Latest AI and machine learning research in covid-19 for healthcare professionals.
Delta-Rule recurrent models maintain a fixed-size state, enabling $O(1)$ inference memory but potentially becoming unstable under extreme-context extrapolation. By tracking RWKV-7 over sequences of up to 100M tokens, we empirically identify a distinct failure pattern: \textbf{localized norm explosion atop a relatively sparse substrate}, rather than global state saturation. Analysis of the recurren...
Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-to-mask translation must preserve target structure while remaining computationally efficient. We propose Generative Embedding Translation (GET), a structured embedding-translation framework that progressively transforms image embeddings into mask embed...
Protein phosphatase 2A containing the B56{delta} regulatory subunit (PP2A-B56{delta}) is a critical signaling enzyme whose dysregulation is associated...
Background. Phthalates are hypothesised to act as metabolic disruptors, and machine learning applied to the National Health and Nutrition Examination ...
Intrinsically disordered proteins and regions (IDPs/IDRs) mediate diverse cellular functions through binding segments whose functional properties are ...
Chimeric antigen receptor (CAR) cell therapy has achieved transformative clinical success through targeting of CD19 in refractory B cell malignancies,...
Promptable segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target segmentation (IRSTD) exposes a mis...
AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel ...
Modern neural network training increasingly uses matrix-aware optimizers, yet their conditioned matrix step is typically added directly to the weight,...
Automated plant traits recognition from herbarium images is essential for plant sciences, yet remains challenging because background elements (e.g., t...
Omnimodal generation is central to a wide range of content creation and editing applications. In-context conditioning is essential to this paradigm. I...
The VHH-Fc antibody scaffold is an emerging therapeutic modality. No public large-scale, standardized developability VHH-Fc dataset exists. Filling th...
Single-molecule protein sequencing promises to democratize clinical proteomics, but platforms retrofitting static DNA-sequencing nanopores face a fund...
In this work, we present a machine learning model for identifying pathogenic DNA variants. The model was learned from the analysis of normal and patho...
As AI systems are increasingly deployed in safety-critical application domains (e.g., autonomous driving), associated risks increase too. Deep learnin...
As AI systems are increasingly deployed in safety-critical application domains (e.g., autonomous driving), associated risks increase too. Deep learnin...
Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography...
Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-leve...
Developing robots capable of understanding and manipulating objects requires compact, interpretable, and generalizable representations. This work prop...
Generative novel view synthesis from sparse input images is rarely all reconstruction or all generation: pixels visible in some source view have a uni...