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Progress on intelligent metasurfaces for signal relay,transmitter,and processor

Light(Science & Applications) 2025年第4期14卷 886-901页

作者： Chao Qian Longwei Tian Hongsheng Chen ZJU-UIUC Institute Interdisciplinary Center for Quantum InformationState Key Laboratory of Extreme Photonics and InstrumentationZhejiang UniversityHangzhouChina ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab.of Advanced Micro/Nano Electronic Devices&Smart Systems of ZhejiangZhejiang UniversityHangzhouChina Jinhua Institute of Zhejiang University Zhejiang UniversityJinhuaChina Shanghai Key Laboratory of Navigation and Location-Based Services Shanghai Jiao Tong UniversityShanghaiChina

Pursuing higher data rate with limited spectral resources is a longstanding topic that has triggered the fast growth of modern wireless communication ***,the massive deployment of active nodes to compensate for propagation loss necessitates high hardware expenditure,energy consumption,and maintenance cost,as well as complicated network interference *** metasurfaces,composed of a number of subwavelength passive or active meta-atoms,have recently found to be a new paradigm to actively reshape wireless communication environment in a green way,distinct from conventional works that passively adapt to the *** this review,we offer a unified perspective on how intelligent metasurfaces can facilitate wireless communication in three manners:signal relay,signal transmitter,and signal *** start by the basic modeling of wireless channel and the evolution of metasurfaces from passive,active to intelligent *** with various deep learning algorithms,intelligent metasurfaces adapt to cater for the ever-changing environments without human ***,we overview specific experimental advancements using intelligent *** conclude by identifying key issues in the practical implementations of intelligent metasurfaces,and surveying new directions,such as gain metasurfaces and knowledge migration.

关键词： gain metasurfaces intelligent metasurfaces signal transmitter signal processor active nodes signal relay wireless communication wireless communication techniqueshoweverthe

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Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding

arXiv

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arXiv 2024年

作者： Liu, Pengkun Wang, Yikai Sun, Fuchun Li, Jiafang Xiao, Hang Xue, Hongxiang Wang, Xinzhou Academy for Engineering and Technology Fudan University Shanghai China State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Tongji University Shanghai China ShengShu Beijing China

Encouraged by the growing availability of pre-trained 2D diffusion models, image-to-3D generation by leveraging Score Distillation Sampling (SDS) is making remarkable progress. Most existing methods combine novel-view lifting from 2D diffusion models which usually take the reference image as a condition while applying hard L2 image supervision at the reference view. Yet heavily adhering to the image is prone to corrupting the inductive knowledge of the 2D diffusion model leading to flat or distorted 3D generation frequently. In this work, we reexamine image-to-3D in a novel perspective and present Isotropic3D, an image-to-3D generation pipeline that takes only an image CLIP embedding as input. Isotropic3D allows the optimization to be isotropic w.r.t. the azimuth angle by solely resting on the SDS loss. The core of our framework lies in a two-stage diffusion model fine-tuning. Firstly, we fine-tune a text-to-3D diffusion model by substituting its text encoder with an image encoder, by which the model preliminarily acquires image-to-image capabilities. Secondly, we perform fine-tuning using our Explicit Multi-view Attention (EMA) which combines noisy multi-view images with the noise-free reference image as an explicit condition. CLIP embedding is sent to the diffusion model throughout the whole process while reference images are discarded once after fine-tuning. As a result, with a single image CLIP embedding, Isotropic3D is capable of generating multi-view mutually consistent images and also a 3D model with more symmetrical and neat content, well-proportioned geometry, rich colored texture, and less distortion compared with existing image-to-3D methods while still preserving the similarity to the reference image to a large extent. The project page is available at https://***/. The code and models are available at https://***/pkunliu/Isotropic3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Small Scale Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Liu, He Wang, Yikai Liu, Huaping Sun, Fuchun Yao, Anbang China Telecom Cloud Technology Co. Ltd. China BNRist Center State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Intel Labs China China

Data-free knowledge distillation is able to utilize the knowledge learned by a large teacher network to augment the training of a smaller student network without accessing the original training data, avoiding privacy, security, and proprietary risks in real applications. In this line of research, existing methods typically follow an inversion-and-distillation paradigm in which a generative adversarial network on-the-fly trained with the guidance of the pre-trained teacher network is used to synthesize a large-scale sample set for knowledge distillation. In this paper, we reexamine this common data-free knowledge distillation paradigm, showing that there is considerable room to improve the overall training efficiency through a lens of "small-scale inverted data for knowledge distillation". In light of three empirical observations indicating the importance of how to balance class distributions in terms of synthetic sample diversity and difficulty during both data inversion and distillation processes, we propose Small Scale Data-free Knowledge Distillation (SSD-KD). In formulation, SSD-KD introduces a modulating function to balance synthetic samples and a priority sampling function to select proper samples, facilitated by a dynamic replay buffer and a reinforcement learning strategy. As a result, SSD-KD can perform distillation training conditioned on an extremely small scale of synthetic samples (e.g., 10× less than the original training data scale), making the overall training efficiency one or two orders of magnitude faster than many mainstream methods while retaining superior or competitive model performance, as demonstrated on popular image classification and semantic segmentation benchmarks. The code is available at https://***/OSVAI/SSD-KD. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic Control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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COLD: A Benchmark for Chinese Offensive Language Detection Disclaimer: The paper contains content that may be profane, vulgar, or offensive

