arXiv:2507.11732v2 Announce Type: replace Abstract: Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or minimally informed initial feature representations, where poor initialization…
arXiv:2510.13205v2 Announce Type: replace Abstract: Healthcare fraud detection remains a critical challenge due to limited availability of labeled data, constantly evolving fraud tactics, and the high dimensionality of medical records. Traditional supervised methods are challenged by extreme label scarcity, while…
arXiv:2602.18519v1 Announce Type: new Abstract: Traditional approaches to measuring visual exploratory behavior in soccer rely on counting visual exploratory actions (VEAs) based on rapid head movements exceeding 125{deg}/s, but this method suffer from player position bias (i.e., a focus on…
arXiv:2502.03771v5 Announce Type: replace Abstract: Semantic caches return cached responses for semantically similar prompts to reduce LLM inference latency and cost. They embed cached prompts and store them alongside their response in a vector database. Embedding similarity metrics assign a…
arXiv:2602.18515v1 Announce Type: new Abstract: Partial differential equations (PDEs) form a central component of scientific computing. Among recent advances in deep learning, evolutionary neural networks have been developed to successively capture the temporal dynamics of time-dependent PDEs via parameter evolution.…
arXiv:2602.18518v1 Announce Type: new Abstract: Content safety teams need metrics that reflect what users actually experience, not only what is reported. We study prevalence: the fraction of user views (impressions) that went to content violating a given policy on a…
arXiv:2602.18493v1 Announce Type: new Abstract: Long-context LLMs and Retrieval-Augmented Generation (RAG) systems process information passively, deferring state tracking, contradiction resolution, and evidence aggregation to query time, which becomes brittle under ultra long streams with frequent updates. We propose the Unified…
arXiv:2602.18486v1 Announce Type: new Abstract: Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is better modeled…
arXiv:2602.18473v1 Announce Type: new Abstract: Accurate analysis of medical time series (MedTS) data, such as electroencephalography (EEG) and electrocardiography (ECG), plays a pivotal role in healthcare applications, including the diagnosis of brain and heart diseases. MedTS data typically exhibit two…
arXiv:2602.19816v1 Announce Type: cross Abstract: Long-context modeling is essential for symbolic music generation, since motif repetition and developmental variation can span thousands of musical events. However, practical composition and performance workflows frequently rely on resource-limited devices (e.g., electronic instruments and…
