Analyzing inconsistency toward enhancing integration of biological molecular databases

The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.

Interacting amino acid preferences of 3D pattern pairs at the binding sites of transient and obligate protein complexes

To assess the physico-chemical characteristics of protein-protein interactions, protein sequences and overall structural folds have been analyzed previously. To highlight this, discovery and examination of amino acid patterns at the binding sites defined by structural proximity in 3-dimensional (3D) space are essential. In this paper, we investigate the interacting preferences of 3D pattern pairs discovered separately in transient and obligate protein complexes. These 3D pattern pairs are not necessarily sequence-consecutive, but each residue in two groups of amino acids from two proteins in a complex is within certain °A threshold to most residues in the other group. We develop an algorithm called AA-pairs by which every pair of interacting proteins is represented as a bipartite graph, and it discovers all maximal quasi-bicliques from every bipartite graph to form our 3D pattern pairs. From 112 and 2533 highly conserved 3D pattern pairs discovered in the transient and obligate complexes respectively, we observe that Ala and Leu is the highest occuring amino acid in interacting 3D patterns of transient (20.91%) and obligate (33.82%) complexes respectively. From the study on the dipeptide composition on each side of interacting 3D pattern pairs, dipeptides Ala-Ala and Ala-Leu are popular in 3D patterns of both transient and obligate complexes. The interactions between amino acids with large hydrophobicity difference are present more in the transient than in the obligate complexes. On contrary, in obligate complexes, interactions between hydrophobic residues account for the top 5 most occuring amino acid pairings.

Future Asian space : projecting the urban space of new East Asia

The idea of "Asian space" is undergoing a transformation as a result of rapid technological, economic, social and cultural changes. Following the shift to a global economy and an urban population explosion, Asian cities have been presented as a mainstay of progress, national pride, identity, and positioning on the global stage. The extraordinary pace and intensity of the changes have created a situation unique in the history of urban development. Despite the immense diversity of Asian countries, "Asia-ness" is often treated as a distinctive quality that has emerged from unique recent circumstances affecting Asian urbanizations as a whole. In Future Asian Space, 15 authors explore broad concepts relating to the creation and re-creation of "Asian space" and contemporary Asian identity, and their examination of different sites and research approaches highlights the difficulty of pinpointing what Asia-ness is, or might become. Appropriate design and planning of cities is a critical element in building a sustainable future and coping with environmental, social and cultural problems. Future Asian Space is designed to stimulate interest and engagement in discussions of the Asian city, and its trajectories in architecture and urbanism. The authors' conclusions are important for academics, theorists and practitioners, but they will also intrigue anyone interested in the future of cities and urban life in Asia.

Future Asian space : introduction

At the turn of this century, the rise of urban development had become a global phenomenon. Rapid urbanization had been at its most dramatic since the Industrial Revolution. With the emergence of global economies, innovative technologies, communication, global migration, rise of population, etc., as well as growing awareness of climate and environmental issues, our world is once again challenged to rethink its future with great urgency. All aspects of our lives are unpredictably becoming altered at an incredible pace and complexity that, indeed, one may argue that "nothing about cities in the 21st century is insignificant. These numerous and unpredictable changes affect everyone, both in the developed and developing regions of the world. In fact, as David N. Buck argues, the speed of change is so rapid that old definitions of developed and developing worlds do not seem to be relevant anymore.

The idea of creative reuse urbanism — the roles of local creativities in culturally sustainable place-making : Tokyo, Bangkok, Singapore

Gentrification is a part of the process of urban renewal which generates significant, and often negative, social impact on existing neighborhood structures, as it tends to be driven predominantly by simple rules of an economy and imperatives of a globalized, "free" market, insensitive to the subtleties of local culture and values. This idea of creative reuse urbanism questions the necessity of extreme, damaging impacts of gentrification. It argues for an urbanism which is culturally rooted, locally related and deeply contextualized. This chapter shows that such urbanism is not just another utopian concept, but a living reality. Examples from Tokyo, Bangkok and Singapore demonstrate a rich spectrum of possibilities, a kind of pre-gentrification which has the capacity to get involved in creative reuse and recycling of existing stocks and inheritances, thus becoming a positive contributor to sustainable urban regeneration. Presented reused buildings and introduced activities are active ingredients in larger process of place-making in the three cities. Tokyo, Bangkok and Singapore were observed through the lenses of sustainability and cultural difference, with main focus on intersections between the practices of reuse and local creativity.

