Fast Search for Dynamic Multi-Relational Graphs

Acting on time-critical events by processing ever growing social media or news streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Continuous queries or techniques to search for rare events that typically arise in monitoring applications have been studied extensively for relational databases. This work is dedicated to answer the question that emerges naturally: how can we efficiently execute a continuous query on a dynamic graph? This paper presents an exact subgraph search algorithm that exploits the temporal characteristics of representative queries for online news or social media monitoring. The algorithm is based on a novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the structural and semantic characteristics of the underlying multi-relational graph. The paper concludes with extensive experimentation on several real-world datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM), 201

Modelling of content-aware indicators for effective determination of shot boundaries in compressed MPEG videos

In this paper, a content-aware approach is proposed to design multiple test conditions for shot cut detection, which are organized into a multiple phase decision tree for abrupt cut detection and a finite state machine for dissolve detection. In comparison with existing approaches, our algorithm is characterized with two categories of content difference indicators and testing. While the first category indicates the content changes that are directly used for shot cut detection, the second category indicates the contexts under which the content change occurs. As a result, indications of frame differences are tested with context awareness to make the detection of shot cuts adaptive to both content and context changes. Evaluations announced by TRECVID 2007 indicate that our proposed algorithm achieved comparable performance to those using machine learning approaches, yet using a simpler feature set and straightforward design strategies. This has validated the effectiveness of modelling of content-aware indicators for decision making, which also provides a good alternative to conventional approaches in this topic

Understanding Co-evolution in Large Multi-relational Social Networks

Understanding dynamics of evolution in large social networks is an important problem. In this paper, we characterize evolution in large multi-relational social networks. The proliferation of online media such as Twitter, Facebook, Orkut and MMORPGs\footnote{Massively Multi-player Online Role Playing Games} have created social networking data at an unprecedented scale. Sony's Everquest 2 is one such example. We used game multi-relational networks to reveal the dynamics of evolution in a multi-relational setting by macroscopic study of the game network. Macroscopic analysis involves fragmenting the network into smaller portions for studying the dynamics within these sub-networks, referred to as `communities'. From an evolutionary perspective of multi-relational network analysis, we have made the following contributions. Specifically, we formulated and analyzed various metrics to capture evolutionary properties of networks. We find that co-evolution rates in trust based `communities' are approximately $60\%$ higher than the trade based `communities'. We also find that the trust and trade connections within the `communities' reduce as their size increases. Finally, we study the interrelation between the dynamics of trade and trust within `communities' and find interesting results about the precursor relationship between the trade and the trust dynamics within the `communities'

SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

Visual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table

Whole-blood sorting, enrichment and in situ immunolabeling of cellular subsets using acoustic microstreaming

Analyzing undiluted whole human blood is a challenge due to its complex composition of hematopoietic cellular populations, nucleic acids, metabolites, and proteins. We present a novel multi-functional microfluidic acoustic streaming platform that enables sorting, enrichment and in situ identification of cellular subsets from whole blood. This single device platform, based on lateral cavity acoustic transducers (LCAT), enables (1) the sorting of undiluted donor whole blood into its cellular subsets (platelets, RBCs, and WBCs), (2) the enrichment and retrieval of breast cancer cells (MCF-7) spiked in donor whole blood at rare cell relevant concentrations (10 mL− 1), and (3) on-chip immunofluorescent labeling for the detection of specific target cellular populations by their known marker expression patterns. Our approach thus demonstrates a compact system that integrates upstream sample processing with downstream separation/enrichment, to carry out multi-parametric cell analysis for blood-based diagnosis and liquid biopsy blood sampling