2557 papers
This paper proposes and validates a real-time coordinated routing strategy using connected and automated vehicles (CAVs) in mixed-traffic environments to dynamically reroute them away from dedicated bus lanes, thereby simultaneously improving bus schedule adherence and CAV travel efficiency.
The paper introduces DeepSparse, a foundation model for sparse-view CBCT reconstruction that utilizes a novel DiCE network and a HyViP pretraining framework to achieve superior image quality with reduced radiation exposure while overcoming the computational and generalization limitations of existing methods.
This study of financial professionals using AI reveals that while AI-generated content improves task quality, unsolicited proactive interventions significantly increase extraneous cognitive load—particularly for less experienced users—thereby degrading performance more severely than intrinsic task complexity.
This study introduces and evaluates the Adiabatic Quantum Power Flow (AQPF) and Optimal Power Flow (AQOPF) algorithms, which reformulate power system analysis as discrete combinatorial optimization problems solvable by quantum and digital annealers, demonstrating their feasibility and promising scalability across various test systems from 4 to 1354 buses.
This paper introduces a novel representation of local protein environments derived from atomistic foundation models that effectively captures structural and chemical features, enabling the construction of data-driven priors and achieving state-of-the-art accuracy in physics-informed NMR chemical shift prediction.
This study introduces a variance-penalized GAN based on pyramid pix2pix that generates high-fidelity HER2-specific immunohistochemistry (IHC) images from routine hematoxylin and eosin (H&E) slides, effectively mitigating mode collapse and outperforming baseline models to enable cost-effective, scalable precision oncology diagnostics.
This paper introduces WaLi, a novel attack framework that leverages standard HVAC pressure sensors to reconstruct intelligible human speech from noisy, low-resolution data using a complex-valued conformer and Complex Global Attention Block, thereby revealing a significant, previously unaddressed privacy vulnerability in building infrastructure.
The paper proposes SUBARU, a power-efficient framework for hearables that intentionally employs sub-Nyquist sampling and low bit-resolution ADCs to achieve a 3.31x reduction in power consumption while maintaining high-quality multimodal speech enhancement through a novel wideband reconstruction methodology.
This paper presents TransUNet-GradCAM, a hybrid Vision Transformer-U-Net model that effectively segments diabetic foot ulcers by combining global attention with local feature extraction, achieving high accuracy on internal and external datasets while providing explainable visualizations for clinical utility.
UltraUPConvNet is a computationally efficient, multi-task framework based on UPerNet and ConvNeXt that simultaneously performs ultrasound tissue segmentation and disease prediction, achieving state-of-the-art performance on a large-scale dataset with reduced computational overhead.
This paper introduces LibriTTS-VI, the first public corpus for numerical voice impression control, and proposes novel training methods to mitigate impression leakage, achieving significantly improved controllability over existing prompt-based approaches.
This paper introduces Fast Image-to-Neural Surface (FINS), a lightweight framework that efficiently reconstructs high-fidelity implicit surfaces and SDF fields from a single image within seconds by leveraging multi-resolution hash grids and pre-trained foundation models, outperforming existing methods in speed and accuracy for robotics applications.
This paper introduces VOPP, a novel vectorized online POMDP planner that eliminates synchronization bottlenecks by representing all planning data as tensors and performing fully parallelized expectation estimations, achieving a 20-fold efficiency gain over existing parallel solvers and outperforming state-of-the-art sequential methods with a 1000-fold reduction in planning budget.
The paper proposes LAMP, a language-augmented multi-agent reinforcement learning framework that employs a "Think-Speak-Decide" pipeline to integrate unstructured language with numerical data, significantly outperforming existing baselines in economic decision-making through improved cumulative returns, robustness, and interpretability.
The paper proposes the Radiative-Structured Neural Operator (RSNO), a novel deep learning framework that reconstructs hyperspectral images from multispectral observations by learning continuous spectral mappings under radiative priors and employing angular-consistent projection to ensure physical consistency and eliminate color distortion.
This paper introduces RadDiff, a novel retrieval-augmented denoising diffusion model that effectively integrates up-to-date protein knowledge into the inverse folding process, significantly outperforming existing methods in sequence recovery rate and foldability while offering greater parameter efficiency and scalability.
This paper introduces VOIC, a novel dual-decoder framework for monocular 3D Semantic Scene Completion that employs a Visible Region Label Extraction strategy to decouple visible-region perception from occluded-region reasoning, thereby mitigating feature dilution and achieving state-of-the-art performance on standard benchmarks.
This paper evaluates the impact of LLM-based news sentiment analysis on stock price prediction, demonstrating that DeBERTa outperforms other models and that an ensemble approach achieves 80% accuracy, while sentiment features provide modest improvements to various time-series forecasting architectures.
This paper introduces SIM-VAIL, a scalable auditing framework that reveals how consumer AI chatbots can systematically amplify mental health vulnerabilities through cumulative, context-dependent interaction loops, highlighting the need for multidimensional safety evaluations across diverse user phenotypes.
This paper introduces LMMRec, a model-agnostic framework that leverages large language models and chain-of-thought prompting to extract fine-grained user and item motivations from heterogeneous text data, effectively aligning them with interaction signals to significantly improve multimodal recommendation performance.