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Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

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工程与科学中的计算机建模(英文) 2024年第1期138卷 827-843页

作者： Tao Yin Changgen Peng Weijie Tan Dequan Xu Hanlin Tang State Key Laboratory of Public big data Guizhou UniversityGuiyang550025China Guizhou big data Academy Guizhou UniversityGuiyang550025China Key Laboratory of Advanced Manufacturing Technology Ministry of EducationGuizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China ChinadataPay Company Guiyang550025China

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie *** traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a *** address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data *** algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the *** determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate *** homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting *** tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging *** assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

关键词： Rate setting Tweedie distribution generalized linear models federated learning homomorphic encryption

Cell softness reveals tumorigenic potential via ITGB8/AKT/glycolysis signaling in a mice model of orthotopic bladder cancer

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Chinese Medical Journal 2024年第2期137卷 209-221页

作者： Shi Qiu Yaqi Qiu Linghui Deng Ling Nie Liming Ge Xiaonan Zheng Di Jin Kun Jin Xianghong Zhou Xingyang Su Boyu Cai Jiakun Li Xiang Tu Lina Gong Liangren Liu Zhenhua Liu Yige Bao Jianzhong Ai Tianhai Lin Lu Yang Qiang Wei Department of Urology Institute of UrologyNational Clinical Research Center for Geriatrics and Center of Biomedical Big DataWest China Hospital of Sichuan UniversityChengduSichuan 610041China Department of Molecular Oncology Institute of Oncology Research(IOR)Oncology Institute of Southern Switzerland(IOSI)Bellinzona 6500Switzerland Department of Science and Drug Technology University of TurinTurinItaly National Clinical research center of Geriatrics The Center of Gerontology and GeriatricsWest China HospitalSichuan UniversityChengduSichuan 610000China Department of Pathology West China HospitalSichuan UniversityChengduSichuan 610041China Department of Pharmaceutics and Bioengineering School of Chemical EngineeringSichuan UniversityChengduSichuan 610000China

Background:Bladder cancer,characterized by a high potential of tumor recurrence,has high lifelong monitoring and treatment *** date,tumor cells with intrinsic softness have been identified to function as cancer stem cells in several cancer ***,the existence of soft tumor cells in bladder tumors remains ***,our study aimed to develop a microbarrier microfluidic chip to efficiently isolate deformable tumor cells from distinct types of bladder cancer ***:The stiffness of bladder cancer cells was determined by atomic force microscopy(AFM).The modified microfluidic chip was utilized to separate soft cells,and the 3D Matrigel culture system was to maintain the softness of tumor *** patterns of integrinβ8(ITGB8),protein kinase B(AKT),and mammalian target of rapamycin(mTOR)were determined by Western *** immunostaining was conducted to examine the interaction between F-actin and tripartite motif containing 59(TRIM59).The stem-cell-like characteristics of soft cells were explored by colony formation assay and in vivo studies upon xenografted tumor ***:Using our newly designed microfluidic approach,we identified a small fraction of soft tumor cells in bladder cancer *** importantly,the existence of soft tumor cells was confirmed in clinical human bladder cancer specimens,in which the number of soft tumor cells was associated with tumor ***,we demonstrated that the biomechanical stimuli arising from 3D Matrigel activated the F-actin/ITGB8/TRIM59/AKT/mTOR/glycolysis pathways to enhance the softness and tumorigenic capacity of tumor ***,we detected a remarkable up-regulation in ITGB8,TRIM59,and phospho-AKT in clinical bladder recurrent tumors compared with their non-recurrent ***:The ITGB8/TRIM59/AKT/mTOR/glycolysis axis plays a crucial role in modulating tumor softness and ***,the soft tumor cells become more sensitive to chemother

关键词： Soft tumor cells 3D Matrigel Stemness Non-muscle invasive bladder neoplasms Microfluidic analytical techniques

