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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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Multiple valence states of Fe boosting SERS activity of Fe_(3)O_(4) nanoparticles and enabling effective SERS-MRI bimodal cancer imaging

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Fundamental research 2024年第4期4卷 858-867页

作者： Jie Lin Xuehua Ma Anran Li Ozioma Udochukwu Akakuru Chunshu Pan Meng He Chenyang Yao Wenzhi Ren Yanying Li Dinghu Zhang Yi Cao Tianxiang Chen Aiguo Wu Cixi Institute of Biomedical Engineering International Cooperation Base of Biomedical Materials Technology and ApplicationChinese Academy of Science Key Laboratory of Magnetic Materials and DevicesZhejiang Engineering Research Center for Biomedical MaterialsNingbo Institute of Materials Technology and EngineeringCASNingbo315201China School of Engineering Medicine Key Laboratory of Big Data-Based Precision Medicine(Beihang University)Ministry of Industry and Information TechnologyBeihang UniversityBeijing100191China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China University of Chinese Academy of Sciences Bejing 100049China

Developing novel nanoparticle-based bioprobes utilized in clinical settings with imaging resolutions ranging from cell to tissue levels is a major challenge for tumor diagnosis and ***,an optimized strategy for designing a Fe_(3)O_(4)-based bioprobe for dual-modal cancer imaging based on surface-enhanced Raman scattering(SERS)and magnetic resonance imaging(MRI)is *** SERS activity of ultrasmall Fe_(3)O_(4) nanoparticles(NPs)was discovered,and a 5×10^(-9)M limit of detection for crystal violet molecules was successfully *** high-efficiency interfacial photon-induced charge transfer in Fe_(3)O_(4) NPs was promoted by multiple electronic energy levels ascribed to the multiple valence states of Fe,which was observed using ultraviolet-visible diffuse reflectance *** functional theory calculations were utilized to reveal that the narrow band gap and high electron density of states of ultrasmall Fe_(3)O_(4) NPs significantly boosted the vibronic coupling resonances in the SERS system upon *** subtypes of cancer cells were accurately recognized via high-resolution SERS imaging in vitro using the prepared Feg Og-based bioprobe with high sensitivity and good ***,Fe_(3)O_(4)-based bioprobes simultaneously exhibited T,-weighted MRI contrast enhancement with an active targeting capability for tumors in *** the best of our knowledge,this is the first report on the use of pure semiconductor-based SERS-MRI dual-modal nanoprobes in tumor imaging in vivo and in vitro,which has been previously realized only using semiconductor-metal complex *** non-metallic materials with SERS-MRI dual-modal imaging established in this report are a promising cancer diagnostic platform,which not only showed excellent performance in early tumor diagnosis but also possesses great potential for image-guided tumor treatment.

关键词： Fe_(3)O_(4)NPs SERS-MRI dual-modal nanoprobes PICT Multiple valence states Cancer diagnosis

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Improving Day-Ahead Electricity Price Forecasting Accuracy in Australia’s National Electricity Market with Kolmogorov-Arnold Networks

Improving Day-Ahead Electricity Price Forecasting Accuracy i...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： He, Yubin Xu, Mingze Hu, Yaping Gu, Huijie Xie, Xinglang Lei, Shunbo Power Dispatch and Control Center China Southern Power Grid Guangdong Guangzhou510530 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Digital Grid Research Institute China Southern Power Grid Guangdong Guangzhou510600 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

ISBN: (纸本)9789819618552

Accurate day-ahead electricity price forecasting is crucial for both power companies’ interests and systems’ stability. However, it faces numerous challenges due to various uncertainties, especially the increasingly frequent extreme weather events. Australia’s National Electricity Market (ANEM) experiences the highest price volatility. To improve the forecasting accuracy in ANEM, this paper proposes a residual electricity price forecasting method with Kolmogorov-Arnold Networks (KAN) for day-ahead 30-min electricity price pre-diction. Unlike the Multi-Layer Perceptron (MLP), KAN includes learnable activation functions and shifts the previous learning from points to edges, making it more sensitive to features. Thus, KAN can capture more complicated nonlinearities of electricity prices in ANEM. Furthermore, we use discrete Fourier transform to create activation functions to reduce the effects of wild price swings and ensure a stable learning process. Our experimental results, based on actual electricity price data from the Australian Energy Market Operator, demonstrate that our method is more accurate than the traditional MLP approach. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Discrete Fourier transforms

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

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

ISBN: (纸本)9798331314385

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.

