Entity Linking for Queries by Searching Wikipedia Sentences

We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset

Indole contributes to tetracycline resistance via the outer membrane protein OmpN in Vibrio splendidus

As an interspecies and interkingdom signaling molecule, indole has recently received attention for its diverse effects on the physiology of both bacteria and hosts. In this study, indole increased the tetracycline resistance of Vibrio splendidus. The minimal inhibitory concentration of tetracycline was 10 mu g/mL, and the OD600 of V. splendidus decreased by 94.5% in the presence of 20 mu g/mL tetracycline; however, the OD600 of V. splendidus with a mixture of 20 mu g/mL tetracycline and 125 mu M indole was 10- or 4.5-fold higher than that with only 20 mu g/mL tetracycline at different time points. The percentage of cells resistant to 10 mu g/mL tetracycline was 600-fold higher in the culture with an OD600 of approximately 2.0 (higher level of indole) than that in the culture with an OD600 of 0.5, which also meant that the level of indole was correlated to the tetracycline resistance of V. splendidus. Furthermore, one differentially expressed protein, which was identified as the outer membrane porin OmpN using SDS-PAGE combined with MALDI-TOF/TOF MS, was upregulated. Consequently, the expression of the ompN gene in the presence of either tetracycline or indole and simultaneously in the presence of indole and tetracycline was upregulated by 1.8-, 2.54-, and 6.01-fold, respectively, compared to the control samples. The combined results demonstrated that indole enhanced the tetracycline resistance of V. splendidus, and this resistance was probably due to upregulation of the outer membrane porin OmpN

Magnetic control of the pair creation in spatially localized supercritical fields

We examine the impact of a perpendicular magnetic field on the creation mechanism of electron-positron pairs in a supercritical static electric field, where both fields are localized along the direction of the electric field. In the case where the spatial extent of the magnetic field exceeds that of the electric field, quantum field theoretical simulations based on the Dirac equation predict a suppression of pair creation even if the electric field is supercritical. Furthermore, an arbitrarily small magnetic field outside the interaction zone can bring the creation process even to a complete halt, if it is sufficiently extended. The mechanism for this magnetically induced complete shutoff can be associated with a reopening of the mass gap and the emergence of electrically dressed Landau levels

Stability and Generalization of ℓp\ell_p-Regularized Stochastic Learning for GCN

Graph convolutional networks (GCN) are viewed as one of the most popular representations among the variants of graph neural networks over graph data and have shown powerful performance in empirical experiments. That $\ell_2$-based graph smoothing enforces the global smoothness of GCN, while (soft) $\ell_1$-based sparse graph learning tends to promote signal sparsity to trade for discontinuity. This paper aims to quantify the trade-off of GCN between smoothness and sparsity, with the help of a general $\ell_p$-regularized $(1<p\leq 2)$ stochastic learning proposed within. While stability-based generalization analyses have been given in prior work for a second derivative objectiveness function, our $\ell_p$-regularized learning scheme does not satisfy such a smooth condition. To tackle this issue, we propose a novel SGD proximal algorithm for GCNs with an inexact operator. For a single-layer GCN, we establish an explicit theoretical understanding of GCN with the $\ell_p$-regularized stochastic learning by analyzing the stability of our SGD proximal algorithm. We conduct multiple empirical experiments to validate our theoretical findings.Comment: Accepted to IJCAI 202