Stability Analysis and Performance Evaluation of Additive Mixed-Precision Runge-Kutta Methods

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Communications on Applied Mathematics and Computation 2024年第1期6卷 705-738页

作者： Ben Burnett Sigal Gottlieb Zachary J.Grant center for Scientific Computing and data Science research UMass DartmouthNorth DartmouthMassachusettsUSA CSME Michigan State UniversityEast LansingMichiganUSA

Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were previously proposed and *** specially designed methods use reduced precision for the implicit computations and full precision for the explicit *** this work,we analyze the stability properties of these methods and their sensitivity to the low-precision rounding errors,and demonstrate their performance in terms of accuracy and *** develop codes in FORTRAN and Julia to solve nonlinear systems of ODEs and PDEs using the mixed-precision additive Runge-Kutta(MP-ARK)*** convergence,accuracy,and runtime of these methods are *** show that for a given level of accuracy,suitably chosen MP-ARK methods may provide significant reductions in runtime.

关键词： Mixed precision Runge-Kutta methods Additive methods Accuracy

Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF1-WFV satellite data

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JOURNAL OF OCEANOLOGY AND LIMNOLOGY 2024年第4期42卷 1-13页

作者： Cao, Chang Li, Junsheng Jia, Xiaodong Wang, Shenglei Wan, Bo Liaoning Earthquake Agcy Shenyang 11000 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr big data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance, especially for the Beijing-Tianjin-Hebei (BTH) region of China where water resources are relatively scarce and inland water bodies are generally small. The remote sensing data of the GF1 satellite launched in 2013 have characteristics of high spatial and temporal resolution, which can be used for the dynamic monitoring of the water environment in small lakes and reservoirs. However, the water quality remote sensing monitoring model based on the GF1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and reservoirs. In this paper, the typical reservoirs in BTH-Guanting Reservoir, Yuqiao Reservoir, Panjiakou Reservoir, and Daheiting Reservoir are taken as the study areas. In the atmospheric correction of GF1-WFV, the relative radiation normalized atmospheric correction was adopted after comparing with other methods, such as 6S and FLAASH. In the water clarity retrieval, a water color hue angle based model was proposed and outperformed other available published models, with the R2 of 0.74 and MRE of 31.7%. The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF1-WFV data. Based on the products, temporal and spatial changes in clarity were analyzed, and the main influencing factors for each water body were discussed. It was found that the clarity of Guanting, Daheiting, and Panjiakou reservoirs showed an upward trend during this period, while that of Yuqiao Reservoir showed a downward trend. In the influencing factors, the water level of the water bodies can be an important factor related to the water clarity changes in this region.

关键词： GF1 satellite atmospheric correction clarity Beijing-Tianjin-Hebei spatial and temporal change analysis AUTOMATIC RADIOMETRIC NORMALIZATION LONG-TERM MODIS LAKE CLASSIFICATION MERIS MAD

Multi-view Feature Learning for the Over-penalty in Adversarial Domain Adaptation

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data Intelligence 2024年第1期6卷 183-200页

作者： Yuhong Zhang Jianqing Wu Qi Zhang Xuegang Hu School of Computer and Information Engineering Hefei University of TechnologyHefei 230601China Key Laboratory of Knowledge Engineering with big data(Hefei University of Technology) The Ministry of Education of ChinaHefei 230009China

Domain adaptation aims to transfer knowledge from the labeled source domain to an unlabeled target domain that follows a similar but different ***,adversarial-based methods have achieved remarkable success due to the excellent performance of domain-invariant feature presentation ***,the adversarial methods learn the transferability at the expense of the discriminability in feature representation,leading to low generalization to the target *** this end,we propose a Multi-view Feature Learning method for the Over-penalty in Adversarial Domain ***,multi-view representation learning is proposed to enrich the discriminative information contained in domain-invariant feature representation,which will counter the over-penalty for discriminability in adversarial ***,the class distribution in the intra-domain is proposed to replace that in the inter-domain to capture more discriminative information in the learning of transferrable *** experiments show that our method can improve the discriminability while maintaining transferability and exceeds the most advanced methods in the domain adaptation benchmark datasets.

