arXiv:2604.22496v1 Announce Type: new
Abstract: In this study, deep learning is used to estimate kinetic parameters for modeling itaconic acid production based on real batch experiments conducted at different agitation speeds and reactor scales. Two d…
arXiv:2511.07003v2 Announce Type: replace
Abstract: Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains challenging. In this pap…
arXiv:2604.22050v1 Announce Type: cross
Abstract: Transformers are mostly relying on softmax attention, which introduces quadratic complexity with respect to sequence length and remains a major bottleneck for efficient inference. Prior work on linear …
arXiv:2502.17011v2 Announce Type: replace-cross
Abstract: Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In this paper, we propose a novel framework that leve…
arXiv:2604.21993v1 Announce Type: new
Abstract: We study the detection of transient liquidity erosion (“crumbling quotes”) in electronic limit order books, where observable quote deterioration may reflect either mechanical liquidity withdrawal or info…
arXiv:2505.17639v3 Announce Type: replace
Abstract: Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization. We introduce PreMoE…
arXiv:2511.21777v3 Announce Type: replace
Abstract: Methane is a potent greenhouse gas, responsible for roughly 30% of warming since pre-industrial times. A small number of large point sources account for a disproportionate share of emissions, creatin…
arXiv:2604.22579v1 Announce Type: cross
Abstract: We study whether deep networks for medical imaging learn useful nonrobust features – predictive input patterns that are not human interpretable and highly susceptible to small adversarial perturbations…
arXiv:2509.20979v2 Announce Type: replace
Abstract: In modern GPU inference, cache efficiency remains a major bottleneck, and heuristic policies such as \textsc{LRU} can perform far worse than the offline optimum. Existing learning-based caching syste…
arXiv:2510.12328v5 Announce Type: replace
Abstract: Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Graph Neural Networks (…