Does the degree of polarization of vector beams remain unchanged on atmospheric propagation?

All roads lead to Rome. In this article we propose a novel theoretical framework to demonstrate vector beams whose degree of polarization does not change on atmospheric propagation. Inspired by the Fresnel equations, we derive the reflected and refracted field of vector beams propagating through a phase screen by employing the continuity of electromagnetic field. We generalize the conventional split-step beam propagation method by considering the vectorial properties in the vacuum diffraction and the refractive properties of a single phase screen. Based on this vectorial propagation model, we extensively calculate the change of degree of polarization (DOP) of vector beams under different beam parameters and turbulence parameters both in free-space and satellite-mediated links. Our result is that whatever in the free-space or satellite-mediated regime, the change of DOP mainly fluctuates around the order of 10-13 to 10-6, which is almost negligible.

Generation and measurement of high-order optical vortices by using the cross phase.

We investigate a method for the generation and measurement of high-order optical vortices (OVs) by using the cross phase (CP), which is applied to implement interconversion between Laguerre-Gauss (LG) beams and Hermite-Gaussian beams in the far-field. Experimentally, we generate LG beams, which are a kind of typical OVs, with 20 radial nodes, and measure OVs with topological charges up to 200 via the CP. On this basis, we discuss the relationship between intensity distributions and the waist radius of initial light beams. This work provides an alternative method to generate and measure high-order OVs, which is useful in the fields of optical micro-manipulation, high-dimensional quantum entanglement, and remote sensing of the angular rotation of structured objects.

Improving Automatic Detection of ECG Abnormality with Less Manual Annotations using Siamese Network.

Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. Recently many works also focused on the design of automatic ECG abnormality detection algorithms. However, clinical electrocardiogram datasets often suffer from their heavy needs for expert annotations, which are often expensive and hard to obtain. In this work, we proposed a weakly supervised pretraining method based on the Siamese neural network, which utilizes the original diagnostic information written by physicians to produce useful feature representations of the ECG signal which improves performance of ECG abnormality detection algorithms with fewer expert annotations. The experiment showed that with the proposed weekly supervised pretraining, the performance of ECG abnormality detection algorithms that was trained with only 1/8 annotated ECG data outperforms classical models that was trained with fully annotated ECG data, which implies a large proportion of annotation resource could be saved. The proposed technique could be easily extended to other tasks beside abnormality detection provided that the text similarity metric is specifically designed for the given task.Clinical Relevance-This work proposes a novel framework for the automatic detection of cardiovascular disease based on electrocardiogram.

Segment Origin Prediction: A Self-supervised Learning Method for Electrocardiogram Arrhythmia Classification.

The automatic arrhythmia classification system has made a significant contribution to reducing the mortality rate of cardiovascular diseases. Although the current deep-learning-based models have achieved ideal effects in arrhythmia classification, their performance still needs to be further improved due to the small scale of the dataset. In this paper, we propose a novel self-supervised pre-training method called Segment Origin Prediction (SOP) to improve the model's arrhythmia classification performance. We design a data reorganization module, which allows the model to learn ECG features by predicting whether two segments are from the same original signal without using annotations. Further, by adding a feed-forward layer to the pre-training stage, the model can achieve better performance when using labeled data for arrhythmia classification in the downstream stage. We apply the proposed SOP method to six representative models and evaluate the performances on the PhysioNet Challenge 2017 dataset. After using the SOP pre-training method, all baseline models gain significant improvement. The experimental results verify the effectiveness of the proposed SOP method.