Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Why do Particle Clouds Generate Electric Charges?

Grains in desert sandstorms spontaneously generate strong electrical charges; likewise volcanic dust plumes produce spectacular lightning displays. Charged particle clouds also cause devastating explosions in food, drug and coal processing industries. Despite the wide-ranging importance of granular charging in both nature and industry, even the simplest aspects of its causes remain elusive, because it is difficult to understand how inert grains in contact with little more than other inert grains can generate the large charges observed. Here, we present a simple yet predictive explanation for the charging of granular materials in collisional flows. We argue from very basic considerations that charge transfer can be expected in collisions of identical dielectric grains in the presence of an electric field, and we confirm the model's predictions using discrete-element simulations and a tabletop granular experiment

Effect of four plant species on soil 15N-access and herbage yield in temporary agricultural grasslands

Positive plant diversity-productivity relationships have been reported for experimental semi-natural grasslands (Cardinale et al. 2006; Hector et al. 1999; Tilman et al. 1996) as well as temporary agricultural grasslands (Frankow-Lindberg et al. 2009; Kirwan et al. 2007; Nyfeler et al. 2009; Picasso et al. 2008). Generally, these relationships are explained, on the one hand, by niche differentiation and facilitation (Hector et al. 2002; Tilman et al. 2002) and, on the other hand, by greater probability of including a highly productive plant species in high diversity plots (Huston 1997). Both explanations accept that diversity is significant because species differ in characteristics, such as root architecture, nutrient acquisition and water use efficiency, to name a few, resulting in composition and diversity being important for improved productivity and resource use (Naeem et al. 1994; Tilman et al. 2002). Plant diversity is generally low in temporary agricultural grasslands grown for ruminant fodder production. Grass in pure stands is common, but requires high nitrogen (N) inputs. In terms of N input, two-species grass-legume mixtures are more sustainable than grass in pure stands and consequently dominate low N input grasslands (Crews and Peoples 2004; Nyfeler et al. 2009; Nyfeler et al. 2011). In temperate grasslands, N is often the limiting factor for productivity (Whitehead 1995). Plant available soil N is generally concentrated in the upper soil layers, but may leach to deeper layers, especially in grasslands that include legumes (Scherer-Lorenzen et al. 2003) and under conditions with surplus precipitation (Thorup-Kristensen 2006). To improve soil N use efficiency in temporary grasslands, we propose the addition of deep-rooting plant species to a mixture of perennial ryegrass and white clover, which are the most widespread forage plant species in temporary grasslands in a temperate climate (Moore 2003). Perennial ryegrass and white clover possess relatively shallow root systems (Kutschera and Lichtenegger 1982; Kutschera and Lichtenegger 1992) with effective rooting depths of <0.7 m on a silt loamy site (Pollock and Mead 2008). Grassland species, such as lucerne and chicory, grow their tap-roots into deep soil layers and exploit soil nutrients and water in soil layers that the commonly grown shallow-rooting grassland species cannot reach (Braun et al. 2010; Skinner 2008). Chicory grown as a catch crop after barley reduced the inorganic soil N down to 2.5 m depth during the growing season, while perennial ryegrass affected the inorganic soil N only down to 1 m depth (Thorup-Kristensen 2006). Further, on a Wakanui silt loam in New Zealand chicory extracted water down to 1.9 m and lucerne down to 2.3 m soil depth, which resulted in greater herbage yields compared with a perennial ryegrass-white clover mixture, especially for dryland plots (Brown et al. 2005). There is little information on both the ability of deep- and shallow-rooting grassland species to access soil N from different vertical soil layers and the relation of soil N-access and herbage yield in temporary agricultural grasslands. Therefore, the objective of the present work was to test the hypotheses 1) that a mixture comprising both shallow- and deep-rooting plant species has greater herbage yields than a shallow-rooting binary mixture and pure stands, 2) that deep-rooting plant species (chicory and lucerne) are superior in accessing soil N from 1.2 m soil depth compared with shallow-rooting plant species, 3) that shallow-rooting plant species (perennial ryegrass and white clover) are superior in accessing soil N from 0.4 m soil depth compared with deep-rooting plant species, 4) that a mixture of deep- and shallow-rooting plant species has greater access to soil N from three soil layers compared with a shallow-rooting two-species mixture and that 5) the leguminous grassland plants, lucerne and white clover, have a strong impact on grassland N acquisition, because of their ability to derive N from the soil and the atmosphere

