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IEEE International Conference on Communications (ICC)

作者： Chaofan Li Huixiang Zhu Yong Xiao Yingyu Li Dusit Niyato Sumei Sun Walid Saad School of Elect. Inform. & Commun. Huazhong Univ. of Science & Technology China Peng Cheng Laboratory China Pazhou Lab (Huangpu) China School of Mech. Eng. and Elect. Inform. China University of Geosciences China School of Computer Science and Engineering at Nanyang Technological University Singapore Institute for Infocomm Research A*STAR Singapore Bradley Department of Electrical and Computer Engineering Virginia Tech VA USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications without requiring any extra devices to be carried out by human users. However, previous studies have shown that wireless sensing performance can be significantly degraded if the relative locations of the transmitters, human users, and receivers are non-ideal and/or the distances between the user and receivers are large. These constraints hinder the wide applications of wireless sensing in many practical scenarios. A promising approach for wireless sensing is through the use of reconfigurable intelligent surfaces (RISs) that can control the propagation environment to create a customizable wireless environment for wireless sensing. To this end, in this paper, the use of RIS-assisted wireless sensing for enhanced human gesture recognition is investigated. A novel RIS-assisted distributed wireless sensing framework that utilizes federated learning (FL) is proposed to enable collaborative model training among decentralized receivers. Then, a novel metric, called human-influencing signal-to-interference ratio (HSIR), is introduced to characterize the quality of locally recorded data as well as its impact on the performance of wireless sensing. To alleviate the model draft problem of FL-assisted wireless sensing, caused by spatial heterogeneity of the quality of wireless sensing data at different receivers, the optimal amplitudes and phases of the RIS are derived so as to improve the HSIR of a set of low-performance receivers located at non-ideal locations. Simulation results show that the proposed RIS-assisted system can significantly improve wireless sensing accuracy by up to 20.1% compared to traditional distributed system.

关键词： Wireless communication Training Wireless sensor networks Accuracy Transmitters Simulation Receivers

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Balanced Self-Paced Learning for AUC Maximization

arXiv

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arXiv 2022年

作者： Gu, Bin Zhang, Chenkang Xiong, Huan Huang, Heng MBZUAI United Arab Emirates School of Computer & Software Nanjing University of Information Science & Technology China Institute for Advanced Study in Mathematics Harbin Institute of Technology China Department of Electrical & Computer Engineering University of Pittsburgh PA United States

Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. However, existing self-paced learning methods are limited to pointwise learning, while AUC maximization is a pairwise learning problem. To solve this challenging problem, we innovatively propose a balanced self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide a statistical objective for self-paced AUC. Based on this, we propose our self-paced AUC maximization formulation, where a novel balanced self-paced regularization term is embedded to ensure that the selected positive and negative samples have proper proportions. Specially, the sub-problem with respect to all weight variables may be non-convex in our formulation, while the one is normally convex in existing self-paced problems. To address this, we propose a doubly cyclic block coordinate descent method. More importantly, we prove that the sub-problem with respect to all weight variables converges to a stationary point on the basis of closed-form solutions, and our BSPAUC converges to a stationary point of our fixed optimization objective under a mild assumption. Considering both the deep learning and kernel-based implementations, experimental results on several large-scale datasets demonstrate that our BSPAUC has a better generalization performance than existing state-of-the-art AUC maximization methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Recasting Self-Attention with Holographic Reduced Representations

arXiv

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arXiv 2023年

作者： Alam, Mohammad Mahmudul Raff, Edward Biderman, Stella Oates, Tim Holt, James Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States Laboratory for Physical Sciences College ParkMD United States Booz Allen Hamilton McLeanVA United States EleutherAI

In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the O(T2) memory and O(T2H) compute costs can make using transformers infeasible. Motivated by problems in malware detection, where sequence lengths of T ≥ 100, 000 are a roadblock to deep learning, we re-cast self-attention using the neuro-symbolic approach of Holographic Reduced Representations (HRR). In doing so we perform the same high-level strategy of the standard self-attention: a set of queries matching against a set of keys, and returning a weighted response of the values for each key. Implemented as a "Hrrformer" we obtain several benefits including O(TH log H) time complexity, O(TH) space complexity, and convergence in 10× fewer epochs. Nevertheless, the Hrrformer achieves near state-of-the-art accuracy on LRA benchmarks and we are able to learn with just a single layer. Combined, these benefits make our Hrrformer the first viable Transformer for such long malware classification sequences and up to 280× faster to train on the Long Range Arena benchmark. Code is available at https://***/NeuromorphicComputa tionResearchProgram/Hrrformer. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Slow-Wave Hybrid Magnonics

