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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction

arXiv

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arXiv 2023年

作者： Deng, Zeshuai Chen, Zhuokun Niu, Shuaicheng Li, Thomas H. Zhuang, Bohan Tan, Mingkui South China University of Technology China Pazhou Lab China ZIP Lab Monash University Australia Key Laboratory of Big Data and Intelligent Robot Ministry of Education Peking University Shenzhen Graduate School China

Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. Conventional SR methods typically gather the paired training data by synthesizing LR images from HR images using a predetermined degradation model, e.g., Bicubic down-sampling. However, the realistic degradation type of test images may mismatch with the training-time degradation type due to the dynamic changes of the real-world scenarios, resulting in inferior-quality SR images. To address this, existing methods attempt to estimate the degradation model and train an image-specific model, which, however, is quite time-consuming and impracticable to handle rapidly changing domain shifts. Moreover, these methods largely concentrate on the estimation of one degradation type (e.g., blur degradation), overlooking other degradation types like noise and JPEG in real-world test-time scenarios, thus limiting their practicality. To tackle these problems, we present an efficient test-time adaptation framework for SR, named SRTTA, which is able to quickly adapt SR models to test domains with different/unknown degradation types. Specifically, we design a second-order degradation scheme to construct paired data based on the degradation type of the test image, which is predicted by a pre-trained degradation classifier. Then, we adapt the SR model by implementing feature-level reconstruction learning from the initial test image to its second-order degraded counterparts, which helps the SR model generate plausible HR images. Extensive experiments are conducted on newly synthesized corrupted DIV2K datasets with 8 different degradations and several real-world datasets, demonstrating that our SRTTA framework achieves an impressive improvement over existing methods with satisfying speed. The source code is available at https://***/DengZeshuai/SRTTA. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical resolving power

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An improved Low Power Wide Area Network Node Location Algorithm for Forest Fire Monitoring System 2021

An improved Low Power Wide Area Network Node Location Algori...

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2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture

作者： Wenli Lei Xincheng Ren Bin Li Zhouzhou Liu Northwestern Polytechnical University China and Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data China Xi'an Aeronautical Polytechnic Institute China

ISBN: (纸本)9781450385046

Aiming at the problem of node location in forest fire monitoring, an improved Low Power Wide Area Network node location algorithm for forest fire monitoring system is proposed this paper. This location algorithm makes full use of the location information of known anchor nodes to modify the location information of unknown nodes without adding other hard nodes in the network. At the same time, the positioning accuracy is further improved. The test results show that the algorithm has better positioning effect than other non-ranging algorithms.

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

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KAN-Face: Efficient Resource Usage and Precision Lip-Sync in Talking Head Generation

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guanwen Feng Siyu Jin Zhihao Qian Yunan Li Qiguang Miao School of Computer Science and Technology Xidian University Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Vision Xidian University Xi’an China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Despite significant progress in NeRF-based talking head generation, problems like poor lip synchronization and inefficient resource usage remain. To solve these, we propose KANFace, a lightweight framework. In preprocessing, we introduce a Lip-Sync Enhancement Module that uses Wav2Lip to extract high-resolution audio features and map them to an explicit intermediate representation, ensuring precise lip movement alignment with the speaker’s identity. These predicted lip-sync features are combined with fundamental audio-extracted lip features and injected into the rendering module to improve synchronization. For rendering, we introduce FastKAN to map spatial points to color values. As a variant of KAN, FastKAN’s sensitivity to 3D scenes and efficient structure enable precise, fast color prediction. Our framework reduces resource consumption while enhancing lip-sync accuracy and facial reconstruction, making it ideal for talking head generation tasks in resource-limited settings. Project: https://***/KAN-Face/

关键词： Accuracy Three-dimensional displays Lips Color Signal processing Rendering (computer graphics) Feature extraction Synchronization Speech processing Faces

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Agent Planning with World Knowledge Model

arXiv

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arXiv 2024年

作者： Qiao, Shuofei Fang, Runnan Zhang, Ningyu Zhu, Yuqi Chen, Xiang Deng, Shumin Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China National University of Singapore NUS-NCS Joint Lab Singapore Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Recent endeavors towards directly using large language models (LLMs) as agent models to execute interactive planning tasks have shown commendable results. Despite their achievements, however, they still struggle with brainless trial-and-error in global planning and generating hallucinatory actions in local planning due to their poor understanding of the "real" physical world. Imitating humans’ mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric World Knowledge Model (WKM) to facilitate agent planning. Concretely, we steer the agent model to self-synthesize knowledge from both expert and sampled trajectories. Then we develop WKM, providing prior task knowledge to guide the global planning and dynamic state knowledge to assist the local planning. Experimental results on three complex real-world simulated datasets with three state-of-the-art open-source LLMs, Mistral-7B, Gemma-7B, and Llama-3-8B, demonstrate that our method can achieve superior performance compared to various strong baselines. Other interesting findings include: 1) our instance-level task knowledge can generalize better to unseen tasks, 2) weak WKM can guide strong agent model planning, and 3) unified WKM training has promising potential for further development Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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KCPMA: k-degree Contact Pattern Mining Algorithms for Moving Objects

