Twins in words and long common subsequences in permutations

A large family of words must contain two words that are similar. We investigate several problems where the measure of similarity is the length of a common subsequence. We construct a family of n^{1/3} permutations on n letters, such that LCS of any two of them is only cn^{1/3}, improving a construction of Beame, Blais, and Huynh-Ngoc. We relate the problem of constructing many permutations with small LCS to the twin word problem of Axenovich, Person and Puzynina. In particular, we show that every word of length n over a k-letter alphabet contains two disjoint equal subsequences of length cnk^{-2/3}. Many problems are left open.Comment: 18+epsilon page

Bioinformatics analysis based on crucial genes of endothelial cells in rheumatoid

Objectives: Synovial neovascularization is an early and remarkable event that promotes the development of rheumatoid arthritis (RA) synovial hyperplasia. This study aimed to find potential diagnostic markers and molecular therapeutic targets for RA at the mRNA molecular level.Method: We download the expression profile dataset GSE46687 from the gene expression ontology (GEO) microarray, and used R software to screen out the differentially expressed genes between the normal group and the disease group. Then we performed functional enrichment analysis, used the STRING database to construct a protein-protein interaction (PPI) network, and identify candidate crucial genes, infiltration of the immune cells and targeted molecular drug.Results: Rheumatoid arthritis datasets included 113 differentially expressed genes (DEGs) including 104 upregulated and 9 downregulated DEGs. The enrichment analysis of genes shows that the differential genes are mainly enriched in condensed chromosomes, ribosomal subunits, and oxidative phosphorylation. Through PPI network analysis, seven crucial genes were identified: RPS13, RPL34, RPS29, RPL35, SEC61G, RPL39L, and RPL37A. Finally, we find the potential compound drug for RA.Conclusion: Through this method, the pathogenesis of RA endothelial cells was further explained. It provided new therapeutic targets, but the relationship between these genes and RA needs further research to be validated in the future

Discussion on the application of the medina model to the flow curve of hot deformation

The application of the Medina model to the flowcurve of hot deformation has been discussed. Usingthe quick fit function of 1stOpt software, therelationship between various model coefficientsand a dimensionless parameter Z/A has been built.Using a classification method of the Medina model,the flow curves of 50A1300 silicon steel are builtand R^2 of 0.979436 and the average relative errorof 7,3517% are obtained. Then using arecrystallization model of the Medina model, theflow curves of 45K-DDQ are built and R^2 of0,974373 and the average relative error of6,1453% are obtained. In addition, the flow curvemodel is programmed in Matlab environment sothat true stress could be automatically obtained.Finally, the whole fitting method and piecewisefitting method are compared when using theMedina model for solving the model parameters ofthe dynamic recrystallization flow curve

Topology Architecture and Routing Algorithms of Octagon-Connected Torus Interconnection Network

Two important issues in the design of interconnection networks for massively parallel computers are scalability and small diameter. A new interconnection network topology, called octagon-connected torus (OCT), is proposed. The OCT network combines the small diameter of octagon topology and the scalability of torus topology. The OCT network has better properties, such as small diameter, regular, symmetry and the scalability. The nodes of the OCT network adopt the Johnson coding scheme which can make routing algorithms simple and efficient. Both unicasting and broadcasting routing algorithms are designed for the OCT network, and it is based on the Johnson coding scheme. A detailed analysis shows that the OCT network is a better interconnection network in the properties of topology and the performance of communication

Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub-optimal performance on target languages, the pseudo labels are often noisy and limit the overall performance. In this work, we aim to improve self-training for cross-lingual NER by combining representation learning and pseudo label refinement in one coherent framework. Our proposed method, namely ContProto mainly comprises two components: (1) contrastive self-training and (2) prototype-based pseudo-labeling. Our contrastive self-training facilitates span classification by separating clusters of different classes, and enhances cross-lingual transferability by producing closely-aligned representations between the source and target language. Meanwhile, prototype-based pseudo-labeling effectively improves the accuracy of pseudo labels during training. We evaluate ContProto on multiple transfer pairs, and experimental results show our method brings in substantial improvements over current state-of-the-art methods.Comment: Accepted by ACL202