Optical bulk-boundary dichotomy in a quantum spin Hall insulator

The bulk-boundary correspondence is a key concept in topological quantum materials. For instance, a quantum spin Hall insulator features a bulk insulating gap with gapless helical boundary states protected by the underlying Z2 topology. However, the bulk-boundary dichotomy and distinction are rarely explored in optical experiments, which can provide unique information about topological charge carriers beyond transport and electronic spectroscopy techniques. Here, we utilize mid-infrared absorption micro-spectroscopy and pump-probe micro-spectroscopy to elucidate the bulk-boundary optical responses of Bi4Br4, a recently discovered room-temperature quantum spin Hall insulator. Benefiting from the low energy of infrared photons and the high spatial resolution, we unambiguously resolve a strong absorption from the boundary states while the bulk absorption is suppressed by its insulating gap. Moreover, the boundary absorption exhibits a strong polarization anisotropy, consistent with the one-dimensional nature of the topological boundary states. Our infrared pump-probe microscopy further measures a substantially increased carrier lifetime for the boundary states, which reaches one nanosecond scale. The nanosecond lifetime is about one to two orders longer than that of most topological materials and can be attributed to the linear dispersion nature of the helical boundary states. Our findings demonstrate the optical bulk-boundary dichotomy in a topological material and provide a proof-of-principal methodology for studying topological optoelectronics.Comment: 26 pages, 4 figure

Farmland Obstacle Detection from the Perspective of UAVs Based on Non-local Deformable DETR

In precision agriculture, unmanned aerial vehicles (UAVs) are playing an increasingly important role in farmland information acquisition and fine management. However, discrete obstacles in the farmland environment, such as trees and power lines, pose serious threats to the flight safety of UAVs. Real-time detection of the attributes of obstacles is urgently needed to ensure their flight safety. In the wake of rapid development of deep learning, object detection algorithms based on convolutional neural networks (CNN) and transformer architectures have achieved remarkable results. Detection Transformer (DETR) and Deformable DETR combine CNN and transformer to achieve end-to-end object detection. The goal of this work is to use Deformable DETR for the task of farmland obstacle detection from the perspective of UAVs. However, limited by local receptive fields and local self-attention mechanisms, Deformable DETR lacks the ability to capture long-range dependencies to some extent. Inspired by non-local neural networks, we introduce the global modeling capability to the front-end ResNet to further improve the overall performance of Deformable DETR. We refer to the improved version as Non-local Deformable DETR. We evaluate the performance of Non-local Deformable DETR for farmland obstacle detection through comparative experiments on our proposed dataset. The results show that, compared with the original Deformable DETR network, the mAP value of the Non-local Deformable DETR is increased from 71.3% to 78.0%. Additionally, Non-local Deformable DETR also presents great performance for detecting small and slender objects. We hope this work can provide a solution to the flight safety problems encountered by UAVs in unstructured farmland environments

Predictive Value of Machine Learning Based on Retinal Structural Changes for Early Parkinson's Disease Diagnosis

Background The diagnosis of Parkinson disease (PD) is mainly based on clinical symptoms, and there is a lack of objective methods for correct diagnosis. At present, there have been studies on retinal structural changes as a biomark for early diagnosis of PD, but machine learning based on retinal structural changes for predicting early PD has not yet been studied. Objective To construct a machine learning model based on the characteristics of retinal structural changes, explore its value in early PD diagnosis, and the accuracy of different machine learning algorithms for early PD diagnosis. Methods From October 2021 to September 2022, 49 PD patients aged 40 to 70 years old (PD group) who attended outpatient clinics and were hospitalized in the department of neurology of Henan Provincial People's Hospital (PD group) and 39 healthy people with matching age and sex (healthy control group) who came to the hospital for physical examination were collected. All study subjects underwent swept-source optical coherence tomography and swept-source optical coherence tomography angiography, the thickness and vessel density of the macular retina were also quantitatively analyzed. The 88 subjects were randomly divided into the 62 training sets and 26 validation set according to the ratio of 7∶3. Variables with significant differences between the PD group and healthy control group were selected as the characteristic variables for inclusion in the machine learning model, and Logistic regression (LR) , K-nearest neighbor algorithm (KNN) , decision tree (DT) , random forest (RF) and extreme gradient boosting (XGboost) models were constructed in the training set. The area under the curve (AUC) , accuracy, sensitivity and specificity of the receiver operating characteristic (ROC) curve were used to evaluate the predictive value of the machine learning model based on retinal structural changes for early PD. Results Compared with the healthy control group, the density of the upper outer ring (A6) , the outer temporal outer ring (A7) , the lower outer ring (A8) and the outer nasal ring (A9) of the superficial capillaries in the PD group were reduced, the thickness of the upper inner ring (A2) , the inner temporal inner ring (A3) , the inferior inner ring (A4) , the inner ring of the nasal side (A5) of the retinal layer, A6, A7, A8 and A9, the thickness of A6 of the ganglion cell complex layer, the thickness of A7 of the nerve fiber layer, A2 and A4, A5, A6, A7, A8, A9 became thinner (P<0.05) . The reductions in A2 thickness of the retinal layer (OR=0.781, 95%CI=0.659-0.926) , A3 thickness of the retinal layer (OR=1.190, 95%CI=1.019-1.390) , A2 thickness of the outer retina (OR=0.748, 95%CI=0.603-0.929) , A6 thickness of the outer retina (OR=2.264, 95%CI=1.469-3.490) , A8 thickness of the outer retina (OR=0.723, 95%CI=0.576-0.906) , and A7 thickness of the nerve fiber layer (OR=0.592, 95%CI=0.454-0.773) , and the decrease in A7 density of the superficial capillaries (OR=1.966, 95%CI=1.399-2.765) were independent risk factors for the occurrence of early PD (P<0.05) . The above variables were involved to construct the machine learning model, the results showed that among the five models constructed, the LR model had the highest overall performance, with an AUC of 0.841, while the DT model has the highest accuracy at 0.846. Conclusion Machine learning model based on retinal features can accurately predict early PD, among which the DT model has high accuracy for early PD diagnosis