2744 papers
This paper proposes and evaluates a safety-aware adaptive edge intelligence controller for assistive vision systems that dynamically balances processing modes based on real-time network, computational, and risk conditions to minimize latency and ensure timely feedback, while outlining a framework for future experimental validation.
This paper proposes a risk-aware adaptive video-processing controller for low-latency assistive vision that dynamically separates frame admission from inference-mode selection based on network, compute, and navigation risk metrics, thereby significantly reducing end-to-end latency and shedding stale non-critical frames while ensuring critical-risk observations receive full-fidelity processing.
The paper introduces FrameShift-CAD, a diagnostic benchmark that reveals a significant reliability gap in text-to-CAD models, showing they struggle to correctly execute pure rotational coordinate-frame transformations compared to translations, even when explicit mapping instructions are provided.
This paper proposes and evaluates two ensemble models for cardiovascular disease prediction, finding that a simpler combination of three classifiers (DNL) outperforms a more complex 24-classifier framework (MEDPOLY) by achieving 98% accuracy while avoiding overfitting and offering better interpretability.
This paper proposes CINN, a convolution-based invertible neural network that integrates global-local feature coupling and multi-channel attention pooling to simultaneously enhance the security, imperceptibility, and recovery fidelity of image steganography, outperforming existing state-of-the-art methods across multiple datasets and steganalysis evaluations.
This paper proposes a "Sheaf-State Language Model" architecture that replaces computationally excessive dense self-attention with a lower-complexity framework using local state-space dynamics and sparse, typed gluing morphisms to efficiently manage context and dependencies.
GaussCast is a dependency-aware delivery framework for multi-user layered 3D Gaussian Splatting that aggregates concurrent user demands at an edge proxy to fetch shared prerequisite blocks only once, thereby significantly reducing upstream traffic and improving startup latency and rendering quality compared to independent per-user delivery strategies.
This paper proposes the State-Sensing Adaptive Artificial Bee Colony (SSA-ABC) algorithm, which overcomes standard ABC's limitations through dimensionality-aware initialization, dynamic search adjustment, and rank-based selection mechanisms to achieve superior performance in high-dimensional optimization and robot path planning.
This study demonstrates that visual distances derived from large vision models significantly correlate with expert-coded historical-artistic relatedness across diverse cultural units and epochs, suggesting that internet-scale visual training corpora inherently encode statistical regularities reflecting art-historical relationships.
This paper proposes a Constrained Soft Actor-Critic (CSAC) approach that decouples stringent delay constraints from the reward function and employs a two-stage mapping mechanism to effectively maximize throughput utility while minimizing delay violations in heterogeneous queuing systems with bursty traffic, outperforming both unconstrained learning and heuristic baselines.