Full-range Gate-controlled Terahertz Phase Modulations with Graphene Metasurfaces

Local phase control of electromagnetic wave, the basis of a diverse set of applications such as hologram imaging, polarization and wave-front manipulation, is of fundamental importance in photonic research. However, the bulky, passive phase modulators currently available remain a hurdle for photonic integration. Here we demonstrate full-range active phase modulations in the Tera-Hertz (THz) regime, realized by gate-tuned ultra-thin reflective metasurfaces based on graphene. A one-port resonator model, backed by our full-wave simulations, reveals the underlying mechanism of our extreme phase modulations, and points to general strategies for the design of tunable photonic devices. As a particular example, we demonstrate a gate-tunable THz polarization modulator based on our graphene metasurface. Our findings pave the road towards exciting photonic applications based on active phase manipulations

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL

Undiagnosed diabetic retinopathy in Northeast China: prevalence and determinants

ObjectiveTo report the prevalence and contributing factors of undiagnosed diabetic retinopathy (DR) in a population from Northeastern China.Subjects/MethodsA total of 800 subjects from the Fushun Diabetic Retinopathy Cohort Study were enrolled. A questionnaire assessing incentives and barriers to diagnosis of DR was administered. Logistic regression was used to identify clinical and sociodemographic factors associated with undiagnosed DR. In a prespecified subgroup analysis, we divided patients into vision-threatening diabetic retinopathy (VTDR) and non-VTDR (NVTDR) subgroups.ResultsAmong 800 participants with DR, 712 (89.0%) were undiagnosed. Among 601 with NVTDR, 566 (94.2%) were undiagnosed. Among 199 with VTDR, 146 (73.4%) were undiagnosed. The risk factors affecting the timely diagnosis of NVTDR and VTDR exhibit significant disparities. In multivariate models, factors associated with undiagnosed VTDR were age over 60 years (OR = 2.966; 95% CI = 1.205-7.299; P = 0.018), duration of diabetes over 10 years (OR = 0.299; 95% CI = 0.118-0753; P = 0.010), visual impairment or blindness (OR = 0.310; 95% CI = 0.117-0.820; P = 0.018), receiving a reminder to schedule an eye examination (OR = 0.380; 95% CI = 0.163-0.883; P = 0.025), and the belief that “people with diabetes are unlikely to develop an eye disease” (OR = 4.691; 95% CI = 1.116-19.724; P = 0.035). However, none of the factors were associated with undiagnosed NVTDR (all P ≥ 0.145).ConclusionOur research has uncovered a disconcerting trend of underdiagnosis in cases of DR within our population. Addressing determinants of undiagnosed DR may facilitate early detection

Giant Phonon-induced Conductance in Scanning Tunneling Spectroscopy of Gate-tunable Graphene

The honeycomb lattice of graphene is a unique two-dimensional (2D) system where the quantum mechanics of electrons is equivalent to that of relativistic Dirac fermions. Novel nanometer-scale behavior in this material, including electronic scattering, spin-based phenomena, and collective excitations, is predicted to be sensitive to charge carrier density. In order to probe local, carrier-density dependent properties in graphene we have performed atomically-resolved scanning tunneling spectroscopy measurements on mechanically cleaved graphene flake devices equipped with tunable back-gate electrodes. We observe an unexpected gap-like feature in the graphene tunneling spectrum which remains pinned to the Fermi level (E_F) regardless of graphene electron density. This gap is found to arise from a suppression of electronic tunneling to graphene states near E_F and a simultaneous giant enhancement of electronic tunneling at higher energies due to a phonon-mediated inelastic channel. Phonons thus act as a "floodgate" that controls the flow of tunneling electrons in graphene. This work reveals important new tunneling processes in gate-tunable graphitic layers