COLD: A Benchmark for Chinese Offensive Language Detection D...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Deng, Jiawen Zhou, Jingyan Sun, Hao Zheng, Chujie Mi, Fei Meng, Helen Huang, Minlie The CoAI group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong Huawei Noah's Ark Lab

Offensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models. However, this task in Chinese is still under exploration due to the scarcity of reliable datasets. To this end, we propose a benchmark - COLD for Chinese offensive language analysis, including a Chinese Offensive Language Dataset - COLDATASET and a baseline detector - COLDETECTOR which is trained on the dataset. We show that the COLD benchmark contributes to Chinese offensive language detection which is challenging for existing resources. We then deploy the COLDETECTOR and conduct detailed analyses on popular Chinese pre-trained language models. We first analyze the offensiveness of existing generative models and show that these models inevitably expose varying degrees of offensive issues. Furthermore, we investigate the factors that influence the offensive generations, and we find that anti-bias contents and keywords referring to certain groups or revealing negative attitudes trigger offensive outputs easier. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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AMOR: A Recipe for Building Adaptable Modular Knowledge Agents Through Process Feedback

arXiv

引用

arXiv 2024年

作者： Guan, Jian Wu, Wei Wen, Zujie Xu, Peng Wang, Hongning Huang, Minlie The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Ant Group China

The notable success of large language models (LLMs) has sparked an upsurge in building language agents to complete various complex tasks. We present AMOR, an agent framework based on open-source LLMs, which reasons with external knowledge bases and adapts to specific domains through human supervision to the reasoning process. AMOR builds reasoning logic over a finite state machine (FSM) that solves problems through autonomous executions and transitions over disentangled modules. This allows humans to provide direct feedback to the individual modules, and thus naturally forms process supervision. Based on this reasoning and feedback framework, we develop AMOR through two-stage fine-tuning: warm-up and adaptation. The former fine-tunes the LLM with examples automatically constructed from various public datasets, enabling AMOR to generalize across different knowledge environments, while the latter tailors AMOR to specific domains using process feedback. Extensive experiments across multiple domains demonstrate the advantage of AMOR to strong baselines, thanks to its FSM-based reasoning and process feedback mechanism. The code and data are publicly available at https://***/JianGuanTHU/AMOR. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Automatic Comment Generation for Chinese Student Narrative Essays

Automatic Comment Generation for Chinese Student Narrative E...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhang, Zhexin Guan, Jian Xu, Guowei Tian, Yixiang Huang, Minlie The CoAI Group DCST China Institute for Artificial Intelligence United States State Key Lab of Intelligent Technology and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua University Beijing100084 China TAL Education Group China

ISBN: (纸本)9781959429418

Automatic essay evaluation can help reduce teachers' workload and enable students to refine their works rapidly. Previous studies focus mainly on giving discrete scores for either the holistic quality or several distinct traits. However, real-world teachers usually provide detailed comments in natural language, which are more informative than single scores. In this paper, we present the comment generation task, which aims to generate comments for specified segments from given student narrative essays. To tackle this task, we propose a planning-based generation model, which first plans a sequence of keywords, and then expands these keywords into a complete comment. To improve the correctness and informativeness of generated comments, we adopt two following techniques: (1) training an error correction module to filter out incorrect keywords, and (2) recognizing fine-grained structured features from source essays to enrich the key word. To support the evaluation of the task, we collect a human-written Chinese dataset, which contains 22,399 essay-comment pairs. Extensive experiments show that our model outperforms strong baselines significantly. Moreover, we exert explicit control on our model to generate comments to describe the strengths or weaknesses of inputs with a 91% success rate. We deploy the model at http://***/static/essayComment/. A demo video is available at https://***/IuFVk8dUxbI. Our code and data are available at https://***/thu-coai/EssayCommentGen. © 2022 Association for Computational Linguistics.

关键词： Students

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

引用

CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024

关键词： Dispersion compensation

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Towards Identifying Social Bias in Dialog systems: Framework, Dataset, and Benchmark

Towards Identifying Social Bias in Dialog Systems: Framework...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhou, Jingyan Deng, Jiawen Mi, Fei Li, Yitong Wang, Yasheng Huang, Minlie Jiang, Xin Liu, Qun Meng, Helen Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Huawei Noah's Ark Lab Huawei Technologies Ltd

Warning: this paper contains content that may be offensive or upsetting. Among the safety concerns that hinder the deployment of open-domain dialog systems (e.g., offensive languages, biases, and toxic behaviors), social bias presents an insidious challenge. Addressing this challenge requires rigorous analyses and normative reasoning. In this paper, we focus our investigation on social bias measurement to facilitate the development of unbiased dialog systems. We first propose a novel DIAL-BIAS FRAMEWORK for analyzing the social bias in conversations using a holistic method beyond bias lexicons or dichotomous annotations. Leveraging the proposed framework, we further introduce the CDIAL-BIAS DATASET which is, to the best of our knowledge, the first annotated Chinese social bias dialog dataset. We also establish a fine-grained dialog bias measurement benchmark, and conduct in-depth analyses to shed light on the utility of detailed annotations in the proposed dataset. Lastly, we evaluate several representative Chinese generative models using our classifiers to unveil the presence of social bias in these systems. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

引用

2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical fibers

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