Latest AI and machine learning research in lupus for healthcare professionals.
The fine-tuning technique for text-to-image diffusion models facilitates image customization but risks privacy breaches and opinion manipulation. Current research focuses on prompt- or image-level adversarial attacks for anti-customization, yet it overlooks the correlation between these two levels and the relationship between internal modules and inputs. This hinders anti-customization performan...
Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors using a single model, addressing the limitations of traditional SR methods constrained to fixed-scale factors (\textit{e.g.}, $\times$ 2). Recent advances leveraging implicit neural representation (INR) have achieved great progress by modeling ...
Although diffusion-based techniques have shown remarkable success in image generation and editing tasks, their abuse can lead to severe negative soc...
Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we int...
This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously...
Simultaneous localization and mapping (SLAM) based on particle filtering has been extensively employed in indoor scenarios due to its high efficienc...
We present a novel deep learning network for Active Speech Cancellation (ASC), advancing beyond Active Noise Cancellation (ANC) methods by effective...
The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly b...
Functional peptides are short amino acid fragments that have a wide range of beneficial functions for living organisms. The majority of previous studi...
Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discove...
The widespread adoption of facial recognition (FR) models raises serious concerns about their potential misuse, motivating the development of anti-f...
This study explores how causal inference models, specifically the Linear Non-Gaussian Acyclic Model (LiNGAM), can extract causal relationships betwe...
Lateral inhibition models coupled with Hebbian plasticity have been shown to learn factorised causal representations of input stimuli, for instance,...
Face Anti-Spoofing (FAS) is essential for ensuring the security and reliability of facial recognition systems. Most existing FAS methods are formula...
Recent advance in text-to-image diffusion models have significantly facilitated the generation of high-quality images, but also raising concerns abo...
Single cell multi-omic investigation opens-up new opportunities to understand mechanisms of gene regulation. Existing methods for inferring transcript...
Despite its potential in cancer therapy, single-atom nanozyme (SAzyme) faces challenges like low atomic loading and rapid cancer metabolism. Here, a h...
Identifying genomic regions shaped by natural selection is a central goal in evolutionary ge-nomics. Existing machine learning methods for this task a...
Influenza A Virus (IAV) poses a persistent threat to global public health due to its broad host adaptability, frequent anti-genic variation, and poten...
Characterizing the ultrastructure of myelin in the human brain is key to understanding the neurobiology of both health and disease. In postmortem huma...