A modeling platform for efficient characterization of phase-locked loop /spl Delta/-/spl Sigma/ frequency synthesizers

The aim of this paper is to determine the stability of higher-order /spl Delta/-/spl Sigma/ modulators using the describing function method. The maximum stable input limits for third-, fourth- and fifth-order Chebyshev Type II based /spl Delta/-/spl Sigma/ modulators are established. These results are useful for optimising the design of higher-order /spl Delta/-/spl Sigma/ modulators

New giant radio sources and underluminous radio halos in two galaxy clusters

The aim of this work is to analyse the radio properties of the massive and dynamical disturbed clusters Abell 1451 and Zwcl 0634.1+4750, especially focusing on the possible presence of diffuse emission. We present new GMRT 320 MHz and JVLA 1.5 GHz observations of these two clusters. We found that both Abell 1451 and Zwcl 0634.1+4750 host a radio halo with a typical spectrum ($\alpha\sim1-1.3$). Similarly to a few other cases reported in the recent literature, these radio halos are significantly fainter in radio luminosity with respect to the current radio power-mass correlations and they are smaller than classical giant radio halos. These underluminous sources might contribute to shed light on the complex mechanisms of formation and evolution of radio halos. Furthermore, we detected a candidate radio relic at large distance from the cluster center in Abell 1451 and a peculiar head tail radio galaxy in Zwcl 0634.1+4750, which might be interacting with a shock front.Comment: 15 pages, 13 figures, accepted for publication in A&

On the absence of radio halos in clusters with double relics

Pairs of radio relics are believed to form during cluster mergers, and are best observed when the merger occurs in the plane of the sky. Mergers can also produce radio halos, through complex processes likely linked to turbulent re-acceleration of cosmic-ray electrons. However, only some clusters with double relics also show a radio halo. Here, we present a novel method to derive upper limits on the radio halo emission, and analyse archival X-ray Chandra data, as well as galaxy velocity dispersions and lensing data, in order to understand the key parameter that switches on radio halo emission. We place upper limits on the halo power below the $P_{\rm 1.4 \, GHz}\, M_{500}$ correlation for some clusters, confirming that clusters with double relics have different radio properties. Computing X-ray morphological indicators, we find that clusters with double relics are associated with the most disturbed clusters. We also investigate the role of different mass-ratios and time-since-merger. Data do not indicate that the merger mass ratio has an impact on the presence or absence of radio halos (the null hypothesis that the clusters belong to the same group cannot be rejected). However, the data suggests that the absence of radio halos could be associated with early and late mergers, but the sample is too small to perform a statistical test. Our study is limited by the small number of clusters with double relics. Future surveys with LOFAR, ASKAP, MeerKat and SKA will provide larger samples to better address this issue.Comment: 12 pages, 7 figures, MNRAS accepte

AstroStat—A VO tool for statistical analysis

AstroStat is an easy-to-use tool for performing statistical analysis on data. It has been designed to be compatible with Virtual Observatory (VO) standards thus enabling it to become an integral part of the currently available collection of VO tools. A user can load data in a variety of formats into AstroStat and perform various statistical tests using a menu driven interface. Behind the scenes, all analyses are done using the public domain statistical software—R and the output returned is presented in a neatly formatted form to the user. The analyses performable include exploratory tests, visualizations, distribution fitting, correlation & causation, hypothesis testing, multivariate analysis and clustering. The tool is available in two versions with identical interface and features—as a web service that can be run using any standard browser and as an offline application. AstroStat will provide an easy-to-use interface which can allow for both fetching data and performing power statistical analysis on them

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

Perceived risk factors for severe Covid-19 symptoms and their association with health behaviours: Findings from the HEBECO study

