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15th International Conference on Scalable Uncertainty Management, SUM 2022

作者： Jenhani, Ilyes Khlifi, Ghaith Sidiropoulos, Panagiotis Jansen, Henk Frangou, George College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar31952 Saudi Arabia Massive Analytic Ltd. London United Kingdom Mullard Space Science Laboratory University College London London United Kingdom UCL Quantum Science & Technology Institute London United Kingdom Autonomous Systems Dynamics and Control Research Group Centre for Autonomous and Cyber Physical Systems Cranfield University Bedford United Kingdom

ISBN: (纸本)9783031188428

Data imperfection and information uncertainty are inevitable in many application domains, including medicine, finance, meteorology, cybersecurity, etc. Sources of uncertainty include the lack of information of the solicited experts, sensor unreliability for automatically collected data, conflicting information sources, etc. Classification techniques, such as the non-specificity based possibilistic decision trees (NSPDT) have been proposed to deal with such uncertainty. However, most of the introduced possibilistic methods are limited to categorical features, i.e. they can not process data input that is continuous. This paper address this limitation through a supervised possibilistic discretization method, which transforms continuous features into categorical features in datasets where class labels are imperfect and represented by possibility distributions. The proposed method can be used as a pre-processing step to make it possible to use methods such as NSPDT in datasets that initially have continuous features. The proposed non-specificity based discretization algorithm has provided good quality categorical features which have led to obtain NSPDT trees showing an accuracy higher than 80% in most cases. With original datasets, where the instances have certain (i.e. non-possibilistic) class labels, the proposed possibilistic discretization method has shown a robust performance compared to the standard discretization algorithm which is the default approach in this case. The difference in accuracy has not exceeded 5% for all used datasets and all tested classifiers, and in some cases, the possibilistic discretization outperformed the standard discretization. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Roblets: Robotic Tablets That Self-Assemble and Self-Fold into a Robot

Roblets: Robotic Tablets That Self-Assemble and Self-Fold in...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Junyi Han Daniela Rus Shuhei Miyashita Automatic Control and Systems Engineering Department University of Sheffield Sheffield UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Insigneo Institute for in silico Medicine

Inspired by human proteins that are synthesized from only 20 types of amino acids, the development of self-assembly methods that allow robots to be built simply by randomly stirring the parts has been explored for many years. The key challenges include how to synthesize parts in pieces into a three-dimensional functional structure in a practical time, and subsequently, achieve a controlled robotic motion, all with minimal human intervention. This study proposes a method of self-assembling a 3D robot by first self-assembling random parts into a 2D structure and then self-folding it into a 3D shape. Once self-folded, the robot, whose compositional parts contain magnets, becomes capable of performing basic tasks such as block-pushing upon an application of an external magnetic field. Self-assembly from parts into a two-dimensional structure was performed by repeatedly colliding the parts with each other, and combining them with complementary-shaped parts, like matching jigsaw puzzle pieces. Self-folding was performed by shrinking a heat-responsive film attached across the hinge of each assembly part in hot water, causing the entire 2D structure to self-fold. The experiment demonstrated a series of 13 parts self-assembling into the shape of a 3D beetle, then walking and pushing an object in 13 minutes. The self-assembly process is programmed (mechanically) to generate the same geometry even if the number of parts is greater than the necessary number for the structure, thus is capable of generating multiple structures simultaneously.

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Resource Allocation for Cognitive Radio Inspired Non-Orthogonal Multiple Access Networks: A Quantum Soft Actor-Critic Method

Resource Allocation for Cognitive Radio Inspired Non-Orthogo...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Huang Ying He Fei Yu Peigen Zeng Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

With the growth of the communication industry, the demand for spectrum resources has been increasing steadily. However, the spectrum resources are limited and the utilization rate is low. In this case, Cognitive Radio (CR) technologies and Non-Orthogonal Multiple Access (NOMA) technology are proposed to improve the number of user access and spectrum resource utilization. In this paper, we consider a CR-inspired NOMA network and propose a power allocation problem based on a time-varying system. Due to the dynamic nature of CR, traditional methods are often faced with problems such as low training efficiency and high network volatility. To address the problem, we propose a novel Quantum Reinforcement Learning (QRL) algorithm with quantum world model, interacting with the environment in the quantum way. Specifically, we construct a quantum soft actor-critic algorithm using variable quantum circuit (VQC), and the experience data is encoded into a quantum sum tree and transformed into quantum world model. Simulation results show the superior performance of the proposed framework.

