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    Low-virtuality splitting in the Standard Model by Filippo Nardi, Lorenzo Ricci, Andrea Wulzer

    Published 2024-10-01
    “…We compute at tree-level the amplitudes describing all the splittings that take place in the Standard Model when the collision energy is much above the electroweak scale. …”
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    Non-Convex Split Feasibility Problems: Models, Algorithms and Theory by Gibali, Aviv, Sabach, Shoham, Voldman, Sergey

    Published 2020-10-01
    “…In this paper, we propose a catalog of iterative methods for solving the Split Feasibility Problem in the non-convex setting. …”
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    Equivalent elastic model of splitting grouting reinforcement in weak strata by Quanwei Liu, Jia Deng, Linsheng Liu, Shoujie Ye, Shaolong Duan, Changzhi Shao, Guangxuan Zhu

    Published 2025-04-01
    “…Based on the diffusion mechanism of splitting grouting in weak strata and engineering practice, a typical combination structure of grout veins and soil in grouted reinforced strata is summarized and proposed. …”
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    Coscattering in next-to-minimal dark matter and split supersymmetry by F. Brümmer

    Published 2020-01-01
    “…Abstract In some models of thermal relic dark matter, the relic abundance may be set by inelastic scattering processes (rather than annihilations) becoming inefficient as the universe cools down. …”
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    Model split-based data privacy protection method for federated learning by CHEN Ka

    Published 2024-09-01
    “…Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. …”
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    Recent Advances in the Modeling of Transmission Lines Loaded with Split Ring Resonators by Jordi Naqui, Lijuan Su, Javier Mata, Ferran Martín

    Published 2015-01-01
    “…This paper reviews the recent advances in the modeling of transmission lines loaded with split ring resonators (SRRs). …”
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    Solution Procedure for Transport Modeling in Effluent Recharge Based on Operator-Splitting Techniques by Shutang Zhu, Gang Zhao, Yuanle Ma

    Published 2008-01-01
    “…It has been found very difficult to solve such a coupled model either analytically or numerically. The present study adopts operator-splitting techniques to decompose the coupled model into two submodels with different intrinsic characteristics. …”
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    Determination of Parameters of the Discrete Element Bond Model for Asphalt Mixture Based on Splitting Test by Jingsong Shan, Yang Du, Dahai Fan, Laiyao Guo

    Published 2019-01-01
    “…According to the real load-displacement curve’s slope at the beginning stage and peak load applied, the range of DEM bond model parameters is determined. On the basis of DEM results of the splitting test, the relationships between simulation load-displacement curve’s characteristics and bond model parameters are fitted. …”
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    Advanced evaluation of performance of machine learning models for soapstock splitting optimisation under uncertainty by Bartosz Szeląg, Krzysztof Barbusiński, Michał Stachura, Przemysław Kowal, Adam Kiczko, Eldon R. Rene

    Published 2025-06-01
    “…The objective was to evaluate and select the modeling approaches based on (i) data availability, (ii) model complexity, (iii) predictive accuracy, and (iv) sensitivity to input uncertainty. …”
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    Systematic trends in the spin-orbit splitting toward weak-binding by Jie Chen

    Published 2025-02-01
    Subjects: “…spin–orbital splitting…”
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    Context modulates evidence accumulation in split-second handball penalty decisions by Henrietta Weinberg, Florian Müller, Rouwen Cañal-Bruland

    Published 2025-02-01
    “…In Exp. 1 we validated the applicability of hierarchical drift–diffusion modeling (HDDM) for the chosen split-second handball penalty scenario. …”
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    Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm Optimization by Mohit Agarwal, Garima Jaiswal, Rohit Kumar Kaliyar, Akansha Singh, Krishna Kant Singh, S. S. Askar, Mohamed Abouhawwash

    Published 2024-01-01
    “…Federated learning uses the concept of decentralized training of n number of local clients for a small number of epochs say 2-5, and then averaging the learned weights of all local clients, and evaluating on test dataset with the average weights loaded to a global model. The train dataset is split into n clusters and each cluster acts as a distributed data for each local model. …”
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