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38th Conference on Neural information processing systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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SAR Change Detection Based on keypoint Graph Structure and Cosegmentation-Based Refinement

SAR Change Detection Based on Keypoint Graph Structure and C...

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Geology, Mapping and Remote Sensing (ICGMRS), International Conference on

作者： Jingxing Zhu Rui Liu Niangang Jiao Feng Wang Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350365719

ISBN: (纸本)9798350365726

Change detection between high-resolution Synthetic Aperture Radar (SAR) images remains a challenging task. Due to the effectively integration of structure and intensity information, graph-based methods have made some progress in SAR change detection. But it still faces the issues of robustness of graph construction and incomplete change detection outcomes. To tackle these challenges, we propose an improved keypoint-based graph structure change detection approach. First, we propose a keypoint extraction strategy guided by a coarse change map to better describe potential change areas. Second, we construct stable graph structures on these keypoints with employing a robust distance measurement that considers the statistical properties of SAR images. Then, the keypoint difference image (KDI) is derived through measuring the consistency of graph structures between pre-and post-time SAR images. Next, keypoint change map (KCM) is generated through clustering KDI by k-means. Finally, we use co-segmentation superpixel mask to refine the KCM and obtain accurate and complete binary change map. We validate the effective of proposed framework on GF-3 dataset. Experimental results demonstrate the superior performance of our method compared to alternative method.

关键词： Image segmentation Geology Noise Focusing Speckle Radar polarimetry Robustness

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Transient Stability Evaluation of Independent Microgrid based on Message Passing Graph Neural Network 6

Transient Stability Evaluation of Independent Microgrid base...

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6th International Conference on Electronics Technology, ICET 2023

作者： Zhen, Jiubao Huang, Shufan Jia, Xuwen Du, Haocheng China Agricultural University College of Information and Electrical Engineering Beijing100083 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan250061 China

ISBN: (纸本)9798350337693

Virtual synchronous generator is used to control independent microgrid to provide inertia and damping support, but it often brings transient stability problems. Due to the large number of nonlinear and non-smooth characteristics of highly electronic independent microgrid, it is difficult to analyze it by the traditional transient stability analysis method. In this paper, the independent microgrid controlled by virtual synchronization is taken as the object. Firstly, we compare the impact of detailed modeling of energy storage and simplified modeling on the transient stability, and then discuss the impact of current limiting link of virtual synchronous machine on the transient stability. Secondary, combined with the idea of graph neural network, we propose an independent microgrid transient stability evaluation model based on message passing graph neural network. The information corresponding to bus and transmission lines in microgrid is aggregated and updated by message passing mechanism, so as to depict the topological connection relationship of microgrid, and then consider the influence of topology structure on transient stability evaluation. Last, a WSCC-9 system with virtual synchronous machine is taken as an example. The results show that considering the energy storage detailed model and current limiting link can effectively improve the accuracy of stability analysis. The proposed method has stronger generalization ability and is more applicable to the microgrid system with variable topological structure. © 2023 IEEE.

关键词： Stability

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Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

arXiv

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arXiv 2024年

作者： He, Fan He, Mingzhen Shi, Lei Huang, Xiaolin Suykens, Johan A.K. STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Ridgeless regression has garnered attention among researchers, particularly in light of the "Benign Overfitting" phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless regression does not always perform well due to the lack of flexibility. This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels, incorporating kernel learning techniques to improve performance in both experiments and theory. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit regularization in the proposed model, its optimization is equivalent to solving an 0-regularized problem in the integral space of RKHSs, elucidating the origin of its generalization ability. Taking an approximation analysis viewpoint, we introduce an lq-norm analysis technique (with 0 © 2024, CC BY.

关键词： Vector spaces

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Research on Optimization of Two-Stage Scheduling for Aluminum Extrusion Machine Manufacturing Line

Research on Optimization of Two-Stage Scheduling for Aluminu...