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Application research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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SST effect on the pre-monsoon intraseasonal oscillation over the South China Sea based on atmospheric-coupled GCM comparison

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Journal of Oceanology and Limnology 2023年第2期41卷 409-417页

作者： Yun LIANG Yan DU Shang-Ping XIE State Key Laboratory of Tropical Oceanography&Key Laboratory of Science and Technology on Operational Oceanography South China Sea Institute of OceanologyChinese Academy of Sciences&Innovation Academy of South China Sea Ecology and Environmental EngineeringGuangzhou 510301China College of Marine Science University of Chinese Academy of SciencesBeijing 100049China Peking University Chongqing Research Institute of Big Data Chongqing 401332China Southern Marine Science and Engineering Guangdong Laboratory Guangzhou 510301China Scripps Institution of Oceanography University of CaliforniaSan DiegoLa JollaCA 92093USA

The role of sea surface temperature(SST)variability in the pre-monsoonal(April to July)intraseasonal oscillation(ISO)over the South China Sea(SCS)is investigated using the Community Earth System Model Version 2(CESM2).An Atmospheric Model Intercomparison Project(AMIP)simulation forced by daily sea surface temperatures(SSTs)derived from a parallel coupled general circulation model(CGCM)run was compared with observations and the mother coupled *** the coupled model,the SST warming leads the peak convection about 1/4 period as in *** paralell uncoupled model fails to simulate this phase relationship,implying the importance of air-sea coupling in reproducing realistic *** to the near-quadrature phase relationship between SST and precipitation ISOs during the ISO events,it is difficult to distinguish the active/passive role of SST from observations *** correlation in intraseasonal precipitation between the daily SST-forced AMIP and mother CGCM runs indicates that SST plays a role in driving the atmospheric ISO.

关键词： intraseasonal variability South China Sea Community Earth System Model Version 2(CESM2) sea surface temperature

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RadSplatter: Extending 3D Gaussian Splatting to Radio Frequencies for Wireless Radiomap Extrapolation

arXiv

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arXiv 2025年

作者： Wang, Yiheng Xue, Ye Zhang, Shutao Chang, Tsung-Hui Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong-Shenzhen Shenzhen China School of Science and Engineering The Chinese University of Hong Kong-Shenzhen Shenzhen China

A radiomap represents the spatial distribution of wireless signal strength, critical for applications like network optimization and autonomous driving. However, constructing radiomap relies on measuring radio signal power across the entire system, which is costly in outdoor environments due to large network scales. We present RadSplatter, a framework that extends 3D Gaussian Splatting (3DGS) to radio frequencies for efficient and accurate radiomap extrapolation from sparse measurements. RadSplatter models environmental scatterers and radio paths using 3D Gaussians, capturing key factors of radio wave propagation. It employs a relaxed-mean (RM) scheme to reparameterize the positions of 3D Gaussians from noisy and dense 3D point clouds. A camera-free 3DGS-based projection is proposed to map 3D Gaussians onto 2D radio beam patterns. Furthermore, a regularized loss function and recursive fine-tuning using highly structured sparse measurements in real-world settings are applied to ensure robust generalization. Experiments on synthetic and real-world data show state-of-the-art extrapolation accuracy and execution speed. © 2025, CC BY.

关键词： Extrapolation

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DanceFix: An Exploration in Group Dance Neatness Assessment Through Fixing Abnormal Challenges of Human Pose 39

DanceFix: An Exploration in Group Dance Neatness Assessment ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Huangbiao Ke, Xiao Wu, Huanqi Xu, Rui Li, Yuezhou Xu, Peirong Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou 350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou 350116 China

