A semantic contrastive study of Chinese and English verb “跑/run” from the perspective of cognitive semantics

Based on corpus data, this paper finds that the semantics of “跑/run” have similarities, which lie in the consistency of the central meanings and common usages. The differences are: 1) the meanings of “run” are significantly more than those of “跑”, resulting in a great divergence in the numbers of meanings; 2) compared with “跑”, the implications of “run” display a higher level of specificity. On the basis of cognitive theories, the paper has yielded the following findings. The semantic overlaps of “跑/run” root in their identical prototypical meanings. The semantic differences are generated by the following factors: 1) the image-schema distribution of “run” is wider with more abstract representations than that of “跑”, due to which many unique semantic items come into being; 2) semantic systems of “跑/run” adopt different metaphorical methods, contributing to more comprehensive cognitive domains of the semantic mapping and closer semantic interrelations of “run” than those of “跑”

Non-local Attention Optimized Deep Image Compression

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics

An efficient contradiction separation based automated deduction algorithm for enhancing reasoning capability

Automated theorem prover (ATP) for first-order logic (FOL), as a significant inference engine, is one of the hot research areas in the field of knowledge representation and automated reasoning. E prover, as one of the leading ATPs, has made a significant contribution to the development of theorem provers for FOL, particularly equality handling, after more than two decades of development. However, there are still a large number of problems in the TPTP problem library, the benchmark problem library for ATPs, that E has yet to solve. The standard contradiction separation (S-CS) rule is an inference method introduced recently that can handle multiple clauses in a synergized way and has a few distinctive features which complements to the calculus of E. Binary clauses, on the other hand, are widely utilized in the automated deduction process for FOL because they have a minimal number of literals (typically only two literals), few symbols, and high manipulability. As a result, it is feasible to improve a prover's deduction capability by reusing binary clause. In this paper, a binary clause reusing algorithm based on the S-CS rule is firstly proposed, which is then incorporated into E with the objective to enhance E’s performance, resulting in an extended E prover. According to experimental findings, the performance of the extended E prover not only outperforms E itself in a variety of aspects, but also solves 18 problems with rating of 1 in the TPTP library, meaning that none of the existing ATPs are able to resolve them

A Simple and Efficient Lattice Summation Method for Metallic Electrodes in Constant Potential Molecular Dynamics Simulation

The constant potential molecular dynamics simulation method proposed by Siepmann and Sprik and reformulated later by Reed (SR-CPM) has been widely employed to investigate the metallic electrolyte/electrode interfaces, especially for conducting nanochannels with complex connectivity, *e.g.*, carbide-derived carbon or graphene-assembled membrane. This work makes substantial extensions of this seminal SR-CPM approach. First, we introduce two numerical techniques to determine electrode atom charges with an order of magnitude improvement in computational efficiency compared with those widely employed methods. The first numerical technique dramatically accelerates the to calculation of the Ewald interaction matrix $\mathbf{E}$, which takes advantage of the existing highly optimised electrostatic codes. The second technique introduces a new preconditioning technique in the conjugate gradient method to considerably increase the computational efficiency of a linear equation system that determines electrode atomic charges. Our improved SR-CPM implemented in the LAMMPS package can handle extra-large systems, *e.g.*, over 8.1 million electrode atoms. Second, after demonstrating the importance of the electroneutrality constraint, we propose a two-step method to enforce electroneutrality in the following post-treatment step, applicable for matrix and iterative techniques. Third, we propose a solid theoretical analysis for the adjustable parameter $\alpha_i$ (namely the atomic Hubbard-U $U_i^0$), which is arbitrarily selected in many SR-CPM simulation practices. We proposed that the optimised $\alpha_i$ or $U_i^0$ should compensate for the electrical potential/energy discrepancy between the discrete atomistic model and the continuum limit. The analytical and optimal ${\alpha}_i^0$ values are derived for a series of 2D materials

Design and analysis of actuator system of electromagnetic shell with high-overload resistances

The components of an electromagnetic shell system should be able to sustain the impact of high-strength instantaneous acceleration when the system is launched. The dynamic characteristics of high overload present significant challenges in the component (electronic and mechanical) design and part assembly of a steering gear system. This paper proposes a new design strategy for the servo system of a high-overload electromagnetic projectile. First, according to the special environment index parameters of a high-overload electromagnetic shell steering system, a new anti-overload deceleration mechanism that combines a triangular thread lead screw, a shift fork, and the entire anti-high-overload mechanical structure is proposed. The transient dynamic vibration characteristics of the entire high overload are analyzed. Based on the integrated module method for complex mechanical and electrical equipment, a mathematical model of the full closed-loop electromagnetic shell actuator system is established, and its dynamic characteristics are analyzed. Finally, a prototype of the high-overload electromagnetic projectile steering system is manufactured. By testing the maximum rudder deflection angle and the frequency and step responses of the system, the dynamic characteristics of the new high-overload electromagnetic shell actuator system are verified. This study provides a new method for designing high-overload electromagnetic shell steering gears

Hypermethylated gene ANKDD1A is a candidate tumor suppressor that interacts with FIH1 and decreases HIF1α stability to inhibit cell autophagy in the glioblastoma multiforme hypoxia microenvironment.

Ectopic epigenetic mechanisms play important roles in facilitating tumorigenesis. Here, we first demonstrated that ANKDD1A is a functional tumor suppressor gene, especially in the hypoxia microenvironment. ANKDD1A directly interacts with FIH1 and inhibits the transcriptional activity of HIF1α by upregulating FIH1. In addition, ANKDD1A decreases the half-life of HIF1α by upregulating FIH1, decreases glucose uptake and lactate production, inhibits glioblastoma multiforme (GBM) autophagy, and induces apoptosis in GBM cells under hypoxia. Moreover, ANKDD1A is highly frequently methylated in GBM. The tumor-specific methylation of ANKDD1A indicates that it could be used as a potential epigenetic biomarker as well as a possible therapeutic target