Stacking-fault energies for Ag, Cu, and Ni from empirical tight-binding potentials

The intrinsic stacking-fault energies and free energies for Ag, Cu, and Ni are derived from molecular-dynamics simulations using the empirical tight-binding potentials of Cleri and Rosato [Phys. Rev. B 48, 22 (1993)]. While the results show significant deviations from experimental data, the general trend between the elements remains correct. This allows to use the potentials for qualitative comparisons between metals with high and low stacking-fault energies. Moreover, the effect of stacking faults on the local vibrational properties near the fault is examined. It turns out that the stacking fault has the strongest effect on modes in the center of the transverse peak and its effect is localized in a region of approximately eight monolayers around the defect.Comment: 5 pages, 2 figures, accepted for publication in Phys. Rev.

Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution

Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-art methods.Comment: Accepted to Proc. European Conf. Computer Vision 201

Multi-view Face Detection Using Deep Convolutional Neural Networks

In this paper we consider the problem of multi-view face detection. While there has been significant research on this problem, current state-of-the-art approaches for this task require annotation of facial landmarks, e.g. TSM [25], or annotation of face poses [28, 22]. They also require training dozens of models to fully capture faces in all orientations, e.g. 22 models in HeadHunter method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks. The proposed method has minimal complexity; unlike other recent deep learning object detection methods [9], it does not require additional components such as segmentation, bounding-box regression, or SVM classifiers. Furthermore, we analyzed scores of the proposed face detector for faces in different orientations and found that 1) the proposed method is able to detect faces from different angles and can handle occlusion to some extent, 2) there seems to be a correlation between dis- tribution of positive examples in the training set and scores of the proposed face detector. The latter suggests that the proposed methods performance can be further improved by using better sampling strategies and more sophisticated data augmentation techniques. Evaluations on popular face detection benchmark datasets show that our single-model face detector algorithm has similar or better performance compared to the previous methods, which are more complex and require annotations of either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR

Development of a tight-binding potential for bcc-Zr. Application to the study of vibrational properties

We present a tight-binding potential based on the moment expansion of the density of states, which includes up to the fifth moment. The potential is fitted to bcc and hcp Zr and it is applied to the computation of vibrational properties of bcc-Zr. In particular, we compute the isothermal elastic constants in the temperature range 1200K < T < 2000K by means of standard Monte Carlo simulation techniques. The agreement with experimental results is satisfactory, especially in the case of the stability of the lattice with respect to the shear associated with C'. However, the temperature decrease of the Cauchy pressure is not reproduced. The T=0K phonon frequencies of bcc-Zr are also computed. The potential predicts several instabilities of the bcc structure, and a crossing of the longitudinal and transverse modes in the (001) direction. This is in agreement with recent ab initio calculations in Sc, Ti, Hf, and La.Comment: 14 pages, 6 tables, 4 figures, revtex; the kinetic term of the isothermal elastic constants has been corrected (Eq. (4.1), Table VI and Figure 4

Interatomic potentials for atomistic simulations of the Ti-Al system

Semi-empirical interatomic potentials have been developed for Al, alpha-Ti, and gamma-TiAl within the embedded atomic method (EAM) by fitting to a large database of experimental as well as ab-initio data. The ab-initio calculations were performed by the linear augmented plane wave (LAPW) method within the density functional theory to obtain the equations of state for a number of crystal structures of the Ti-Al system. Some of the calculated LAPW energies were used for fitting the potentials while others for examining their quality. The potentials correctly predict the equilibrium crystal structures of the phases and accurately reproduce their basic lattice properties. The potentials are applied to calculate the energies of point defects, surfaces, planar faults in the equilibrium structures. Unlike earlier EAM potentials for the Ti-Al system, the proposed potentials provide reasonable description of the lattice thermal expansion, demonstrating their usefulness in the molecular dynamics or Monte Carlo studies at high temperatures. The energy along the tetragonal deformation path (Bain transformation) in gamma-TiAl calculated with the EAM potential is in a fairly good agreement with LAPW calculations. Equilibrium point defect concentrations in gamma-TiAl are studied using the EAM potential. It is found that antisite defects strongly dominate over vacancies at all compositions around stoichiometry, indicating that gamm-TiAl is an antisite disorder compound in agreement with experimental data.Comment: 46 pages, 6 figures (Physical Review B, in press

In situ size sorting in CVD synthesis of Si microspheres

[EN] Silicon microspheres produced in gas-phase by hot-wall CVD offer unique quality in terms of sphericity, surface smoothness, and size. However, the spheres produced are polydisperse in size, which typically range from 0.5 mu m to 5 mu m. In this work we show through experiments and calculations that thermophoretic forces arising from strong temperature gradients inside the reactor volume effectively sort the particles in size along the reactor. These temperature gradients are shown to be produced by a convective gas flow. The results prove that it is possible to select the particle size by collecting them in a particular reactor region, opening new possibilities towards the production by CVD of size-controlled high-quality silicon microspheres.The authors acknowledge financial support from the following projects: ENE2013-49984-EXP, MAT2012-35040, MAT2015-69669-P and ESP2014-54256-C4-2-R of the Spanish Ministry of Economy and Competitiveness (MINECO), and PROMETEOII/2014/026 of the Regional Valencian Government.Garín Escrivá, M.; Fenollosa Esteve, R.; Kowalski, L. (2016). In situ size sorting in CVD synthesis of Si microspheres. Scientific Reports. 6:1-10. https://doi.org/10.1038/srep38719S110

Online change detection in exponential families with unknown parameters

International audienceThis paper studies online change detection in exponential families when both the parameters before and after change are unknown. We follow a standard statistical approach to sequential change detection with generalized likelihood ratio test statistics. We interpret these statistics within the framework of information geometry, hence providing a unified view of change detection for many common statistical models and corresponding distance functions. Using results from convex duality, we also derive an efficient scheme to compute the exact statistics sequentially, which allows their use in online settings where they are usually approximated for the sake of tractability. This is applied to real-world datasets of various natures, including onset detection in audio signals

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types