ISBN: (纸本)157735897X

The fair and objective assessment of performances and competitions is a common pursuit and challenge in human society. The application of computer vision technology offers hope for this purpose, but it still faces obstacles such as occlusion and motion blur. To address these hindrances, our DanceFix proposes a bidirectional spatial-temporal context optical flow correction (BOFC) method. This approach leverages the consistency and complementarity of motion information between two modalities: optical flow, which excels at pixel capture, and lightweight skeleton data. It enables the extraction of pixel-level motion changes and the correction of abnormal skeleton data. Furthermore, we propose a part-level dance dataset (Dancer Parts) and part-level motion feature extraction based on task decoupling (PETD). This aims to decouple complex whole-body parts tracking into fine-grained limb-level motion extraction, enhancing the confidence of temporal information and the accuracy of correction for abnormal data. Finally, we present the DNV dataset, which simulates fully neat group dance scenes and provides reliable labels and validation methods for the newly introduced group dance neatness assessment (GDNA). To the best of our knowledge, this is the first work to develop quantitative criteria for assessing limb and joint neatness in group dance. We conduct experiments on DNV and video-based public JHMDB datasets. Our method effectively corrects abnormal skeleton points, flexibly embeds, and improves the accuracy of existing pose estimation algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Edge-AI: IoT Request Service Provisioning in Federated Edge Computing Using Actor-Critic Reinforcement Learning

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IEEE Transactions on engineering Management 2024年 71卷 12519-12528页

作者： Baghban, Hojjat Rezapour, Amir Hsu, Ching-Hsien Nuannimnoi, Sirapop Huang, Ching-Yao National Yang Ming Chiao Tung University Department of Electronics Engineering and Institute of Electronics Hsinchu30010 Taiwan Asia University Department of Computer Science and Information Engineering Taichung41354 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Logitech Inc. Hsinchu30010 Taiwan China Medical University Department of Medical Research and China Medical University Hospital Taichung404 Taiwan Foshan University Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big Data Foshan528000 China National Yang-Ming Chiao Tung University Department of Electronics Engineering Institute of Electronics Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Department of Electronics Engineering Institute of Electronics Hsinchu30010 Taiwan

Edge computing plays a critical role in the Internet of Things (IoT) environment as it potentially supports the time-critical IoT applications' resources as well as latency requirements. In the near future, most edge service providers are envisioned to receive revenue from deploying these applications with the expenditures proportional to placing the offloaded requests from IoT devices and allocating the required resources. One way to maximize the edge profit and minimize the response latency is to integrate the edge nodes and form the edge federation. Therefore, edge service providers can have interoperability to distribute the IoT requests on the appropriate edge nodes in the light of providing satisfactory service levels to meet their objectives. Since the edge nodes are volatile and IoT time-critical applications are increasing, the edge nodes are envisioned to face massive traffic from IoT devices. Therefore, exploiting the traditional dynamic requests placement approaches cannot meet the SLA requirement of both IoT devices and edge service providers. In this article, we designed an intelligent reinforcement learning-based request service provisioning system (i.e., here, we call Edge-AI) as part of a smart edge orchestrator in the edge federation. We implement the proposed method, which is called DRL-Dispatcher, and compare it with greedy and random algorithms in edge federation. The experimental results show that the proposed DRL-Dispatcher performs better in terms of profit and low response latency as compared with the baseline approaches. © 1988-2012 IEEE.

关键词： Internet of things

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Machine learning strategies for small sample size in materials science

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science China Materials 2025年第2期68卷 387-405页

作者： Qiuling Tao JinXin Yu Xiangyu Mu Xue Jia Rongpei Shi Zhifu Yao Cuiping Wang Haijun Zhang Xingjun Liu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhenShenzhen 518055China School of Computer Science&Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China Advanced Institute for Materials Research(WPI-AIMR) Tohoku UniversitySendai 980-8577Japan Department of Materials Science and Engineering College of MaterialsXiamen UniversityXiamen 361005China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhenShenzhen 518055China

Machine learning (ML) has been widely used todesign and develop new materials owing to its low computational cost and powerful predictive capabilities. In recentyears, the shortcomings of ML in materials science have gradually emerged, with a primary concern being the scarcity ofdata. It is challenging to build reliable and accurate ML modelsusing limited data. Moreover, the small sample size problemwill remain long-standing in materials science because of theslow accumulation of material data. Therefore, it is importantto review and categorize strategies for small-sample learningfor the development of ML in materials science. This reviewsystematically sorts the research progress of small-samplelearning strategies in materials science, including ensemblelearning, unsupervised learning, active learning, and transferlearning. The directions for future research are proposed, including few-shot learning, and virtual sample *** importantly, we emphasize the significance of embedding material domain knowledge into ML and elaborate on thebasic idea for implementing this strategy.

关键词： material design machine learning small sample size few-shot learning material domain knowledge

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