Situated event bootstrapping and capture guidance for automated home movie authoring

This paper describes a novel interactive media authoring framework, MediaTE, that enables amateurs to create videos of higher narrative or aesthetic quality with a completely mobile lifecycle. A novel event bootstrapping dialog is used to derive shot suggestions that yield both targetted footage and annotation enabling an automatic Computational Media Aesthetics-aware editing phase, the manual performance of which is typically a barrier to the amateur. This facilitates a move away from requiring a prior-conception of the events or locale being filmed, in the form of a template, to at-capture bootstrapping of this information. Metadata gathered as part of the critical path of media creation also has implications for the longevity and reuse of captured media assets. Results of an evaluation performed on both the usability and delivered media aspects of the system are discussed, which highlight the tenability of the proposed framework and the quality of the produced media.

Topic transition detection using hierarchical hidden Markov and semi-Markov models

In this paper we introduce a probabilistic framework to exploit hierarchy, structure sharing and duration information for topic transition detection in videos. Our probabilistic detection framework is a combination of a shot classification step and a detection phase using hierarchical probabilistic models. We consider two models in this paper: the extended Hierarchical Hidden Markov Model (HHMM) and the Coxian Switching Hidden semi-Markov Model (S-HSMM) because they allow the natural decomposition of semantics in videos, including shared structures, to be modeled directly, and thus enabling efficient inference and reducing the sample complexity in learning. Additionally, the S-HSMM allows the duration information to be incorporated, consequently the modeling of long-term dependencies in videos is enriched through both hierarchical and duration modeling. Furthermore, the use of the Coxian distribution in the S-HSMM makes it tractable to deal with long sequences in video. Our experimentation of the proposed framework on twelve educational and training videos shows that both models outperform the baseline cases (flat HMM and HSMM) and performances reported in earlier work in topic detection. The superior performance of the S-HSMM over the HHMM verifies our belief that duration information is an important factor in video content modeling.

Exploiting side information in distance dependent Chinese restaurant processes for data clustering

Multimedia contents often possess weakly annotated data such as tags, links and interactions. The weakly annotated data is called side information. It is the auxiliary information of data and provides hints for exploring the link structure of data. Most clustering algorithms utilize pure data for clustering. A model that combines pure data and side information, such as images and tags, documents and keywords, can perform better at understanding the underlying structure of data. We demonstrate how to incorporate different types of side information into a recently proposed Bayesian nonparametric model, the distance dependent Chinese restaurant process (DD-CRP). Our algorithm embeds the affinity of this information into the decay function of the DD-CRP when side information is in the form of subsets of discrete labels. It is flexible to measure distance based on arbitrary side information instead of only the spatial layout or time stamp of observations. At the same time, for noisy and incomplete side information, we set the decay function so that the DD-CRP reduces to the traditional Chinese restaurant process, thus not inducing side effects of noisy and incomplete side information. Experimental evaluations on two real-world datasets NUS WIDE and 20 Newsgroups show exploiting side information in DD-CRP significantly improves the clustering performance.

Learning sparse latent representation and distance metric for image retrieval

The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG.

Data clustering using side information dependent Chinese restaurant processes

Side information, or auxiliary information associated with documents or image content, provides hints for clustering. We propose a new model, side information dependent Chinese restaurant process, which exploits side information in a Bayesian nonparametric model to improve data clustering. We introduce side information into the framework of distance dependent Chinese restaurant process using a robust decay function to handle noisy side information. The threshold parameter of the decay function is updated automatically in the Gibbs sampling process. A fast inference algorithm is proposed. We evaluate our approach on four datasets: Cora, 20 Newsgroups, NUS-WIDE and one medical dataset. Types of side information explored in this paper include citations, authors, tags, keywords and auxiliary clinical information. The comparison with the state-of-the-art approaches based on standard performance measures (NMI, F1) clearly shows the superiority of our approach.

Dirichlet process mixture models with pairwise constraints for data clustering

The Dirichlet process mixture (DPM) model, a typical Bayesian nonparametric model, can infer the number of clusters automatically, and thus performing priority in data clustering. This paper investigates the influence of pairwise constraints in the DPM model. The pairwise constraint, known as two types: must-link (ML) and cannot-link (CL) constraints, indicates the relationship between two data points. We have proposed two relevant models which incorporate pairwise constraints: the constrained DPM (C-DPM) and the constrained DPM with selected constraints (SC-DPM). In C-DPM, the concept of chunklet is introduced. ML constraints are compiled into chunklets and CL constraints exist between chunklets. We derive the Gibbs sampling of the C-DPM based on chunklets. We further propose a principled approach to select the most useful constraints, which will be incorporated into the SC-DPM. We evaluate the proposed models based on three real datasets: 20 Newsgroups dataset, NUS-WIDE image dataset and Facebook comments datasets we collected by ourselves. Our SC-DPM performs priority in data clustering. In addition, our SC-DPM can be potentially used for short-text clustering.