Enhancing single-pixel imaging reconstruction using hybrid transformer network with adaptive feature refinement.

Single-pixel imaging (SPI) is a novel imaging technique that applies to acquiring spatial information under low light, high absorption, and backscattering conditions. The existing reconstruction techniques, such as pattern analysis and signal-recovery algorithms, are inefficient due to their iterative behaviors and substantial computational requirements. In this paper, we address these issues by proposing a hybrid convolutional-transformer network for efficient and accurate SPI reconstruction.

Strongly enhanced shift current at exciton resonances in a noncentrosymmetric wide-gap semiconductor.

Excitons are fundamental quasiparticles that are ubiquitous in photoexcited semiconductors and insulators. Despite causing a sharp and strong photoabsorption near the interband absorption edge, charge-neutral excitons do not yield photocurrent in conventional photovoltaic processes unless dissociated into free charge carriers. Here, we experimentally demonstrate that excitons can directly contribute to photocurrent generation through a nonlinear light-matter interaction in a noncentrosymmetric semiconductor CuI.

Enhancing sonothrombolysis outcomes with dual-frequency ultrasound: Insights from an in silico microbubble dynamics study.

Sonothrombolysis is a technique that employs the ultrasound waves to break down the clot. Recent studies have demonstrated significant improvement in the treatment efficacy when combining two ultrasound waves of different frequencies. Nevertheless, the findings remain conflicted on the ideal frequency pairing that leads to an optimal treatment outcome.

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

Background And Objective: Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events.

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions.

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10).