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Federated Learning’s Role in Next-Gen TV Ad Optimization

Computers, Materials & Continua 2025年第1期82卷 675-712页

作者： Gabriela Dobrita Simona-Vasilica Oprea Adela Bâra department of Economic informatics and Cybernetics Bucharest University of Economic StudiesBucharest010374Romania

In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become *** methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data *** article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data *** combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot *** into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization ***,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient Boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data *** FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity ***,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.

关键词： Ad scheduling prescriptive-predictive approach federated learning KNN

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The linear self-attracting diffusion driven by the weighted-fractional Brownian motion II: The parameter estimation

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Science China Mathematics 2025年

作者： Litan Yan Rui Guo Wenyi Pei department of statistics College of Science Donghua University School of statistics and Mathematics Zhejiang Gongshang University

Let Ba,bbe a weighted-fractional Brownian motion with Hurst indexes a and b such that a>-1and 0≤b<1∧（1+a）.In this paper,we consider the linear self-attracting diffusion ■ interacting diffusion (see Cranston and Le Jan （1995）).Under the continuous observation,we study asymptotic ■ converges in distribution to the sum of the chi-square random variable with 1 degree of freedom and the random variable Z1a,b.

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High-dimensional large-scale mixed-type data imputation under missing at random

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Science China Mathematics 2025年

作者： Wei Liu Guizhen Li Ling Zhou Lan Luo School of Mathematics Sichuan University Center of Statistical Research and School of statistics Southwestern University of Finance and Economics department of statistics and Actuarial Science University of Iowa

Missingness in mixed-type variables is commonly encountered in a variety of areas. The requirement of complete observations necessities data imputation when a moderate or large proportion of data is ***, inappropriate imputation would downgrade the performance of machine learning algorithms, leading to bad predictions and unreliable statistical inference. For high-dimensional large-scale mixed-type missing data, we develop a computationally efficient imputation method, missing value imputation via generalized factor models（MIG）, under missing at random. The proposed MIG method allows missing variables to be of different types,including continuous, binary, and count variables, and are scalable to both data size n and variable dimension p while existing imputation methods rely on restrictive assumptions such as the same type of missing variables,the low dimensionality of variables, and a limited sample size. We explicitly show that the imputation error of the proposed MIG method diminishes to zero with the rate Op(max{n-1/2, p-1/2}) as both n and p tend to infinity. Five real datasets demonstrate the superior empirical performance of the proposed MIG method over existing methods that the average normalized absolute imputation error is reduced by 5.3%–34.1%.

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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 579-607页

作者： Pongsakon Promsawat Weerapan Sae-dan Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana department of Civil Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand department of Computer Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand department of statistics Faculty of ScienceRamkhamkaeng UniversityBangkok10240Thailand

The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction *** comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest *** the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 ***,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer *** Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time *** numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

关键词： Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal

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A novel method for clustering cellular data to improve classification

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Neural Regeneration Research 2025年第9期20卷 2697-2705页

作者： Diek W.Wheeler Giorgio A.Ascoli Center for Neural informatics Structures&PlasticityKrasnow Institute for Advanced Study Bioengineering department Volgenau School of Engineering George Mason University FairfaxVAUSA

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are *** recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within *** we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical *** general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical *** demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

关键词： cellular data clustering dendrogram data classification Levene's one-tailed statistical test unsupervised hierarchical clustering

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Rough singular integrals associated to surfaces of revolution on product spaces

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Acta Mathematica Sinica,English Series 2025年

作者： Hussain AL-QASSEM Leslie CHENG Yibiao PAN Mathematics Program Department of Mathematics Statistics and Physics College of Arts and Sciences Qatar University department of Mathematics Bryn Mawr College department of Mathematics University of Pittsburgh

We study the Lpboundedness of singular integral operators along surfaces of revolution on product spaces. The Lpboundedness for these operators are obtained under very weak conditions on kernels. Our results are new and they improve several previously known results. Furthermore, they are natural extensions of many known results on singular integrals in the one-parameter setting and they subsume many other corresponding results on the product space setting.