关键词： domain adaptation adversarial learning multi-view learning

Accurate crop row recognition of maize at the seedling stage using lightweight network

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International Journal of Agricultural and Biological Engineering 2024年第1期17卷 189-198,I0001页

作者： Jian Wei Mengfan Zhang Caicong Wu Qin Ma Weitao Wang Chuanfeng Wan College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and big data Applications Ministry of Agriculture and Rural AffairsBeijing 100083China

Accurate extraction of crop row is very important for automation of agricultural *** rows are required for accurate machine guidance in agricultural production such as fertilization,plant protection,weeding and *** this study,an efficient crop row detection algorithm called Crop-BiSeNet V2 was proposed,which combined BiSeNet V2 with a spatial convolutional neural *** proposed Crop-BiSeNet V2 detected crop rows in color images without the use of threshold and other pre-information such as number of rows.A data set had 2697 maize crop images was constructed in challenging field trial conditions such as variable light,shadows,presence of weeds,and irregular crop *** proposed system was experimentally determined to overcome the interference of different complex *** it can be applied to crop rows of different numbers,straight lines and *** analyses were performed to check the robustness of the *** this algorithm with the Fully Convolutional Networks(FCN)algorithm,it exhibited superior performance and saved 84.85 *** accuracy rate reached 0.9811,and the detection speed reached 65.54 ms/*** Crop-BiSeNet V2 algorithm proposed in this study show strong generalization performance for seedling crop row *** provides high-reliability technical support for crop row detection research and assists in the study of intelligent field operation machinery navigation.

关键词： computer vision crop row detection precision agriculture semantic segmentation

Two-Stage Online Debiased Lasso Estimation and Inference for High-Dimensional Quantile Regression with Streaming data

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Journal of Systems Science & Complexity 2024年第3期37卷 1251-1270页

作者： PENG Yanjin WANG Lei School of Statistics and data Science KLMDASRLEBPS and LPMCNankai UniversityTianjin 300071China

In this paper,the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression(QR)models in the presence of streaming *** the first stage,the authors modify the QR score function based on kernel smoothing and obtain the online lasso smoothed QR estimator through iterative *** estimation process only involves the current data batch and specific historical summary statistics,which perfectly accommodates to the special structure of streaming *** the second stage,an online debiasing procedure is carried out to eliminate biases caused by the lasso penalty as well as the accumulative approximation error so that the asymptotic normality of the resulting estimator can be *** authors conduct extensive numerical experiments to evaluate the performance of the proposed *** experiments demonstrate the effectiveness of the proposed method and support the theoretical *** application to the Beijing PM2.5 dataset is also presented.

关键词： Adaptive tuning asymptotic normality debiased lasso online updating quantile regres-sion

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A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

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Journal of Energy Chemistry 2024年第4期91卷 417-432页

作者： Sijia Yang Caiping Zhang Haoze Chen Jinyu Wang Dinghong Chen Linjing Zhang Weige Zhang National Active Distribution Network Technology research center(NANTEC) Beijing Jiaotong UniversityBeijing 100044China

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered ***,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity *** address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled *** traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory *** generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate *** only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating *** high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

关键词： Lithium-ion battery pack Capacity estimation Label generation Multi-machine learning model Real-world operating

Privacy-Preserving Federated Mobility Prediction with Compound data and Model Perturbation Mechanism

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China Communications 2024年第3期21卷 160-173页

作者： Long Qingyue Wang Huandong Chen Huiming Jin Depeng Zhu Lin Yu Li Li Yong Beijing National research center for Information Science and Technology(BNRist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China China Mobile research Beijing 100032China

Human mobility prediction is important for many ***,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important *** rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the ***,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy *** this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction *** experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.

关键词： federated learning mobility prediction privacy