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Chinese Automation Congress (CAC)

作者： Aoran Zhou Lipeng Qiu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In view of the problems of high energy consumption, long inventory time and low production efficiency in the production process of traditional aluminum extrusion machine, this paper establishes an efficient two-stage parallel scheduling optimization model based on the integration of flexible job shop scheduling and assembly sequence planning (IFJSS-ASP). On this basis, an evolutionary algorithm combining strong dominance relationship and simple grouping method (SDREA-SGM) is proposed. The algorithm utilizes a two-stage hybrid chromosome encoding and decoding method to establish a rational mapping from the decision space to the solution space. Furthermore, in order to prevent the model from easily falling into local optima, an adaptive intra-class and inter-class pairing selection strategy has been adopted by the algorithm. This enhances global search and improves local search flexibility. Finally, the feasibility and effectiveness of the algorithm are verified by solving problems of different scales.

关键词： Energy consumption Job shop scheduling Optimization models Aluminum Production Evolutionary computation Encoding Planning Decoding Optimization

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PORTABLE ELECTROCHEMICAL SENSOR BASED ON MOLECULARLY IMPRINTED POLYMER FOR OXYTETRACYCLINE DETECTION 10

PORTABLE ELECTROCHEMICAL SENSOR BASED ON MOLECULARLY IMPRINT...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Tang, Yuxin Hu, Jingfang Wang, Linzhe Wei, Wensong Song, Yu Li, Yansheng Gao, Guowei Beijing key Laboratory of sensor Beijing Information Science & Technology University Beijing100101 China Key Laboratory of Agricultural Product Processing Ministry of Agriculture Institute of Food Science and Technology Chinese Academy of Agricultural Sciences Beijing100193 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science & Technology Unive rsity Beijing100192 China Zibo Institute for Digital Agriculture and Rural Research Zibo255051 China

ISBN: (纸本)9781837242672

Reduced graphene oxide (rGO) plays a critical role in enhancing the performance of electrochemical sensors through its excellent electrical conductivity, large surface area, ease of functionalization, chemical stability, and synergy with other nanomaterials. In this paper, A novel electrochemical sensor was developed based on rGO and molecularly imprinted polymer (MIP) for detection of trace oxytetracycline (OTC) in water. The MIP/rGO composites are synthesized by in-situ electrochemical modification on a disposable screen-printed electrode (SPE). Due to the large surface area and good electron transport rate of rGO, as well as the specific recognition for TC of MIP, the sensitive determination of trace TC at MIP/rGO/SPE was realized. Linear sweep voltammetry (LSV) was performed on the sensor for the electrochemical analysis of OTC. Under optimized conditions, the peak current of MIP/rGO/SPE exhibited a good linear correlation with OTC concentration in a wide linear range (1-25 (j,mol/L), possessed a detection limit of 0.34 (j,mol/L. Spiked detection of OTC was applied for the sensor in actual water samples, with relative standard deviations (RSDs) of 93.2%-108.7%. These results indicate that the proposed MIP/rGO/SPE sensor has the ability to accurately, rapidly and selectively detect OTC in water samples. © The Institution of Engineering & Technology 2024.

关键词： Reduced Graphene Oxide

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Terahertz metamaterial narrowband absorber based on metal split ring

Terahertz metamaterial narrowband absorber based on metal sp...

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2023 Applied Optics and Photonics China: Optical Sensing, Imaging, and Display Technology and Applications;and Biomedical Optics, AOPC 2023

作者： Ji, Xiaodi Ge, Hongyi Jiang, Yuying Wu, Xuyang Jia, Zhiyuan Sun, Zhenyu Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Henan China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Henan Zhengzhou450001 China

ISBN: (纸本)9781510672321

Multi-band terahertz metamaterial absorbers offer new perspectives to achieve perfect absorption and multipoint information matching, which enable an ever-growing number of applications. In this study, a dual-band terahertz metamaterial absorber based on the metal split ring is designed. The absorber has perfect absorption peaks at 1.15 THz and 2.47 THz, and the absorption rate is more than 99%. The absorber produces a harp peak with a bandwidth of 0.008 at 2.47 THz, which has an extremely high quality factor of 308. The distribution of the electric field and surface current at two resonance points is analyzed using the finite element integration method. Through full wave simulation calculation, the maximum sensitivity of the analyte refractive index of the absorber is 400 GHz/RIU, and the maximum sensitivity of the thickness is 35 GHz/μm. The results show that the absorber can achieve highly sensitive detection of trace substances. © 2023 SPIE. All rights reserved.

