All non-classical correlations can be activated into distillable entanglement

We devise a protocol in which general non-classical multipartite correlations produce a physically relevant effect, leading to the creation of bipartite entanglement. In particular, we show that the relative entropy of quantumness, which measures all non-classical correlations among subsystems of a quantum system, is equivalent to and can be operationally interpreted as the minimum distillable entanglement generated between the system and local ancillae in our protocol. We emphasize the key role of state mixedness in maximizing non-classicality: Mixed entangled states can be arbitrarily more non-classical than separable and pure entangled states.Comment: 4+4 pages, 1 figure. Published versio

Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti

Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.Comment: 11 pages, 9 figures, accepted for 25th International Conference on Neural Information Processing (ICONIP 2018), 14-16 December, 2018 (Siem Reap, Cambodia

Gut inflammation provides a respiratory electron acceptor for Salmonella.

Salmonella enterica serotype Typhimurium (S. Typhimurium) causes acute gut inflammation by using its virulence factors to invade the intestinal epithelium and survive in mucosal macrophages. The inflammatory response enhances the transmission success of S. Typhimurium by promoting its outgrowth in the gut lumen through unknown mechanisms. Here we show that reactive oxygen species generated during inflammation react with endogenous, luminal sulphur compounds (thiosulphate) to form a new respiratory electron acceptor, tetrathionate. The genes conferring the ability to use tetrathionate as an electron acceptor produce a growth advantage for S. Typhimurium over the competing microbiota in the lumen of the inflamed gut. We conclude that S. Typhimurium virulence factors induce host-driven production of a new electron acceptor that allows the pathogen to use respiration to compete with fermenting gut microbes. Thus the ability to trigger intestinal inflammation is crucial for the biology of this diarrhoeal pathogen

Existence and stability of singular patterns in a Ginzburg–Landau equation coupled with a mean field

We study singular patterns in a particular system of parabolic partial differential equations which consist of a Ginzburg–Landau equation and a mean field equation. We prove the existence of the three simplest concentrated periodic stationary patterns (single spikes, double spikes, double transition layers) by composing them of more elementary patterns and solving the corresponding consistency conditions. In the case of spike patterns we prove stability for sufficiently large spatial periods by first showing that the eigenvalues do not tend to zero as the period goes to infinity and then passing in the limit to a nonlocal eigenvalue problem which can be studied explicitly. For the two other patterns we show instability by using the variational characterization of eigenvalues

A Dual X-Ray Absorptiometry Validated Geometric Model for the Calculation of Body Segment Inertial Parameters of Young Females

The purpose of this study was to validate a new geometric solids model, developed to address the lack of female specific models for body segment inertial parameter estimation. A second aim was to determine the effect of reducing the number of geometric solids used to model the limb segments on model accuracy. The ‘full’ model comprised 56 geometric solids, the ‘reduced’ 31, and the ‘basic’ 16. Predicted whole-body inertial parameters were compared with direct measurements (reaction board, scales), and predicted segmental parameters with those estimated from whole-body DXA scans for 28 females. The percentage root mean square error (%RMSE) for whole-body volume was <2.5% for all models, and 1.9% for the full model. The %RMSE for whole-body center of mass location was <3.2% for all models. The %RMSE whole-body mass was <3.3% for the full model. The RMSE for segment masses was <0.5 kg (<0.5%) for all segments; Bland-Altman analysis showed the full and reduced models could adequately model thigh, forearm, foot and hand segments, but the full model was required for the trunk segment. The proposed model was able to accurately predict body segment inertial parameters for females, more geometric solids are required to more accurately model the trunk

White Beam Synchrotron X-Ray Topography of Gallium Arsenide

The defect structure of gallium arsenide is being examined using white beam transmission topography. The specimens under examination are cut-and-polished, three inch diameter, single crystal substrates from various suppliers in the “as received” condition. The goal of this continuing program is to first document the existence of various crystallographic defect structures and then to establish their effect on the performance of microwave integrated circuits subsequently fabricated on the wafers. Success in establishing such a correlation might permit the use of an x-ray diffraction measurement to screen incoming material, eliminating marginal substrates and achieving a corresponding increase in yield

Sedimentological characterization of Antarctic moraines using UAVs and Structure-from-Motion photogrammetry

In glacial environments particle-size analysis of moraines provides insights into clast origin, transport history, depositional mechanism and processes of reworking. Traditional methods for grain-size classification are labour-intensive, physically intrusive and are limited to patch-scale (1m2) observation. We develop emerging, high-resolution ground- and unmanned aerial vehicle-based ‘Structure-from-Motion’ (UAV-SfM) photogrammetry to recover grain-size information across an moraine surface in the Heritage Range, Antarctica. SfM data products were benchmarked against equivalent datasets acquired using terrestrial laser scanning, and were found to be accurate to within 1.7 and 50mm for patch- and site-scale modelling, respectively. Grain-size distributions were obtained through digital grain classification, or ‘photo-sieving’, of patch-scale SfM orthoimagery. Photo-sieved distributions were accurate to <2mm compared to control distributions derived from dry sieving. A relationship between patch-scale median grain size and the standard deviation of local surface elevations was applied to a site-scale UAV-SfM model to facilitate upscaling and the production of a spatially continuous map of the median grain size across a 0.3 km2 area of moraine. This highly automated workflow for site scale sedimentological characterization eliminates much of the subjectivity associated with traditional methods and forms a sound basis for subsequent glaciological process interpretation and analysis