Generation of Isolated Flat Bands with Tunable Numbers through Moiré Engineering.

In contrast to the Dirac-cone materials in which the low-energy spectrum features a pseudospin-1/2 structure, Lieb and Dice lattices both host triply degenerate low-energy excitations. Here, we discuss moiré structures involving twisted bilayers of these lattices, which are shown to exhibit a tunable number of isolated flat bands near the Fermi level due to the bipartite nature of their structures. These flat bands remain isolated from the high-energy bands even in the presence of small higher-order terms and chiral-symmetry-breaking interlayer tunneling.

Terahertz deep learning fusion computed tomography.

Terahertz (THz) tomographic imaging based on time-resolved THz signals has raised significant attention due to its non-invasive, non-destructive, non-ionizing, material-classification, and ultrafast-frame-rate nature for object exploration and inspection. However, the material and geometric information of the tested objects is inherently embedded in the highly distorted THz time-domain signals, leading to substantial computational complexity and the necessity for intricate multi-physics models to extract the desired information. To address this challenge, we present a THz multi-dimensional tomographic framework and multi-scale spatio-spectral fusion Unet (MS3-Unet), capable of fusing and collaborating the THz signals across diverse signal domains.

Rapid sensing of hidden objects and defects using a single-pixel diffractive terahertz sensor.

Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements.

Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration.

Unlabelled: Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The diffraction-limited THz signals highly constrain the performances of existing restoration methods.

Terahertz spatio-temporal deep learning computed tomography.

Terahertz computed tomography (THz CT) has drawn significant attention because of its unique capability to bring multi-dimensional object information from invisible to visible. However, current physics-model-based THz CT modalities present low data use efficiency on time-resolved THz signals and low model fusion extensibility, limiting their application fields' practical use. In this paper, we propose a supervised THz deep learning computed tomography (THz DL-CT) framework based on time-domain information.

Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network.

Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning. Among different approaches, diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive, free-space optical layers. Here, we introduce a polarization-multiplexed diffractive processor to all-optically perform multiple, arbitrarily-selected linear transformations through a single diffractive network trained using deep learning.

Plasma matrix metalloproteinase 1 improves the detection and survival prediction of esophageal squamous cell carcinoma.

This study aimed to identify noninvasive protein markers capable of detecting the presence and prognosis of esophageal squamous-cell carcinoma (ESCC). Analyzing microarray expression data collected from 17-pair ESCC specimens, we identified one protein, matrix metalloproteinase-1 (MMP1), as a possibly useful marker. Plasma MMP1 was then measured by enzyme-linked immunosorbent assay (ELISA) in 210 ESCC patients and 197 healthy controls.

Development of risk scoring system for stratifying population for hepatocellular carcinoma screening.

Unlabelled: The age and risk level that warrants hepatocellular carcinoma (HCC) screening remains to be defined. To develop risk scores for stratifying average-risk population for mass HCC screening, we conducted a pooled analysis using data from three cohorts involving 12,377 Taiwanese adults 20-80 years of age. During 191,240.