Interference phenomena, chiral bosons and Lorentz invariance

We have studied the theory of gauged chiral bosons and proposed a general theory, a master action, that encompasses different kinds of gauge field couplings in chiral bosonized theories with first-class chiral constraints. We have fused opposite chiral aspects of this master action using the soldering formalism and applied the final action to several well known models. The Lorentz rotation permitted us to fix conditions on the parameters of this general theory in order to preserve the relativistic invariance. We also have established some conditions on the arbitrary parameter concerned in a chiral Schwinger model with a generalized constraint, investigating both covariance and Lorentz invariance. The results obtained supplements the one that shows the soldering formalism as a new method of mass generation.Comment: 11 pages, RevTex(twocolumn). Final version to appear in Physiscal Review

On Pruning for Score-Based Bayesian Network Structure Learning

Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variable in the data. Constructing such collections naively is computationally intensive since the number of parent sets grows exponentially with the number of variables. Thus, pruning techniques are not only desirable but essential. While good pruning rules exist for the Bayesian Information Criterion (BIC), current results for the Bayesian Dirichlet equivalent uniform (BDeu) score reduce the search space very modestly, hampering the use of the (often preferred) BDeu. We derive new non-trivial theoretical upper bounds for the BDeu score that considerably improve on the state-of-the-art. Since the new bounds are mathematically proven to be tighter than previous ones and at little extra computational cost, they are a promising addition to BNSL methods

On tidal forces in f(R) theories of gravity

Despite the extraordinary attention that modified gravity theories have attracted over the past decade, the geodesic deviation equation in this context has not received proper formulation thus far. This equation provides an elegant way to investigate the timelike, null and spacelike structure of spacetime geometries. In this investigation we provide the full derivation of this equation in situations where General Relativity has been extended in Robertson-Walker background spacetimes. We find that for null geodesics the contribution arising from the geometrical new terms is in general non-zero. Finally we apply the results to a well known class of f(R) theories, compare the results with General Relativity predictions and obtain the equivalent area distance relation.Comment: 9 pages, 2 figure

Resistant arterial hypertension in a patient with adrenal incidentaloma multiple steno-obstructive vascular lesions and antiphospholipid syndrome

Resistant hypertension is defined as above of blood pressure (≤ 140/90 mmHg) despite therapy with three or more antihypertensive drugs of different classes at maximum tolerable doses with one bling a diuretic. An important consideration in defining a patient with resistant hypertension is the mislabeling of secondary hypertension as resistant hypertension. Here, we report a patients with resistant hypertension caused by multiple stenoocclusive arteries due to antiphospholipid syndrome and coexisting with subclinical Cushing’s syndrome

Joints in Random Forests

Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist. Due to their discriminative nature, however, they lack principled methods to process inputs with missing features or to detect outliers, which requires pairing them with imputation techniques or a separate generative model. In this paper, we demonstrate that DTs and RFs can naturally be interpreted as generative models, by drawing a connection to Probabilistic Circuits, a prominent class of tractable probabilistic models. This reinterpretation equips them with a full joint distribution over the feature space and leads to Generative Decision Trees (GeDTs) and Generative Forests (GeFs), a family of novel hybrid generative-discriminative models. This family of models retains the overall characteristics of DTs and RFs while additionally being able to handle missing features by means of marginalisation. Under certain assumptions, frequently made for Bayes consistency results, we show that consistency in GeDTs and GeFs extend to any pattern of missing input features, if missing at random. Empirically, we show that our models often outperform common routines to treat missing data, such as K-nearest neighbour imputation, and moreover, that our models can naturally detect outliers by monitoring the marginal probability of input features

Gli interventi educativi per i pazienti con scompenso cardiaco: una sintesi della letteratura

Patient education is recognized as a central component of heart failure care and reduces hospital readmissions. Nurses have an important role in providing patient education and modifying self-care behaviors. The aim of this article is to examine characteristics of educational interventions for heart failure patients, their measured outcomes and the role of nurses in providing education. We conducted a literature review of the last 10 years and considered 30 articles. Multisession motivational interventions, repeated over time and with different follow-up interventions seem to produce the best results. However, some aspects remain controversial