6985 papers
This paper presents a unified Lie-group framework based on Cosserat rod theory and SE(3) cumulative parameterization that overcomes existing modeling limitations to provide efficient, constraint-free analytical expressions for the kinematics, statics, and dynamics of diverse continuum soft robotic structures.
This study leverages the MICCAI 2024 UWF4DR dataset to benchmark state-of-the-art deep learning models, including CNNs, Vision Transformers, and foundation models, in both spatial and frequency domains for image quality assessment, referable diabetic retinopathy detection, and diabetic macular edema identification using ultra-widefield imaging, demonstrating that feature-level fusion and frequency-domain representations yield robust and explainable results.
This paper proposes a physics-guided preprocessing framework for millimeter-wave human pose estimation that explicitly models signal correlations and kinematics to achieve real-time, lightweight performance with significantly fewer parameters than existing data-driven baselines while maintaining competitive accuracy.
SiMO introduces a novel collaborative perception framework that utilizes Length-Adaptive Multi-Modal Fusion (LAMMA) and a "Pretrain-Align-Fuse-RD" training strategy to overcome sensor failures and semantic mismatches, ensuring robust performance across all individual modalities while maintaining effective multimodal integration.
This paper proposes a topologically stable variant of the Hough transform for line detection in point clouds, which replaces the traditional discretized voting scheme with a continuous score function and utilizes persistent homology to identify candidate lines via an efficient algorithm.
This paper proposes a novel flow-based conceptual design for coupling European capacity markets that, by harnessing cross-border capacity while respecting network constraints, reduces system costs and improves investment efficiency compared to isolated national mechanisms.
This paper introduces DynamicVGGT, a unified feed-forward framework that extends static 3D perception to dynamic 4D scene reconstruction for autonomous driving by jointly predicting current and future point maps, utilizing a Motion-aware Temporal Attention module for temporal coherence, and employing a Dynamic 3D Gaussian Splatting Head to explicitly model point motion and refine geometry.
The paper proposes WaDi, a novel one-step image synthesis framework that leverages the insight that weight direction changes are more critical than norm changes during distillation, introducing the parameter-efficient LoRaD adapter to achieve state-of-the-art performance with only 10% of trainable parameters.
Seed2Scale is a self-evolving data engine that overcomes data bottlenecks in embodied AI by synergizing a lightweight "SuperTiny" model for robust data collection with a large Vision-Language Model for autonomous quality verification, enabling a target model to achieve a 131.2% performance improvement starting from just four seed demonstrations.
The paper introduces FinToolBench, the first real-world, runnable benchmark that evaluates LLM agents on 760 executable financial tools using a novel framework assessing timeliness, intent, and regulatory compliance, alongside a proposed finance-aware baseline named FATR to advance trustworthy agentic AI in finance.
This paper proposes a learning-free method for 6D object pose tracking that fuses event-based optical flow for high-speed pose propagation with a template-based correction strategy, demonstrating superior performance over state-of-the-art algorithms in highly dynamic scenarios where traditional RGB-D cameras struggle with motion blur and low frame rates.
SAIL is a test-time scaling framework that enhances one-shot robot imitation learning by reframing trajectory generation as an iterative refinement process guided by Monte Carlo Tree Search, an automated retrieval archive, and a vision-language model-based scoring mechanism, thereby significantly improving success rates across diverse manipulation tasks.
This paper introduces a prototype-guided approach that leverages the intrinsic embedding geometry of diffusion models to identify and cluster concept representations, enabling the reliable erasure of broad, multi-faceted concepts while preserving overall image quality.
This paper demonstrates that explicitly reducing observation dimensionality and implementing locality-aware credit assignment in a communication-free multi-agent system enhances robustness and performance in asymmetric 3D pursuit-evasion tasks within cluttered environments.
The paper proposes OSCAR, a label-free method that utilizes coupled latent spaces and neural implicit representations to accurately reconstruct complete 3D vertebral anatomy from partial ultrasound images by implicitly modeling acoustic shadowing and signal transmission, achieving an 80% improvement in HD95 score over state-of-the-art techniques.
This paper presents BladeChain, a blockchain-based system that integrates multi-stakeholder endorsement, automated scheduling, and AI model provenance to provide immutable, auditable traceability for aircraft engine blade inspections across the entire component life cycle.
The paper introduces SP-CLIP, a lightweight zero-shot action recognition framework that significantly improves performance on fine-grained and compositional actions by augmenting frozen vision-language models with structured, multi-level semantic prompts without requiring any additional parameter training or visual encoder modifications.
This paper systematically reviews recent advancements in Multimodal Mathematical Reasoning by proposing a unified Perception-Alignment-Reasoning paradigm, categorizing existing approaches around four fundamental questions regarding information extraction, representation, reasoning, and evaluation, while outlining future research challenges.
This paper introduces Silicone Ethernet (SEth), a wireless, battery-free system that embeds sensing, communication, and power transfer capabilities directly into a conductive silicone substrate to overcome the cabling limitations of fine-grained robotic touch sensing.
This paper presents the first systematic analysis of Attack Graph and Intrusion Detection System integration, proposing a novel unifying lifecycle framework that establishes a continuous feedback loop to enhance threat detection and incident response.