Choosing a random distribution with prescribed risks

We describe several simulation algorithms that yield random probability distributions with given values of risk measures. In case of vanilla risk measures, the algorithms involve combining and transforming random cumulative distribution functions or random Lorenz curves obtained by simulating rather general random probability distributions on the unit interval. A new algorithm based on the simulation of a weighted barycentres array is suggested to generate random probability distributions with a given value of the spectral risk measure.

Interactive Diffraction from Biological Nanostructures

We describe a technique for interactive rendering of diffraction effects produced by biological nanostructures such as snake skin surface gratings. Our approach uses imagery from atomic force microscopy that accurately captures the nanostructures responsible for structural coloration, that is, coloration due to wave interference, in a variety of animals. We develop a rendering technique that constructs bidirectional reflection distribution functions (BRDFs) directly from the measured data and leverages precomputation to achieve interactive performance. We demonstrate results of our approach using various shapes of the surface grating nanostructures. Finally, we evaluate the accuracy of our precomputation-based technique and compare to a reference BRDF construction technique.

Measurement of the inclusive jet cross section in pp collisions at sqrt(s)=2.76 TeV and comparison to the inclusive jet cross section at sqrt(s)=7 TeV using the ATLAS detector

The inclusive jet cross-section has been measured in proton-proton collisions at root s = 2.76 TeV in a dataset corresponding to an integrated luminosity of 0.20 pb(-1) collected with the ATLAS detector at the Large Hadron Collider in 2011. Jets are identified using the anti-k(t) algorithm with two radius parameters of 0.4 and 0.6. The inclusive jet double-differential cross-section is presented as a function of the jet transverse momentum p(T) and jet rapidity y, covering a range of 20 <= p(T) < 430 GeV and vertical bar y vertical bar < 4.4. The ratio of the cross-section to the inclusive jet cross-section measurement at root s = 7 TeV, published by the ATLAS Collaboration, is calculated as a function of both transverse momentum and the dimensionless quantity x(T) = 2p(T)/root s, in bins of jet rapidity. The systematic uncertainties on the ratios are significantly reduced due to the cancellation of correlated uncertainties in the two measurements. Results are compared to the prediction from next-to-leading order perturbative QCD calculations corrected for non-perturbative effects, and next-to-leading order Monte Carlo simulation. Furthermore, the ATLAS jet cross-section measurements at root s = 2.76 TeV and root s = 7 TeV are analysed within a framework of next-to-leading order perturbative QCD calculations to determine parton distribution functions of the proton, taking into account the correlations between the measurements.

Interactive Diffraction from Biological Nanostructures

We describe a technique for interactive rendering of diffraction effects produced by biological nanostructures, such as snake skin surface gratings. Our approach uses imagery from atomic force microscopy that accurately captures the geometry of the nanostructures responsible for structural colouration, that is, colouration due to wave interference, in a variety of animals. We develop a rendering technique that constructs bidirectional reflection distribution functions (BRDFs) directly from the measured data and leverages pre-computation to achieve interactive performance. We demonstrate results of our approach using various shapes of the surface grating nanostructures. Finally, we evaluate the accuracy of our pre-computation-based technique and compare to a reference BRDF construction technique.

Measurement of dijet cross sections in pp collisions at 7 TeV centre-of-mass energy using the ATLAS detector

Double-differential dijet cross-sections measured in pp collisions at the LHC with a 7TeV centre-of-mass energy are presented as functions of dijet mass and half the rapidity separation of the two highest-pT jets. These measurements are obtained using data corresponding to an integrated luminosity of 4.5 fb−1, recorded by the ATLAS detector in 2011. The data are corrected for detector effects so that cross-sections are presented at the particle level. Cross-sections are measured up to 5TeV dijet mass using jets reconstructed with the anti-kt algorithm for values of the jet radius parameter of 0.4 and 0.6. The cross-sections are compared with next-to-leading-order perturbative QCD calculations by NLOJet++ corrected to account for non-perturbative effects. Comparisons with POWHEG predictions, using a next-to-leading-order matrix element calculation interfaced to a partonshower Monte Carlo simulation, are also shown. Electroweak effects are accounted for in both cases. The quantitative comparison of data and theoretical predictions obtained using various parameterizations of the parton distribution functions is performed using a frequentist method. In general, good agreement with data is observed for the NLOJet++ theoretical predictions when using the CT10, NNPDF2.1 and MSTW 2008 PDF sets. Disagreement is observed when using the ABM11 and HERAPDF1.5 PDF sets for some ranges of dijet mass and half the rapidity separation. An example setting a lower limit on the compositeness scale for a model of contact interactions is presented, showing that the unfolded results can be used to constrain contributions to dijet production beyond that predicted by the Standard Model.

Putative functions of extracellular matrix glycoproteins in secondary palate morphogenesis

