Results 201 to 210 of about 5,565,025 (258)
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction. [PDF]
Wen J, Ahmad R, Schniter P.
europepmc +1 more source
CoSP: Reconfigurable Metamaterial Inverse Design via Contrastive Pretrained Large Language Model
In this work, CoSP (contrastive multi‐state pretrain), an intelligent inverse design method for reconfigurable metamaterials based on a contrastive pretrained large language model, is proposed. Numerical experiments demonstrate that CoSP can design reconfigurable metamaterial structures for multi‐state, multi‐band optical responses, showing great ...
Shujie Yang +4 more
wiley +1 more source
Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems. [PDF]
Alçalar YU, Akçakaya M.
europepmc +1 more source
Toward High‐Resolution Polymeric Neural Interfaces: A Practical Guide for Electron Beam Lithography
Electron beam lithography (EBL) enables sub‐micron patterning of polymer‐based neural implants, overcoming the resolution limits of optical lithography. By addressing challenges such as charging, non‐planarity, and substrate heating, it supports higher channel density, improved miniaturization, and better long‐term tissue integration.
Hanna Karlsson‐Fernberg, Maria Asplund
wiley +1 more source
Entropy-Regularized Iterative Weighted Shrinkage-Thresholding Algorithm (ERIWSTA) for inverse problems in imaging. [PDF]
Ma L, Wu B, Yao Y, Teng Y.
europepmc +1 more source
The nanoparticle loading enables effective control over the liquid domain size in bicontinuous interfacially jammed emulsion gels (bijels) produced through solvent‐transfer induced phase separation (STrIPS). The relationship between the nanoparticle loading and domain size profiles in STrIPS bijels is resolved through confocal microscopy and explained ...
Jesse M. Steenhoff, Martin F. Haase
wiley +1 more source
Multi-Scale Energy (MuSE) framework for inverse problems in imaging. [PDF]
Chand JR, Jacob M.
europepmc +1 more source
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi +7 more
wiley +1 more source
Quantitative photoacoustic tomography: modeling and inverse problems. [PDF]
Tarvainen T, Cox B.
europepmc +1 more source

