8464 papers
This paper introduces an interactive, no-code framework with a graphical interface and graph-based representation to democratize scenario generation for autonomous driving validation in CARLA, enabling non-technical users to efficiently create, manage, and execute diverse test scenarios without programming expertise.
This paper introduces a novel SCC-constrained bilevel model to demonstrate that synchronous generators at advantageous grid locations can exploit the localized nature of short-circuit current services to exercise market power, significantly inflating revenues and consumer costs, thereby highlighting the urgent need for specific market design mitigations.
DianJin-OCR-R1 is a reasoning-enhanced vision-language model that improves OCR accuracy by interleaving its own recognition with expert tool outputs, guiding the model to iteratively re-examine images and integrate evidence to reduce hallucinations and enhance fine-grained perception.
This paper introduces PyloChain, a hierarchical sharded blockchain that balances performance and availability by utilizing speculative execution across local chains and a DAG-based main chain to efficiently handle global transactions while ensuring data availability and fault tolerance.
This paper systematically evaluates the potential of carbon-aware execution strategies for scientific workflows, demonstrating that leveraging their inherent flexibility through temporal shifting and dynamic resource scaling can reduce carbon emissions by over 80% and 67%, respectively.
This paper introduces -Motif, a GPU-accelerated subgraph isomorphism algorithm that reformulates graph matching as scalable database operations on motifs, achieving speedups of up to 595 over traditional methods like VF2 while enabling efficient quantum circuit compilation through familiar relational abstractions.
This paper proposes SSL-SLR, a self-supervised learning framework for sign language recognition that addresses the limitations of standard contrastive methods by introducing free-negative pairs and a novel data augmentation technique to better handle video redundancy and shared movements, thereby achieving significant accuracy improvements across various evaluation settings.
This paper introduces RED, a robust event-guided motion deblurring network that employs a robustness-oriented perturbation strategy and a modality-specific disentanglement mechanism to effectively reconstruct sharp images from fragmented event data caused by real-world sensor under-reporting.
This paper presents a multimodal framework that integrates natural language understanding of pilot radio calls with trajectory data to significantly improve the accuracy of aircraft goal prediction in non-towered airspace compared to motion-only baselines.
The paper presents GLIDE, a cooperative search-and-rescue framework that pairs two specialized UAVs with a UGV to enable rapid, safe navigation in unknown environments through real-time victim detection, terrain scouting, and guided long-horizon planning.
This paper presents a vision-based framework for legged robots that enables robust decision-driven semantic exploration by integrating confidence-calibrated perception, controlled-growth topological memory, and utility-driven subgoal selection to overcome the limitations of conventional geometry-centric navigation in open-world environments.
DeCLIP is a replay-free, parameter-efficient framework for Multi-Label Class-Incremental Learning that decouples CLIP representations through class-specific prompting and Adaptive Similarity Tempering to effectively mitigate catastrophic forgetting and reduce false-positive rates without violating CLIP's single image-text alignment paradigm.
The paper introduces MARLIN, a decentralized reservoir management framework that combines multi-agent reinforcement learning inspired by starling murmurations with LLM-guided reward shaping to effectively handle environmental uncertainties, significantly improving flood response times and computational efficiency compared to traditional methods.
This paper introduces ROSflight 2.0, a modular, open-source ROS 2-based autopilot ecosystem designed to lower barriers for UAV research and accelerate the transition from simulation to hardware, featuring a lean architecture that successfully controls multirotors at 400 Hz with all loops running on a companion computer.
ROSplane 2.0 is an open-source, ROS 2-based fixed-wing autopilot framework designed by researchers to accelerate UAV experimentation through a lean, modular architecture, enhanced control algorithms, and a streamlined aerodynamic modeling pipeline that simplifies the transition from simulation to real-world testing.
This paper introduces HVAC-EAR, a novel system that reconstructs intelligible human speech from low-resolution, noisy pressure data in HVAC systems using a complex-valued conformer, demonstrating significant privacy risks by achieving clear eavesdropping up to 1.2 meters in real-world deployments.
PriorWeaver is an interactive visualization system that simplifies Bayesian prior elicitation for novice analysts by allowing them to iteratively construct and refine datasets reflecting their beliefs, resulting in priors that are more aligned with their expectations compared to traditional methods.
This paper proposes a bio-inspired, digital pheromone-based framework that mimics ant colony behavior to dynamically classify industrial production lines as In-Control or Out-of-Control and forecast maintenance needs by aggregating real-time temperature data, threat signals, and decaying environmental scores into a single adaptive indicator.
This paper presents "Pre/Absence," a virtual reality experience that leverages the dialectic of presence and absence to transform the interpretation of lost architectural heritage from static factual summaries into a nuanced, emotionally engaging narrative that fosters deeper cultural awareness and critical reflection on the evolving meanings of heritage.
The paper introduces BOUND, an open-world object detector that advances beyond simple "unknown" labeling by inferring coarse-grained, hierarchical categories for unseen objects to enable more informed decision-making while maintaining high performance on known classes.