arXiv:2511.07649v1 Announce Type: new Abstract: Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interconnected reservoirs. We introduce AdaTrip as an adaptive, time-varying graph learning framework for…
arXiv:2511.05540v2 Announce Type: replace-cross Abstract: We challenge the long-standing assumption that exhaustive scene modeling is required for high-performance end-to-end autonomous driving (E2EAD). Inspired by cognitive science, we propose that effective planning arises not from reconstructing the world, but from the…
arXiv:2511.07651v1 Announce Type: new Abstract: Effective crime linkage analysis is crucial for identifying serial offenders and enhancing public safety. To address limitations of traditional crime linkage methods in handling high-dimensional, sparse, and heterogeneous data, we propose a Siamese Autoencoder framework…
arXiv:2511.08360v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are used in many applications, but their large size and high computational cost make them hard to run on devices with limited resources. Two widely used techniques to address this challenge…
arXiv:2511.07657v1 Announce Type: new Abstract: Enterprise relational databases increasingly contain vast amounts of non-semantic data – IP addresses, product identifiers, encoded keys, and timestamps – that challenge traditional semantic analysis. This paper introduces a novel Character-Level Autoencoder (CAE) approach that…
arXiv:2511.08424v1 Announce Type: cross Abstract: Process-structure-property relationships are fundamental in materials science and engineering and are key to the development of new and improved materials. Symbolic regression serves as a powerful tool for uncovering mathematical models that describe these relationships.…
arXiv:2511.07658v1 Announce Type: new Abstract: Although recent advancements in learning-based analog circuit design automation have tackled tasks such as topology generation, device sizing, and layout synthesis, efficient performance evaluation remains a major bottleneck. Traditional SPICE simulations are time-consuming, while existing…
arXiv:2511.08577v1 Announce Type: cross Abstract: Improving reasoning capabilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Prior work proposes recurrent transformers, which allocate a fixed number of extra iterations per token to improve generation…
arXiv:2501.05530v2 Announce Type: replace-cross Abstract: This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the interpretability of outlier detection results. Both OSs employ…
arXiv:2509.15810v2 Announce Type: replace Abstract: To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the…