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Physical Review Letters 2024年第11期132卷 116701-116701页

作者： Jing Xu Changchun Zhong Shihao Zhuang Chen Qian Yu Jiang Amin Pishehvar Xu Han Dafei Jin Josep M. Jornet Bo Zhen Jiamian Hu Liang Jiang Xufeng Zhang Center for Nanoscale Materials Argonne National Laboratory Lemont Illinois 60439 USA Pritzker School of Molecular Engineering University of Chicago Chicago Illinois 60637 USA Department of Materials Science and Engineering University of Wisconsin—Madison Madison Wisconsin 53706 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Electrical and Computer Engineering Northeastern University Boston Massachusetts 02115 USA Department of Physics and Astronomy University of Notre Dame Notre Dame Indiana 46556 USA Department of Physics Northeastern University Boston Massachusetts 02115 USA

Cavity magnonics is an emerging research area focusing on the coupling between magnons and photons. Despite its great potential for coherent information processing, it has been long restricted by the narrow interaction bandwidth. In this Letter, we theoretically propose and experimentally demonstrate a novel approach to achieve broadband photon-magnon coupling by adopting slow waves on engineered microwave waveguides. To the best of our knowledge, this is the first time that slow wave is combined with hybrid magnonics. Its unique properties promise great potentials for both fundamental research and practical applications, for instance, by deepening our understanding of the light-matter interaction in the slow wave regime and providing high-efficiency spin wave transducers. The device concept can be extended to other systems such as optomagnonics and magnomechanics, opening up new directions for hybrid magnonics.

关键词： Magnons Spin waves Surface plasmon polariton Waveguides

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Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis

arXiv

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arXiv 2023年

作者： Théate, Thibaut Sutera, Antonio Ernst, Damien Department of Electrical Engineering and Computer Science University of Liège Liège Belgium Haulogy Intelligent Systems Solutions Braine-le-Comte Belgium Information Processing and Communications Laboratory Institut Polytechnique de Paris Paris France

The energy transition is expected to significantly increase the share of renewable energy sources whose production is intermittent in the electricity mix. Apart from key benefits, this development has the major drawback of generating a mismatch between power supply and demand. The innovative dynamic pricing approach may significantly contribute to mitigating that critical problem by taking advantage of the flexibility offered by the demand side. At its core, this idea consists in providing the consumer with a price signal which is evolving over time, in order to influence its consumption. This novel approach involves a challenging decision-making problem that can be summarised as follows: how to determine a price signal maximising the synchronisation between power supply and demand under the constraints of maintaining the producer/retailer’s profitability and benefiting the final consumer at the same time? As a contribution, this research work presents a detailed formalisation of this particular decision-making problem. Moreover, the paper discusses the diverse algorithmic components required to efficiently design a dynamic pricing policy: different forecasting models together with an accurate statistical modelling of the demand response to dynamic prices. © 2023, CC BY.

关键词： Decision making

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Risk-Sensitive Policy with Distributional Reinforcement Learning

arXiv

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arXiv 2022年

作者： Théate, Thibaut Ernst, Damien Department of Electrical Engineering and Computer Science University of Liège Liège Belgium Information Processing and Communications Laboratory Institut Polytechnique de Paris Paris France

Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the Q function generally standing at the core of learning schemes in RL by another function taking into account both the expected return and the risk. Named the risk-based utility function U, it can be extracted from the random return distribution Z naturally learnt by any distributional RL algorithm. This enables to span the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, and with an emphasis on the interpretability of the resulting decision-making process. © 2022, CC BY.

关键词： Reinforcement learning

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An Enhanced Aquila-Based Resource Allocation for Efficient Indoor IoT Visible Light Communication

An Enhanced Aquila-Based Resource Allocation for Efficient I...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Selma Yahia Yassine Meraihi Sylia Mekhmoukh Taleb Seyedali Mirjalili Amar Ramdane-Cherif Tu Dac Ho Hossien B. Eldeeb Sami Muhaidat LIST Laboratory University of M’Hamed Bougara Boumerdes Boumerdes Algeria Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane Australia LISV Laboratory University of Versailles St-Quentin-en-Yvelines Velizy France Department of Electrical Engineering Faculty of Engineering Science and Technology UiT Arctic University of Norway Electrical Engineering Computer Science Center of Cyber-Physical Systems Khalifa University UAE

Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.

