5725 papers
This paper introduces SurgSync, a time-synchronized multi-modal data collection framework implemented on a da Vinci Research Kit that addresses the lack of training data for surgical AI by integrating advanced sensors and synchronization tools to generate a validated dataset of 214 surgical instances for skill assessment and autonomous control development.
The paper proposes DLRMamba, a novel framework for edge-based multispectral object detection that combines a Low-Rank SS2D module to reduce parameter redundancy with a Structure-Aware Distillation strategy to preserve feature fidelity, achieving superior efficiency and accuracy on resource-constrained hardware.
This paper introduces Load-Aware Informative Path Planning (LIPP), a generalization of classical Informative Path Planning formulated as a Mixed-Integer Quadratic Program that optimizes routing, visitation order, and sampling counts under an energy budget by explicitly modeling the coupling between information gain and load-dependent traversal costs, thereby achieving superior energy efficiency and data collection compared to traditional methods in physical sampling scenarios.
This paper introduces ESM-YOLO+, a lightweight visible-infrared fusion network that employs a Mask-Enhanced Attention Fusion module and training-time Structural Representation enhancement to achieve high-precision small-target detection in complex remote sensing scenes while significantly reducing model complexity compared to baselines.
MindfulAgents is a large language model-driven multi-agent system that personalizes mindfulness meditation through expert-aligned script generation and real-time adaptation, significantly improving user engagement, self-awareness, and stress reduction in both short-term and long-term studies.
This paper proposes a contrastive few-shot RGB-D segmentation framework that integrates sparse 1D laser depth with a negative contrastive learning branch to significantly improve indoor traversability detection and obstacle avoidance compared to existing methods.
This paper presents a physics-informed model derived from hexapedal robot experiments on granular slopes that identifies delayed anchoring and backward slip as primary failure mechanisms, enabling the construction of phase diagrams for quantitative risk estimation of locomotion on deformable inclines.
This paper presents LLM2SMT, a case study demonstrating that an LLM coding agent can autonomously build a complete, competitive DPLL(T)-style SMT solver for QF_UF—including the Nieuwenhuis-Oliveras congruence closure algorithm and Lean proof emission—without any human-written code.
This paper proposes HIERAMP, a method that leverages the coarse-to-fine generation capability of Vision Autoregressive (VAR) models to amplify hierarchical semantics through dynamic class token injection, thereby improving dataset distillation performance by better capturing object structures and details without explicitly optimizing global proximity.
This paper proposes an Adaptive Gain Nonlinear Observer (AGNO) that leverages a full nonlinear dynamic model to accurately and robustly estimate external interaction wrenches in human-UAV physical payload transportation without dedicated force-torque sensors, demonstrating superior performance over Extended Kalman Filters through rigorous stability analysis and simulation.
This study presents a 3D histomorphometric analysis pipeline using nnU-Net segmentation on optically cleared prostatectomy specimens to extract features related to perineural and lymphovascular invasion, demonstrating that 3D perineural invasion features significantly outperform their 2D counterparts in predicting 5-year biochemical recurrence in prostate cancer.
This paper introduces work schedule-aware robust and chance-constrained optimization formulations to demonstrate that intelligently coordinating EV charging with hybrid work schedules and rooftop PV generation can effectively increase EV hosting capacity and manage grid demands on residential distribution transformers.
This paper proposes a unified two-stage framework that restores feasibility for conflicting Signal Temporal Logic (STL) specifications by first applying minimal relaxation and then refining the solution through Pareto-optimal multi-objective optimization, thereby enabling interpretable decision-making and avoiding deadlocks in safety-critical applications like autonomous driving.
Space-Control is a hardware-software co-design that addresses the critical security gap of missing process-level isolation in CXL-based disaggregated memory by authenticating execution contexts and enforcing fine-grained access control with minimal performance overhead.
This tutorial critically examines the gap between theoretical Control Barrier Function (CBF) guarantees and practical implementation in robotics, revealing how common misuses and hidden assumptions often lead to tautological safety claims in passively safe systems while offering guidelines and interactive tools to construct valid safety arguments for systems with input constraints.
This paper proposes a rotating equilibrium strategy for collaborative tethered aerial transport, where steady circular motion generates centrifugal forces to maintain tether tension, thereby enabling quadrotors to produce purely vertical thrust and reducing total power consumption by up to 20% compared to conventional static lifting methods.
This paper introduces Virtual Intraoperative CT (viCT), a method that sequentially updates preoperative CT scans during endoscopic sinus surgery by integrating monocular endoscopic video-derived 3D reconstructions to visualize evolving tissue resection boundaries with submillimeter accuracy, thereby addressing the limitations of static image guidance.
This report synthesizes developmental science and industry practice to establish non-negotiable design guardrails for anthropomorphic conversational AI, ensuring these systems prioritize adolescent safety, autonomy, and skill development.
The paper presents pqRPKI, a practical post-quantum RPKI framework that utilizes a multi-layer Merkle Tree Ladder and a redesigned manifest structure to significantly reduce bandwidth and validation costs compared to naive post-quantum signature swaps, while enabling efficient dual-stack deployment with minimal overhead.
This review paper analyzes the dual threats posed by quantum computing and artificial intelligence to current cryptographic systems, concluding that a comprehensive defense requires a dynamic, multi-layered strategy combining post-quantum algorithms, implementation hardening, and cryptographic agility to address the limitations of any single solution.