Web service QoS prediction using improved software source code metrics

Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services' QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics

Spatial synchronization and extinction of species under external forcing

We study the interplay between synchronization and extinction of a species. Using a general model we show that under a common external forcing, the species with a quadratic saturation term in the population dynamics first undergoes spatial synchronization and then extinction, thereby avoiding the rescue effect. This is because the saturation term reduces the synchronization time scale but not the extinction time scale. The effect can be observed even when the external forcing acts only on some locations provided there is a synchronizing term in the dynamics. Absence of the quadratic saturation term can help the species to avoid extinction.Comment: 4 pages, 2 figure

ASMOOTH: A simple and efficient algorithm for adaptive kernel smoothing of two-dimensional imaging data

An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language (IDL). The functional form of the kernel can be varied (top-hat, Gaussian etc.) to allow different weighting of the event counts registered within the smoothing region. For each individual pixel the algorithm increases the smoothing scale until the signal-to-noise ratio (s.n.r.) within the kernel reaches a preset value. Thus, noise is suppressed very efficiently, while at the same time real structure, i.e. signal that is locally significant at the selected s.n.r. level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The ASMOOTH algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all pixel values share the same signal-to-noise ratio above the background. We apply ASMOOTH to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the ASMOOTHed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, i.e. they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.Comment: 9 pages, 5 figures, to be published in MNRA

Stripped Spiral Galaxies as Promising Targets for the Determination of the Cepheid distance to the Virgo Cluster

The measurement of precise galaxy distances by Cepheid observations out to the distance of the Virgo cluster is important for the determination of the Hubble constant ($H_0$). The Virgo cluster is thereby often used as an important stepping stone. The first HST measurement of the distance of a Virgo galaxy (M100) using Cepheid variables provided a value for $H_0=80(\pm 17)$ km/s/Mpc (Freedman et al. 1994). This measurement was preceeded by a ground based study of the Virgo spiral NGC4571 (Pierce et al. 1994) formally providing $H_0= 87\pm7$ km/s/Mpc. These determinations rely on the accuracy with which the position of this observed spiral galaxy can be located with respect to the Virgo cluster center. This uncertainty introduces a major error in the determination of $H_0$, together with the uncertainty in the adopted Virgo infall velocity of the Local Group. Here we propose the use of spiral galaxies which show clear signs of being stripped off their interstellar medium by the intracluster gas of the Virgo cluster as targets for the Cepheid distance measurements. We show that the stripping process and the knowledge of the intracluster gas distribution from ROSAT X-ray observations allow us to locate these galaxies with an at least three times higher precision with respect to M87 than in the case of other spirals like M100. The X-ray observations further imply that M87 is well centered within the intracluster gas halo of the Virgo cluster and that M86 is associated with a group of galaxies and a larger dark matter halo. The combination of these informations could enable us to locate the two stripped spiral galaxies quite precisely within the Virgo cluster and could greatly improve the determination of the Virgo cluster distance.Comment: 21 pages, Latex(aaspp.sty), including 6 figures, accepted for publication in ApJL (shortened abstract:

Features in the Primordial Spectrum from WMAP: A Wavelet Analysis

Precise measurements of the anisotropies in the cosmic microwave background enable us to do an accurate study on the form of the primordial power spectrum for a given set of cosmological parameters. In a previous paper (Shafieloo and Souradeep 2004), we implemented an improved (error sensitive) Richardson-Lucy deconvolution algorithm on the measured angular power spectrum from the first year of WMAP data to determine the primordial power spectrum assuming a concordance cosmological model. This recovered spectrum has a likelihood far better than a scale invariant, or, `best fit' scale free spectra (\Delta ln L = 25 w.r.t. Harrison Zeldovich, and, \Delta ln L = 11 w.r.t. power law with n_s=0.95). In this paper we use Discrete Wavelet Transform (DWT) to decompose the local features of the recovered spectrum individually to study their effect and significance on the recovered angular power spectrum and hence the likelihood. We show that besides the infra-red cut off at the horizon scale, the associated features of the primordial power spectrum around the horizon have a significant effect on improving the likelihood. The strong features are localised at the horizon scale.Comment: 8 pages, 4 figures, uses Revtex4, matches version accepted to Phys. Rev. D, main results and conclusions unchanged, references adde

Making On-Demand Routing Efficient with Route-Request Aggregation

In theory, on-demand routing is very attractive for mobile ad hoc networks (MANET), because it induces signaling only for those destinations for which there is data traffic. However, in practice, the signaling overhead of existing on-demand routing protocols becomes excessive as the rate of topology changes increases due to mobility or other causes. We introduce the first on-demand routing approach that eliminates the main limitation of on-demand routing by aggregating route requests (RREQ) for the same destinations. The approach can be applied to any existing on-demand routing protocol, and we introduce the Ad-hoc Demand-Aggregated Routing with Adaptation (ADARA) as an example of how RREQ aggregation can be used. ADARA is compared to AODV and OLSR using discrete-event simulations, and the results show that aggregating RREQs can make on-demand routing more efficient than existing proactive or on-demand routing protocols

3D functional models of monkey brain through elastic registration of histological sections

In this paper we describe a method for the reconstruction and visualization of functional models of monkey brains. Models are built through the registration of high resolution images obtained from the scanning of histological sections with reference photos taken during the brain slicing. From the histological sections it is also possible to acquire specifically activated neuron coordinates introducing functional information in the model. Due to the specific nature of the images (texture information is useless and the sections could be deformed when they were cut and placed on glass) we solved the registration problem by extracting corresponding cerebral cortex borders (extracted with a snake algorithm), and computing from their deformation an image transform modeled as an affine deformation plus a non-linear field evaluated as an elastically constrained deformation minimizing contour distances. Registered images and contours are used then to build 3D models of specific brains by a software tool allowing the interactive visualization of cortical volumes together with the spatially referenced neurons classified and differently colored according to their functionalities

Intrinsically disordered proteins and conformational noise: Implications in cancer

Intrinsically disordered proteins, IDPs, are proteins that lack a rigid 3D structure under physiological conditions, at least in vitro. Despite the lack of structure, IDPs play important roles in biological processes and transition from disorder to order upon binding to their targets. With multiple conformational states and rapid conformational dynamics, they engage in myriad and often “promiscuous” interactions. These stochastic interactions between IDPs and their partners, defined here as conformational noise, is an inherent characteristic of IDP interactions. The collective effect of conformational noise is an ensemble of protein network configurations, from which the most suitable can be explored in response to perturbations, conferring protein networks with remarkable flexibility and resilience. Moreover, the ubiquitous presence of IDPs as transcriptional factors and, more generally, as hubs in protein networks, is indicative of their role in propagation of transcriptional (genetic) noise. As effectors of transcriptional and conformational noise, IDPs rewire protein networks and unmask latent interactions in response to perturbations. Thus, noise-driven activation of latent pathways could underlie state-switching events such as cellular transformation in cancer. To test this hypothesis, we created a model of a protein network with the topological characteristics of a cancer protein network and tested its response to a perturbation in presence of IDP hubs and conformational noise. Because numerous IDPs are found to be epigenetic modifiers and chromatin remodelers, we hypothesize that they could further channel noise into stable, heritable genotypic changes