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Finite-Size Analysis of Prepare-and-Measure and Decoy-State Quantum Key Distribution via Entropy Accumulation

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PRX Quantum 2025年第2期6卷 020342-020342页

作者： Lars Kamin Amir Arqand Ian George Norbert Lütkenhaus Ernest Y.-Z. Tan Institute for Quantum Computing and Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

An important goal in quantum key distribution (QKD) is the task of providing a finite-size security proof without the assumption of collective attacks. For prepare-and-measure QKD, one approach for obtaining such proofs is the generalized entropy-accumulation theorem (GEAT) but thus far it has only been applied to study a small selection of protocols. In this work, we present techniques for applying the GEAT in finite-size analysis of generic prepare-and-measure protocols, with a focus on decoy-state protocols. In particular, we present an improved approach for computing entropy bounds for decoy-state protocols, which has the dual benefits of providing tighter bounds than previous approaches (even asymptotically) and being compatible with methods for computing min-trade-off functions in the GEAT. Furthermore, we develop methods to incorporate some improvements to the finite-size terms in the GEAT and implement techniques to automatically optimize the min-trade-off function. Our approach also addresses some numerical-stability challenges specific to prepare-and-measure protocols, which have not been addressed in previous works.

关键词： Numerical methods

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Predictive Prescription of Unit Commitment Decisions Under Net Load Uncertainty

arXiv

引用

arXiv 2022年

作者： Yurdakul, Ogun Qiu, Feng Albayrak, Sahin Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Energy Systems Division Argonne National Laboratory LemontIL60439 United States

To take unit commitment (UC) decisions under uncertain net load, most studies utilize a stochastic UC (SUC) model that adopts a one-size-fits-all representation of uncertainty. Disregarding contextual information such as weather forecasts and temporal information, these models are typically plagued by a poor out-of-sample performance. To effectively exploit contextual information, in this paper, we formulate a conditional SUC problem that is solved given a covariate observation. The presented problem relies on the true conditional distribution of net load and so cannot be solved in practice. To approximate its solution, we put forward a predictive prescription framework, which leverages a machine learning model to derive weights that are used in solving a reweighted sample average approximation problem. In contrast with existing predictive prescription frameworks, we manipulate the weights that the learning model delivers based on the specific dataset, present a method to select pertinent covariates, and tune the hyperparameters of the framework based on the out-of-sample cost of its policies. We conduct extensive numerical studies, which lay out the relative merits of the framework vis-à-vis various benchmarks. © 2022, CC BY.

关键词： Stochastic models

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Multidomain Dependency-Aware Guided Unified-Stage Coronary Artery Branch Recognition Network

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Sigeng Fan, Jingfan Ai, Danni Xiao, Deqiang Lin, Yucong Song, Hong Liu, Hongli Yu, Wenyuan Yu, Yang Yang, Jian Beijing Institute of Technology Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing100081 China Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Capital Medical University Department of Cardiac Surgery Beijing Anzhen Hospital Beijing China Yellwin Co. Ltd Beijing China

Clinical scoring in X-ray coronary angiography image sequences is widely used for revascularization decision-making in cases of coronary artery disease. Accurately recognizing coronary artery branches is a fundamental step in assessing the severity of quantitative stenosis. Existing methods employ a multistage process that includes view separation, skeletonization, graph building, and classification using topological features. However, the graph often suffers from skeleton errors, leading to incorrect topological connections during the classification stage, which requires manual correction. To address these issues, we propose a unified-stage coronary artery branch recognition network (UniCABR) that integrates the segmentation, skeletonization, and graph-building stages. Specifically, we design a dependency-aware module to build dependency graphs in both semantic and spatial domains, avoiding the use of rigid inter-branch topological connections and thus eliminating the need for manual correction of misconnections resulting from skeleton errors. Furthermore, to suppress nontarget branches according to clinical criteria and enhance the performance of side branches, we introduce a small feature supplementation module coupled with an adaptive merged binary supervision method at the pixel level. Extensive experiments on two datasets and a generalization study demonstrate the superiority of UniCABR in performance and generalization ability for coronary artery branch recognition tasks. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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