperforms the fully-connected FPA array. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Joint Admission and Power Control for Big Data Access Management Using GAT

Joint Admission and Power Control for Big Data Access Manage...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mengke Yang Daosen Zhai Ruonan Zhang Haotong Cao Lin Cai F. Richard Yu School of Electronics and Information Northwestern Polytechnical University Xi'an Shaanxi China National Mobile Communications Research Laboratory Southeast University Nanjing Jiangsu China Nanjing University of Posts and Telecommunications Nanjing Jiangsu China Department of Electrical and Computer Engineering University of Victoria Victoria Canada Department of Systems and Computer Engineering Carleton University Ottawa ON

The emerging artificial intelligence (AI) puts forward high requirement for big data acquisition, which is difficult to be met with the existing communication technologies in real time. In this paper, we investigate new graph learning based access management scheme for supporting the real-time big data acquisition in the sixth-generation mobile communication system (6G). We model the network scene with a mass of communication links as a fully connected graph which takes into account the accumulative interference of all links. Then, the joint admission and power control problem is formulated as a combinatorial optimization problem. We propose a graph attention network (GAT) based algorithm which can learn the system features by weighted aggregation of neighbor nodes. In addition, we construct a differentiable loss function that can accurately express the optimization objective and train the network by the change of loss. Based on the output of the GAT, we iteratively optimize the link admission and power to active more links. Simulation results demonstrate that the proposed algorithm is superior to the traditional convex optimization based algorithms and the nonmodified GAT based algorithms in the number of activated links. Moreover, the training of the constructed network is unsupervised with high computational efficiency, which makes them suitable for the big data access management.

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VoiceGuard: An Effective and Practical Approach for Detecting and Blocking Unauthorized Voice Commands to Smart Speakers

VoiceGuard: An Effective and Practical Approach for Detectin...

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International Conference on Dependable systems and Networks (DSN)

作者： Xuening Xu Chenglong Fu Xiaojiang Du E. Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems UNC Charlotte Charlotte NC USA Information Directorate Air Force Research Laboratory Rome NY USA

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.

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Purcell-enhanced X-ray Imaging in Ultra-thin Scintillators

Purcell-enhanced X-ray Imaging in Ultra-thin Scintillators

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Conference on Lasers and Electro-Optics (CLEO)

作者： Roman Schuetz Yaniv Kurman Neta Lahav Avner Shultzman Charles Roques-Carmes Alon Lifshits Segev Zaken Rotem Strassberg Orr Be’Er Yehonadav Bekenstein Ido Kaminer Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel Department of Materials Science and Engineering Technion - Israel Institute of Technology Haifa Israel Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA

We demonstrate improved X-ray imaging using nanophotonic scintillators. Our scintillators rely on Purcell enhancement for brighter and faster emission. Applying this concept in radiology and nuclear medicine could ena... 详细信息

ISBN: (纸本)9781957171258

关键词： Positron emission tomography Purcell effect Spatial frequency Spontaneous emission X ray imaging X rays

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Extracting Stress-Related EEG Patterns from Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency

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IEEE Transactions on Neural systems and Rehabilitation engineering 2024年 32卷 1817-1827页

作者： Su, Cheng-Hua Ko, Li-Wei Jung, Tzyy-Ping Onton, Julie Tzou, Shey-Cherng Juang, Jia-Chi Hsu, Chung-Yao Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Department of Biomedical Science and Environmental Biology Kaohsiung807 Taiwan University of California San Diego Swartz Center for Computational Neuroscience La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan University of California San Diego Institute for Neural Computation La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Biological Science and Technology Hsinchu300 Taiwan Kaohsiung Medical University Hospital Sleep Disorders Center Department of Neurology Kaohsiung807 Taiwan Kaohsiung Medical University College of Medicine Department of Neurology Kaohsiung807 Taiwan

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution. © 2001-2011 IEEE.

关键词： Entropy

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