2543 papers
This paper presents a 1.6-fJ/spike subthreshold analog Leaky Integrate-and-Fire neuron fabricated in 28 nm CMOS, which achieves a 300 kHz spiking frequency at 250 mV and demonstrates 82.5% accuracy on the MNIST dataset when used in a quantized Spiking Neural Network, validating its potential for energy-efficient embedded machine learning.
This mixed-methods study investigates the lifecycle and sustainability of taboo knowledge artifacts on Wikipedia, revealing that their successful development relies on resilient leadership, engaged organizations, and emergent governance to navigate conflict and limited identifiability.
This paper introduces the Multimodal Laryngoscopic Video Analyzing System (MLVAS), a novel framework that integrates audio keyword spotting with diffusion-refined video segmentation to automatically extract key metrics and features for the objective assisted diagnosis of Vocal Fold Paralysis.
This paper explores how integrating artificial intelligence into software testing transforms the generation and validation of test cases by enhancing efficiency, accuracy, and scalability while addressing challenges related to data quality, model transparency, and the balance between automation and human oversight.
The paper proposes PiVOT, a visual prompting mechanism that leverages a pretrained CLIP foundation model to automatically generate and refine online visual prompts, thereby enhancing generic object tracking by effectively suppressing distractors through contrastive guidance.
ExpGest is a novel diffusion-based framework that generates expressive, controllable full-body gestures by leveraging synchronized audio and text guidance, along with a specialized noise emotion classifier, to overcome the limitations of existing methods that often produce stiff, upper-body-only movements.
This paper demonstrates that competing algorithmic agents using multi-armed bandit learning can spontaneously develop "naive collusion" through action synchronicity, even without knowledge of competitors, with the likelihood of such outcomes depending critically on whether the agents employ deterministic, greedy-in-the-limit, or persistently random policies.
This paper proposes a deep learning-based framework that integrates historical outage and weather data to predict event-level power system resilience, validated through both simulated and real-world datasets, to guide targeted investments in distributed energy resources for vulnerable regions.
This paper proposes a novel, problem-agnostic diffusion model that enhances inverse problems by decomposing the conditional score function into an unconditional pretrained component and a Gaussian-prior-based MAP-guided term, resulting in superior content preservation and structural coherence compared to state-of-the-art methods.
Prompt-SID is a self-supervised single-image denoising framework that leverages a latent diffusion-based structural representation generator and a scale replay training mechanism to preserve detailed structural information without relying on expensive paired datasets.
This study proposes a high-accuracy glioma subclass classification framework that fuses 2D and 3D UNET-segmented multimodal MRI images using weighted averaging and classifies them via a pre-trained ResNet50 model, achieving a 99.25% accuracy rate.
This paper proposes a deep learning-based approach utilizing UNET, Inception, and ResNet architectures to achieve automatic 2D and 3D glioma segmentation on BraTS datasets, demonstrating that a ResNet model effectively balances computational efficiency and spatial accuracy to significantly improve diagnosis with high accuracy and Dice scores.
This paper introduces DEBT control, a joint routing and flow-control protocol for payment channel networks that uses price-based mechanisms and gradient descent optimization to guide the network toward an optimal steady-state operating condition while providing convergence guarantees.
This paper proposes a novel dual-thread framework that enables concurrent planning and acting with interruptible execution for LLM-based multi-agent systems, overcoming the limitations of serialized paradigms to enhance real-time responsiveness and adaptability in dynamic environments like Minecraft.
This paper proposes a novel transformer-based geometric deep learning model that tokenizes tetrahedral meshes with anatomical landmarks to accurately classify Alzheimer's disease and predict brain amyloid positivity in medium-risk individuals, offering a robust alternative to costly and invasive PET scans.
ViLAM is a novel method that distills vision-language reasoning from large Vision-Language Models into spatial attention maps to guide socially compliant robot navigation, achieving significant improvements in success rates through real-world validation.
The paper introduces iProg, an interactive tool that leverages a structured communication protocol between humans and large language models to decompose scientific data analysis tasks into declarative Data Flow Diagrams and generate corresponding code, thereby achieving significantly faster development, higher code quality, and better performance than traditional Low Code/No Code alternatives.
This paper proposes a unified framework for occlusion-robust two-hand reconstruction that combines a fusion-alignment encoder to implicitly integrate heterogeneous 2D structural priors from vision foundation models with a penetration-free diffusion model that guides 3D pose generation toward collision-free, kinematically coherent interactions.
This paper introduces MediTools, an AI-powered prototype application that leverages large language models to revolutionize medical education through interactive dermatology case simulations, enhanced literature analysis, and automated medical news summaries, while validating its potential through a survey of medical professionals and students.
The paper proposes SFIBA, a spatial-based full-target invisible backdoor attack that ensures trigger specificity and stealthiness in black-box settings by restricting triggers to local spatial regions and employing a frequency-domain injection method, thereby achieving high attack performance while evading existing defenses.