8106 papers
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.
SurgCUT3R is a novel framework that addresses the challenges of data scarcity and pose drift in monocular endoscopic video reconstruction by leveraging a synthetic data generation pipeline, hybrid supervision, and a hierarchical inference strategy to achieve robust, accurate, and efficient 3D surgical scene understanding.
The paper proposes T2SGrid, a novel framework that reformulates video temporal grounding as a spatial understanding task by arranging video frames into composite grid images via overlapping sliding windows, thereby overcoming the limitations of existing temporal encoding methods and achieving superior performance on standard benchmarks.
This paper introduces VSL-Skin, a novel voxel-based phase-change system that bridges soft and rigid robotics by enabling individually addressable, centimeter-scale stiffness modulation, 30% axial compression, and autonomous self-repair to create programmable virtual joints and variable-stiffness morphologies.
This paper introduces a configuration-driven runtime orchestration framework that dynamically generates execution graphs from configuration at request time, enabling low-latency, dependency-aware parallel data retrieval across distributed microservices and APIs without requiring workflow code redeployment.
This paper proposes a novel approach to image-based shape retrieval that leverages pre-aligned multi-modal encoders and a hard contrastive learning loss to achieve state-of-the-art performance in both zero-shot and supervised settings, eliminating the need for explicit view-based supervision or view synthesis.
This paper proposes a perception-aware multimodal reasoning framework that enhances Vision-Language Models' spatial understanding in monocular driving scenarios by representing objects with Visual Reference Tokens and utilizing a Multimodal Chain-of-Thought dataset, achieving significant performance gains on the SURDS benchmark through standard supervised fine-tuning.
This paper introduces ADAS-TO, the first large-scale naturalistic multimodal dataset of 15,659 ADAS-to-manual takeover events from 327 drivers, which combines kinematic and vision-language analysis to characterize safety-critical scenarios and demonstrate that actionable visual cues often precede takeovers by over three seconds.
This paper introduces a lightweight, probabilistic world model built on a pretrained vision foundation model that generates uncertainty-based runtime monitors to accurately detect anomalous failures in bimanual manipulators, outperforming existing baselines while requiring significantly fewer trainable parameters.
MipSLAM is a novel 3D Gaussian Splatting SLAM framework that achieves high-fidelity anti-aliased rendering and robust pose estimation by integrating an Elliptical Adaptive Anti-aliasing algorithm, a Spectral-Aware Pose Graph Optimization module, and a local frequency-domain perceptual loss to overcome aliasing artifacts and trajectory drift.
AdaGen introduces a general, learnable framework that employs reinforcement learning with an adversarial reward to dynamically adapt step-specific parameters during iterative image synthesis, thereby overcoming the limitations of static, manually-designed schedules and achieving superior performance across diverse generative models with reduced inference costs.
This paper introduces a closed-loop, large-language-model-driven evolutionary framework that automatically reconstructs all seven key components of Adaptive Large Neighborhood Search (ALNS), achieving superior performance on TSPLIB benchmarks and revealing novel design patterns through a MAP-Elites guided process.