关键词： Metamaterials

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Rethinking Distribution Alignment for Inter-class Fairness 1

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Ye, Jinhuang Wu, Jiawei Li, Zuoyong Zheng, Xianghan College of Computer and Big Data Fuzhou University Fuzhou350108 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789819997886

ISBN: (纸本)9789819997879

Semi-supervised learning (SSL) is a successful paradigm that can use unlabelled data to alleviate the labelling cost problem in supervised learning. However, the excellent performance brought by SSL does not transfer well to the task of class imbalance. The reason is that the class bias of pseudo-labelling further misleads the decision boundary. To solve this problem, we propose a new plug-and-play approach to handle the class imbalance problem based on a theoretical extension and analysis of distribution alignment. The method, called Basis Transformation Based Distribution Alignment (BTDA), efficiently aligns class distributions while taking into account inter-class *** implements the basis transformation through a learnable transfer matrix, thereby reducing the performance loss caused by pseudo-labelling biases. Extensive experiments show that our proposed BTDA approach can significantly improve performance in class imbalance tasks in terms of both accuracy and recall metrics when integrated with advanced SSL algorithms. Although the idea of BTDA is not complex, it can show advanced performance on datasets such as CIFAR and SVHN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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A semantic and emotion-based dual latent variable generation model for a dialogue system

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 319-330页

作者： Ming Yan Xingrui Lou Chien Aun Chan Yan Wang Wei Jiang State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijingChina School of Information and Communications Engineering Communication University of ChinaBeijingChina Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of ChinaBeijingChina Department of Electrical and Electronic Engineering The University of MelbourneMelbourneVictoriaAustralia School of Data Science and Intelligent Media Communication University of ChinaBeijingChina

With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human *** emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate ***,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the *** propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional *** from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer ***,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and *** content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features ***,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the *** results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.

关键词： conditional variational autoencoder dual latent space emotional responses latent variable generation

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Spatial Heterogeneity and Influencing Factors of HFRS Epidemics in Rural and Urban Areas:A Study in Guanzhong Plain of Shaanxi Province,China

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Biomedical and Environmental Sciences 2022年第11期35卷 1012-1024页

作者： ZHU Ling Li LI Yan Ping LU Liang LI Shu Juan REN Hong Yan National Institute for Nutrition and Health Chinese Center for Disease Control and PreventionBeijing 100050China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Xi’an Eighth Hospital Xi’an 710061ShaanxiChina State Key Laboratory of Infectious Disease Prevention and Control National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 102206China

Objective The Guanzhong Plain of Shaanxi Province is a severely afflicted hemorrhagic fever with renal syndrome(HFRS)epidemic area,while HFRS prevalence has decreased in most epidemic areas in *** information is available regarding the leading fine-scale influencing factors in this highly HFRSconcentrated area and the roles of natural environmental and socioeconomic *** investigate this,two regions in the Guanzhong Plain,that is,the Chang’an District and Hu County,with similar geographical environments,different levels of economic development,and high epidemic prevalence,were chosen as representative areas of the HFRS *** Maximum entropy models were constructed based on HFRS cases and fine-scale influencing factors,including meteorological,natural environmental,and socioeconomic factors,from 2014 to *** More than 95% of the HFRS cases in the study area were located in the northern plains,which has an altitude of less than 800 m,with topography contributed 84.1% of the impact on the spatial differentiation of the HFRS *** the northern plains,precipitation and population density jointly affected the spatial differentiation of the HFRS epidemic,with contribution rates of 60.7% and 28.0%,*** comparing the influencing factors of the northern plains of Chang’an District and Hu County,we found that precipitation and the normalized difference vegetation index(NDVI)dominated the HFRS epidemic in the relatively developed Chang’an District,while land-use type,temperature,precipitation and population density dominated the HFRS epidemic in the relatively undeveloped Hu *** Topography was the primary key factor for HFRS prevalence in the Chang’an District and Hu County,and the spatial differentiation of HFRS was dominated by precipitation and population density in the northern *** with the influencing factors of the relatively developed Chang’an District,the developing Hu County was more affected

关键词： Hemorrhagic fever with renal syndrome(HFRS) Spatial heterogeneity Influencing factors Economic development stages Fine scale Maximum Entropy model

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