DOSnet as a Non-Black-Box PDE Solver: When Deep Learning Meets Operator Splitting

Deep neural networks (DNNs) recently emerged as a promising tool for analyzing and solving complex differential equations arising in science and engineering applications. Alternative to traditional numerical schemes, learning-based solvers utilize the representation power of DNNs to approximate the input-output relations in an automated manner. However, the lack of physics-in-the-loop often makes it difficult to construct a neural network solver that simultaneously achieves high accuracy, low computational burden, and interpretability. In this work, focusing on a class of evolutionary PDEs characterized by having decomposable operators, we show that the classical ``operator splitting'' numerical scheme of solving these equations can be exploited to design neural network architectures. This gives rise to a learning-based PDE solver, which we name Deep Operator-Splitting Network (DOSnet). Such non-black-box network design is constructed from the physical rules and operators governing the underlying dynamics contains learnable parameters, and is thus more flexible than the standard operator splitting scheme. Once trained, it enables the fast solution of the same type of PDEs. To validate the special structure inside DOSnet, we take the linear PDEs as the benchmark and give the mathematical explanation for the weight behavior. Furthermore, to demonstrate the advantages of our new AI-enhanced PDE solver, we train and validate it on several types of operator-decomposable differential equations. We also apply DOSnet to nonlinear Schr\"odinger equations (NLSE) which have important applications in the signal processing for modern optical fiber transmission systems, and experimental results show that our model has better accuracy and lower computational complexity than numerical schemes and the baseline DNNs

Testing and Data Reduction of the Chinese Small Telescope Array (CSTAR) for Dome A, Antarctica

The Chinese Small Telescope ARray (hereinafter CSTAR) is the first Chinese astronomical instrument on the Antarctic ice cap. The low temperature and low pressure testing of the data acquisition system was carried out in a laboratory refrigerator and on the 4500m Pamirs high plateau, respectively. The results from the final four nights of test observations demonstrated that CSTAR was ready for operation at Dome A, Antarctica. In this paper we present a description of CSTAR and the performance derived from the test observations.Comment: Accepted Research in Astronomy and Astrophysics (RAA) 1 Latex file and 20 figure

Topological triply-degenerate point with double Fermi arcs

Unconventional chiral particles have recently been predicted to appear in certain three dimensional (3D) crystal structures containing three- or more-fold linear band degeneracy points (BDPs). These BDPs carry topological charges, but are distinct from the standard twofold Weyl points or fourfold Dirac points, and cannot be described in terms of an emergent relativistic field theory. Here, we report on the experimental observation of a topological threefold BDP in a 3D phononic crystal. Using direct acoustic field mapping, we demonstrate the existence of the threefold BDP in the bulk bandstructure, as well as doubled Fermi arcs of surface states consistent with a topological charge of 2. Another novel BDP, similar to a Dirac point but carrying nonzero topological charge, is connected to the threefold BDP via the doubled Fermi arcs. These findings pave the way to using these unconventional particles for exploring new emergent physical phenomena

Is Z^+(4430) a loosely bound molecular state?

Since $Z^+(4430)$ lies very close to the threshold of $D^\ast{\bar D}_1$, we investigate whether $Z^+(4430)$ could be a loosely bound S-wave state of $D^\ast{\bar D}_1$ or $D^\ast{\bar D}^\prime_1$ with $J^P=0^-, 1^-, 2^-$, i.e., a molecular state arising from the one-pion-exchange potential. The potential from the crossed diagram is much larger than that from the diagonal scattering diagram. With various trial wave functions, we notice that the attraction from the one pion exchange potential alone is not strong enough to form a bound state with realistic pionic coupling constants deduced from the decay widths of $D_1$ and $D^\prime_1$.Comment: 8 pages, 4 figures, 4 tables. Typos corrected, more discussions adde

The Role of the Lactadherin in Promoting Intestinal DCs Development In Vivo and Vitro

Lactadherin, as one of the immune components in the breast milk, might play a role in the intestinal immune system of newborn. Therefore, we investigated the effect of lactadherin-feeding in early time on the development of intestinal immune system compared with naturally rearing and artificially rearing (non-lactadherin). In the present study, we observed that the Peyer's Patches (PP) from the pups of artificially reared group with lactadherin added were characterized by an excess of OX62+CD4+SIRP+ DC cells and a higher expression of CD3+CD4+CD25+T cells. Additionally, this study also demonstrated that IL-10 production was dramatically increased when lactadherin was present in culture medium compared with lactadherin-absent culture. These results suggested that lactadherin could adjust intestinal DCs activity, induce CD3+CD4+CD25+T cell differentiation, and enhance IL-10 production

Semi-supervised learning for multi-label cardiovascular diseases prediction: a multi-dataset study

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision. Code is available at https://github.com/KAZABANA/ECGMatch