————————————————————————— # 150-feature observation layout: # [ 0– 48] Sec 1 — 7×7 local grid 49f # [ 49– 80] Sec 2 — 8-dir whiskers ×4 32f # [ 81– 94] Sec 3 — Space & Voronoi 14f # [ 95–119] Sec 4 — 5 closest enemies (A*) 25f # [120–125] Sec 5 — Self state 6f # [126–131] Sec 6 — Open space pull + dominance 6f # [132–149] Sec 7 — Tactical signals 18f # [132–135] […]
Non Pneumatic tires offer a promising alternative to pneumatic tires. However, their discontinuous spoke structures present challenges in stiffness tuning, durability, and high speed vibration. This study introduces an integrated generative design and machine learning driven framework to optimize UPTIS type spoke geometries for passenger vehicles. Upper and lower spoke profiles were parameterized using high order polynomial representations, enabling the creation of approximately 250 generative designs through PCHIP based geometric variation. Machine learning models like KRR for stiffness […]
Human action recognition (HAR) remains challenging, particularly for skeleton-based methods due to issues like domain shift and limited deep semantic understanding. Traditional Graph Convolutional Networks often struggle with effective cross-domain adaptation and inferring complex semantic relationships. To address these limitations, we propose CD-SEAFNet, a novel framework meticulously designed to significantly enhance robustness and cross-domain generalization for skeleton-based action recognition. CD-SEAFNet integrates three core modules: an Adaptive Spatio-Temporal Graph Feature Extractor that dynamically learns and adjusts graph structures to […]
arXiv:2604.04969v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Multimodal Large Language Models (MLLMs), yet existing systems struggle with complex cross-modal reasoning. Flat vector retrieval often ignores structural dependencies, while current graph-based methods rely on costly “translation-to-text” pipelines that discard fine-grained visual information. To address these limitations, we propose textbf{MG$^2$-RAG}, a lightweight textbf{M}ulti-textbf{G}ranularity textbf{G}raph textbf{RAG} framework that jointly improves graph construction, modality fusion, and cross-modal retrieval. MG$^2$-RAG constructs a hierarchical multimodal knowledge graph by combining lightweight […]
Following our report last week that Verizon is forcing people to wait 35 days for phone unlocks after paying off device installment plans, Verizon is apparently trying to eliminate the inconvenient delay. But Verizon hasn’t confirmed the plan to Ars, and a statement to another news outlet did not provide any timeline for implementing the change. As a refresher, the latest version of Verizon’s device unlocking policy for postpaid customers imposes a 35-day waiting period when a customer […]
This paper investigates continuous-time and discrete-time firing-rate and Hopfield recurrent neural networks (RNNs), with applications in nonlinear control design and implicit deep learning. First, we introduce a nonlinear separation principle that guarantees global exponential stability for the interconnection of a contracting state-feedback controller and a contracting observer, alongside parametric extensions for robustness and equilibrium tracking. Second, we derive sharp linear matrix inequality (LMI) conditions that guarantee the contractivity of both firing rate and Hopfield neural network architectures. We […]
Electrocardiograms (ECGs) are among the most widely used diagnostic tools for cardiovascular diseases, and a large amount of ECG data worldwide appears only in image form. However, most existing automated ECG analysis methods rely on access to raw signal recordings, limiting their applicability in real-world and resource-constrained settings. In this paper, we present ECG-Scan, a self-supervised framework for learning clinically generalized representations from ECG images through dual physiological-aware alignments: 1) Our approach optimizes image representation learning using multimodal […]
Exploration remains a central challenge in reinforcement learning, especially in sparse-reward settings where extrinsic feedback alone is often insufficient to guide effective behavior. In this work, we develop a curiosity-driven framework that combines a hybrid intrinsic reward with compact predictive representation learning. Specifically, curiosity is quantified by integrating prediction error with the rarity of state-action pairs in a learned latent space. To make novelty estimation more meaningful for high-dimensional observations such as raw pixels, we employ the Information […]
Three years after saying it had stopped buying location data of Americans without a warrant, the FBI acknowledged it has restarted the purchases. During questioning at a Senate Select Committee on Intelligence hearing yesterday, FBI Director Kash Patel said the location data purchases have produced valuable information, and he did not commit to stopping the practice. In March 2023, then-FBI Director Christopher Wray confirmed that the agency had previously bought location data of US citizens without obtaining a […]
Six steps to reset the dynamic.