2,257 research outputs found
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
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
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