Spontaneous and deliberate future thinking: A dual process account

© 2019 Springer Nature.This is the final published version of an article published in Psychological Research, licensed under a Creative Commons Attri-bution 4.0 International License. Available online at: https://doi.org/10.1007/s00426-019-01262-7.In this article, we address an apparent paradox in the literature on mental time travel and mind-wandering: How is it possible that future thinking is both constructive, yet often experienced as occurring spontaneously? We identify and describe two ‘routes’ whereby episodic future thoughts are brought to consciousness, with each of the ‘routes’ being associated with separable cognitive processes and functions. Voluntary future thinking relies on controlled, deliberate and slow cognitive processing. The other, termed involuntary or spontaneous future thinking, relies on automatic processes that allows ‘fully-fledged’ episodic future thoughts to freely come to mind, often triggered by internal or external cues. To unravel the paradox, we propose that the majority of spontaneous future thoughts are ‘pre-made’ (i.e., each spontaneous future thought is a re-iteration of a previously constructed future event), and therefore based on simple, well-understood, memory processes. We also propose that the pre-made hypothesis explains why spontaneous future thoughts occur rapidly, are similar to involuntary memories, and predominantly about upcoming tasks and goals. We also raise the possibility that spontaneous future thinking is the default mode of imagining the future. This dual process approach complements and extends standard theoretical approaches that emphasise constructive simulation, and outlines novel opportunities for researchers examining voluntary and spontaneous forms of future thinking.Peer reviewe

Assessing the impact of a health intervention via user-generated Internet content

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of user-generated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the prevalence of a health event in a population from Internet data. This model is applied to identify control location groups that correlate historically with the areas, where a specific intervention campaign has taken place. We then determine the impact of the intervention by inferring a projection of the disease rates that could have emerged in the absence of a campaign. Our case study focuses on the influenza vaccination program that was launched in England during the 2013/14 season, and our observations consist of millions of geo-located search queries to the Bing search engine and posts on Twitter. The impact estimates derived from the application of the proposed statistical framework support conventional assessments of the campaign

Search For Heavy Pointlike Dirac Monopoles

We have searched for central production of a pair of photons with high transverse energies in $p\bar p$ collisions at $\sqrt{s} = 1.8$ TeV using $70 pb^{-1}$ of data collected with the D\O detector at the Fermilab Tevatron in 1994--1996. If they exist, virtual heavy pointlike Dirac monopoles could rescatter pairs of nearly real photons into this final state via a box diagram. We observe no excess of events above background, and set lower 95% C.L. limits of $610, 870, or 1580 GeV/c^2$ on the mass of a spin 0, 1/2, or 1 Dirac monopole.Comment: 12 pages, 4 figure

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

The activation of eco-driving mental models: can text messages prime drivers to use their existing knowledge and skills?

Eco-driving campaigns have traditionally assumed that drivers lack the necessary knowledge and skills and that this is something that needs rectifying. Therefore, many support systems have been designed to closely guide drivers and fine-tune their proficiency. However, research suggests that drivers already possess a substantial amount of the necessary knowledge and skills regarding eco-driving. In previous studies, participants used these effectively when they were explicitly asked to drive fuel-efficiently. In contrast, they used their safe driving skills when they were instructed to drive as they would normally. Hence, it is assumed that many drivers choose not to engage purposefully in eco-driving in their everyday lives. The aim of the current study was to investigate the effect of simple, periodic text messages (nine messages in 2 weeks) on drivers’ eco- and safe driving performance. It was hypothesised that provision of eco-driving primes and advice would encourage the activation of their eco-driving mental models and that comparable safety primes increase driving safety. For this purpose, a driving simulator experiment was conducted. All participants performed a pre-test drive and were then randomly divided into four groups, which received different interventions. For a period of 2 weeks, one group received text messages with eco-driving primes and another group received safety primes. A third group received advice messages on how to eco-drive. The fourth group were instructed by the experimenter to drive fuel-efficiently, immediately before driving, with no text message intervention. A post-test drive measured behavioural changes in scenarios deemed relevant to eco- and safe driving. The results suggest that the eco-driving prime and advice text messages did not have the desired effect. In comparison, asking drivers to drive fuel-efficiently led to eco-driving behaviours. These outcomes demonstrate the difficulty in changing ingrained habits. Future research is needed to strengthen such messages or activate existing knowledge and skills in other ways, so driver behaviour can be changed in cost-efficient ways

Проблемы технического нормирования шумовых характеристик текстильных машин

Для целей оценки соответствия шумовых характеристик машин требованиям санитарных норм предложено использовать обобщенные предельно допустимые шумовые характеристики, которые задают предельно допустимые характеристики для близких по типу машин, объединенных в группы с учетом характерной плотности их установки и условий эксплуатации. Для уточненного определения этих характеристик целесообразно использовать методику, учитывающую звукопоглощение и рассеяние шума поверхностью машин, плотность тел рассеяния в поперечном сечении производственного помещения и его акустические и геометрические характеристики.For the purposes of assessing the compliance of noise characteristics of machines with the requirements of sanitary standards, it is suggested to use generalized maximum permissible noise characteristics that set the maximum permissible characteristics for similar machines, grouped together, taking into account the characteristic density of their installation and operating conditions. For an accurate definition of these characteristics, it is advisable to use a technique that takes into account the sound absorption and noise scattering by the machine surface, the density of scattering bodies in the cross section of the production room and its acoustic and geometric characteristics