Cleft palate is a common birth defect in humans. Elevation and fusion of paired palatal shelves are coordinated by growth and transcription factors, and mutations in these can cause malformations. Among the effector genes for growth factor signaling are extracellular matrix (ECM) glycoproteins. These provide substrates for cell adhesion (e.g., fibronectin, tenascins), but also regulate growth factor availability (e.g., fibrillins). Cleft palate in Bmp7 null mouse embryos is caused by a delay in palatal shelf elevation. In contrast, palatal shelves of Tgf-β3 knockout mice elevate normally, but a cleft develops due to their failure to fuse. However, nothing is known about a possible functional interaction between specific ECM proteins and Tgf-β/Bmp family members in palatogenesis. To start addressing this question, we studied the mRNA and protein distribution of relevant ECM components during secondary palate development, and compared it to growth factor expression in wildtypewild type and mutant mice. We found that fibrillin-2 (but not fibrillin-1) mRNA appeared in the mesenchyme of elevated palatal shelves adjacent to the midline epithelial cells, which were positive for Tgf-β3 mRNA. Moreover, midline epithelial cells started expressing fibronectin upon contact of the two palatal shelves. These findings support the hypothesis that fibrillin-2 and fibronectin are involved in regulating the activity of Tgf-β3 at the fusing midline. In addition, we observed that tenascin-W (but not tenascin-C) was misexpressed in palatal shelves of Bmp7-deficient mouse embryos. In contrast to tenascin-C, tenascin-W secretion was strongly induced by Bmp7 in embryonic cranial fibroblasts in vitro. These results are consistent with a putative function for tenascin-W as a target of Bmp7 signaling during palate elevation. Our results indicate that distinct ECM proteins are important for morphogenesis of the secondary palate, both as downstream effectors and as regulators of Tgf-β/Bmp activity.

Distribution of the glutamate transporters GLT-1 (SLC1A2) and GLAST (SLC1A3) in peripheral organs

The glutamate transporters GLT-1 and GLAST are widely expressed in astrocytes in the brain where they fulfill important functions during glutamatergic neurotransmission. The present study examines their distribution in peripheral organs using in situ hybridization (ISH) and immunocytochemistry. GLAST was found to be more widely distributed than GLT-1. GLAST was expressed primarily in epithelial cells, cells of the macrophage-lineage, lymphocytes, fat cells, interstitial cells, and salivary gland acini. GLT-1 was primarily expressed in glandular tissue, including mammary gland, lacrimal gland, and ducts and acini in salivary glands, but also by perivenous hepatocytes and follicular dendritic cells in spleen and lymph nodes. The findings demonstrate that, although expressed by the same cells in the brain, these two glutamate transporters have different distribution patterns in peripheral tissues and that they fulfill glutamate transport functions apart from glutamatergic neurotransmission in these areas.

Subcellular distribution of the insulin-like growth factor (IGF) binding proteins (IGFBPs) 2 and 3 in articular chondrocytes

The insulin-like growth factor (IGF) is a major anabolic regulator in articular cartilage. The IGF-binding proteins (IGFBPs) are increased during osteoarthritis (OA), but the function of the later proteins remains unknown. In general, the IGFBPs are pluripotential effectors capable of IGF regulation and of acting on their own to control key cell functions, including survival and proliferation. The independent functions are often associated with their cell location, and therefore this study explores the distribution of IGFBP-2 and IGFBP-3 in articular chondrocytes. Immunohistochemistry was used to localize IGFBP-2 in normal human articular cartilage. Bovine chondrocytes were used for subcellular fractionation (hypotonic cell lysis) under nonreducing conditions and nuclear purification (centrifugation on sucrose cushions). Cell fraction markers and IGFBPs were assayed in the subcellular fractions by Western immunoblot. The IHC results showed association of IGFBP-2 with chondrocytes, but not with the nuclei. Subcellular fractionation of isolated chondrocytes yielded intact nuclei as assessed at the light microscopic level; the nuclear marker histone H1 was exclusively associated with this fraction. More than 90% of the cytoplasmic marker GAPDH and all the detectable IGFBP-2 were in the cytoplasmic fraction. Immunoreactive IGFBP-3 was found in the cytoplasmic and peri-nuclear/nuclear fractions. Chondrocytes contain intracellular IGFBP-2 and IGFBP-3 but only IGFBP-3 is associated with nuclei. This suggests the hypothesis that the actions of these IGFBPs in articular cartilage extend beyond the classic modulation of IGF receptor action.

Calculating track thrust with track functions

In e+e− event shapes studies at LEP, two different measurements were sometimes performed: a “calorimetric” measurement using both charged and neutral particles and a “track-based” measurement using just charged particles. Whereas calorimetric measurements are infrared and collinear safe, and therefore calculable in perturbative QCD, track-based measurements necessarily depend on nonperturbative hadronization effects. On the other hand, track-based measurements typically have smaller experimental uncertainties. In this paper, we present the first calculation of the event shape “track thrust” and compare to measurements performed at ALEPH and DELPHI. This calculation is made possible through the recently developed formalism of track functions, which are nonperturbative objects describing how energetic partons fragment into charged hadrons. By incorporating track functions into soft-collinear effective theory, we calculate the distribution for track thrust with next-to-leading logarithmic resummation. Due to a partial cancellation between nonperturbative parameters, the distributions for calorimeter thrust and track thrust are remarkably similar, a feature also seen in LEP data.

A new Bayesian approach to the reconstruction of spectral functions

We present a novel approach for the reconstruction of spectra from Euclidean correlator data that makes close contact to modern Bayesian concepts. It is based upon an axiomatically justified dimensionless prior distribution, which in the case of constant prior function m(ω) only imprints smoothness on the reconstructed spectrum. In addition we are able to analytically integrate out the only relevant overall hyper-parameter α in the prior, removing the necessity for Gaussian approximations found e.g. in the Maximum Entropy Method. Using a quasi-Newton minimizer and high-precision arithmetic, we are then able to find the unique global extremum of P[ρ|D] in the full Nω » Nτ dimensional search space. The method actually yields gradually improving reconstruction results if the quality of the supplied input data increases, without introducing artificial peak structures, often encountered in the MEM. To support these statements we present mock data analyses for the case of zero width delta peaks and more realistic scenarios, based on the perturbative Euclidean Wilson Loop as well as the Wilson Line correlator in Coulomb gauge.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().