Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy

We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergraph partitioning. By assigning vertex weights through the LPT algorithm, we generate a prior hypergraph under a relaxed balance constraint. With the prior hypergraph, we have defined the Wasserstein discrepancy to coordinate the optimal transport of coarsening process. And the optimal transport matrix is solved by Sinkhorn algorithm. Our coarsening scheme fully takes into account the minimization of connectivity metric (objective function). For the initial partitioning stage, we define a normalized cut function induced by Fiedler vector, which is theoretically proved to be a concave function. Thereby, a three-point algorithm is designed to find the best cut under the balance constraint

Semantic Information Extraction for Text Data with Probability Graph

In this paper, the problem of semantic information extraction for resource constrained text data transmission is studied. In the considered model, a sequence of text data need to be transmitted within a communication resource-constrained network, which only allows limited data transmission. Thus, at the transmitter, the original text data is extracted with natural language processing techniques. Then, the extracted semantic information is captured in a knowledge graph. An additional probability dimension is introduced in this graph to capture the importance of each information. This semantic information extraction problem is posed as an optimization framework whose goal is to extract most important semantic information for transmission. To find an optimal solution for this problem, a Floyd's algorithm based solution coupled with an efficient sorting mechanism is proposed. Numerical results testify the effectiveness of the proposed algorithm with regards to two novel performance metrics including semantic uncertainty and semantic similarity

Preparation and properties of compound Arnebiae radix microemulsion gel

Background: Compound Arnebiae radix oil has been clinically applied to treat burns and scalds for a long time. However, it is unstable and inconvenient to use. The aim of this study was to prepare a compound Arnebiae radix microemulsion gel for transdermal delivery system and evaluate its characteristics.Materials and Methods: Based on the solubility of Shikonin, the active component of Arnebiae radix and the results of phase studies, adequate ratio of each component in microemulsion was determined. The optimized microemulsion gel was prepared using Carbomer 940. The gels were characterized in terms of appearance, preliminary stability test and the content of Shikonin in the compound Arnebiae radix microemulsion gel with HPLC analysis.Results: The optimized conditions for preparing microemulsion were Tween-80, glycerin, isopropyl myristate (IPM) with the ratio of 6:3:2. The optimal microemulsion gel was obtained with Carbomer 940 (1.0%).Conclusion: The prepared compound Arnebiae radix microemulsion gel showed good stability over time. It is more convenience in application than the previous used formulations.Keywords: Compound Arnebiae radix oil, microemulsion gel, pseudo-ternary phase diagram, characterizatio

A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks

Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations, based on network prior knowledge, induce different search behaviors with similar characteristics to various perspectives. Specifically, it is difficult for users to determine a suitable transformation a priori. This article proposes a multi-transformation evolutionary framework for influence maximization (MTEFIM) with convergence guarantees to exploit the potential similarities and unique advantages of alternate transformations and to avoid users manually determining the most suitable one. In MTEFIM, multiple transformations are optimized simultaneously as multiple tasks. Each transformation is assigned an evolutionary solver. Three major components of MTEFIM are conducted via: 1) estimating the potential relationship across transformations based on the degree of overlap across individuals of different populations, 2) transferring individuals across populations adaptively according to the inter-transformation relationship, and 3) selecting the final output seed set containing all the transformation's knowledge. The effectiveness of MTEFIM is validated on both benchmarks and real-world social networks. The experimental results show that MTEFIM can efficiently utilize the potentially transferable knowledge across multiple transformations to achieve highly competitive performance compared to several popular IM-specific methods. The implementation of MTEFIM can be accessed at https://github.com/xiaofangxd/MTEFIM.Comment: This work has been submitted to the IEEE Computational Intelligence Magazine for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Level of Orthographic Knowledge Helps to Reveal Automatic Predictions in Visual Word Processing

The brain generates predictions about visual word forms to support efficient reading. The “interactive account” suggests that the predictions in visual word processing can be strategic or automatic (non-strategic). Strategic predictions are frequently demonstrated in studies that manipulated task demands, however, few studies have investigated automatic predictions. Orthographic knowledge varies greatly among individuals and it offers a unique opportunity in revealing automatic predictions. The present study grouped the participants by level of orthographic knowledge and recorded EEGs in a non-linguistic color matching task. The visual word-selective N170 response was much stronger to pseudo than to real characters in participants with low orthographic knowledge, but not in those with high orthographic knowledge. Previous work on predictive coding has demonstrated that N170 is a good index for prediction errors, i.e., the mismatches between predictions and visual inputs. The present findings provide unambiguous evidence that automatic predictions modulate the early stage of visual word processing

CTCF Mediates the Cell-Type Specific Spatial Organization of the Kcnq5 Locus and the Local Gene Regulation

Chromatin loops play important roles in the dynamic spatial organization of genes in the nucleus. Growing evidence has revealed that the multivalent functional zinc finger protein CCCTC-binding factor (CTCF) is a master regulator of genome spatial organization, and mediates the ubiquitous chromatin loops within the genome. Using circular chromosome conformation capture (4C) methodology, we discovered that CTCF may be a master organizer in mediating the spatial organization of the kcnq5 gene locus. We characterized the cell-type specific spatial organization of the kcnq5 gene locus mediated by CTCF in detail using chromosome conformation capture (3C) and 3C-derived techniques. Cohesion also participated in mediating the organization of this locus. RNAi-mediated knockdown of CTCF sharply diminished the interaction frequencies between the chromatin loops of the kcnq5 gene locus and down-regulated local gene expression. Functional analysis showed that the interacting chromatin loops of the kcnq5 gene locus can repress the gene expression in a luciferase reporter assay. These interacting chromatin fragments were a series of repressing elements whose contacts were mediated by CTCF. Therefore, these findings suggested that the dynamical spatial organization of the kcnq5 locus regulates local gene expression