Psoriasis

Inverse Materials Design by Large Language Model-Assisted Generative Framework

Deep generative models hold great promise for inverse materials design, yet their efficiency and accuracy remain constrained by data scarcity and model architecture. Here, we introduce AlloyGAN, a closed-loop framework that integrates Large Language Model (LLM)-assisted text mining with Conditional Generative Adversarial Networks (CGANs) to enhance data diversity and improve inverse design. Taki...

FIG: Forward-Inverse Generation for Low-Resource Domain-specific Event Detection

Event Detection (ED) is the task of identifying typed event mentions of interest from natural language text, which benefits domain-specific reasoning in biomedical, legal, and epidemiological domains. However, procuring supervised data for thousands of events for various domains is a laborious and expensive task. To this end, existing works have explored synthetic data generation via forward (ge...

Predicting psoriasis severity using machine learning: a systematic review.

BACKGROUND: In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from expe...

Inverse Surrogate Model of a Soft X-Ray Spectrometer using Domain Adaptation

In this study, we present a method to create a robust inverse surrogate model for a soft X-ray spectrometer. During a beamtime at an electron storag...

Bayesian Physics Informed Neural Networks for Linear Inverse problems

Inverse problems arise almost everywhere in science and engineering where we need to infer on a quantity from indirect observation. The cases of med...

Data-Efficient Limited-Angle CT Using Deep Priors and Regularization

Reconstructing an image from its Radon transform is a fundamental computed tomography (CT) task arising in applications such as X-ray scans. In many...

Inverse Flow and Consistency Models

Inverse generation problems, such as denoising without ground truth observations, is a critical challenge in many scientific inquiries and real-worl...

OMG: Opacity Matters in Material Modeling with Gaussian Splatting

Decomposing geometry, materials and lighting from a set of images, namely inverse rendering, has been a long-standing problem in computer vision and...

NPSim: Nighttime Photorealistic Simulation From Daytime Images With Monocular Inverse Rendering and Ray Tracing

Semantic segmentation is an important task for autonomous driving. A powerful autonomous driving system should be capable of handling images under a...

Multi-view 3D surface reconstruction from SAR images by inverse rendering

3D reconstruction of a scene from Synthetic Aperture Radar (SAR) images mainly relies on interferometric measurements, which involve strict constrai...

A Deep Inverse-Mapping Model for a Flapping Robotic Wing

In systems control, the dynamics of a system are governed by modulating its inputs to achieve a desired outcome. For example, to control the thrust ...

Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo

A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to th...

Diffusion Models for Inverse Problems in the Exponential Family

Diffusion models have emerged as powerful tools for solving inverse problems, yet prior work has primarily focused on observations with Gaussian mea...

Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo

In image processing, solving inverse problems is the task of finding plausible reconstructions of an image that was corrupted by some (usually known...

Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction

Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view...

Revisiting convolutive blind source separation for identifying spiking motor neuron activity: From theory to practice

Objective: Identifying the activity of motor neurons (MNs) non-invasively is possible by decomposing signals from muscles, e.g., surface electromyog...

A Mixture-Based Framework for Guiding Diffusion Models

Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion m...

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model

We propose a multi-scale deep energy model that is strongly convex in the local neighbourhood around the data manifold to represent its probability ...

Efficient sampling approaches based on generalized Golub-Kahan methods for large-scale hierarchical Bayesian inverse problems

Uncertainty quantification for large-scale inverse problems remains a challenging task. For linear inverse problems with additive Gaussian noise and...

AI-driven materials design: a mini-review

Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be i...