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FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding

FedTC: Enabling Communication-Efficient Federated Learning v...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yixuan Guan Xuefeng Liu Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated learning (FL) enables distributed training via periodically synchronizing model updates among participants. Communication overhead becomes a dominant constraint of FL since participating clients usually suffer from limited bandwidth. To tackle this issue, top-k based gradient compression techniques are broadly explored in FL context, manifesting powerful capabilities in reducing gradient volumes via picking significant entries. However, previous studies are primarily conducted on the raw gradients where massive spatial redundancies exist and positions of non-zero (top-k) entries vary greatly between gradients, which both impede the achievement of deeper compressions. Top-k may also degrade the performance of trained models due to biased gradient estimations. Targeting the above issues, we propose FedTC, a novel transform coding based compression framework. FedTC transforms gradients into a new domain with more compact energy distributions, which facilitates reducing spatial redundancies and biases in subsequent sparsification. Furthermore, non-zero entries across clients from different rounds become highly aligned in the transform domain, motivating us to partition the gradients into smaller entry blocks with various alignment levels to better exploit these alignments. Lastly, positions and values of non-zero entries are independently compressed in a block-wise manner with our customized designs, through which a higher compression ratio is achieved. Theoretical analysis and extensive experiments consistently demonstrate the effectiveness of our approach.

关键词： Training Quantization (signal) Costs Federated learning Redundancy Transform coding Estimation

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M3OFF: Module-Compositional Model-Free Computation Offloading in Multi-Environment MEC

M3OFF: Module-Compositional Model-Free Computation Offloadin...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Tao Ren Zheyuan Hu Jianwei Niu Weikun Feng Hang He State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Computation offloading is one of the key issues in mobile edge computing (MEC) that alleviates the tension between user equipment's limited capabilities and mobile application's high requirements. To achieve model-free computation offloading when reliable MEC dynamics are unavailable, deep reinforcement learning (DRL) has become a popular methodology. However, most existing DRL-based offloading approaches are developed for a single MEC environment, with invariant system bandwidth, edge capability, task types, etc., while realistic MEC scenarios tend to be of high diversity. Unfortunately, in multi-MEC environments, DRL-based offloading faces at least two challenges, learning inefficiency and interference of offloading experiences. To address the challenges, we propose a DRL-based Multi-environmental Module-compositional Modelfree computation OFFloading (M 3 OFF) framework. M 3 OFF generates offloading policies using module composition instead of a single DRL network so that learning efficiency could be improved by reusing the same modules and learning interference could be reduced by composing different modules. Furthermore, we design multiple module composition-specific training methods for M 3 OFF, including alternate modules-and-composer updates to improve training stability, loss-regularization to avoid module degeneration, and module-dropout to mitigate overfitting. Extensive experimental results on both simulation and testbed demonstrate that M 3 OFF outperforms the performances of most state-of-the-arts in multi-MEC and reaches close to single-MEC.

关键词： Training Visualization Multi-access edge computing Computational modeling Interference Bandwidth Stability analysis

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Nonreciprocal field transformation with active acoustic metasurfaces

arXiv

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arXiv 2023年

作者： Wen, Xinhua Cho, Choonlae Zhu, Xinghong Park, Namkyoo Li, Jensen Department of Physics The Hong Kong University of Science and Technology Kowloon Hong Kong Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Field transformation, as an extension of the transformation optics, provides a unique means for nonreciprocal wave manipulation, while the experimental realization remains a significant challenge as it requires stringent material parameters of the metamaterials, e.g., purely nonreciprocal bianisotropic parameters. Here, we develop and demonstrate a nonreciprocal field transformation in a 2D acoustic system, using an active metasurface that can independently control all constitutive parameters and achieve purely nonreciprocal Willis coupling. The field-transforming metasurface enables tailor-made field distribution manipulation, achieving localized field amplification by a predetermined ratio. Interestingly, the metasurface demonstrates the self-adaptive capability to various excitation conditions and can extend to other geometric shapes. The metasurface also achieves nonreciprocal wave propagation for internal and external excitations, demonstrating a one-way acoustic device. Such a field-transforming metasurface not only extends the framework of the transformation theory for nonreciprocal wave manipulation, but also holds significant potential in applications such as ultra-sensitive sensors and nonreciprocal communication. Copyright © 2023, The Authors. All rights reserved.