Risk perceptions are important influences on health behaviours. We used descriptive statistics and multivariable logistic regression models to assess cross-sectionally risk perceptions for severe Covid-19 symptoms and their health behaviour correlates among 2206 UK adults from the HEBECO study. The great majority (89-99%) classified age 70+, having comorbidities, being a key worker, overweight, and from an ethnic minority as increasing the risk. People were less sure about alcohol drinking, vaping, and nicotine replacement therapy use (17.4-29.5% responding 'don't know'). Relative to those who did not, those who engaged in the following behaviours had higher odds of classifying these behaviours as (i) decreasing the risk: smoking cigarettes (adjusted odds ratios, aORs, 95% CI = 2.26, 1.39-3.37), and using e-cigarettes (aORs = 5.80, 3.25-10.34); (ii) having no impact: smoking cigarettes (1.98; 1.42-2.76), using e-cigarettes (aORs = 2.63, 1.96-3.50), drinking alcohol (aORs = 1.75, 1.31-2.33); and lower odds of classifying these as increasing the risk: smoking cigarettes (aORs: 0.43, 0.32-0.56), using e-cigarettes (aORs = 0.25, 0.18-0.35). Similarly, eating more fruit and vegetables was associated with classifying unhealthy diet as 'increasing risk' (aOR = 1.37, 1.12-1.69), and exercising more with classifying regular physical activity as 'decreasing risk' (aOR = 2.42, 1.75-3.34). Risk perceptions for severe Covid-19 among UK adults were lower for their own health behaviours, evidencing optimism bias. These risk perceptions may form barriers to changing people's own unhealthy behaviours, make them less responsive to interventions that refer to the risk of Covid-19 as a motivating factor, and exacerbate inequalities in health behaviours and outcomes

Perceived risk factors for severe Covid-19 symptoms and their association with health behaviours: Findings from the HEBECO study

Background: There remains uncertainty about Covid-19 risk factors. We examined UK adults’ risk perceptions for severe Covid-19 symptoms and whether engaging in concurrent health behaviours is associated with risk perceptions. / Methods: Cross-sectional analysis of data from the HEBECO study where 2206 UK adults classified potential factors (age 70+, ethnic minority, medical comorbidities, vaping, smoking cigarettes, alcohol drinking, regular physical activity, being overweight, eating unhealthy foods, using nicotine replacement therapy – NRT, lower income, poor housing, being a keyworker) as either increasing, decreasing, or having no impact on severe Covid-19 symptoms. Logistic regressions examined whether engaging in health behaviours was associated with risk perceptions after adjusting for socio-demographic characteristics, health conditions and other behaviours. / Results: The great majority (89-99%) of adults classified age 70+, having comorbidities, being a key worker, overweight, and from an ethnic minority as increasing the risk. People were less sure about alcohol drinking, vaping, and nicotine replacement therapy use (17.4-29.5% responding ‘don’t know’). Relative to those who did not, those who smoked tobacco, vaped and consumed alcohol had significantly (all p<0.015) higher odds (aORs=1.58 to 5.80) for classifying these behaviours as ‘no impact’ or ‘decreasing risk’, and lower odds (aORs=.25 to .72) for classifying as ‘increasing risk’. Similarly, eating more fruit and vegetables was associated with classifying unhealthy diet as ‘increasing risk’ (aOR=1.37,1.12-1.69), and exercising more with classifying regular physical activity as ‘decreasing risk’ (aOR=2.42,1.75-3.34). / Conclusions: Risk perceptions for severe Covid-19 symptoms were lower for adults’ own health behaviours, evidencing optimism bias. / Implications: These risk perceptions may form barriers to changing one’s own unhealthy behaviours or make one less responsive to interventions that refer to the risk of Covid-19 as a motivating factor. Thus, in some cases risk perceptions could help sustain unhealthy behaviours and exacerbate inequalities in health behaviours and outcomes

Phase Field Model for Three-Dimensional Dendritic Growth with Fluid Flow

We study the effect of fluid flow on three-dimensional (3D) dendrite growth using a phase-field model on an adaptive finite element grid. In order to simulate 3D fluid flow, we use an averaging method for the flow problem coupled to the phase-field method and the Semi-Implicit Approximated Projection Method (SIAPM). We describe a parallel implementation for the algorithm, using Charm++ FEM framework, and demonstrate its efficiency. We introduce an improved method for extracting dendrite tip position and tip radius, facilitating accurate comparison to theory. We benchmark our results for two-dimensional (2D) dendrite growth with solvability theory and previous results, finding them to be in good agreement. The physics of dendritic growth with fluid flow in three dimensions is very different from that in two dimensions, and we discuss the origin of this behavior

Evolution of Ultracold, Neutral Plasmas

We present the first large-scale simulations of an ultracold, neutral plasma, produced by photoionization of laser-cooled xenon atoms, from creation to initial expansion, using classical molecular dynamics methods with open boundary conditions. We reproduce many of the experimental findings such as the trapping efficiency of electrons with increased ion number, a minimum electron temperature achieved on approach to the photoionization threshold, and recombination into Rydberg states of anomalously-low principal quantum number. In addition, many of these effects establish themselves very early in the plasma evolution ($\sim$ ns) before present experimental observations begin.Comment: 4 pages, 3 figures, submitted to PR