Determining Principal Component Cardinality through the Principle of Minimum Description Length

PCA (Principal Component Analysis) and its variants areubiquitous techniques for matrix dimension reduction and reduced-dimensionlatent-factor extraction. One significant challenge in using PCA, is thechoice of the number of principal components. The information-theoreticMDL (Minimum Description Length) principle gives objective compression-based criteria for model selection, but it is difficult to analytically applyits modern definition - NML (Normalized Maximum Likelihood) - to theproblem of PCA. This work shows a general reduction of NML prob-lems to lower-dimension problems. Applying this reduction, it boundsthe NML of PCA, by terms of the NML of linear regression, which areknown.Comment: LOD 201

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Combustion in thermonuclear supernova explosions

Type Ia supernovae are associated with thermonuclear explosions of white dwarf stars. Combustion processes convert material in nuclear reactions and release the energy required to explode the stars. At the same time, they produce the radioactive species that power radiation and give rise to the formation of the observables. Therefore, the physical mechanism of the combustion processes, as reviewed here, is the key to understand these astrophysical events. Theory establishes two distinct modes of propagation for combustion fronts: subsonic deflagrations and supersonic detonations. Both are assumed to play an important role in thermonuclear supernovae. The physical nature and theoretical models of deflagrations and detonations are discussed together with numerical implementations. A particular challenge arises due to the wide range of spatial scales involved in these phenomena. Neither the combustion waves nor their interaction with fluid flow and instabilities can be directly resolved in simulations. Substantial modeling effort is required to consistently capture such effects and the corresponding techniques are discussed in detail. They form the basis of modern multidimensional hydrodynamical simulations of thermonuclear supernova explosions. The problem of deflagration-to-detonation transitions in thermonuclear supernova explosions is briefly mentioned.Comment: Author version of chapter for 'Handbook of Supernovae,' edited by A. Alsabti and P. Murdin, Springer. 24 pages, 4 figure

Increased insolation threshold for runaway greenhouse processes on Earth like planets

Because the solar luminosity increases over geological timescales, Earth climate is expected to warm, increasing water evaporation which, in turn, enhances the atmospheric greenhouse effect. Above a certain critical insolation, this destabilizing greenhouse feedback can "runaway" until all the oceans are evaporated. Through increases in stratospheric humidity, warming may also cause oceans to escape to space before the runaway greenhouse occurs. The critical insolation thresholds for these processes, however, remain uncertain because they have so far been evaluated with unidimensional models that cannot account for the dynamical and cloud feedback effects that are key stabilizing features of Earth's climate. Here we use a 3D global climate model to show that the threshold for the runaway greenhouse is about 375 W/m$^2$, significantly higher than previously thought. Our model is specifically developed to quantify the climate response of Earth-like planets to increased insolation in hot and extremely moist atmospheres. In contrast with previous studies, we find that clouds have a destabilizing feedback on the long term warming. However, subsident, unsaturated regions created by the Hadley circulation have a stabilizing effect that is strong enough to defer the runaway greenhouse limit to higher insolation than inferred from 1D models. Furthermore, because of wavelength-dependent radiative effects, the stratosphere remains cold and dry enough to hamper atmospheric water escape, even at large fluxes. This has strong implications for Venus early water history and extends the size of the habitable zone around other stars.Comment: Published in Nature. Online publication date: December 12, 2013. Accepted version before journal editing and with Supplementary Informatio

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Heterogeneity in glucose response curves during an oral glucose tolerance test and associated cardiometabolic risk

We aimed to examine heterogeneity in glucose response curves during an oral glucose tolerance test with multiple measurements and to compare cardiometabolic risk profiles between identified glucose response curve groups. We analyzed data from 1,267 individuals without diabetes from five studies in Denmark, the Netherlands and the USA. Each study included between 5 and 11 measurements at different time points during a 2-h oral glucose tolerance test, resulting in 9,602 plasma glucose measurements. Latent class trajectories with a cubic specification for time were fitted to identify different patterns of plasma glucose change during the oral glucose tolerance test. Cardiometabolic risk factor profiles were compared between the identified groups. Using latent class trajectory analysis, five glucose response curves were identified. Despite similar fasting and 2-h values, glucose peaks and peak times varied greatly between groups, ranging from 7-12 mmol/L, and 35-70 min. The group with the lowest and earliest plasma glucose peak had the lowest estimated cardiovascular risk, while the group with the most delayed plasma glucose peak and the highest 2-h value had the highest estimated risk. One group, with normal fasting and 2-h values, exhibited an unusual profile, with the highest glucose peak and the highest proportion of smokers and men. The heterogeneity in glucose response curves and the distinct cardiometabolic risk profiles may reflect different underlying physiologies. Our results warrant more detailed studies to identify the source of the heterogeneity across the different phenotypes and whether these differences play a role in the development of type 2 diabetes and cardiovascular disease

Planet Populations as a Function of Stellar Properties

Exoplanets around different types of stars provide a window into the diverse environments in which planets form. This chapter describes the observed relations between exoplanet populations and stellar properties and how they connect to planet formation in protoplanetary disks. Giant planets occur more frequently around more metal-rich and more massive stars. These findings support the core accretion theory of planet formation, in which the cores of giant planets form more rapidly in more metal-rich and more massive protoplanetary disks. Smaller planets, those with sizes roughly between Earth and Neptune, exhibit different scaling relations with stellar properties. These planets are found around stars with a wide range of metallicities and occur more frequently around lower mass stars. This indicates that planet formation takes place in a wide range of environments, yet it is not clear why planets form more efficiently around low mass stars. Going forward, exoplanet surveys targeting M dwarfs will characterize the exoplanet population around the lowest mass stars. In combination with ongoing stellar characterization, this will help us understand the formation of planets in a large range of environments.Comment: Accepted for Publication in the Handbook of Exoplanet

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

'Special K' and a loss of cell-to-cell adhesion in proximal tubule-derived epithelial cells: modulation of the adherens junction complex by ketamine

Ketamine, a mild hallucinogenic class C drug, is the fastest growing ‘party drug’ used by 16–24 year olds in the UK. As the recreational use of Ketamine increases we are beginning to see the signs of major renal and bladder complications. To date however, we know nothing of a role for Ketamine in modulating both structure and function of the human renal proximal tubule. In the current study we have used an established model cell line for human epithelial cells of the proximal tubule (HK2) to demonstrate that Ketamine evokes early changes in expression of proteins central to the adherens junction complex. Furthermore we use AFM single-cell force spectroscopy to assess if these changes functionally uncouple cells of the proximal tubule ahead of any overt loss in epithelial cell function. Our data suggests that Ketamine (24–48 hrs) produces gross changes in cell morphology and cytoskeletal architecture towards a fibrotic phenotype. These physical changes matched the concentration-dependent (0.1–1 mg/mL) cytotoxic effect of Ketamine and reflect a loss in expression of the key adherens junction proteins epithelial (E)- and neural (N)-cadherin and β-catenin. Down-regulation of protein expression does not involve the pro-fibrotic cytokine TGFβ, nor is it regulated by the usual increase in expression of Slug or Snail, the transcriptional regulators for E-cadherin. However, the loss in E-cadherin can be partially rescued pharmacologically by blocking p38 MAPK using SB203580. These data provide compelling evidence that Ketamine alters epithelial cell-to-cell adhesion and cell-coupling in the proximal kidney via a non-classical pro-fibrotic mechanism and the data provides the first indication that this illicit substance can have major implications on renal function. Understanding Ketamine-induced renal pathology may identify targets for future therapeutic intervention