arXiv:2510.17890v1 Announce Type: new Abstract: Recent advances in deep learning have led to a surge of open-source models across diverse domains. While model merging offers a promising way to combine their strengths, existing approaches often suffer from parameter conflicts that…
arXiv:2510.17887v1 Announce Type: new Abstract: We present a comprehensive, physics aware deep learning framework for constructing fast and accurate surrogate models of rarefied, shock containing micro nozzle flows. The framework integrates three key components, a Fusion DeepONet operator learning architecture…
arXiv:2510.17846v1 Announce Type: new Abstract: Prognostic Health Management (PHM) systems monitor and predict equipment health. A key task is Remaining Useful Life (RUL) estimation, which predicts how long a component, such as a rolling element bearing, will operate before failure.…
arXiv:2510.15946v2 Announce Type: replace Abstract: Internet memes have emerged as a popular multimodal medium, yet they are increasingly weaponized to convey harmful opinions through subtle rhetorical devices like irony and metaphor. Existing detection approaches, including MLLM-based techniques, struggle with these…
arXiv:2510.17901v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative decentralized training across multiple parties (nodes) while keeping raw data private. There are two main paradigms in FL: Horizontal FL (HFL), where all participant nodes share the same feature space…
arXiv:2502.01546v2 Announce Type: replace-cross Abstract: Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object’s physical properties and friction with the floor contain significant uncertainties, which makes the task…
arXiv:2510.17914v1 Announce Type: new Abstract: We introduce NeuCo-Bench, a novel benchmark framework for evaluating (lossy) neural compression and representation learning in the context of Earth Observation (EO). Our approach builds on fixed-size embeddings that act as compact, task-agnostic representations applicable…
arXiv:2508.11503v2 Announce Type: replace-cross Abstract: Reliable autonomous navigation across the unstructured terrains of distant planetary surfaces is a critical enabler for future space exploration. However, the deployment of learning-based controllers is hindered by the inherent sim-to-real gap, particularly for the…
arXiv:2510.17915v1 Announce Type: new Abstract: Despite extensive research on neural network calibration, existing methods typically apply global transformations that treat all predictions uniformly, overlooking the heterogeneous reliability of individual predictions. Furthermore, the relationship between improved calibration and effective uncertainty-aware decision-making…
arXiv:2510.18637v1 Announce Type: cross Abstract: Semantic segmentation of electron microscopy (EM) images of biological samples remains a challenge in the life sciences. EM data captures details of biological structures, sometimes with such complexity that even human observers can find it…
