Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description

In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention.

Graph Based Multi-Layer K-Means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces

In this paper, we focus on developing a novel unsupervised machine learning algorithm, named graph based multi-layer k-means++ (G-MLKM), to solve the data-target association problem when targets move on a constrained space and minimal information of the targets can be obtained by sensors. Instead of employing the traditional data-target association methods that are based on statistical probabilities, the G-MLKM solves the problem via data clustering. We first develop the multi-layer k-means++ (MLKM) method for data-target association at a local space given a simplified constrained space situation. Then a p-dual graph is proposed to represent the general constrained space when local spaces are interconnected. Based on the p-dual graph and graph theory, we then generalize MLKM to G-MLKM by first understanding local data-target association, extracting cross-local data-target association mathematically, and then analyzing the data association at intersections of that space. To exclude potential data-target association errors that disobey physical rules, we also develop error correction mechanisms to further improve the accuracy. Numerous simulation examples are conducted to demonstrate the performance of G-MLKM, which yields an average data-target association accuracy of 92.2%.

Structural basis for piRNA-targeting

SummaryPiwi proteins use PIWI-interacting RNAs (piRNAs) to identify and silence the transposable elements (TEs) pervasively found in animal genomes. The Piwi targeting mechanism is proposed to be similar to targeting by Argonaute proteins, which employ microRNA (miRNA) guides to repress cellular mRNAs, but has not been characterized in detail. We present cryo-EM structures of a Piwi-piRNA complex with and without target RNAs and analysis of target recognition. Resembling Argonaute, Piwi identifies targets using the piRNA seed-region. However, Piwi creates a much weaker seed so that prolonged target association requires further piRNA-target pairing. Beyond the seed, Piwi creates wide central cleft wide for unencumbered piRNA-target pairing, enabling long-lived Piwi-piRNA-target interactions that are tolerant of mismatches. Piwi ensures targeting fidelity by blocking propagation of the piRNA-target duplex in the absence of faithful seed pairing, and by requiring extended piRNA-target pairing to reach an endonucleolytically active conformation. This mechanism allows Piwi to minimize off-targeting cellular mRNAs and adapt piRNA sequences to evolving genomic threats.

Inducing a Target Association between Ordinal Variables by Using a Parametric Copula Family

The need for building and generating statistically dependent random variables arises in various fields of study where simulation has proven to be a useful tool.In this work, we present an approach for constructing ordinal variables with arbitrarily assigned marginal distributions and value of association or correlation, expressed in terms of either Goodman and Kruskal's gamma or Pearson's linear correlation. The approach first constructs a class of bivariate copula-based distributions matching the assigned margins, and then, within this class, identifies the distribution matching the assigned association or correlation, by calibrating the copula parameter. A numerical example and a possible application are illustrated.