The Physical Propagator of a Slowly Moving Charge

We consider an electron which is electromagnetically dressed in such a way that it is both gauge invariant and that it has the associated electric and magnetic fields expected of a moving charge. We study the propagator of this dressed electron and, for small velocities, show explicitly at one loop that at the natural (on-shell) renormalisation point, $p_0=m$, ${\bold p}= m{\bold v}$, one can renormalise the propagator multiplicatively. Furthermore the renormalisation constants are infra-red finite. This shows that the dressing we use corresponds to a slowly moving, physical asymptotic field.Comment: 10 pages, plain TeX, 1 Figure (uuencoded needs epsfig.sty

Density estimation with Gaussian processes for gravitational-wave posteriors

The properties of black-hole and neutron-star binaries are extracted from gravitational-wave signals using Bayesian inference. This involves evaluating a multi-dimensional posterior probability function with stochastic sampling. The marginal probability density distributions from which the samples are drawn are usually interpolated with kernel density estimators. Since most post-processing analysis within the field is based on these parameter estimation products, interpolation accuracy of the marginals is essential. In this work, we propose a new method combining histograms and Gaussian Processes as an alternative technique to fit arbitrary combinations of samples from the source parameters. This method comes with several advantages such as flexible interpolation of non-Gaussian correlations, Bayesian estimate of uncertainty, and efficient re-sampling with Hamiltonian Monte Carlo

Il futuro non (è) scritto

Review of Recensione diCuratori: Paolo Carlotti, Anna Irene Del Monaco, Dina NenciniTitolo: L’ampliamento della Camera dei Deputati. Letture e prospettive per il progetto. Collana: Lettura e progetto. Nuova Serie FrancoAngeliLingua: italiano Editore: FrancoAngeliCaratteristiche: formato 14x21,5 cm, brossura, b/nISBN: 9788891751027Anno: 201

Pedagogically Semi-Equivalent Repertoire Analogies: Soprano Fach

For decades, conservatories have divided singing into two tracks: classical (voce chiusa) or musical theater (CCM). Young singers are forced to decide which track they will continue on—seemingly for the rest of their careers. But the demand for a single skill set does not reflect today’s job market nor the vocal demands singers will face throughout their careers. Singers, instead, must be readily able to compete at high levels on more than one track. Yet the majority of programs have not restructured to accommodate this change. Such recalcitrance is detrimental to singers as they navigate high-stakes, high-pressure careers with little to no knowledge of crucial skills they may need. Often this can lead to over-singing, poor vocal health, and sometimes the end of a career. I propose a way in which both tracks—voce chiusa and CCM—can be used symbiotically. This combinatorial approach is good for future careers, but more importantly, it is beneficial to a singer’s intrinsic laryngeal muscle development. Finding a healthy belt for voce chiusa-trained singers is equally beneficial as finding a strong head-voice for CCM singers. This synthesis of vocal techniques actually yields a super-technique—a more flexible, healthy, and individualized sound that is unified through the use of the breath. This document is an exposition of the pedagogical benefits of learning both traditional, voce chiusa and CCM repertoire concurrently in vocal training. I also offer repertoire that supports this combinatorial approach in order to make its utility in the voice studio more accessible. This repertoire features pedagogically semi-equivalent song pairings with a focus on the soprano Fach. I will highlight the major areas of equivalence and pedagogical efficacy shared by the voce chiusa and CCM repertoire paired. Twenty-five style pairings are offered as a sample of the future catalog I hope to aggregate for all major voice types. The purpose of the catalog is to incorporate pluralistic style in the studio by combining the pedagogical methods of voce chiusa and CCM singing in order to train singers to have greater vocal development, flexibility, and health

Applicazioni del metodo variazionale ad alcuni problemi di fisica atomica e nucleare

Utilizzando il metodo variazionale si studiano alcuni sistemi con l’obiettivo di approssimare l’energia del loro stato fondamentale. I casi trattati in questa tesi vertono su problemi di natura atomica, quali la descrizione degli atomi di idrogeno, elio e dell’atomo muonico, e su problemi di carattere nucleare, quali la stima dell’ampiezza del potenziale di Yukawa e la descrizione del deutone

Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils

Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (ρb), and soil organic carbon content (OC). In this study, application of PTFs was carried out for 359 Sicilian soils by implementing five different artificial neural networks (ANNs) to estimate the parameter of the van Genuchten (vG) model for water retention curves. The raw data used to train the ANNs were soil texture, ρb, OC, and porosity. The ANNs were evaluated in their ability to predict both the vG parameters, on the basis of the normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE), and the water retention data. The Akaike's information criterion (AIC) test was also used to assess the most efficient network. Results confirmed the high predictive performance of ANNs with four input parameters (clay, sand, and silt fractions, and OC) in simulating soil water retention data, with a prediction accuracy characterized by MAE = 0.026 and RMSE = 0.069. The AIC efficiency criterion indicated that the most efficient ANN model was trained with a relatively low number of input nodes