arXiv:2512.09013v1 Announce Type: new Abstract: Intracranial aneurysms remain a major cause of neurological morbidity and mortality worldwide, where rupture risk is tightly coupled to local hemodynamics particularly wall shear stress and oscillatory shear index. Conventional computational fluid dynamics simulations provide…
arXiv:2511.12783v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has been widely used to optimize expensive and gradient-free objective functions across various domains. However, existing BO methods have not addressed the objective where both inputs and outputs are functions, which increasingly…
arXiv:2512.09054v1 Announce Type: new Abstract: Accurate and reliable probability predictions are essential for multi-class supervised learning tasks, where well-calibrated models enable rational decision-making. While isotonic regression has proven effective for binary calibration, its extension to multi-class problems via one-vs-rest calibration…
arXiv:2512.09779v1 Announce Type: cross Abstract: Few-shot learning (FSL) mitigates data scarcity in cardiac MRI segmentation but typically relies on semi-supervised techniques sensitive to domain shifts and validation bias, restricting zero-shot generalizability. We propose PathCo-LatticE, a fully supervised FSL framework that…
arXiv:2512.09059v1 Announce Type: new Abstract: Accurate precipitation forecasting is essential for hydrometeorological risk management, especially for anticipating extreme rainfall that can lead to flash flooding and infrastructure damage. This study introduces a diffusion-based deep learning (DL) framework that systematically compares…
arXiv:2004.10919v2 Announce Type: replace Abstract: Automatic question-answering (QA) systems have boomed during last few years, and commonly used techniques can be roughly categorized into Information Retrieval (IR)-based and generation-based. A key solution to the IR based models is to retrieve…
arXiv:2512.09067v1 Announce Type: new Abstract: Neural networks, while effective for tackling many challenging scientific tasks, are not known to perform well out-of-distribution (OOD), i.e., within domains which differ from their training data. Understanding neural network OOD generalization is paramount to…
arXiv:2502.04499v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a popular method of transferring knowledge from a large “teacher” model to a small “student” model. Previous work has explored various layer-selection strategies (e.g., forward matching and in-order random matching) for…
arXiv:2512.09074v1 Announce Type: new Abstract: Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing historical mortality, predicting an incoming deadly heatwave remains a challenge due to the…
arXiv:2505.17918v2 Announce Type: replace Abstract: Tabular data have been playing a vital role in diverse real-world fields, including healthcare, finance, etc. With the recent success of Large Language Models (LLMs), early explorations of extending LLMs to the domain of tabular…