关键词： Acoustic fields

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Quantum Reinforcement Learning with Quantum World Model

Quantum Reinforcement Learning with Quantum World Model

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peigen Zeng Ying He F. Richard Yu Victor C.M. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

Quantum reinforcement learning (QRL) can outperform classical reinforcement learning (RL) by utilizing quantum parallel theory and quantum phenomena such as superposition and entanglement. Although some excellent work has been done on QRL, most existing works either fail to show the exponential advantage of quantum computation over classical computation in terms of performance or are too demanding on quantum devices. In this paper, we provide a novel perspective on combining quantum computing and RL with faster convergence speed and relatively relaxed demands on quantum devices. Specifically, we propose a method to construct a world model with quantum circuit that allows it to interact in a quantum way. In addition, we use Grover's algorithm to efficiently extract high-value information from the quantum world model. Extensive simulation results show that the proposed method can have superior performance compared to classical RL algorithms.

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Improving Multi-Person Pose Tracking with A Confidence Network

arXiv

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arXiv 2023年

作者： Fu, Zehua Zuo, Wenhang Hu, Zhenghui Liu, Qingjie Wang, Yunhong The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Hangzhou Innovation Institute Behang University Hangzhou310051 China

Human pose estimation and tracking are fundamental tasks for understanding human behaviors in videos. Existing top-down framework-based methods usually perform three-stage tasks: human detection, pose estimation and tracking. Although promising results have been achieved, these methods rely heavily on high-performance detectors and may fail to track persons who are occluded or miss-detected. To overcome these problems, in this paper, we develop a novel keypoint confidence network and a tracking pipeline to improve human detection and pose estimation in top-down approaches. Specifically, the keypoint confidence network is designed to determine whether each keypoint is occluded, and it is incorporated into the pose estimation module. In the tracking pipeline, we propose the Bbox-revision module to reduce missing detection and the ID-retrieve module to correct lost trajectories, improving the performance of the detection stage. Experimental results show that our approach is universal in human detection and pose estimation, achieving state-of-the-art performance on both PoseTrack 2017 and 2018 datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models

HIR-Diff: Unsupervised Hyperspectral Image Restoration Via I...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Li Pang Xiangyu Rui Long Cui Hongzhong Wang Deyu Meng Xiangyong Cao School of Computer Science and Technology Xi'an Jiaotong University Xi'an China MOEKLINNS Laboratory Xi'an Jiaotong University Xi'an China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an China Macao Institute of Systems Engineering Macau University of Science and Technology Taipa Macao

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Hyperspectral image (HSI) restoration aims at recov-ering clean images from degraded observations and plays a vital role in downstream tasks. Existing model-based methods have limitations in accurately modeling the com-plex image characteristics with handcraft priors, and deep learning-based methods suffer from poor generalization ability. To alleviate these issues, this paper proposes an unsupervised HSI restoration framework with pre-trained diffusion model (HIR-Diff), which restores the clean HSls from the product of two low-rank components, i.e., the re-duced image and the coefficient matrix. Specifically, the re-duced image, which has a low spectral dimension, lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled. The coefficient matrix can be effectively pre-estimated based on singular value decomposition (SVD) and rank-revealing QR (RRQR) factorization. Fur-thermore, a novel exponential noise schedule is proposed to accelerate the restoration process (about 5 x acceleration for denoising) with little performance decrease. Ex-tensive experimental results validate the superiority of our method in both performance and speed on a variety of HSI restoration tasks, including HSI denoising, noisy HSI super-resolution, and noisy HSI inpainting. The code is available at https://***/LiPang/HIRDiff.

关键词： Schedules TV Noise reduction Noise Superresolution Diffusion models Image restoration

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Cross-Modal Generative Semantic Communications for Mobile AIGC: Joint Semantic Encoding and Prompt engineering

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Zhang, Ping Shen, Xuemin Sherman School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Auburn University Auburn United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering University of Waterloo Canada

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services can become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modal Generative Semantic Communications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize a series of cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perpetual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward. © 2024, CC BY.

关键词： Semantics

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