(Universitat Pompeu Fabra - Barcelona) Expansions by groups of humans were common during prehistoric times, after the adoption of agriculture. Among other factors, this is due to population growth of farmers which was greater than of that hunter-gatherers. We can find one example of this during the Neolithic period, when farming was introduced to Europe by migrations from the Middle East.

Macworld UK’s Acting Editor David Price takes the reins for this edition, and is joined by Online Editor of Computerworld UK Scott Carey to chat about billions and billions of pounds and the acquisition of ARM by SoftBank. Second up, Staff Writer at PC Advisor and Macworld UK Christopher Minasians plugs in to the haunting world of graphics cards and makes sense of it all for the rest of us. Digital Arts Staff Writer Mimi Launder then explains why Twitter has slapped a troll right in the face in order to stand up to Internet bullying.  

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1 June 2012 , Volume 68, Number 4

Eyes in the skies keeping watch on a planet under stress. Click on the PDF link to view the graphic

Primary hyperparathyroidism (PHPT) is a common endocrine disorder, definitive treatment usually requiring surgical removal of the offending parathyroid glands. To perform focused surgical approaches, it is necessary to localize all hyperfunctioning glands. The aim of the study was to compare the efficiency of established conventional scintigraphic imaging modalities with emerging ¹⁸F-fluorocholine PET/CT imaging in preoperative localization of hyperfunctioning parathyroid glands in a larger series of PHPT patients. Methods: In total, 103 patients with PHPT were imaged preoperatively with ¹⁸F-fluorocholine PET/CT and conventional scintigraphic imaging methods, consisting of ^99mTc-sestamibi SPECT/CT, ^99mTc-sestamibi/pertechnetate subtraction imaging, and ^99mTc-sestamibi dual-phase imaging. The results of histologic analysis, as well as intact parathyroid hormone and serum calcium values obtained 1 d after surgery and on follow-up, served as the standard of truth for evaluation of imaging results. Results: Diagnostic performance of ¹⁸F-fluorocholine PET/CT surpassed conventional scintigraphic methods (separately or combined), with calculated sensitivity of 92% for PET/CT and 39%–56% for conventional imaging (65% for conventional methods combined) in the entire patient group. Subgroup analysis, differentiating single and multiple hyperfunctioning parathyroid glands, showed PET/CT to be most valuable in the group with multiple hyperfunctioning glands, with sensitivity of 88%, whereas conventional imaging was significantly inferior, with sensitivity of 22%–34% (44% combined). Conclusion: ¹⁸F-fluorocholine PET/CT is a diagnostic modality superior to conventional imaging methods in patients with PHPT, allowing for accurate preoperative localization.

As federal and state governments ramp up efforts to implement the Workforce Innovation and Opportunity Act, these fact sheets compare key characteristics of the foreign born and the U.S. born that are relevant to understanding needs for adult education and workforce training services. The fact sheets cover the United States, the 20 states and 25 counties with the largest immigrant populations, and New York City.

As the share of U.S. children under age 8 who are Dual Language Learners (DLLs) increases, state policies have an important role to play in ensuring all young learners are able to get their education off to a good start. These fact sheets compare key characteristics of DLLs and their peers nationwide and in 30 states, and identify state policies that support equitable access to high-quality early childhood education and care programs.

States are in the midst of designing new policies to hold schools accountable for the education of English Learner (EL) students, as mandated by the federal Every Student Succeeds Act (ESSA). This series of fact sheets sketches the characteristics of immigrant and EL students in 25 states, the gaps between their educational outcomes and those of their peers, and the accountability policies each state is developing.

With many countries in East Asia facing unfavorable demographic shifts in the form of aging populations, low fertility, and shrinking workforces, governments in 2016 continued to explore immigration as a potential policy solution. However, a tradition of cultural homogeneity and wariness among publics about increased immigration is leading policymakers to test the waters with very small steps.

These fact sheets provide a sociodemographic sketch of parents with children ages 0 to 8 in the 30 states with the largest number of immigrant families, offering data and analysis of some of the key parental characteristics to help stakeholders identify populations that could be targets for early childhood and parent-focused programs working to improve child and parent outcomes.

Graphic novels -- History and criticism.

