Exploring Communities in Large Profiled Graphs

Given a graph $G$ and a vertex $q\in G$, the community search (CS) problem aims to efficiently find a subgraph of $G$ whose vertices are closely related to $q$. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Extensive experiments show that PCS can identify communities with themes that are common to their vertices, and is more effective than existing CS approaches. As a naive solution for PCS is highly expensive, we have also developed a tree index, which facilitate efficient and online solutions for PCS

LSCD: A Large-Scale Screen Content Dataset for Video Compression

Multimedia compression allows us to watch videos, see pictures and hear sounds within a limited bandwidth, which helps the flourish of the internet. During the past decades, multimedia compression has achieved great success using hand-craft features and systems. With the development of artificial intelligence and video compression, there emerges a lot of research work related to using the neural network on the video compression task to get rid of the complicated system. Not only producing the advanced algorithms, but researchers also spread the compression to different content, such as User Generated Content(UGC). With the rapid development of mobile devices, screen content videos become an important part of multimedia data. In contrast, we find community lacks a large-scale dataset for screen content video compression, which impedes the fast development of the corresponding learning-based algorithms. In order to fulfill this blank and accelerate the research of this special type of videos, we propose the Large-scale Screen Content Dataset(LSCD), which contains 714 source sequences. Meanwhile, we provide the analysis of the proposed dataset to show some features of screen content videos, which will help researchers have a better understanding of how to explore new algorithms. Besides collecting and post-processing the data to organize the dataset, we also provide a benchmark containing the performance of both traditional codec and learning-based methods

Modelling of Capillary Pore Structure Evolution in Portland Cement Pastes Based on Irregular-Shaped Cement Particles

The pore structure plays a crucial role in durability performance of cement-based materials. However, the pore structure in cement pastes is highly dependent on the initial packing of cement particles and cement hydration process, which seems to be related to the shapes of cement particles. This paper proposed a numerical method to investigate the effect of cement particle shapes on capillary pore structures in cement pastes. In this study, irregular-shaped cement particles with various shapes are generated using a novel central growth model, and then incorporated into CEMHYD3D model to simulate Portland cement hydration. Some home-made programs of determining pore structure parameters including porosity, pore size distribution, connectivity and tortuosity are subsequently performed on the extracted three-dimensional network of capillary pore structure in cement pastes. The modelling results indicate that shape-induced large surface area in more non-equiaxed irregular-shaped cement particles can improve pore structure parameters in hardened cement pastes, but this effect will be slight in the later curing period and at a low water-tocement ratio. In addition, the less considered geometric difference plays a role in pore structure evolution especially for extremely non-equiaxed cement particle. However, the geometric attribute has a weak effect on pore structure parameters overall. The modelling results can provide a new insight into durability design in cement-based materials by means of manipulating cement particle shape in the future

Sequential optimization for efficient high-quality object proposal generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING ++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster