Latest AI and machine learning research in psoriasis for healthcare professionals.
Despite the success of direct-acting antivirals, preventing hepatitis C virus (HCV) reinfection remains a critical global challenge. To address this, we leveraged deep learning-based de novo protein design to engineer mini-proteins targeting the large extracellular loop (LEL) of the HCV co-receptor CD81. These mini-proteins are predicted to precisely dock into CD81-LEL, occluding the critical bind...
Small-data inverse design is challenging in engineering informatics when observations are heterogeneous, mixed-type, and constrained by physical relations among design variables. This work proposes a topology-aware surrogate framework guided by an Incremental Transformer (INCRT) for physics-constrained inverse design, applied to geopolymer mixture design. The method integrates intrinsic-dimensiona...
Accurate immune receptor design requires modeling the coupled variation of amino-acid sequence, full-atom conformation, and target-binding geometry ac...
Inferring latent physical properties from sensory observations is a fundamental challenge in machine perception. Among available sensing modalities, t...
We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning. The unknown ...
Many problems in science and engineering are difficult to model accurately, either due to unknown physical mechanisms, poorly quantified measurement u...
Lensless imaging enables compact and versatile computational cameras by replacing bulky optics with thin coded elements. However, reconstruction from ...
Mirror Illusion Art is a novel reflection-conditioned 3D illusion where one object yields two target appearances (front and mirror). The task is formu...
Inverse rendering aims to recover both 3D geometry and physically meaningful material properties from images, enabling applications such as relighting...
Reconstructing physics-based 3D assets -- geometry, materials, and illumination -- from multi-view images is a core problem in computer graphics and v...
A major challenge for cognitive neuroscience is to explain how value of a goal-directed behavior is computed in complex and naturalistic environments....
Multimodal Large Language Models (MLLMs) inherit rich relational priors from their language backbones, yet often fail when asked to apply these relati...
Implicit Neural Representations (INRs) parameterized by multilayer perceptrons excel at modeling continuous signals. However, a key challenge persists...
Neural materials can represent complex specular reflections and scattering effects in a compact, universal basis. However, acquiring and authoring suc...
Neural materials can represent complex specular reflections and scattering effects in a compact, universal basis. However, acquiring and authoring suc...
Image hiding aims to conceal image-level messages within cover images at the same resolution. Invertible neural networks (INN)-based image hiding has ...
Reliable structural health monitoring (SHM) of offshore wind turbine (OWT) support structures requires fast state estimation from sparse measurements....
Training of neural networks for histopathology classification tasks typically relies on data encoding into latent space, which reduces complexity and ...
A growing family of training-free solvers -- FlowDPS, FLOWER, PnP-Flow and their diffusion ancestors (DPS, DAPS) -- repurpose a pretrained flow-matchi...
Controllable video generation demands independent command of the camera and the subject, yet 2D conditioning entangles them: camera- and object-induce...