Latest AI and machine learning research in psoriasis for healthcare professionals.
Time-of-Flight non-line-of-sight (ToF NLOS) imaging techniques provide state-of-the-art reconstructions of scenes hidden around corners by inverting the optical path of indirect photons scattered by visible surfaces and measured by picosecond resolution sensors. The emergence of a wide range of ToF NLOS imaging methods with heterogeneous formulae and hardware implementations obscures the assessmen...
Smart city research envisions a future in which data-driven solutions and sustainable infrastructure work together to define urban living at the crossroads of urbanization and technology. Within this framework, smart parking systems play an important role in reducing urban congestion and supporting sustainable transportation. Automating parking solutions have considerable benefits, such as increas...
Magnetoencephalography (MEG) and electroencephalography (EEG) source imaging requires solving an ill-posed inverse problem for which numerous algorith...
PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific...
Diffusion models have gained prominence as powerful generative tools for solving inverse problems due to their ability to model complex data distribut...
Volume denoising is a foundational problem in computational imaging, as many 3D imaging inverse problems face high levels of measurement noise. Inspir...
Tagged MRI enables tracking internal tissue motion non-invasively. It encodes motion by modulating anatomy with periodic tags, which deform along with...
Psoriasis affects over 125 million people worldwide, yet the mechanistic understanding of keratinocyte-driven inflammation remains incomplete, limitin...
We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. Our metho...
The choice of prior is central to solving ill-posed imaging inverse problems, making it essential to select one consistent with the measurements $y$ t...
Inverse problems in imaging are ill-posed, leading to infinitely many solutions consistent with the measurements due to the non-trivial null-space of ...
Graph foundation models have emerged as powerful tools for drug repurposing by enabling the prediction of novel drug-disease indications from large bi...
Inverse lithography (ILT) is critical for modern semiconductor manufacturing but suffers from highly non-convex objectives that often trap optimizatio...
This paper presents a novel method for recovering sparse vectors from linear models corrupted by Poisson noise. The contribution is twofold. First, an...
This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems. The focus is on cases where...
Background: Natural language processing (NLP) systems integrated into clinical workflows show promise for detecting early cognitive impairment, yet ca...
Projections (or dimensionality reduction) methods $P$ aim to map high-dimensional data to typically 2D scatterplots for visual exploration. Inverse pr...
We develop an operator-theoretic framework for stability and statistical concentration in nonlinear inverse problems with block-structured parameters....
Scientific diagrams convey explicit structural information, yet modern text-to-image models often produce visually plausible but structurally incorrec...
World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely imag...