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Understanding the dynamical structure of pulsating stars. HARPS spectroscopy of the delta Scuti stars rho Pup and DX Cet

High-resolution spectroscopy is a powerful tool to study the dynamical structure of pulsating stars atmosphere. We aim at comparing the line asymmetry and velocity of the two delta Sct stars rho Pup and DX Cet with previous spectroscopic data obtained on classical Cepheids and beta Cep stars. We obtained, analysed and discuss HARPS high-resolution spectra of rho Pup and DX Cet. We derived the same physical quantities as used in previous studies, which are the first-moment radial velocities and the bi-Gaussian spectral line asymmetries. The identification of f=7.098 (1/d) as a fundamental radial mode and the very accurate Hipparcos parallax promote rho Pup as the best standard candle to test the period-luminosity relations of delta Sct stars. The action of small-amplitude nonradial modes can be seen as well-defined cycle-to-cycle variations in the radial velocity measurements of rho Pup. Using the spectral-line asymmetry method, we also found the centre-of-mass velocities of rho Pup and DX Cet, V_gamma = 47.49 +/- 0.07 km/s and V_gamma = 25.75 +/- 0.06 km/s, respectively. By comparing our results with previous HARPS observations of classical Cepheids and beta Cep stars, we confirm the linear relation between the atmospheric velocity gradient and the amplitude of the radial velocity curve, but only for amplitudes larger than 22.5 km/s. For lower values of the velocity amplitude (i.e., < 22.5 km/s), our data on rho Pup seem to indicate that the velocity gradient is null, but this result needs to be confirmed with additional data. We derived the Baade-Wesselink projection factor p = 1.36 +/- 0.02 for rho Pup and p = 1.39 +/- 0.02 for DX Cet. We successfully extended the period-projection factor relation from classical Cepheids to delta Scuti stars.Comment: Accepted for publication in A&A (in press

Electronic spectra of linear HC5_5H and cumulene carbene H2_2C5_5

The $1 ^3\Sigma_u^- \leftarrow X^3\Sigma_g^-$ transition of linear HC$_5$H (A) has been observed in a neon matrix and gas phase. The assignment is based on mass-selective experiments, extrapolation of previous results of the longer HC$_{2n+1}$H homologues, and density functional and multi-state CASPT2 theoretical methods. Another band system starting at 303 nm in neon is assigned as the $1 ^1 A_1 \leftarrow X ^1 A_1$ transition of the cumulene carbene pentatetraenylidene H$_2$C$_5$ (B).Comment: 7 pages, 4 figures, 5 table

The CoRoT B-type binary HD50230: a prototypical hybrid pulsator with g-mode period and p-mode frequency spacings

B-type stars are promising targets for asteroseismic modelling, since their frequency spectrum is relatively simple. We deduce and summarise observational constraints for the hybrid pulsator, HD50230, earlier reported to have deviations from a uniform period spacing of its gravity modes. The combination of spectra and a high-quality light curve measured by the CoRoT satellite allow a combined approach to fix the position of HD50230 in the HR diagram. To describe the observed pulsations, classical Fourier analysis was combined with short-time Fourier transformations and frequency spacing analysis techniques. Visual spectra were used to constrain the projected rotation rate of the star and the fundamental parameters of the target. In a first approximation, the combined information was used to interpret multiplets and spacings to infer the true surface rotation rate and a rough estimate of the inclination angle. We identify HD50230 as a spectroscopic binary and characterise the two components. We detect the simultaneous presence of high-order g modes and low-order p and g-modes in the CoRoT light curve, but were unable to link them to line profile variations in the spectroscopic time series. We extract the relevant information from the frequency spectrum, which can be used for seismic modelling, and explore possible interpretations of the pressure mode spectrum.Comment: 26 pages, 12+6 figures, accepted for publication in Astronomy and Astrophysic

A new method for the spectroscopic identification of stellar non-radial pulsation modes. II. Mode identification of the Delta Scuti star FG Virginis

We present a mode identification based on new high-resolution time-series spectra of the non-radially pulsating Delta Scuti star FG~Vir (HD 106384, V = 6.57, A5V). From 2002 February to June a global Delta Scuti Network (DSN) campaign, utilizing high-resolution spectroscopy and simultaneous photometry has been conducted for FG~Vir in order to provide a theoretical pulsation model. In this campaign we have acquired 969 Echelle spectra covering 147 hours at six observatories. The mode identification was carried out by analyzing line profile variations by means of the Fourier parameter fit method, where the observational Fourier parameters across the line are fitted with theoretical values. This method is especially well suited for determining the azimuthal order m of non-radial pulsation modes and thus complementary with the method of Daszynska-Daszkiewicz (2002) which does best at identifying the degree l. 15 frequencies between 9.2 and 33.5 c/d were detected spectroscopically. We determined the azimuthal order m of 12 modes and constrained their harmonic degree l. Only modes of low degree (l <= 4) were detected, most of them having axisymmetric character mainly due to the relatively low projected rotational velocity of FG Vir. The detected non-axisymmetric modes have azimuthal orders between -2 and 1. We derived an inclination of 19 degrees, which implies an equatorial rotational rate of 66 km/s.Comment: 14 pages, 26 figure

Grid Loss: Detecting Occluded Faces

Detection of partially occluded objects is a challenging computer vision problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of the detection window are occluded, since not every sub-part of the window is discriminative on its own. To address this issue, we propose a novel loss layer for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a convolution layer independently rather than over the whole feature map. This results in parts being more discriminative on their own, enabling the detector to recover if the detection window is partially occluded. By mapping our loss layer back to a regular fully connected layer, no additional computational cost is incurred at runtime compared to standard CNNs. We demonstrate our method for face detection on several public face detection benchmarks and show that our method outperforms regular CNNs, is suitable for realtime applications and achieves state-of-the-art performance.Comment: accepted to ECCV 201

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, e.g. heavy facial occlusions, extremely low resolutions, strong illumination, exceptionally pose variations, image or video compression artifacts, etc. In this paper, we present a face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above. Similar to the region-based CNNs, our proposed network consists of the region proposal component and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. Firstly, the multi-scale information is grouped both in region proposal and RoI detection to deal with tiny face regions. Secondly, our proposed network allows explicit body contextual reasoning in the network inspired from the intuition of human vision system. The proposed approach is benchmarked on two recent challenging face detection databases, i.e. the WIDER FACE Dataset which contains high degree of variability, as well as the Face Detection Dataset and Benchmark (FDDB). The experimental results show that our proposed approach trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE Dataset by a large margin, and consistently achieves competitive results on FDDB against the recent state-of-the-art face detection methods

Property Inference Attacks on Convolutional Neural Networks:Influence and Implications of Target Model's Complexity

Machine learning models' goal is to make correct predictions for specific tasks by learning important properties and patterns from data. By doing so, there is a chance that the model learns properties that are unrelated to its primary task. Property Inference Attacks exploit this and aim to infer from a given model (i.e., the target model) properties about the training dataset seemingly unrelated to the model's primary goal. If the training data is sensitive, such an attack could lead to privacy leakage. This paper investigates the influence of the target model's complexity on the accuracy of this type of attack, focusing on convolutional neural network classifiers. We perform attacks on models that are trained on facial images to predict whether someone's mouth is open. Our attacks' goal is to infer whether the training dataset is balanced gender-wise. Our findings reveal that the risk of a privacy breach is present independently of the target model's complexity: for all studied architectures, the attack's accuracy is clearly over the baseline. We discuss the implication of the property inference on personal data in the light of Data Protection Regulations and Guidelines