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Intelligent Analog Beam Selection and Beamspace Channel Tracking in THz Massive MIMO with Lens Antenna Array

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Intelligent Analog Beam Selection and Beamspace Channel Trac...

作者： Zarini, Hosein Mili, Mohammad Robat Rasti, Mehdi Andreev, Sergey Nardelli, Pedro H. J. Bennis, Mehdi Sharif University of Technology Department of Computer Engineering Tehran*** Iran Pasargad Institute for Advanced Innovative Solutions Tehran199163361 Iran Oulu90014 Finland Tampere University Faculty of Information Technology and Communication Sciences Tampere33100 Finland Lappeenranta-Lahti University of Technology School of Energy Systems Department of Electrical Engineering Lappeenranta53850 Finland University of Oulu Center for Wireless Communications Oulu90014 Finland

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive exogenous (NARX) model for tracking and predicting the beamspace channel over the sequences of time. Benefiting from bounded generalization error, fast convergence, limited prediction variance, and negligible performance loss, the proposed scheme achieves up to 15% spectral efficiency (SE) gain over its counterparts. We further improve this performance by means of an ensemble learning technique for simultaneously training multiple NARX modules in parallel, thus leading to a 23% SE gain. Relying on the predicted beamspace channel, we propose a beamspace analog beam selection technique through fine-tuning the architecture of a pre-trained off-the-shelf GoogleNet, which brings up to 21% SE gain over similar baselines. With the aid of an ensemble learning technique, it is further indicated numerically that up to 34% SE improvement can be achieved, as compared to the counterparts. © 2015 IEEE.

关键词： Learning systems

A New Combined PV Output Power Forecasting Model Based on Optimized LSTM Network

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A New Combined PV Output Power Forecasting Model Based on Op...

Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Azim Heydari Farshid Keynia Afef Fekih Lina Bertling Tjernberg Grazia Barchi David Moser Institute for Renewable Energy Eurac Research Bolzano Italy Department of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman Iran Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette LA USA School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

One of the most challenging problems in designing and implementing effective management strategies and demand responses in renewable-rich grids is the uncertainty associated with the power output (PO) of solar photovoltaic (PV) systems. The exact and trustworthy prediction of PV power can provide substantial decision support for planning and operating power systems. This paper develops an intelligent PV output power (PV-OP) forecasting model. The proposed PV-OP forecasting model consists of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) signal decomposition model to decompose the original PV output power signal to different frequencies, LSTM neural network as a main forecaster engine, and multi-objective NSGA-II optimization algorithm as a hyperparameters optimizer. The developed PV power forecasting model was validated using the PV power datasets of an off-grid village located in Chalokwa, Zambia. The obtained results confirmed the performance and accuracy of the proposed PV output power forecasting model.

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Balanced Self-Paced Learning for AUC Maximization

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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

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels

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arXiv 2024年

作者： Yan, Jianhao Yan, Pingchuan Chen, Yulong Li, Jing Zhu, Xianchao Zhang, Yue Zhejiang University Zhejiang China School of Engineering Westlake University Zhejiang China University College London London United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom LanBridge Group Sichuan China Institute of Advanced Technology Westlake Institute for Advanced Study China

This study presents a comprehensive evaluation of GPT-4’s translation capabilities compared to human translators of varying expertise levels. Through systematic human evaluation using the MQM schema, we assess translations across three language pairs (Chinese←→English, Russian←→English, and Chinese←→Hindi) and three domains (News, Technology, and Biomedical). Our findings reveal that GPT-4 achieves performance comparable to junior-level translators in terms of total errors, while still lagging behind senior translators. Unlike traditional Neural Machine Translation systems, which show significant performance degradation in resource-poor language directions, GPT-4 maintains consistent translation quality across all evaluated language pairs. Through qualitative analysis, we identify distinctive patterns in translation approaches: GPT-4 tends toward overly literal translations and exhibits lexical inconsistency, while human translators sometimes over-interpret context and introduce hallucinations. This study represents the first systematic comparison between LLM and human translators across different proficiency levels, providing valuable insights into the current capabilities and limitations of LLM-based translation systems. © 2024, CC BY.

关键词： Neural machine translation

Design Optimization and Data-driven Shallow Learning for Dynamic Modeling of a Smart Segmented Electroadhesive Clutch

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Design Optimization and Data-driven Shallow Learning for Dyn...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Navid Feizi Zahra Bahrami S. Farokh Atashzar Mehrdad R. Kermani Rajni V. Patel Canadian Surgical Technologies and Advanced Robotics (CSTAR) Lawson Health Research Institute London ON Canada School of Biomedical Engineering Western University London ON Canada Institute of Geography University of Erlangen-Nuremberg Erlangen Germany Department of Mechanical and Aerospace Engineering the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering New York University (NYU) New York NY USA NYU WIRELESS and NYU CUSP Department of Electrical and Computer Engineering Western University London ON Canada Department of Electrical and Computer Engineering the Department of Surgery and the Department of Clinical Neurological Sciences School of Biomedical Engineering Western University London ON Canada

Electroadhesive clutches have attracted a great deal of interest in the last decade as semi-active actuators for human-robot interaction due to their lightweight, low power consumption, and tunable high-torque output capability. However, because of the complexity of their dynamics, in most cases, they are utilized in an ON/OFF -control strategy. In this regard, the non-autonomous (time-dependent) degradation of electroadhesive behavior is an inherent challenge that injects unpredictability and uncertainty into the behavior of this family of semi-active clutches. We propose a novel approach to preventing degradation of electroadhesion using a segmented electrode design that modulates the electrical field on the dielectric surface while using a direct current signal and securing low power consumption. This paper, for the first time, presents an optimization process based on a novel analytic model of the proposed actuator. It also develops a data-driven model augmentation using a hybrid shallow learning approach composed of a long short-term memory (LSTM) architecture which is combined with the analytical model. The performance of the proposed semi-active clutch and the data-driven hybrid model is experimentally validated in this paper.

