Deep learning for structure-preserving universal stable Koopman-inspired embeddings for nonlinear canonical Hamiltonian dynamics by Pawan Goyal, Süleyman Yıldız, Peter Benner

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BiCrossNet: resource-efficient cross-view geolocalization with binary neural networks by Federico Fontana, Thomas Jantos, Jan Steinbrener, Luigi Cinque, Gian Luca Foresti, Bernhard Rinner

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Learning from topology: cosmological parameter estimation from the large-scale structure by Jacky H T Yip, Adam Rouhiainen, Gary Shiu

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Multi-view mixture-of-experts for predicting molecular properties using SMILES, SELFIES, and graph-based representations by Eduardo Soares, Victor Yukio Shirasuna, Emilio Vital Brazil, Indra Priyadarsini, Seiji Takeda

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Rapid likelihood free inference of compact binary coalescences using accelerated hardware by D Chatterjee, E Marx, W Benoit, R Kumar, M Desai, E Govorkova, A Gunny, E Moreno, R Omer, R Raikman, M Saleem, S Aggarwal, M W Coughlin, P Harris, E Katsavounidis

“…We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. …”
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Artificial Intelligence Meets Bioequivalence: Using Generative Adversarial Networks for Smarter, Smaller Trials by Anastasios Nikolopoulos, Vangelis D. Karalis

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Stabilizing training of affine coupling layers for high-dimensional variational inference by Daniel Andrade

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Reinforcement-Learning-Based Trajectory Design and Phase-Shift Control in UAV-Mounted-RIS Communications by Tianjiao Sun, Sixing Yin, Li Deng, F. Richard Yu

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Accelerated development of multi-component alloys in discrete design space using Bayesian multi-objective optimisation by Osman Mamun, Markus Bause, Bhuiyan Shameem Mahmood Ebna Hai

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Randomized tensor network reservoir computing: validity and learnability phase transitions by Shinji Sato, Daiki Sasaki, Chih-Chieh Chen, Kodai Shiba, Tomah Sogabe

“…Reservoir computing (RC) systems, traditionally based on echo state networks (ESN) or liquid state machines, have shown significant potential in dynamic temporal data modeling, such as weather forecasting and astronomical predictions. …”
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A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information by Simone Martino, Domiziano Doria, Chiara Lionello, Matteo Becchi, Giovanni M Pavan

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Energy Minimization for Federated Learning Based Radio Map Construction by Fahui Wu, Yunfei Gao, Lin Xiao, Dingcheng Yang, Jiangbin Lyu

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User Handover Aware Hierarchical Federated Learning for Open RAN-Based Next-Generation Mobile Networks by Amardip Kumar Singh, Kim Khoa Nguyen

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Semi-Supervised Learning via Cross-Prediction-Powered Inference for Wireless Systems by Houssem Sifaou, Osvaldo Simeone

“…The second, meta-CPPI (MCPPI), extends tuned CPPI via the joint optimization of the ML labeling models and of the parameters of interest. …”
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Explainable AI for Enhancing Efficiency of DL-Based Channel Estimation by Abdul Karim Gizzini, Yahia Medjahdi, Ali J. Ghandour, Laurent Clavier

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Federated Analytics With Data Augmentation in Domain Generalization Toward Future Networks by Xunzheng Zhang, Juan Marcelo Parra-Ullauri, Shadi Moazzeni, Xenofon Vasilakos, Reza Nejabati, Dimitra Simeonidou

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Accelerating Energy-Efficient Federated Learning in Cell-Free Networks With Adaptive Quantization by Afsaneh Mahmoudi, Ming Xiao, Emil Bjornson

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Analysis of Radial Force, Vibration, and Noise in Low-speed, High-torque Density Spoke-type PMSMs with Symmetric and Asymmetric Assisted Poles Designs by Sayyed Haleem Shah, Yun-Chong Wang, Dan Shi, Jian-Xin Shen

“…Secondly, Finite element (FE) models are designed for the machine structural analysis. …”
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Enhancing Hierarchical Sales Forecasting with Promotional Data: A Comparative Study Using ARIMA and Deep Neural Networks by Mariana Teixeira, José Manuel Oliveira, Patrícia Ramos

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Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline by Mengnan Cui, Karsten Reuter, Johannes T Margraf

“…Interestingly, it even outperforms TL based on foundation models in some cases, by leveraging an optimal overlap of pre-training and fine-tuning chemical spaces.…”
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