arXiv:2602.00681v2 Announce Type: replace-cross
Abstract: Audio-to-image retrieval offers an interpretable alternative to audio-only classification for bioacoustic species recognition, but learning aligned audio-image representations is challenging du…
arXiv:2604.04545v1 Announce Type: cross
Abstract: Ringkoebing Fjord is an inland water basin on the Danish west coast separated from the North Sea by a set of gates used to control the amount of water entering and leaving the fjord. Currently, human o…
arXiv:2604.04916v1 Announce Type: new
Abstract: Extreme weather events, such as severe storms, hurricanes, snowstorms, and ice storms, which are exacerbated by climate change, frequently cause widespread power outages. These outages halt industrial op…
arXiv:2604.03298v2 Announce Type: cross
Abstract: The rapid scaling of Large Language Models presents significant challenges for their deployment and inference, particularly on resource-constrained specialized AI hardware accelerators such as Huawei’s…
arXiv:2604.03459v1 Announce Type: cross
Abstract: Regional climate information at kilometer scales is essential for assessing the impacts of climate change, but generating it with global climate models is too expensive due to their high computational …
arXiv:2511.20944v4 Announce Type: replace
Abstract: Business Email Compromise (BEC) is a high-impact social engineering threat with extreme operational asymmetry: false negatives can trigger large financial losses, while false positives primarily incu…
arXiv:2601.03484v2 Announce Type: replace
Abstract: Deploying models, especially large language models (LLMs), is becoming increasingly attractive to a broader user base, including those without specialized expertise. However, due to the resource cons…
arXiv:2604.04231v1 Announce Type: new
Abstract: Foundation models, such as large language models (LLMs), are powerful but often require customization before deployment to satisfy practical constraints such as safety, privacy, and task-specific require…
arXiv:2603.23472v2 Announce Type: replace
Abstract: Federated Learning (FL) enables heterogeneous clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy. However, gradients and model …
arXiv:2604.04380v1 Announce Type: new
Abstract: Designing visually diverse and high-quality designs remains a manual, time-consuming process, limiting scalability and personalization in creative workflows. We present a system for generating editable d…