How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics

Multivariate analyses play an important role in high energy physics. Such analyses often involve performing an unbinned maximum likelihood fit of a probability density function (p.d.f.) to the data. This paper explores a variety of unbinned methods for determining the goodness of fit of the p.d.f. to the data. The application and performance of each method is discussed in the context of a real-life high energy physics analysis (a Dalitz-plot analysis). Several of the methods presented in this paper can also be used for the non-parametric determination of whether two samples originate from the same parent p.d.f. This can be used, e.g., to determine the quality of a detector Monte Carlo simulation without the need for a parametric expression of the efficiency.Comment: 32 pages, 12 figure

A New Dental Superalloy System: V. Embrittling Phase Transformations

The σ phase is rich in Ta. When the Ta concentration is less than 14%, σ does not interfere with the slip mechanism; when the Ta concentration is more than 15%, σ interferes with the slip mechanism. The coherent α-Co 3Ta phase forms at Ta concentrations of less than 15%, whereas incoherent γ-Co 2Ta forms only at higher Ta concentrations. The interface between σ and the matrix is an important factor in the failure mechanism.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67929/2/10.1177_00220345740530013301.pd

Chromomagnetic Dipole Moment of the Top Quark Revisited

We study the complete one-loop contributions to the chromagnetic dipole moment $\Delta\kappa$ of the top quark in the Standard Model, two Higgs doublet models, topcolor assited technicolor models (TC2), 331 models and extended models with a single extra dimension. We find that the SM predicts $\Delta\kappa = - 0.056$ and that the predictions of the other models are also consitent with the constraints imposed on $\Delta\kappa$ by low-energy precision measurements.Comment: 20 pages, 5 figures, Updat

Tune in to your emotions: a robust personalized affective music player

The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application

Simulation techniques for cosmological simulations

Modern cosmological observations allow us to study in great detail the evolution and history of the large scale structure hierarchy. The fundamental problem of accurate constraints on the cosmological parameters, within a given cosmological model, requires precise modelling of the observed structure. In this paper we briefly review the current most effective techniques of large scale structure simulations, emphasising both their advantages and shortcomings. Starting with basics of the direct N-body simulations appropriate to modelling cold dark matter evolution, we then discuss the direct-sum technique GRAPE, particle-mesh (PM) and hybrid methods, combining the PM and the tree algorithms. Simulations of baryonic matter in the Universe often use hydrodynamic codes based on both particle methods that discretise mass, and grid-based methods. We briefly describe Eulerian grid methods, and also some variants of Lagrangian smoothed particle hydrodynamics (SPH) methods.Comment: 42 pages, 16 figures, accepted for publication in Space Science Reviews, special issue "Clusters of galaxies: beyond the thermal view", Editor J.S. Kaastra, Chapter 12; work done by an international team at the International Space Science Institute (ISSI), Bern, organised by J.S. Kaastra, A.M. Bykov, S. Schindler & J.A.M. Bleeke

Improved constraints on the expansion rate of the Universe up to z~1.1 from the spectroscopic evolution of cosmic chronometers

We present new improved constraints on the Hubble parameter H(z) in the redshift range 0.15 < z < 1.1, obtained from the differential spectroscopic evolution of early-type galaxies as a function of redshift. We extract a large sample of early-type galaxies (\sim11000) from several spectroscopic surveys, spanning almost 8 billion years of cosmic lookback time (0.15 < z < 1.42). We select the most massive, red elliptical galaxies, passively evolving and without signature of ongoing star formation. Those galaxies can be used as standard cosmic chronometers, as firstly proposed by Jimenez & Loeb (2002), whose differential age evolution as a function of cosmic time directly probes H(z). We analyze the 4000 {\AA} break (D4000) as a function of redshift, use stellar population synthesis models to theoretically calibrate the dependence of the differential age evolution on the differential D4000, and estimate the Hubble parameter taking into account both statistical and systematical errors. We provide 8 new measurements of H(z) (see Tab. 4), and determine its change in H(z) to a precision of 5-12% mapping homogeneously the redshift range up to z \sim 1.1; for the first time, we place a constraint on H(z) at z \neq 0 with a precision comparable with the one achieved for the Hubble constant (about 5-6% at z \sim 0.2), and covered a redshift range (0.5 < z < 0.8) which is crucial to distinguish many different quintessence cosmologies. These measurements have been tested to best match a \Lambda CDM model, clearly providing a statistically robust indication that the Universe is undergoing an accelerated expansion. This method shows the potentiality to open a new avenue in constrain a variety of alternative cosmologies, especially when future surveys (e.g. Euclid) will open the possibility to extend it up to z \sim 2.Comment: 34 pages, 15 figures, 6 tables, published in JCAP. It is a companion to Moresco et al. (2012b, http://arxiv.org/abs/1201.6658) and Jimenez et al. (2012, http://arxiv.org/abs/1201.3608). The H(z) data can be downloaded at http://www.physics-astronomy.unibo.it/en/research/areas/astrophysics/cosmology-with-cosmic-chronometer

Shopping externalities and retail concentration:Evidence from dutch shopping streets

Why do shops cluster in shopping streets? We argue that retail firms benefit from shopping externalities. We identify these externalities for the main Dutch shopping streets by estimating the effect of footfall – the number of pedestrians that pass by – and the number of shops in the vicinity on store owners’ rental income. We address endogeneity issues by exploiting spatial variation within shopping streets combined with historic long-lagged instruments. Our estimates imply an elasticity of rental income with respect to footfall as well as number of shops in the vicinity of (at least) 0.25. We show that these shopping externalities are unlikely to be internalised. It follows that substantial subsidies to shop owners are welfare improving, seemingly justifying current policies. Finally, we find limited evidence for heterogeneity between retail firms located in shopping streets in their willingness to pay for shopping externalities

Anxiety disorders in children and young people: assessment and treatment

Despite significant advancements in our knowledge of anxiety disorders in children and adolescents, they continue to be under-recognised and under-treated. It is critical that these disorders are taken seriously in children and young people as they are highly prevalent, have a negative impact on educational, social and health functioning, create a risk of ongoing anxiety and other mental health disorders across the lifespan and are associated with substantial economic burden. Yet very few children with anxiety disorders access evidence-based treatments and there is an urgent need for widespread implementation of effective interventions. This review aims to provide an overview of recent research developments that will be relevant to clinicians and policymakers, particularly focusing on the development and maintenance of child anxiety disorders and considerations for assessment and treatment. Given the critical need to increase access to effective support we hope this review will contribute to driving forward a step change in treatment delivery for children and young people with anxiety disorders and their families

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).Comment: 17 pages, 4 figure