A study on reliability in graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated.

In this paper we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be.

Enhancing the effectiveness of clustering with spectra analysis

For many clustering algorithms, such as K-Means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters, that is, k, to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images, or biological data. In an effort to improve the effectiveness of clustering, we seek the answer to a fundamental question: How can we effectively estimate the number of clusters in a given data set? We propose an efficient method based on spectra analysis of eigenvalues (not eigenvectors) of the data set as the solution to the above. We first present the relationship between a data set and its underlying spectra with theoretical and experimental results. We then show how our method is capable of suggesting a range of k that is well suited to different analysis contexts. Finally, we conclude with further  empirical results to show how the answer to this fundamental question enhances the clustering process for large text collections.

Polyhedral studies for minimum-span graph labelling with integer distance constraints

This paper studies the polytope of the minimum-span graph labelling problems with integer distance constraints (DC-MSGL). We first introduce a few classes of new valid inequalities for the DC-MSGL defined on general graphs and briefly discuss the separation problems of some of these inequalities. These are the initial steps of a branch-and-cut algorithm for solving the DC-MSGL. Following that, we present our polyhedral results on the dimension of the DC-MSGL polytope, and that some of the inequalities are facet defining, under reasonable conditions, for the polytope of the DC-MSGL on triangular graphs.

Light emission and excitonic effect of boron nitride nanotubes observed by photoluminescent spectra

Photoluminescent (PL) and optical absorption spectra of high-yield multi-wall BN nanotubes (BNNTs) were systematically investigated at room temperature in comparison with commercial hexagonal BN (h-BN) powder. The direct band gap of the BNNTs was determined to be 5.75 eV, just slightly narrower than that of h-BN powder (5.82 eV). Two Frenkel excitons with the binding energy of 1.27 and 1.35 eV were also determined. However, they were not a distinctive characteristic of the BNNTs as reported previously. Observed broad UV–visible–NIR light emission demonstrates the potential of the BNNTs as a nano light source.

Picture this : a simple graph that reveals much ado about research

Funnel graphs provide a simple, yet highly effective, means to identify key features of an empirical literature. This paper illustrates the use of funnel graphs to detect publication selection bias, identify the existence of genuine empirical effects and discover potential moderator variables that can help to explain the wide variation routinely found among reported research findings. Applications include union–productivity effects, water price elasticities, common currency-trade effects, minimum-wage employment effects, efficiency wages and the price elasticity of prescription drugs.

Finding and filling the gaps in the Australian governments' innovation and entrepreneurship support spectra

A national innovation system is concerned with the full process of converting new knowledge into commercially viable results. Governments are policy-active in trying to create productive national innovation systems. This paper reviews ways of thinking about entrepreneurship as the commercialisation component of Australia’s innovation system. The paper explores the impact and relevance of selected existing Australian Commonwealth, and to a lesser extent State government, programs for the commercialisation channels so identified, using four frameworks for the analysis: financial, management/start-up, innovation and entrepreneurial. The analysis indicates program initiatives covering the later development and commercialization phases, but serious gaps in the support available for the entrepreneurship phase involving the act of new entry. This gap is covered by research provider business development people and to a limited extent by incubator and State government initiatives. A critical issue has been and is access to smaller amounts of seed finance. The critical human component is the education of public servants and politicians about the nature and operation of entrepreneurship.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Surface-Enhanced Infrared Spectra of Manganese (III) Tetraphenylporphine Chloride Physisorbed on Gold Island Films

Surface-enhanced infrared absorption (SEIRA) spectra of manganese (III) tetraphenylporphine chloride (Mn(TPP)Cl) on metal island films were measured in transmission mode. Dependences of the enhancement factor of SEIRA on both the sample quantity and the type of evaporated metal were investigated by subsequently increasing the amount of Mn(TPP)Cl on gold and silver substrates. The enhancement increases nonlinearly with the amount of sample and varies slightly with the thickness of metal islands. In particular, the SEIRA transmission method presents an anomalous spectral enhancement by a factor of 579, with substantial spectral shifts, observed only for the physisorbed Mn(TPP)Cl that remained on a 3-nm-thick gold film after immersion of the substrates into acetone. A charge-transfer (CT) interaction between the porphyrinic Mn and gold islands is therefore proposed as an additional factor in the SEIRA mechanism of the porphyrin system. The number of remaining porphyrin molecules was estimated by calibration-based fluorescence spectroscopy to be 2.36×1013 molecules (i.e., ~2.910-11 mol/cm2) for a 3-nm-thick gold film, suggesting that the physisorbed molecules distributed very loosely on the metal island surface as a result of the weak van der Waals interactions. Fluorescence microscopy revealed the formation of microcrystalline porphyrin aggregates during the consecutive increase in sample solution. However, the immersion likely redistributed the porphyrin to be directly attached on the gold surface, as evidenced by an absence of porphyrinic microcrystals and the observed SEIRA enhancement. The distinctive red shift in the UV-visible spectra and the SEIRA-enhanced peaks indicate the presence of a preferred orientation in the form of the porphyrin ring inclined with respect to the gold surface.

Graph matching with subdivision surfaces for texture synthesis on surfaces

Existing texture synthesis-from-example strategies for polygon meshes typically make use of three components: a multi-resolution mesh hierarchy that allows the overall nature of the pattern to be reproduced before filling in detail; a matching strategy that extends the synthesized texture using the best fit from a texture sample; and a transfer mechanism that copies the selected portion of the texture sample to the target surface. We introduce novel alternatives for each of these components. Use of p2-subdivision surfaces provides the mesh hierarchy and allows fine control over the surface complexity. Adaptive subdivision is used to create an even vertex distribution over the surface. Use of the graph defined by a surface region for matching, rather than a regular texture neighbourhood, provides for flexible control over the scale of the texture and allows simultaneous matching against multiple levels of an image pyramid created from the texture sample. We use graph cuts for texture transfer, adapting this scheme to the context of surface synthesis. The resulting surface textures are realistic, tolerant of local mesh detail and are comparable to results produced by texture neighbourhood sampling approaches.

Reliable graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated. In this chapter we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be. We also examined the performance difference of different discovery algorithms. This reveals the impact of discovery process. The experimental results show the superior reliability and robustness of MML method to standard significance tests in the recovery of graph links with small samples and weak links.