arXiv:2509.18107v1 Announce Type: new Abstract: Multivariate time series forecasting involves predicting future values based on historical observations. However, existing approaches primarily rely on predefined single-scale patches or lack effective mechanisms for multi-scale feature fusion. These limitations hinder them from fully…
arXiv:2509.18106v1 Announce Type: new Abstract: The growing use of permanent monitoring systems has increased data availability, offering new opportunities for structural assessment but also posing scalability challenges, especially across large bridge networks. Managing multiple structures requires tracking and comparing long-term…
arXiv:2509.18105v1 Announce Type: new Abstract: We study learning of continuous-time inventory dynamics under stochastic demand and quantify when structure helps or hurts forecasting of the bullwhip effect. BULL-ODE compares a fully learned Neural ODE (NODE) that models the entire right-hand…
arXiv:2508.19356v3 Announce Type: replace Abstract: Graphs are central to the chemical sciences, providing a natural language to describe molecules, proteins, reactions, and industrial processes. They capture interactions and structures that underpin materials, biology, and medicine. This primer, Graph Data Modeling:…
arXiv:2509.18112v1 Announce Type: new Abstract: Foundation models (FMs) and large language models (LLMs) demonstrate remarkable capabilities across diverse domains through training on massive datasets. These models have demonstrated exceptional performance in healthcare applications, yet their potential for electronic fetal monitoring…
arXiv:2509.18114v1 Announce Type: new Abstract: Autoregressive inference in large transformer-based language models (LLMs) presents significant challenges for runtime efficiency, particularly during the decode phase where load imbalance across GPU shards can cause throughput degradation and latency spikes. A DPU-assisted framework…
arXiv:2502.17928v3 Announce Type: replace-cross Abstract: Source localization in graph information propagation is essential for mitigating network disruptions, including misinformation spread, cyber threats, and infrastructure failures. Existing deep generative approaches face significant challenges in real-world applications due to limited propagation data…
arXiv:2509.18115v1 Announce Type: new Abstract: In this paper, we propose a novel Spatial Balance Attention block for spatiotemporal forecasting. To strike a balance between obeying spatial proximity and capturing global correlation, we partition the spatial graph into a set of…
arXiv:2509.16727v2 Announce Type: replace-cross Abstract: Automated pain assessment from facial expressions is crucial for non-communicative patients, such as those with dementia. Progress has been limited by two challenges: (i) existing datasets exhibit severe demographic and label imbalance due to ethical…
arXiv:2509.18116v1 Announce Type: new Abstract: Test-time optimization remains impractical at scale due to prohibitive inference coststextemdash techniques like iterative refinement and multi-step verification can require $10$–$100times$ more compute per query than standard decoding. Latent space test-time optimization methods like LatentSeek…
