arXiv:2508.18025v2 Announce Type: replace-cross Abstract: Successful autonomous planetary exploration hinges on real-time, high-fidelity environmental perception. However, standard deep learning models usually demand far more memory and computation power than space-qualified, radiation-hardened onboard hardware can provide. This creates a fundamental design…
arXiv:2505.15570v2 Announce Type: replace Abstract: Neural network-based radio receivers are expected to play a key role in future wireless systems, making reliable Out-Of-Distribution (OOD) detection essential. We propose a post-hoc, layerwise OOD framework based on channelwise feature aggregation that avoids…
arXiv:2510.15042v2 Announce Type: replace-cross Abstract: Vision-language pre-training, i.e., aligning images with paired text, is a powerful paradigm to create encoders that can be directly used for tasks such as classification, retrieval, and segmentation. In the 3D medical image domain, these…
arXiv:2412.09468v4 Announce Type: replace Abstract: In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. Recently, we have witnessed the rise of variational autoencoder-based latent factor models, which learn latent factors self-adaptively. While…
arXiv:2412.16276v2 Announce Type: replace-cross Abstract: Classifying Antimicrobial Peptides (AMPs) from the vast collection of peptides derived from metagenomic sequencing offers a promising avenue for combating antibiotic resistance. However, most existing AMP classification methods rely primarily on sequence-based representations and fail…
arXiv:2506.15854v3 Announce Type: replace-cross Abstract: Intelligent Transportation Systems (ITS) rely on a variety of devices that frequently process privacy-sensitive data. Roadside units are important because they use AI-equipped cameras to detect traffic violations in Connected and Autonomous Vehicles (CAV). However,…
arXiv:2510.09676v2 Announce Type: replace Abstract: Inverse problems, where the goal is to recover an unknown signal from noisy or incomplete measurements, are central to applications in medical imaging, remote sensing, and computational biology. Diffusion models have recently emerged as powerful…
arXiv:2601.02316v2 Announce Type: replace Abstract: Empirical evaluation serves as the primary compass guiding research progress in foundation models. Despite a large body of work focused on training frontier vision-language models (VLMs), approaches to their evaluation remain nascent. To guide their…
arXiv:2601.08999v1 Announce Type: new Abstract: Accurate prediction of solar energetic particle events is vital for safeguarding satellites, astronauts, and space-based infrastructure. Modern space weather monitoring generates massive volumes of high-frequency, multivariate time series (MVTS) data from sources such as the…
arXiv:2601.09000v1 Announce Type: new Abstract: The Warmup Stable Decay (WSD) learning rate scheduler has recently become popular, largely due to its good performance and flexibility when training large language models. It remains an open question whether the remarkable performance of…
