Sex categorization of faces: The effects of age and experience

The face own-age bias effect refers to the better ability to recognize the face from one's own age compared with other age groups. Here we examined whether an own-age advantage occurs for faces sex categorization. We examined 7- and 9-year-olds' and adults' ability to correctly categorize the sex of 7- and 9-year-olds and adult faces without external cues, such as hair. Results indicated that all ages easily classify the sex of adult faces. They succeeded in classifying the sex of child faces, but their performance was poorer than for adult faces. In adults, processing time increased, and a response bias (male response) was elicited for child faces. In children, response times remained constant, and no bias was observed. Experience with specific category of faces seems to offer some advantage in speed of processing. Overall, sex categorization is more challenging for child than for adult faces due to their reduced sexual dimorphic facial characteristics

Predictors and outcome associated with an Enterococcus positive isolate during intensive care unit admission

This study reports the incidence, risk factors and mortality associated with a positive Enterococcus spp. isolate during admission to two tertiary intensive care units participating in an antibiotic cycling study. Incidence was low, with only 4.2% of admissions (36/852) at Royal Brisbane and Women’s Hospital and 2.8% (31/1104) at Westmead Hospital developing a positive Enterococcus spp. isolate (P=0.087). A positive enterococcal isolate, while not an independent predictor of mortality (odds ratio [OR]=1.6, 95% confidence interval [CI] 0.80 to 3.2, P=0.18), may be a marker of the underlying severity of illness with higher unadjusted in-hospital mortality (26% or 17/66 vs 14% or 250/1855, P=0.007). Independent risk factors for a positive isolate were use of meropenem/imipenem (OR=5.7, 95% CI 2.4 to 14,

Study of azimuthal anisotropy of ϒ(1S) mesons in pPb collisions at sNN = 8.16 TeV

The azimuthal anisotropy of Image 1 mesons in high-multiplicity proton-lead collisions is studied using data collected by the CMS experiment at a nucleon-nucleon center-of-mass energy of 8.16TeV. The Image 1 mesons are reconstructed using their dimuon decay channel. The anisotropy is characterized by the second Fourier harmonic coefficients, found using a two-particle correlation technique, in which the Image 1 mesons are correlated with charged hadrons. A large pseudorapidity gap is used to suppress short-range correlations. Nonflow contamination from the dijet background is removed using a low-multiplicity subtraction method, and the results are presented as a function of Image 1 transverse momentum. The azimuthal anisotropies are smaller than those found for charmonia in proton-lead collisions at the same collision energy, but are consistent with values found for Image 1 mesons in lead-lead interactions at a nucleon-nucleon center-of-mass energy of 5.02 TeV

Measurement of the tt¯ charge asymmetry in events with highly Lorentz-boosted top quarks in pp collisions at s=13 TeV

The measurement of the charge asymmetry in top quark pair events with highly Lorentz-boosted top quarks decaying to a single lepton and jets is presented. The analysis is performed using proton-proton collisions at s=13TeV with the CMS detector at the LHC and corresponding to an integrated luminosity of 138 fb−1. The selection is optimized for top quarks produced with large Lorentz boosts, resulting in nonisolated leptons and overlapping jets. The top quark charge asymmetry is measured for events with a tt¯ invariant mass larger than 750 GeV and corrected for detector and acceptance effects using a binned maximum likelihood fit. The measured top quark charge asymmetry of (0.42−0.69+0.64)% is in good agreement with the standard model prediction at next-to-next-to-leading order in quantum chromodynamic perturbation theory with next-to-leading-order electroweak corrections. The result is also presented for two invariant mass ranges, 750–900 and >900GeV

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors