Bayesian inference on group differences in multivariate categorical data

Multivariate categorical data are common in many fields. We are motivated by election polls studies assessing evidence of changes in voters opinions with their candidates preferences in the 2016 United States Presidential primaries or caucuses. Similar goals arise routinely in several applications, but current literature lacks a general methodology which combines flexibility, efficiency, and tractability in testing for group differences in multivariate categorical data at different---potentially complex---scales. We address this goal by leveraging a Bayesian representation which factorizes the joint probability mass function for the group variable and the multivariate categorical data as the product of the marginal probabilities for the groups, and the conditional probability mass function of the multivariate categorical data, given the group membership. To enhance flexibility, we define the conditional probability mass function of the multivariate categorical data via a group-dependent mixture of tensor factorizations, thus facilitating dimensionality reduction and borrowing of information, while providing tractable procedures for computation, and accurate tests assessing global and local group differences. We compare our methods with popular competitors, and discuss improved performance in simulations and in American election polls studies

Stratified stochastic variational inference for high-dimensional network factor model

There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov Chain Monte Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes.Comment: 25 pages, 1 figures. Corrected compilation issues and minor typo

On the regularity of stochastic currents, fractional Brownian motion and applications to a turbulence model

38 pagesInternational audienceWe study the pathwise regularity of the map $$\varphi \mapsto I(\varphi) = \int_0^T \langle \varphi(X_t), dX_t \rangle$$ where $\varphi$ is a vector function on $\R^d$ belonging to some Banach space $V$, $X$ is a stochastic process and the integral is some version of a stochastic integral defined via regularization. A \emph{stochastic current} is a continuous version of this map, seen as a random element of the topological dual of $V$. We give sufficient conditions for the current to live in some Sobolev space of distributions and we provide elements to conjecture that those are also necessary. Next we verify the sufficient conditions when the process $X$ is a $d$-dimensional fractional Brownian motion (fBm); we identify regularity in Sobolev spaces for fBm with Hurst index $H \in (1/4,1)$. Next we provide some results about general Sobolev regularity of Brownian currents. Finally we discuss applications to a model of random vortex filaments in turbulent fluids

Bayesian inference for tensor factorization models

Multivariate categorical data are routinely collected in several applications, including epidemiology, biology, and sociology, among many others. Popular models dealing with these variables include log-linear and tensor factorization models, with these lasts having the advantage of flexibly characterizing the dependence structure underlying the data. Under such framework, this Thesis aims to provide novel approaches to define compact representations of the dependence structures and to introduce new inference possibilities in tensor factorization approaches. We introduce a new class of GROuped Tensor (GROT) factorizations, which have superior performance in terms of data compression if compared to standard Parafac approach, using relatively few components to represent the joint probability mass function of the data. While popular Parafac factorizations rely on mixing together independent components, GROT mixes together grouped factorizations, equivalent to replacing vector arms in Parafac with low-dimensional tensor arms. We consider a Bayesian approach to inference with Dirichlet priors on the mixing weights and arm components, to obtain a combined low-rank and sparse structure, while facilitating efficient posterior computation via Markov chain Monte Carlo. Motivated by an application on malaria risk assessment, we also introduce a novel multivariate generalization of mixed membership models, which allows identification of correlated profiles related to different domains corresponding to separate groups of variables. We consider as a case study the Machadinho settlement project in Brazil, with the aim of defining survey based environmental and behavioral risk profiles and studying their interaction and evolution. To achieve this goal, we show that the use of correlated multiple membership vectors leads to interpretable inference requiring a lower number of profiles compared to standard formulations while inducing a more compact representation of the population level model. We propose a novel multivariate logistic normal distribution for the membership vectors, which allows easy introduction of auxiliary information in the membership profiles leveraging a multivariate latent logistic regression. A Bayesian approach to inference, relying on Pólya gamma data augmentation, facilitates efficient posterior computation via Markov chain Monte Carlo. The proposed approach is shown to outperform the classical mixed membership model in simulations, and the malaria diffusion application

Effective electrothermal analysis of electronic devices and systems with parameterized macromodeling

