Psoriasis

Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) train a single neural approximation by minimizing multiple physics- and data-derived losses, but the gradients of these losses often interfere and can stall optimization. Existing remedies typically treat this pathology either through scalar loss balancing or full-parameter-space gradient surgery, leaving it unclear which intervention is most appropriate. W...

Neural Network Guided Calibration for Fast Virtual Twin Generation in Cardiovascular ODE Models

Calibration of closed-loop lumped-parameter cardiovascular models remains a major bottleneck for scalable digital-twin generation because inverse estimation is ill-conditioned and typically requires computationally expensive iterative forward simulation. This study investigates whether a supervised neural network (NN) can provide a fast inverse estimator for a paediatric sepsis cardiovascular ODE ...

3DSS: 3D Surface Splatting for Inverse Rendering

We present 3D Surface Splatting (3DSS), the first differentiable surface splatting renderer for physically-based inverse rendering from multi-view ima...

3DSS: 3D Surface Splatting for Inverse Rendering

We present 3D Surface Splatting (3DSS), the first differentiable surface splatting renderer for physically-based inverse rendering from multi-view ima...

Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations

Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in...

Tempered Guided Diffusion

Training-free conditional diffusion provides a flexible alternative to task-specific conditional model training, but existing samplers often allocate ...

Optimizing Diffusion Priors with a Single Observation

While diffusion priors generate high-quality posterior samples across many inverse problems, they are often trained on limited training sets or purely...

SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography

Machine learning has achieved impressive performance in tomographic reconstruction, but supervised training requires paired measurements and ground-tr...

Conflated Inverse Modeling to Generate Diverse and Temperature-Change Inducing Urban Vegetation Patterns

Urban areas are increasingly vulnerable to thermal extremes driven by rapid urbanization and climate change. Traditionally, thermal extremes have been...

Physics-Informed Synthetic Dataset and Denoising TIE-Reconstructed Phase Maps in Transient Flows Using Deep Learning

High-speed quantitative phase imaging enables non-intrusive visualization of transient compressible gas flows and energetic phenomena. However, phase ...

Generative World Renderer

Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing syn...

SGS-Intrinsic: Semantic-Invariant Gaussian Splatting for Sparse-View Indoor Inverse Rendering

We present SGS-Intrinsic, an indoor inverse rendering framework that works well for sparse-view images. Unlike existing 3D Gaussian Splatting (3DGS) b...

ICTPolarReal: A Polarized Reflection and Material Dataset of Real World Objects

Accurately modeling how real-world materials reflect light remains a core challenge in inverse rendering, largely due to the scarcity of real measured...

Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration

Deformable image registration plays a fundamental role in medical image analysis by enabling spatial alignment of anatomical structures across subject...

Statistical Learning for Latent Embedding Alignment with Application to Brain Encoding and Decoding

Brain encoding and decoding aims to understand the relationship between external stimuli and brain activities, and is a fundamental problem in neurosc...

The results of Transcriptome-wide Mendelian Randomization (TWMR) in large-scale populations can directly validate, across scales, the results of causal inference from deep learning combined with double machine learning on single-cell transcriptomes of human samples.

Objective Aiming at the core problems prevalent in biomedical research, including the "translational distance", the difficulty in aligning cross-scale...

Computational Fluid Particle Dynamics-Informed Machine Learning Prototype for a User-Centered Smart Inhaler Enabling Uniform Drug Delivery to Small Airways

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles ...

Single-Pass Discrete Diffusion Predicts High-Affinity Peptide Binders at >1,000 Sequences per Second across 150 Receptor Targets

De novo peptide design methods traditionally couple generation to 3D structure prediction, limiting throughput to seconds or hours per candidate. Here...

Adaptive regularization parameter selection for high-dimensional inverse problems: A Bayesian approach with Tucker low-rank constraints

This paper introduces a novel variational Bayesian method that integrates Tucker decomposition for efficient high-dimensional inverse problem solving....

InversePep: Diffusion-Driven Structure-Based Inverse Folding for Functional Peptides

Designing functional peptides with specific structural and biochemical properties is critical for applications in protein engineering and therapeutic ...