Libraries -- Marketing.

Hutton, George Stewart, 1906-1984.

Edinburgh : Daniel Lizars, 1829-

London : Stacy Bias, 2019.

Emanuel Ben-David, Bala Rajaratnam.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 580--604.

Abstract:
The Wishart distribution defined on the open cone of positive-definite matrices plays a central role in multivariate analysis and multivariate distribution theory. Its domain of parameters is often referred to as the Gindikin set. In recent years, varieties of useful extensions of the Wishart distribution have been proposed in the literature for the purposes of studying Markov random fields and graphical models. In particular, generalizations of the Wishart distribution, referred to as Type I and Type II (graphical) Wishart distributions introduced by Letac and Massam in Annals of Statistics (2007) play important roles in both frequentist and Bayesian inference for Gaussian graphical models. These distributions have been especially useful in high-dimensional settings due to the flexibility offered by their multiple-shape parameters. Concerning Type I and Type II Wishart distributions, a conjecture of Letac and Massam concerns the domain of multiple-shape parameters of these distributions. The conjecture also has implications for the existence of Bayes estimators corresponding to these high dimensional priors. The conjecture, which was first posed in the Annals of Statistics, has now been an open problem for about 10 years. In this paper, we give a necessary condition for the Letac and Massam conjecture to hold. More precisely, we prove that if the Letac and Massam conjecture holds on a decomposable graph, then no two separators of the graph can be nested within each other. For this, we analyze Type I and Type II Wishart distributions on appropriate Markov equivalent perfect DAG models and succeed in deriving the aforementioned necessary condition. This condition in particular identifies a class of counterexamples to the conjecture.

We study the identity testing problem in the context of spin systems or undirected graphical models, where it takes the following form: given the parameter specification of the model $M$ and a sampling oracle for the distribution $mu_{M^*}$ of an unknown model $M^*$, can we efficiently determine if the two models $M$ and $M^*$ are the same? We consider identity testing for both soft-constraint and hard-constraint systems. In particular, we prove hardness results in two prototypical cases, the Ising model and proper colorings, and explore whether identity testing is any easier than structure learning. For the ferromagnetic (attractive) Ising model, Daskalakis et al. (2018) presented a polynomial-time algorithm for identity testing. We prove hardness results in the antiferromagnetic (repulsive) setting in the same regime of parameters where structure learning is known to require a super-polynomial number of samples. Specifically, for $n$-vertex graphs of maximum degree $d$, we prove that if $|eta| d = omega(log{n})$ (where $eta$ is the inverse temperature parameter), then there is no polynomial running time identity testing algorithm unless $RP=NP$. In the hard-constraint setting, we present hardness results for identity testing for proper colorings. Our results are based on the presumed hardness of #BIS, the problem of (approximately) counting independent sets in bipartite graphs.

Motivated by modern applications in which one constructs graphical models based on a very large number of features, this paper introduces a new class of cluster-based graphical models, in which variable clustering is applied as an initial step for reducing the dimension of the feature space. We employ model assisted clustering, in which the clusters contain features that are similar to the same unobserved latent variable. Two different cluster-based Gaussian graphical models are considered: the latent variable graph, corresponding to the graphical model associated with the unobserved latent variables, and the cluster-average graph, corresponding to the vector of features averaged over clusters. Our study reveals that likelihood based inference for the latent graph, not analyzed previously, is analytically intractable. Our main contribution is the development and analysis of alternative estimation and inference strategies, for the precision matrix of an unobservable latent vector Z. We replace the likelihood of the data by an appropriate class of empirical risk functions, that can be specialized to the latent graphical model and to the simpler, but under-analyzed, cluster-average graphical model. The estimators thus derived can be used for inference on the graph structure, for instance on edge strength or pattern recovery. Inference is based on the asymptotic limits of the entry-wise estimates of the precision matrices associated with the conditional independence graphs under consideration. While taking the uncertainty induced by the clustering step into account, we establish Berry-Esseen central limit theorems for the proposed estimators. It is noteworthy that, although the clusters are estimated adaptively from the data, the central limit theorems regarding the entries of the estimated graphs are proved under the same conditions one would use if the clusters were known in advance. As an illustration of the usage of these newly developed inferential tools, we show that they can be reliably used for recovery of the sparsity pattern of the graphs we study, under FDR control, which is verified via simulation studies and an fMRI data analysis. These experimental results confirm the theoretically established difference between the two graph structures. Furthermore, the data analysis suggests that the latent variable graph, corresponding to the unobserved cluster centers, can help provide more insight into the understanding of the brain connectivity networks relative to the simpler, average-based, graph.

