arXiv subjects
Publications and source records attributed to Wen Hao.
We describe the construction of Frobenius manifold out of a cyclic (commutative) $BV_\infty$ algebra $(A,\Delta)$ under the assumption of a Hodge-to-de Rham degeneration property and the existence of a compatible homotopy retract of $A$ onto its cohomology. We then apply it to Jacobi manifolds and Hermitian manifolds, generalizing known results in literature.
We extend our previous demonstration of the first on-sky primary mirror segment closed-loop control on Keck using a vector-Zernike wavefront sensor (vZWFS), which improved the Strehl ratio on the NIRC2 science camera by up to 10 percentage points. Segment co-phasing errors contribute to Keck contrast limits and will be necessary to correct for the segmented Extremely Large Telescopes and future space missions. The goal of the post-AO vZWFS on Keck is to monitor and correct segment co-phasing errors in parallel with science observations. The ZWFS is ideal for measuring phase discontinuities and is one of the most sensitive WFSs, but has limited dynamic range. The Keck vZWFS consists of a metasurface mask imposing two different phase shifts to orthogonal polarizations, split into two pupil images, extending its dynamic range. We report on the vZWFS closed-loop co-phasing performance and early work towards understanding the interactions between the AO system and segment phasing. We discuss a comparison of the AO performance when co-phasing by aligning segment edges, as is currently done at Keck, compared with aligning to the average phase over the segments, as is done by the vZWFS.
In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detection, wave delineation. Although many neural architectures have been proposed in the literature, there is a lack of systematic studies and open-source libraries for ECG deep learning. In this paper, we propose a deep learning framework, named \texttt{torch\_ecg}, which gathers a large number of neural networks, both from literature and novel, for various ECG processing tasks. It establishes a convenient and modular way for automatic building and flexible scaling of the networks, as well as a neat and uniform way of organizing the preprocessing procedures and augmentation techniques for preparing the input data for the models. Besides, \texttt{torch\_ecg} provides benchmark studies using the latest databases, illustrating the principles and pipelines for solving ECG processing tasks and reproducing results from the literature. \texttt{torch\_ecg} offers the ECG research community a powerful tool meeting the growing demand for the application of deep learning techniques.