Research on the Influence of Different Types of Exhibition on the Development of County Economy

The county-level exhibition economy is an industrial economy with the characteristics of region, feature, service and opening which takes the county as the geographical space, the county-level political power as the important impetus, the market as the direction, the exhibition and the festival activity as the core. This paper introduces six types of county-level MICE economy, and analyzes their characteristics while giving some examples of corresponding county-level or city-level exhibition. The urban exhibition competitiveness index in 2018 and 2019 is evaluated by regression analysis to develop the MICE industry more effectively as well as to promote the economic development and the prosperity of the exhibition industry of China.

Image of the future life in ritual texts of the peoples of Middle Volga Region

The subject of this research is the ritual texts of the peoples of Middle Volga Region of the XIX – early XX centuries. The goal lies in constructing image of the future based on ritual texts, as well as finding common motives and themes therein. Leaning on the archival materials, the author determines the key concepts of the regional folklore: desire of happiness, acquisition/loss of freedom, and loneliness. The content analysis is conducted on ritual lamentations and songs, as well as the most meaningful motives that unite all the rituals of the life cycle. The article employs culturological methods of research that allow identifying the spiritual and moral values and representations of the life cycle in the ritual texts of the peoples of Middle Volga Region. The scientific novelty consists in interpretation of the ritual texts of the population of the region as basic elements essential for constructing image of the future life. The conclusion is made that ritual texts of the inhabitants of the region feature similar theme: admiration of children, desire of happiness and freedom in the context of sending for military service and consummation of marriage, leaving home, and fear of loneliness. Shared outlook upon life created a predictable picture of the future with uniform requirements to a person as a bearer of the traditional culture. Such requirements implied the idea of the importance of reproduction of healthy generation and unification of family.

Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors

In spatial analysis applications, measuring the shape similarity of polygons is crucial for polygonal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should involve finding the difference between polygons, as objects in observation, in terms of visual perception and the differences of the regions, boundaries, and structures formed by the polygons from a mathematical point of view. In existing approaches, the shape similarity of polygons is calculated by only comparing their mathematical characteristics while not taking human perception into consideration. Aiming to solve this problem, we use the features of context and texture of polygons, since they are basic visual perception elements, to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into a grid representation, the contour feature is represented as a multiscale statistic feature, and the region feature is transformed into condensed grid of context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes similarity as a correlation between textures extracted by shape features. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor.

DNet: Dynamic Neighborhood Feature Learning in Point Cloud

Neighborhood selection is very important for local region feature learning in point cloud learning networks. Different neighborhood selection schemes may lead to quite different results for point cloud processing tasks. The existing point cloud learning networks mainly adopt the approach of customizing the neighborhood, without considering whether the selected neighborhood is reasonable or not. To solve this problem, this paper proposes a new point cloud learning network, denoted as Dynamic neighborhood Network (DNet), to dynamically select the neighborhood and learn the features of each point. The proposed DNet has a multi-head structure which has two important modules: the Feature Enhancement Layer (FELayer) and the masking mechanism. The FELayer enhances the manifold features of the point cloud, while the masking mechanism is used to remove the neighborhood points with low contribution. The DNet can learn the manifold features and spatial geometric features of point cloud, and obtain the relationship between each point and its effective neighborhood points through the masking mechanism, so that the dynamic neighborhood features of each point can be obtained. Experimental results on three public datasets demonstrate that compared with the state-of-the-art learning networks, the proposed DNet shows better superiority and competitiveness in point cloud processing task.

A Descent Conjugate Gradient Algorithm for Optimization Problems and Its Applications in Image Restoration and Compression Sensing

It is well known that the nonlinear conjugate gradient algorithm is one of the effective algorithms for optimization problems since it has low storage and simple structure properties. This motivates us to make a further study to design a modified conjugate gradient formula for the optimization model, and this proposed conjugate gradient algorithm possesses several properties: (1) the search direction possesses not only the gradient value but also the function value; (2) the presented direction has both the sufficient descent property and the trust region feature; (3) the proposed algorithm has the global convergence for nonconvex functions; (4) the experiment is done for the image restoration problems and compression sensing to prove the performance of the new algorithm.

Is there a non-stationary γ-ray emission zone 42 pc from the 3C 279 core?

We investigate the relationship between the variable gamma-ray emission and jet properties in the blazar 3C 279 by combining the Fermi-LAT data spanning a period of eight years and concurrent radio measurements made at multiple epochs with VLBA at 15 and 43 GHz within the MOJAVE and VLBA-BU monitoring programmes. The aim of this paper is to compare the flux variability of the different components found in the VLBA observations, to the variability in the gamma-rays. This analysis helps us to investigate whether any of the jet components can be associated with the gamma-ray variability. Through Spearman rank correlation we found that the gamma-ray variability is correlated with a particular region (feature B in the MOJAVE images) downstream from the observed base (core) of the jet. This jet component is therefore a likely location where an important fraction of the variable gamma-ray emission is produced. We also calculated the average proper motion of the component with respect to the VLBA core and found that it moves at an apparent superluminal velocity of (3.70 ± 0.35)c, implying that one of the gamma-ray emission zones is not stationary. This jet component is also found between 6.86 mas and 8.68 mas, which translates to a distance from the radio core of at least 42 pc.