Noise resistant generalized parametric validity index of clustering for gene expression data

This article has been made available through the Brunel Open Access Publishing Fund.Validity indices have been investigated for decades. However, since there is no study of noise-resistance performance of these indices in the literature, there is no guideline for determining the best clustering in noisy data sets, especially microarray data sets. In this paper, we propose a generalized parametric validity (GPV) index which employs two tunable parameters α and β to control the proportions of objects being considered to calculate the dissimilarities. The greatest advantage of the proposed GPV index is its noise-resistance ability, which results from the flexibility of tuning the parameters. Several rules are set to guide the selection of parameter values. To illustrate the noise-resistance performance of the proposed index, we evaluate the GPV index for assessing five clustering algorithms in two gene expression data simulation models with different noise levels and compare the ability of determining the number of clusters with eight existing indices. We also test the GPV in three groups of real gene expression data sets. The experimental results suggest that the proposed GPV index has superior noise-resistance ability and provides fairly accurate judgements

Accretion disk around the rotating Damour-Solodukhin wormhole

A new rotating generalization of the Damour-Solodukhin wormhole (RDSWH), called Kerr-like wormhole, has recently been proposed and investigated by Bueno \textit{et al} for echoes in the gravitational wave signal. We show a novel feature of the RDSWH, viz., that the kinematic properties such as the ISCO\ or marginally stable radius $r_{\text{ms}}$, efficiency $\epsilon$ and the disk potential $V_{\text{eff}}$ are \textit{independent} of $\lambda$ (which means they are identical to their KBH counterparts for any given spin). Differences however appear in the emissivity properties for higher values $0.1<\lambda\leq 1$ (say) and for the extreme spin $a_{\star}=0.998$. The kinematic and emissivity are generic properties as variations of the wormhole mass and the rate of accretion within the model preserve these properties. Specifically, the behavior of the luminosity peak is quite opposite to each other for the two objects, which could be useful from the viewpoint of observations. Apart from this, an estimate of the difference $\Delta_{\lambda}$ in the maxima of flux of radiation $F(r)$ shows non-zero values but is too tiny to be observable at present for $\lambda < 10^{-3}$ permitted by the strong lensing bound. The broad conclusion is that RDSWH\ are experimentally indistinguishable from KBH by accretion characteristics.Comment: 9 pages, 3 tables, 13 figure

Dual-layer network representation exploiting information characterization

In this paper, a logical dual-layer representation approach is proposed to facilitate the analysis of directed and weighted complex networks. Unlike the single logical layer structure, which was widely used for the directed and weighted flow graph, the proposed approach replaces the single layer with a dual-layer structure, which introduces a provider layer and a requester layer. The new structure provides the characterization of the nodes by the information, which they provide to and they request from the network. Its features are explained and its implementation and visualization are also detailed. We also design two clustering methods with different strategies respectively, which provide the analysis from different points of view. The effectiveness of the proposed approach is demonstrated using a simplified example. By comparing the graph layout with the conventional directed graph, the new dual-layer representation reveals deeper insight into the complex networks and provides more opportunities for versatile clustering analysis.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Lensing observables: Massless dyonic vis-\`a-vis Ellis wormhole

Stable massless wormholes are theoretically interesting in their own right as well as for astrophysical applications, especially as galactic halo objects. Therefore, the study of gravitational lensing observables for such objects is of importance, and we do here by applying the parametric post-Newtonian method of Keeton and Petters to massless dyonic charged wormholes of the Einstein-Maxwell-Dilaton field theory and to the massless Ellis wormhole of the Einstein minimally coupled scalar field theory. The paper exemplifies how the lensing signatures of two different solutions belonging to two different theories could be qualitatively similar from the observational point of view. Quantitative differences appear depending on the parameter values. Surprisingly, there appears an unexpected divergence in the correction to differential time delay, which seems to call for a review of its original derivation.Comment: 16 pages, 7 figure

Observational evidence for mass ejection during soft X-ray dips in GRS1915+105

We investigate the connection between the X-ray and radio properties of the Galactic microquasar GRS1915+105, by analyzing the X-ray data observed with RXTE, during the presence of a huge radio flare (~450 mJy). The X-ray lightcurve shows two dips of ~100 second duration. Detailed time resolved spectral analysis shows the existence of three spectral components: a multicolor disk-blackbody, a Comptonized component due to hot plasma and a power-law. We find that the Comptonized component is very weak during the dip. This is further confirmed by the PHA ratio of the raw data and ratio of the lightcurves in different energy bands. These results, combined with the fact that the 0.5 -- 10 Hz QPO disappears during the dip and that the Comptonized component is responsible for the QPO lead to the conclusion that during the dips the matter emitting Comptonized spectrum is ejected away. This establishes a direct connection between the X-ray and radio properties of the source.Comment: Replaced with some minor changes, corrected typos. Added Journal Re

Generating Preview Tables for Entity Graphs

Users are tapping into massive, heterogeneous entity graphs for many applications. It is challenging to select entity graphs for a particular need, given abundant datasets from many sources and the oftentimes scarce information for them. We propose methods to produce preview tables for compact presentation of important entity types and relationships in entity graphs. The preview tables assist users in attaining a quick and rough preview of the data. They can be shown in a limited display space for a user to browse and explore, before she decides to spend time and resources to fetch and investigate the complete dataset. We formulate several optimization problems that look for previews with the highest scores according to intuitive goodness measures, under various constraints on preview size and distance between preview tables. The optimization problem under distance constraint is NP-hard. We design a dynamic-programming algorithm and an Apriori-style algorithm for finding optimal previews. Results from experiments, comparison with related work and user studies demonstrated the scoring measures' accuracy and the discovery algorithms' efficiency.Comment: This is the camera-ready version of a SIGMOD16 paper. There might be tiny differences in layout, spacing and linebreaking, compared with the version in the SIGMOD16 proceedings, since we must submit TeX files and use arXiv to compile the file

Characterizing Driving Context from Driver Behavior

Because of the increasing availability of spatiotemporal data, a variety of data-analytic applications have become possible. Characterizing driving context, where context may be thought of as a combination of location and time, is a new challenging application. An example of such a characterization is finding the correlation between driving behavior and traffic conditions. This contextual information enables analysts to validate observation-based hypotheses about the driving of an individual. In this paper, we present DriveContext, a novel framework to find the characteristics of a context, by extracting significant driving patterns (e.g., a slow-down), and then identifying the set of potential causes behind patterns (e.g., traffic congestion). Our experimental results confirm the feasibility of the framework in identifying meaningful driving patterns, with improvements in comparison with the state-of-the-art. We also demonstrate how the framework derives interesting characteristics for different contexts, through real-world examples.Comment: Accepted to be published at The 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2017