A new strategy for probabilistic graphical modeling is developed that draws parallels to community detection analysis. The method jointly estimates an undirected graph and homogeneous communities of nodes. The structure of the communities is taken into account when estimating the graph and at the same time, the structure of the graph is accounted for when estimating communities of nodes. The procedure uses a joint group graphical lasso approach with community detection-based grouping, such that some groups of edges co-occur in the estimated graph. The grouping structure is unknown and is estimated based on community detection algorithms. Theoretical derivations regarding graph convergence and sparsistency, as well as accuracy of community recovery are included, while the method's empirical performance is illustrated in an fMRI context, as well as with simulated examples.

Graphical models are commonly used to represent conditional dependence relationships between variables. There are multiple methods available for exploring them from high-dimensional data, but almost all of them rely on the assumption that the observations are independent and identically distributed. At the same time, observations connected by a network are becoming increasingly common, and tend to violate these assumptions. Here we develop a Gaussian graphical model for observations connected by a network with potentially different mean vectors, varying smoothly over the network. We propose an efficient estimation algorithm and demonstrate its effectiveness on both simulated and real data, obtaining meaningful and interpretable results on a statistics coauthorship network. We also prove that our method estimates both the inverse covariance matrix and the corresponding graph structure correctly under the assumption of network “cohesion”, which refers to the empirically observed phenomenon of network neighbors sharing similar traits.

We present the R package mgm for the estimation of k-order mixed graphical models (MGMs) and mixed vector autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data sets consisting of mixed types of variables (continuous, count, categorical) are ubiquitous. In addition, we allow to relax the stationarity assumption of both models by introducing time-varying versions of MGMs and mVAR models based on a kernel weighting approach. Time-varying models offer a rich description of temporally evolving systems and allow to identify external influences on the model structure such as the impact of interventions. We provide the background of all implemented methods and provide fully reproducible examples that illustrate how to use the package.

Jami J. Mulgrave, Subhashis Ghosal.

Source: Bayesian Analysis, Volume 15, Number 2, 449--475.

Abstract:
Gaussian graphical models have been used to study intrinsic dependence among several variables, but the Gaussianity assumption may be restrictive in many applications. A nonparanormal graphical model is a semiparametric generalization for continuous variables where it is assumed that the variables follow a Gaussian graphical model only after some unknown smooth monotone transformations on each of them. We consider a Bayesian approach in the nonparanormal graphical model by putting priors on the unknown transformations through a random series based on B-splines where the coefficients are ordered to induce monotonicity. A truncated normal prior leads to partial conjugacy in the model and is useful for posterior simulation using Gibbs sampling. On the underlying precision matrix of the transformed variables, we consider a spike-and-slab prior and use an efficient posterior Gibbs sampling scheme. We use the Bayesian Information Criterion to choose the hyperparameters for the spike-and-slab prior. We present a posterior consistency result on the underlying transformation and the precision matrix. We study the numerical performance of the proposed method through an extensive simulation study and finally apply the proposed method on a real data set.

Andrea Cremaschi, Raffaele Argiento, Katherine Shoemaker, Christine Peterson, Marina Vannucci.

Source: Bayesian Analysis, Volume 14, Number 4, 1271--1301.