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New types of singular solutions of conformal Q-curvature equations

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Acta Mathematica Sinica,English Series 2025年

作者： Zongming Guo Zhongyuan Liu Fangshu Wan department of Mathematics Henan Normal University School of Mathematics and statistics Henan University School of Mathematical Sciences Anhui University

We construct radial and non-radial singular solutions u ∈ C4（B{0}） with a non-removable singular point x = 0 for the conformal Q-curvature equation:■where B = {x ∈ R4: |x| D0≥ 0, and satisfying ■Moreover, detailed asymptotic expansions near x = 0 of these radial and non-radial singular solutions can be established. As an application, we can also obtain the existence of two types of solutions u ∈ C4（R4■） to the problem ■ satisfying |x|-2u（x） →-D D0, and satisfying u（x） = o（|x|2） uniformly as |x| → ∞.

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Artificial Circulation System Algorithm:A Novel Bio-Inspired Algorithm

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 635-663页

作者： NerminÖzcan Semih Utku Tolga Berber department of Biomedical Technologies Dokuz Eylül UniversityIzmir35390Turkey department of Biomedical Engineering Iskenderun Technical UniversityIskenderun31200Turkey department of Computer Engineering Dokuz Eylül UniversityIzmir35390Turkey department of statistics and Computer Sciences Karadeniz Technical UniversityTrabzon61080Turkey

Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering appli*** present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm(ACSA).The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this *** work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions,identified as classical benchmark *** method was subsequently examined by application to 12 CEC 2022 benchmark problems of different ***,the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches,including evolutionary,human,physics,and ***,a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing *** results show that the ACSA strategy can quickly reach the global optimum,avoid getting trapped in local optima,and effectively maintain a balance between exploration and *** outperformed 42 algorithms statistically,according to post-hoc *** also outperformed 9 algorithms *** study concludes that ACSA offers competitive solutions in comparison to popüler methods.

关键词： Bio-inspired evolutionary heuristic metaheuristic optimization

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Stabilities of delay stochastic McKean-Vlasov equations in the G-framework

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Science China(Information Sciences) 2025年第1期68卷 255-277页

作者： Haiyan YUAN Quanxin ZHU department of Mathematics Heilongjiang Institute of Technology Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education) School of Mathematics and Statistics Hunan Normal University

This article focuses on a type of stochastic Mc Kean-Vlasov equation in the G-framework(G-SMVE) and addresses the stability issue for delay stochastic Mc Kean-Vlasov equations in the G-framework(G-SMVDEs). Distribution dependence and uncertainty prevent us from directly applying the stochastic analysis method for stochastic Mc Kean-Vlasov equations(SMVEs)to G-SMVEs directly. To overcome this difficulty, we introduce definitions, including the derivative of a function with a law under the G-expectation and the Lions derivatives. Then we construct a new G-It? formula for G-SMVEs according to the G-It? formula for stochastic differential equations in the G-framework(G-SDEs) and the It? formula for SMVEs. Using the new G-It? formula and the Lyapunov functional method, we investigate the moment exponential stability and almost sure asymptotic stability of G-SMVDEs. To overcome the difficulty in obtaining the distribution dependence of the exact solution to the G-SMVDE, we introduce the empirical measure and the corresponding interacting particle system, and then prove the stability equivalence between the underlying G-SMVDE and the corresponding interacting particle system. Two examples are used to confirm our theoretical results.

关键词： delay stochastic McKean-Vlasov equations Wasserstein distance G-Brownian motion Lions derivative stability

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Single-cell technologies: current and near future

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Science China Life Sciences 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji University Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Research Institute of Intelligent Computing Zhejiang Lab Pharmaceutical informatics Institute College of Pharmaceutical SciencesZhejiang University National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang University Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Key Laboratory of Advanced Theory and Application in statistics and Data Science (MOE) School of StatisticsEast China Normal University

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity, tissue organization, and overall organism function(Sun et al.,2024). In 2009, single-cell RNA sequencing(sc RNA-seq) was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology, single-cell approaches have expanded to encompass other omics, such as genomics, epigenomics, proteomics,and metabolomics.

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