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Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

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arXiv 2022年

作者： Angelopoulos, Anastasios N. Kohli, Amit P. Bates, Stephen Jordan, Michael I. Malik, Jitendra Alshaabi, Thayer Upadhyayula, Srigokul Romano, Yaniv Department of Electrical Engineering and Computer Science University of California Berkeley United States Advanced Bioimaging Center Department of Molecular and Cell Biology University of California Berkeley United States Department of Electrical and Computer Engineering and of Computer Science Technion Israel Institute of Technology Israel

Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model's mistakes and hallucinations. To address this, we develop uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems. In particular, we show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value with a user-specified confidence probability. Our methods work in conjunction with any base machine learning model, such as a neural network, and endow it with formal mathematical guarantees-regardless of the true unknown data distribution or choice of model. Furthermore, they are simple to implement and computationally inexpensive. We evaluate our procedure on three image-to-image regression tasks: quantitative phase microscopy, accelerated magnetic resonance imaging, and super-resolution transmission electron microscopy of a Drosophila melanogaster brain. Copyright © 2022, The Authors. All rights reserved.

关键词： Uncertainty analysis

Zero crosstalk in anisotropic TM leaky mode with subwavelength grating metamaterials

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Zero crosstalk in anisotropic TM leaky mode with subwaveleng...

Conference on Lasers and Electro-Optics (CLEO)

作者： Md Faiyaz Kabir Md Borhan Mia Ishtiaque Ahmed Nafiz Jaidye Syed Z. Ahmed Sangsik Kim Department of Electrical and Computer Engineering Texas Tech University Lubbock Texas USA Department of Physics and Astronomy Texas Tech University Lubbock Texas USA School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

We show that a TM leaky mode realized by subwavelength gratings (SWGs) can eliminate crosstalk between proximate waveguides. Oscillating fields i n t he SWGs allow coupling coefficient components to counteract each ot... 详细信息

ISBN: (纸本)9781957171258

关键词： Crosstalk Effective medium theory Electric fields Optical systems Subwavelength gratings Waveguides

Strong Long-Wave Infrared Optical Response in a Topological Semiconductor with a Mexican Hat Band Structure

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arXiv 2025年

作者： Polking, Mark J. Xu, Haowei Sankar, Raman Grossklaus, Kevin Li, Ju Advanced Materials and Microsystems Group Massachusetts Institute of Technology Lincoln Laboratory 02421 United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology 02139 United States Institute of Physics Academia Sinica 11529 Taiwan Department of Electrical and Computer Engineering Tufts University 02155 United States Department of Materials Science and Engineering Massachusetts Institute of Technology 02139 United States

Light sources and photodetectors operating in the far- to mid-infrared (FIR/MIR) band (8-12 μm, 0.1-0.15 eV) remain relatively poorly developed compared to their counterparts operating in the visible and near-infrared ranges, despite extensive application potential for thermal imaging, standoff sensing, and other technologies. This is attributable in part to the lack of narrow-gap materials (10 larger than that of HgxCd1-xTe (MCT), the dominant material for FIR/MIR photodetectors. A previous theoretical investigation indicated that chalcogen p and metal d band inversion in this material creates a Mexican hat band structure (MHBS), which results in a dramatic increase in the joint density of states at the optical transition edge compared to typical semiconductors. This prediction is experimentally validated here using single-crystal specimens of Pb0.7Sn0.3Se measured using temperature-dependent spectroscopic ellipsometry over a wavelength range of 1.7-20 μm (0.73-0.062 eV). These measurements demonstrate a large enhancement in extinction coefficient and refractive index characteristic of a MHBS in the vicinity of the absorption edge, in agreement with theoretical predictions. The realization of topological semiconductors with a MHBS is expected to lead to high-efficiency detectors operating in the FIR/MID range. © 2025, CC BY.

关键词： Single crystals

On Creativity and Open-Endedness

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arXiv 2024年

作者： Soros, L.B. Adams, Alyssa M. Kalonaris, Stefano Witkowski, Olaf Guckelsberger, Christian Department of Computer Science Barnard College United States Riken AIP Japan Cross Labs Cross Compass Ltd. Japan Graduate School of Arts and Sciences University of Tokyo Japan Earth-Life Science Institute Tokyo Institute of Technology Japan Institute for Advanced Study United States Department of Computer Science Aalto University Finland School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Artificial Life (ALife) as an interdisciplinary field draws inspiration and influence from a variety of perspectives. Scientific progress crucially depends, then, on concerted efforts to invite cross-disciplinary dialogue. The goal of this paper is to revitalize discussions of potential connections between the fields of Computational Creativity (CC) and ALife, focusing specifically on the concept of Open-Endedness (OE);the primary goal of CC is to endow artificial systems with creativity, and ALife has dedicated much research effort into studying and synthesizing OE and artificial innovation. However, despite the close proximity of these concepts, their use so far remains confined to their respective communities, and their relationship is largely unclear. We provide historical context for research in both domains, and review the limited work connecting research on creativity and OE explicitly. We then highlight specific questions to be investigated in future work, with the eventual goals of (i) decreasing conceptual ambiguity by highlighting similarities and differences between the concepts of OE and creativity, (ii) identifying synergy effects of a research agenda that encompasses both concepts, and (iii) establishing a dialogue between ALife and CC research. © 2024, CC BY.

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