Abstract:
Gaussian graphical models are useful tools for exploring network structures in multivariate normal data. In this paper we are interested in situations where data show departures from Gaussianity, therefore requiring alternative modeling distributions. The multivariate $t$ -distribution, obtained by dividing each component of the data vector by a gamma random variable, is a straightforward generalization to accommodate deviations from normality such as heavy tails. Since different groups of variables may be contaminated to a different extent, Finegold and Drton (2014) introduced the Dirichlet $t$ -distribution, where the divisors are clustered using a Dirichlet process. In this work, we consider a more general class of nonparametric distributions as the prior on the divisor terms, namely the class of normalized completely random measures (NormCRMs). To improve the effectiveness of the clustering, we propose modeling the dependence among the divisors through a nonparametric hierarchical structure, which allows for the sharing of parameters across the samples in the data set. This desirable feature enables us to cluster together different components of multivariate data in a parsimonious way. We demonstrate through simulations that this approach provides accurate graphical model inference, and apply it to a case study examining the dependence structure in radiomics data derived from The Cancer Imaging Atlas.

Ioannis Ntzoufras, Claudia Tarantola, Monia Lupparelli.

Source: Bayesian Analysis, Volume 14, Number 3, 797--823.

Abstract:
We introduce a novel Bayesian approach for quantitative learning for graphical log-linear marginal models. These models belong to curved exponential families that are difficult to handle from a Bayesian perspective. The likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities. Posterior distributions cannot be directly obtained, and Markov Chain Monte Carlo (MCMC) methods are needed. Finally, a well-defined model requires parameter values that lead to compatible marginal probabilities. Hence, any MCMC should account for this important restriction. We construct a fully automatic and efficient MCMC strategy for quantitative learning for such models that handles these problems. While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm that employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformation. We exploit a conditional conjugate setup to build an efficient proposal on probability parameters. The proposed methodology is illustrated by a simulation study and a real dataset.

Marcos Oliveira Prates, Renato Martins Assunção, Erica Castilho Rodrigues.

Source: Bayesian Analysis, Volume 14, Number 2, 623--647.

Abstract:
Spatial confounding between the spatial random effects and fixed effects covariates has been recently discovered and showed that it may bring misleading interpretation to the model results. Techniques to alleviate this problem are based on decomposing the spatial random effect and fitting a restricted spatial regression. In this paper, we propose a different approach: a transformation of the geographic space to ensure that the unobserved spatial random effect added to the regression is orthogonal to the fixed effects covariates. Our approach, named SPOCK, has the additional benefit of providing a fast and simple computational method to estimate the parameters. Also, it does not constrain the distribution class assumed for the spatial error term. A simulation study and real data analyses are presented to better understand the advantages of the new method in comparison with the existing ones.

Suprateek Kundu, Bani K. Mallick, Veera Baladandayuthapani.

Source: Bayesian Analysis, Volume 14, Number 2, 449--476.

Abstract:
There has been an intense development in the Bayesian graphical model literature over the past decade; however, most of the existing methods are restricted to moderate dimensions. We propose a novel graphical model selection approach for large dimensional settings where the dimension increases with the sample size, by decoupling model fitting and covariance selection. First, a full model based on a complete graph is fit under a novel class of mixtures of inverse–Wishart priors, which induce shrinkage on the precision matrix under an equivalence with Cholesky-based regularization, while enabling conjugate updates. Subsequently, a post-fitting model selection step uses penalized joint credible regions to perform model selection. This allows our methods to be computationally feasible for large dimensional settings using a combination of straightforward Gibbs samplers and efficient post-fitting inferences. Theoretical guarantees in terms of selection consistency are also established. Simulations show that the proposed approach compares favorably with competing methods, both in terms of accuracy metrics and computation times. We apply this approach to a cancer genomics data example.

Gregor Thut
Sep 13, 2006; 26:9494-9502
BehavioralSystemsCognitive

Did you know that cash transfer (CT) programmes in countries of the sub-Saharan Africa actually have a significant impact? In Malawi, these programmes helped families invest in agricultural equipment and livestock to produce their own food and reduce levels of negative coping strategies, like begging and school drop-outs. In Kenya, secondary school attendance rose by 9 percent and access to [...]

The positive outcomes won’t be immediately apparent, but will help reduce the strain on our healthcare system

The new book 'The Rainbow Atlas' invites readers on a vivid journey across the globe

Visitors to the 1900 Paris Exposition would have had the opportunity to view an extraordinary display of photographs, charts, publications and other items meant to demonstrate the progress and resilience of African Americans in the United States, only a few decades after the abolition of slavery. The materials were assembled by African American intellectuals Thomas J. […]

This paper studies the relationship between bank geographic complexity and risk. We use a unique dataset of 96 bank holding companies around the world to measure the geographic dispersion of their affiliates. We study how this dispersion interacts with economic and regulatory conditions to affect the riskiness of the bank.