KCPMA: k-degree Contact Pattern Mining Algorithms for Moving...

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International Conference on Computer Supported Cooperative Work in Design

作者： You Chen Hua Dai Mingfeng Jiang Qu Lu Pengyue Li Bohan Li Geng Yang Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

During infectious disease outbreaks, tracking contacted objects is important for suppressing the spread of the virus and using trajectories of moving objects to discover contacted objects is one of effective approaches. Existing algorithms focus on individual contact event discovery but lack the ability to obtain the k-degree contact events. In this paper, we propose efficient k-degree contact pattern mining algorithms that are capable of mining k-degree contact events. The definition of k-degree contact event is first formulated. Based on the definition, a sliding window-based baseline k-degree contact pattern mining algorithm (KCPMA) is presented. To improve the mining efficiency, the sample-point checking strategy and R-tree index are adopted and the optimized mining algorithm (KCPMA+) is proposed. Comprehensive experiments on real datasets demonstrate that the proposed algorithms are effective and efficient in mining k-degree contact events.

关键词： Costs Infectious diseases Federated learning Computer viruses Trajectory Indexes data mining

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Input Design for Regularized System Identification: Stationary Conditions and Sphere Preserving Algorithm

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第9期68卷 5714-5720页

作者： Mu, Biqiang Kong, He Chen, Tianshi Jiang, Bo Wang, Lei Wu, Junfeng Chinese Acad Sci Inst Syst Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Southern Univ Sci & Technol SUSTech Dept Mech & Energy Engn Shenzhen Key Lab Biomimet Robot & Intelligent Syst Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Nanjing Normal Univ Sch Math Sci Nanjing 210023 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China

This article studies input design of kernel-based regularization methods for linear dynamical systems, which has been formulated as a nonconvex optimization problem with the criterion being a scalar measure of the posterior covariance of the Bayesian estimate, subject to a spherical constraint on the input. The nonconvex nature of such input design problems poses significant challenges in deriving optimality conditions and efficient numerical algorithms. In this work, we first derive a sufficient condition for guaranteeing that a stationary point of the regularized input design problem is a global minimum. Next, we propose a spherical constraint preserving (SCP) algorithm to efficiently reach a stationary point of the design problem. Numerical simulation results show that the SCP algorithm finds the global minimum of the original design problem for all simulated cases and its average computational time is only approximately one tenth of that of the algorithms for previous methods.

关键词： Input design regularized system identification spherical constraint preserving (SCP) algorithm stationary points

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A Multi-Stream Network for Mesh Denoising Via Graph Neural Networks with Gaussian Curvature

A Multi-Stream Network for Mesh Denoising Via Graph Neural N...

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IEEE International Conference on Image Processing

作者： Zhibo Zhao Wenhui Wu Hongjie Liu Yuanhao Gong Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China GuangdongHK Joint Laboratory for Big Data Imaging and Communication Reexen Technology Ltd

3D meshes are getting popular in both research and industry. However, the meshes obtained via the 3D scanning equipment frequently contain a high level of noise. In this paper, we present a Gaussian Curvature Driven Multi-stream Network (GCM-Net) based on graph convolutional networks. This network can remove the noise while preserving the essential features during the 3D mesh denoising process. Our method is the first attempt to apply the high-order feature (i.e., Gaussian curvature) in the denoising task, which is more descriptive for the shape of the mesh. GCM-Net consists of curvature stream, vertex stream, and face normal stream, where the curvature stream focuses on the high-order Gaussian curvature feature of 3D mesh. Our method achieves state-of-the-art results on a publicly available dataset, demonstrating its effectiveness. The proposed method can be applied in various applications, such as 3D human body modeling, metaverse, object tracking and biomedical visualization.

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification

Domain Adaptation Random Vector Functional Link for White Bl...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qin Wang Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells.

关键词： White blood cells Training Deep learning Adaptation models Neural networks Focusing Vectors data models Medical diagnosis Medical diagnostic imaging

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