Product Alert

Diffusion Adversarial Post-Training for One-Step Video Generation

The diffusion models are widely used for image and video generation, but their iterative generation process is slow and expansive. While existing distillation approaches have demonstrated the potential for one-step generation in the image domain, they still suffer from significant quality degradation. In this work, we propose Adversarial Post-Training (APT) against real data following diffusion ...

D$^2$-DPM: Dual Denoising for Quantized Diffusion Probabilistic Models

Diffusion models have achieved cutting-edge performance in image generation. However, their lengthy denoising process and computationally intensive score estimation network impede their scalability in low-latency and resource-constrained scenarios. Post-training quantization (PTQ) compresses and accelerates diffusion models without retraining, but it inevitably introduces additional quantization...

Phase of Flight Classification in Aviation Safety using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset

Safety is the main concern in the aviation industry, where even minor operational issues can lead to serious consequences. This study addresses the ...

Dataset Distillation via Committee Voting

Dataset distillation aims to synthesize a smaller, representative dataset that preserves the essential properties of the original data, enabling eff...

UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

The low-altitude economy (LAE) plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disas...

The State of Post-Hoc Local XAI Techniques for Image Processing: Challenges and Motivations

As complex AI systems further prove to be an integral part of our lives, a persistent and critical problem is the underlying black-box nature of suc...

Recovery of activation propagation and self-sustained oscillation abilities in stroke brain networks

Healthy brain networks usually show highly efficient information communication and self-sustained oscillation abilities. However, how the brain netw...

Demystifying Domain-adaptive Post-training for Financial LLMs

Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finan...

GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology

Explainable AI (XAI) in medical histopathology is essential for enhancing the interpretability and clinical trustworthiness of deep learning models ...

Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding

Background: Healthcare has many manual processes that can benefit from automation and augmentation with Generative Artificial Intelligence (AI), the...

Survival Analysis Revisited: Understanding and Unifying Poisson, Exponential, and Cox Models in Fall Risk Analysis

This paper explores foundational and applied aspects of survival analysis, using fall risk assessment as a case study. It revisits key time-related ...

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: a machine learning approach

Transcranial Magnetic Stimulation (TMS) with simultaneous Electroencephalogram (TMS-EEG) allows assessing the neurophysiological properties of cortica...

Molecular unbalances between striosome and matrix compartments characterize the pathogenesis of Huntington’s disease model mouse

The pathogenesis of Huntington’s disease is still incompletely understood, despite the remarkable advances in identifying the molecular effects of the...

Limitations of de novo sequencing in resolving sequence ambiguity

De novo peptide sequencing enables peptide identification from fragmentation spectra without relying on sequence databases. However, incomplete spectr...

Mapping antigenic evolution of influenza A virus using deep learning-based prediction of hemagglutination inhibition titers

Seasonal influenza remains a significant public health challenge through unpredictable antigenic drift, where accumulated mutations enable immune evas...

Integration of steady-state diffusion MRI with Neural Posterior Estimation (NPE) for post-mortem investigations

Post-mortem diffusion MRI plays a key role in investigative pipelines to characterise tissue microstructure, with long scan times facilitating the acq...

The Use of Artificial Intelligence In Magnetic Resonance Imaging of Epilepsy: A Systematic Review and Meta-Analysis

The application of artificial intelligence (AI)/machine learning (ML) to MRI can be a powerful tool to streamline clinical decision-making, yet variab...

Efficacy and safety evaluation of artificial intelligence-identified antimicrobial peptides for use against avian pathogenic Escherichia coli in the poultry industry

The overuse of antibiotics in both veterinary and human medicine has resulted in the emergence of antibiotic-resistant bacteria, prompting a search fo...

Postprocessing-Enhanced Machine Learning for Reliable Real-Time Sleep Staging in Closed-Loop Neuromodulation

Real-time sleep stage classification is important for closed-loop neuromodulation at certain stages during sleep, yet current models often yield noisy...

Leveraging learned representations and multitask learning for lysine methylation site discovery

Lysine methylation is a dynamic and reversible post-translational modification of proteins carried out by lysine methyltransferase enzymes. The role o...