2529 papers
This paper explicitly establishes that the optimum circuit size changes by at most under truth table perturbations, extending the bound to general Hamming distances and verifying its tightness at through exhaustive SAT-based analysis.
This paper introduces PixelConfig, a framework for reverse-engineering Meta Pixel configurations, which reveals that default settings drive widespread adoption of activity and identity tracking features capable of capturing sensitive health data, while existing tracking restriction mechanisms offer limited practical protection.
EventVGGT is a novel framework that addresses the scarcity of depth annotations and temporal inconsistency in event-based monocular depth estimation by treating event streams as coherent video sequences and distilling spatio-temporal and multi-view geometric priors from the Visual Geometry Grounded Transformer (VGGT) through a tri-level distillation strategy, achieving state-of-the-art performance and robust zero-shot generalization.
The paper proposes MIDAS, a training-free diffusion-based framework that enables coverless multi-image steganography with user-specific access control through latent-level fusion, demonstrating superior performance in image quality, robustness, and security compared to existing methods.
This paper introduces GeoBenchr, an open-source, application-centric benchmarking suite designed to comprehensively evaluate and compare the performance, scalability, and configuration impact of spatiotemporal database platforms across diverse real-world use cases.
This paper proposes a vision-augmented, iterative system identification framework that combines a lightweight CNN for friction priors and an S4 model for temporal dynamics to overcome cold-start failures and improve real-time tire parameter estimation for autonomous racing.
This paper introduces YOLO-NAS-Bench, the first surrogate benchmark for YOLO-style object detectors, which employs a self-evolving mechanism to iteratively refine a LightGBM predictor, enabling efficient and accurate discovery of high-performing architectures that surpass official YOLO baselines.
RiO-DETR is the first real-time oriented object detection transformer that addresses challenges in angle estimation, periodicity, and convergence through novel designs like Content-Driven Angle Estimation and Decoupled Periodic Refinement, achieving a new speed-accuracy trade-off on benchmark datasets.
This paper proposes a unifying framework for Boolean tensor network complexity dichotomy theorems that classifies unresolved problems into nine cases based on finite group structures of binary functions, resolving higher-order cyclic group cases and advancing the order-1 case while identifying barriers involving quaternion subgroups.
CIGPose introduces a Causal Intervention Graph Neural Network framework that enhances whole-body pose estimation robustness by using a Structural Causal Model to identify and replace context-confounded keypoint representations with invariant embeddings, thereby achieving state-of-the-art performance on COCO-WholeBody without relying on extra training data.
The paper proposes MetaDAT, a trajectory prediction framework that combines meta-learning pre-training with a data-adaptive test-time updating mechanism to achieve robust, fast, and accurate online adaptation under distribution shifts by dynamically adjusting learning rates and focusing on informative hard samples.
This paper demonstrates that binary vulnerabilities in WebAssembly modules, such as buffer overflows and use-after-free errors, can compromise web application security by enabling attacks like SQL injections and XS-Leaks, while also offering best practices to mitigate these risks.
This paper constructs a functor to define dynamics for an algebraic model of interacting components, generalizing the AlChemy artificial life model and exploring how category theory can formally connect algebraic structures with dynamical systems in artificial chemistry.
The paper proposes GIIM, a novel graph-based framework that enhances multi-view medical image diagnosis by simultaneously modeling intra-view relationships and inter-view dynamics while effectively handling missing data to improve predictive accuracy and robustness.
ShapeMark is a robust and diversity-preserving watermarking method for diffusion models that encodes watermark bits into structured noise patterns rather than individual values and employs a randomization design to prevent fixed patterns, thereby achieving state-of-the-art robustness against lossy scenarios while maintaining high generation quality.
This paper introduces a preconditioned SE(3) Stein Variational Gradient Descent framework that reformulates point-cloud surface coverage as a manifold-aware sampling problem, enabling robots to generate high-quality, SE(3)-constrained trajectories that outperform existing optimization-based and sampling-as-optimization methods in both simulation and real-world experiments.
The paper introduces SEA-Nav, a reinforcement learning framework that combines differentiable control barrier functions, adaptive collision replay, and kinematic constraints to enable quadruped robots to achieve safe, agile, and efficient navigation in densely cluttered environments with minute-level training time.
TopoOR introduces a novel topological scene representation for surgical operating rooms that leverages higher-order structures and attention mechanisms to preserve complex multimodal relationships and manifold geometry, thereby outperforming traditional graph and LLM-based methods in safety-critical tasks like sterility breach detection and robot phase prediction.
This paper reports on the authors' experience developing three professional software engineering curricula and proposes a systematic, content-mapping-based approach with guiding principles for effectively integrating Requirements Engineering courses into these dynamic and modular programs.
This paper introduces the Patrologia Graeca Corpus, a large-scale open resource featuring OCR-processed, lemmatized, and part-of-speech tagged text from degraded nineteenth-century bilingual Greek-Latin editions, which achieves state-of-the-art recognition accuracy and establishes a new benchmark for noisy polytonic Greek processing.