Bank of Spain Working Papers by Henrique S. Basso and Juan F. Jimeno

Most states require driver education (DE) for novice drivers, and several recent substantial efforts have sought to realign DE with the aim of producing safer drivers. However, teen participation rates and how they differ among relevant subgroups remain unknown.

This study provides national estimates of teen driver participation in formal DE, a recognized gap in the literature, and identifies disparities in behind-the-wheel training among certain racial/ethnic, socioeconomic, and gender groups, particularly in jurisdictions without a DE requirement. (Read the full article)

Little is known about the epidemiology and pathogenicity of Clostridium difficile infection among children, particularly those aged ≤3 years in whom colonization is common and pathogenicity uncertain.

Young children, 1 to 3 years of age, had the highest Clostridium difficile infection incidence. Considering that clinical presentation, outcomes, and disease severity were similar across age groups, C difficile infection in the youngest age group likely represents true disease and not asymptomatic colonization. (Read the full article)

Prescribing patterns in the US pediatric population are changing but not uniformly. A detailed examination of prescription variation is needed to better understand pharmacotherapy of children and to inform future exploration of the causes and consequences of diverse practices.

We examine pediatric pharmacotherapy and quantify payer type differences and small geographic area variation. Substantial payer-type differences and regional variations were found, likely reflecting local practice cultures. Variation was greatest for medications used in situations of diagnostic and therapeutic uncertainty. (Read the full article)

Despite breastfeeding recommendations by the World Health Organization and the American Academy of Pediatrics, there is less agreement on appropriate use of infant solid foods. There are currently no well-established dietary guidelines for US infants that are similar to the Dietary Guidelines for Americans (aged >2 years).

Distinct dietary patterns exist among US infants and have differential influences on growth. Use of "Infant guideline solids" (vegetables, fruits, baby cereal, and meat) with prolonged breastfeeding is a promising healthy dietary pattern for infants after age 6 months. (Read the full article)

Parent refusal and delay of childhood vaccines has increased in recent years and is believed to cluster in communities. Such clustering could pose public health risks and barriers to achieving quality benchmarks for immunization coverage.

We found that underimmunization and vaccine refusal cluster geographically. Spatial scan analysis may be a useful tool to identify locations where clinicians may face challenges to achieving benchmarks for immunization coverage and that deserve special focus for interventions. (Read the full article)

Functional capabilities in patients with spina bifida depend on the spinal level of the lesion and its type. Sociodemographic characteristics have been shown in other conditions to be an important additional influence on outcomes, making them important for risk adjustment.

Males, non-Hispanic blacks, and patients without private insurance have less favorable functional outcomes in spina bifida, and age also has an impact. These attributes need to be considered by clinicians and researchers and used in comparing care outcomes across clinic settings. (Read the full article)

Children with cystic fibrosis demonstrate gas exchange abnormalities and increased respiratory loads during sleep independent of lung function, age, and BMI. Assessment of breathing patterns during sleep provides an opportunity for detection of early lung disease progression.

Children with cystic fibrosis demonstrated increased respiratory loads and gas exchange abnormalities during sleep compared with controls. Based on these findings, sleep assessment in this patient population can identify markers for the early detection of lung disease progression. (Read the full article)

Sixteen design projects created by graphic design students at Pennsylvania College of Technology have been honored in the national Flux Student Design Competition.

Unable to host their senior capstone showcase as an on-campus celebration of their work with family and friends in attendance, graduating students in the Graphic Design undergraduate program in the Stuckeman School at Penn State are turning to Instagram to highlight their design work in a creative way to an even larger potential audience during the week of May 4-8.

Timing, preparation and control are essential to getting paid fairly. This infographic by Adzuna is guide on how you can navigate your way to a salary increase.

"King of King Court" by Travis Dandro, published by Drawn & Quarterly, has won the 2020 Lynd Ward Prize for Graphic Novel of the Year. Penn State University Libraries sponsors the juried award and its administrator, the Pennsylvania Center for the Book.