We propose a parameterized macromodeling methodology to effectively and accurately carry out dynamic electrothermal (ET) simulations of electronic components and systems, while taking into account the influence of key design parameters on the system behavior. In order to improve the accuracy and to reduce the number of computationally expensive thermal simulations needed for the macromodel generation, a decomposition of the frequency-domain data samples of the thermal impedance matrix is proposed. The approach is applied to study the impact of layout variations on the dynamic ET behavior of a state-of-the-art 8-finger AlGaN/GaN high-electron mobility transistor grown on a SiC substrate. The simulation results confirm the high accuracy and computational gain obtained using parameterized macromodels instead of a standard method based on iterative complete numerical analysis

A Hu–Washizu variational approach to self-stabilized virtual elements: 2D linear elastostatics

An original, variational formulation of the Virtual Element Method (VEM) is proposed, based on a Hu–Washizu mixed variational statement for 2D linear elastostatics. The proposed variational framework appears to be ideal for the formulation of VEs, whereby compatibility is enforced in a weak sense and the strain model can be prescribed a priori, independently of the unknown displacement model. It is shown how the ensuing freedom in the definition of the strain model can be conveniently exploited for the formulation of self-stabilized and possibly locking-free low order VEs. The superior performances of the VEs formulated within this framework has been verified by application to several numerical tests

NAD-dependent ADP-ribosylation of the human antimicrobial and immune-modulatory peptide LL-37 by ADP-ribosyltransferase-1

LL-37 is a cationic peptide belonging to the cathelicidin family that has antimicrobial and immune-modulatory properties. Here we show that the mammalian mono-ADP-ribosyltransferase-1 (ART1), which selectively transfers the ADP-ribose moiety from NAD to arginine residues, ADP-ribosylates LL-37 in vitro. The incorporation of ADP-ribose was first observed by Western blot analysis and then confirmed by MALDI-TOF. Mass-spectrometry showed that up to four of the five arginine residues present in LL-37 could be ADP-ribosylated on the same peptide when incubated at a high NAD concentration in the presence of ART1. The attachment of negatively charged ADP-ribose moieties considerably alters the positive charge of the arginine residues thus reducing the cationicity of LL-37. The cationic nature of LL-37 is key for its ability to interact with cell membranes or negatively charged biomolecules, such as DNA, RNA, F-actin and glycosaminoglycans. Thus, the ADP-ribosylation of LL-37 is expected to have the potential to modulate LL-37 biological activities in several physiological and pathological settings

Kaolin protects olive fruits from Bactrocera oleae (Gmelin) infestations unaffecting olive oil quality

The efficacy of the processed kaolin “Surround WP” to control olive fruit fly, Bactrocera oleae Gmelin, field infestations was investigated in east Calabria. The preliminary results showed that fruit infestation levels were significantly reduced on kaolin-treated trees compared with untreated trees. The promising results of these experiments points to the feasibility of using particle film technology composed of a non-toxic material, to avoid olive fly damage as an alternative to the applications of rotenone in organic orchards. Finally, kaolin treatment unaffected the nutritional and sensory quality parameters of the corresponding virgin olive oils obtained by a laboratory scale olive mill, thus satisfying the present quality requirements

Severe bloodstream infection due to KPC-producer e coli in a renal transplant recipient treated with the double-carbapenem regimen and analysis of in vitro synergy testing a case report

Transplant recipients are at high risk of infections caused by multidrug resistant microorganisms. Due to the limited thera- peutic options, innovative antimicrobial combinations against carbape- nem-resistant Enterobacteriaceae causing severe infections are necessary. A 61-year-old woman with a history of congenital solitary kidney underwent renal transplantation. The postoperative course was compli- cated by nosocomial pneumonia due to Stenotrophomonas maltophilia and pan-sensitive Escherichia coli, successfully treated with antimicrobial therapy. On postoperative day 22, diagnosis of surgical site infection and nosocomial pneumonia with concomitant bacteremia due to a Kle- bisella pneumoniae carbapenemase-producer E coli was made. The patient was treated with the double-carbapenem regimen (high dose of merope- nem plus ertapenem) and a potent synergistic and bactericidal activity of this un-conventional therapeutic strategy was observed in vitro. Despite a microbiological response with prompt negativity of blood cultures, the patient faced a worse outcome because of severe hemorrhagic shock. The double-carbapenem regimen might be considered as a rescue therapy in those subjects, including transplant recipients, in whom previous antimicrobial combinations failed or when colistin use might be discouraged. Performing in vitro synergy testing should be strongly encouraged in cases of infections caused by pan-drug resistant strains, especially in high-risk patients