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Abstract Multiferroics are materials exhibiting the coexistence of ferroelectricity and ideally ferromagnetism. Unfortunately, most known magnetoelectric multiferroics combine ferroelectricity with antiferromagnetism or with weak ferromagnetism. Here, following previous theoretical predictions, we provide clear experimental indications that ferroelectricity can be induced by epitaxial tensile strain in the ferromagnetic simple binary oxide EuO. We investigate the ferroelectric phase transition using infrared reflectance spectroscopy, finding that the frequency of the soft optical phonon reduces with increasing tensile strain and decreasing temperature. We observe such a soft mode anomaly at 100 K in (EuO) 2 /(BaO) 2 superlattices grown epitaxially on (LaAlO 3 ) 0.29 -(SrAl 1/2 Ta 1/2 O 3 ) 0.71 substrates, which is a typical signature for a displacive ferroelectric phase transition. The EuO in this superlattice is nominally subjected to 6.4% biaxial tensile strain, i.e., 50% more than believed needed from previously published calculations. We interpret our results with new first-principles density functional calculations using a hybrid functional, which provides a better quantitative agreement with experiment than the previously used local-density approximation and generalized gradient approximation functionals. 

Thin-film electrostatic engineering is used to uncover a hidden antiferroelectric phase. 

Summary In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of chronic obstructive pulmonary disease. Bayesian inference; Chronic obstructive pulmonary disease (COPD); Data integration; Gaussian graphical model; Markov random field prior; Spike and slab prior. 

Abstract This paper develops the concept of flood problem framing to understand decision-makers’ priorities in flood risk management in the Los Angeles Metropolitan Region in California (LA Metro). Problem frames shape an individual’s preferences for particular management strategies and their future behaviors. While flooding is a complex, multifaceted problem, with multiple causes and multiple impacts, a decision-maker is most likely to manage only those dimensions of flooding about which they are aware or concerned. To evaluate flood decision-makers’ primary concerns related to flood exposure, vulnerability, and management in the LA Metro, we draw on focus groups with flood control districts, city planners, nonprofit organizations, and other flood-related decision-makers. We identify numerous concerns, including concerns about specific types of floods (e.g., fluvial vs pluvial) and impacts to diverse infrastructure and communities. Our analyses demonstrate that flood concerns aggregate into three problem frames: one concerned with large fluvial floods exacerbated by climate change and their housing, economic, and infrastructure impacts; one concerned with pluvial nuisance flooding, pollution, and historic underinvestment in communities; and one concerned with coastal and fluvial flooding’s ecosystem impacts. While each individual typically articulated concerns that overlapped with only one problem frame, each problem frame was discussed by numerous organization types, suggesting low barriers to cross-organizational coordination in flood planning and response. This paper also advances our understanding of flood risk perception in a region that does not face frequent large floods. Significance StatementThis paper investigates the primary concerns that planners, flood managers, and other decision-makers have about flooding in Southern California. This is important because the way that decision-makers understand flooding shapes the way that they will plan for and respond to flood events. We find that some decision-makers are primarily concerned with large floods affecting large swaths of infrastructure and housing; others are concerned with frequent, small floods that mobilize pollution in low-income areas; and others are concerned with protecting coastal ecosystems during sea level rise. Our results also highlight key priorities for research and practice, including the need for flexible and accessible flood data and education about how to evacuate.