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arXiv:2604.01878v1 Announce Type: new Abstract: Spectral graph contrastive learning has emerged as a unified paradigm for handling both homophilic and heterophilic graphs by leveraging high-frequency …
arXiv:2604.01870v1 Announce Type: new Abstract: In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure …
arXiv:2604.01845v1 Announce Type: new Abstract: Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. …
arXiv:2604.01830v1 Announce Type: new Abstract: Topology control for power grid operation is a challenging sequential decision making problem because the action space grows combinatorially with …
arXiv:2604.01802v1 Announce Type: new Abstract: Accurate sensing of spatially distributed physical fields typically requires dense instrumentation, which is often infeasible in real-world systems due to …
arXiv:2604.01775v1 Announce Type: new Abstract: Although demand forecasting is a critical component of supply chain planning, actual retail data can exhibit irreconcilable seasonality, irregular spikes, …
arXiv:2604.01769v1 Announce Type: new Abstract: For multi-input and multi-output (MIMO) channels, the optimal channel estimation (CE) based on linear minimum mean square error (LMMSE) requires …
arXiv:2604.01762v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a crucial paradigm for adapting large language models (LLMs) under constrained computational budgets. However, …
arXiv:2604.01740v1 Announce Type: new Abstract: A persistent structural weakness in deep clustering is the disconnect between feature learning and cluster assignment. Most architectures invoke an …
arXiv:2604.01730v1 Announce Type: new Abstract: This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine. A physics-based component-level model is developed …
arXiv:2604.01727v1 Announce Type: new Abstract: Forecasting evolving clinical risks relies on intrinsic pathological dependencies rather than mere chronological proximity, yet current methods struggle with coarse …
arXiv:2604.01712v1 Announce Type: new Abstract: The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital …
