Optical and X-ray properties of the RIXOS AGN: II - Emission lines

We present the optical and UV emission line properties of 160 X-ray selected AGN taken from the RIXOS survey (including Halpha, Hbeta, [OIII]5007, MgII2798 and CIII]1909). This sample is believed to contain a mixture of absorbed and unabsorbed objects, with column densities up to 4e21 cm-2. Although the distribution of the [OIII] EW for the RIXOS AGN is typical of optically selected samples, the Balmer line EWs are relatively low. This is consistent with the presence of a dust absorber between the broad and narrow line regions (eg. a molecular torus), and intrinsically weak optical line emission. We find Baldwin effects in CIII] and MgII, and a positive response of the MgII line to its ionizing continuum. There is a strong correlation between the EW and FWHM of MgII, which may be similar to that seen in other samples for Hbeta. We demonstrate that this is consistent with models which suggest two line-emitting zones, a `very broad line region' (VBLR) and an `intermediate line region' (ILR). The correlation between EW and FWHM in MgII may be a physical characteristic of the ILR or it may reflect a geometric dependence. We found no correlation between the Hbeta FWHM and the slope of the X-ray spectrum, however this may be due to the effects of dust absorption which suppresses the broad Hbeta component, masking any relationship. The Halpha FWHM does tend to be narrow when alpha_X is soft, and broadens as alpha_X hardens, although the formal probability for this correlation is low (91 per cent). If the distribution of alpha_X in the RIXOS sample reflects the level of intrinsic absorption in these AGN, the data suggest a possible link between the velocity of the Balmer line-emitting region and the amount of absorbing material beyond.Comment: 29 pages, 14 figures, to be published in Monthly Notices of the Royal Astronomical Society. Also available from http://www.mssl.ucl.ac.uk/www_astro/preprints/preprints.htm

COLECCIÓN MARÍA DOLORES RAMÍREZ RODRÍGUEZ [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Methodological developments in violence research

Über Jahrzehnte wurde Gewalt durch Interviews mit Betroffenen oder Tätern, durch teilnehmende Beobachtung oder Gewaltstatistiken untersucht, meist unter Verwendung entweder qualitativer oder quantitativer Analysemethoden. Seit der Jahrhundertwende stehen Forschenden eine Reihe neuer Ansätze zur Verfügung: Es gibt immer mehr Videoaufnahmen von gewaltsamen Ereignissen, Mixed Methods-Ansätze werden stetig weiterentwickelt und durch Computational Social Sciences finden Big Data-Ansätze Einzug in immer mehr Forschungsfelder. Diese drei Entwicklungen bieten großes Potenzial für die quantitative und qualitative Gewaltforschung. Der vorliegende Beitrag diskutiert Videodatenanalyse, Triangulation und Mixed Methods-Ansätze sowie Big Data und bespricht den gegenwärtigen und zukünftigen Einfluss der genannten Entwicklungen auf das Forschungsfeld. Das Augenmerk liegt besonders darauf, (1) wie neuere Videodaten genutzt werden können, um Gewalt zu untersuchen und wo ihre Vor- und Nachteile liegen, (2) wie Triangulation und Mixed Methods-Ansätze umfassendere Analysen und theoretische Verknüpfungen in der Gewaltforschung ermöglichen und (3) wo Anwendungen von Big Data und Computational Social Science in der Gewaltforschung liegen können.For decades violence research has relied on interviews with victims and perpetrators, on participant observation, and on survey methods, and most studies focused on either qualitative or quantitative analytic strategies. Since the turn of the millennium, researchers can draw on a range of new approaches: there are increasing amounts of video data of violent incidents, triangulation and mixed methods approaches become ever more sophisticated, and computational social sciences introduce big data analysis to more and more research fields. These three developments hold great potential for quantitative and qualitative violence research. This paper discusses video data analysis, mixed methods, and big data in the context of current and future violence research. Specific focus lies on (1) potentials and challenges of new video data for studying violence; (2) the role of triangulation and mixed methods in enabling more comprehensive violence research from multiple theoretical perspectives, and (3) what potential uses of big data and computational social science in violence research may look like

The Cyber Trust Tension in E-Government: Balancing Identity, Privacy, Security

The growing use of the Internet and other information and communication technologies (ICTs) in e-government services raises important new issues of 'cyber trust' that could have a significant influence on governance structures and practices in the future. This paper argues that at the heart of debates about cyber trust in e-government is a 'trust tension' between the need to collect data on individuals as the basis for providing services and fears about the inappropriate use of personal information gathered, stored, and analysed using ICTs. It draws on studies of experiences in e-commerce and e-business, as well as e-government, to illuminate the nature of cyber trust and its wider social dimensions, including the main related challenges faced in e-government and some strategies, products and services for dealing with them

Contribution a l'etude et a la modelisation de la reponse acoustique de cibles impenetrables de formes quelconques

CNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Fatty acids and sugars in commercial baked goods

In Spain the consumption of bakery products is increasing, while that of bread is decreasing. Baked goods have a high fat and sugar content, and their intake accounts for a high percentage of the food consumed by the population for breakfast, mid-morning and mid-afternoon meals. Twenty products, with and without cream and chocolate, were analysed. The nutrients examined were proteins, fats and fatty acids, carbohydrates, sugars, starch, and fibre. The values for carbohydrates ranged between 36.8% and 62.3%, and for sugars between 9.0% and 33.8%. The fat content ranged from 6.0% to 36.8%, while 76% of the saturated fatty acids (SFA) determined were atherogenic acids. In accordance with daily energy intake recommendations for SFA and sugars, the intake of one serving of the product provides 25% or more of the recommended energy from SFA for nine of the twenty baked goods tested, and more than 15% of the energy recommended from sugars for fourteen of these products

A Counter-Forensic Method for CNN-Based Camera Model Identification

An increasing number of digital images are being shared and accessed through websites, media, and social applications. Many of these images have been modified and are not authentic. Recent advances in the use of deep convolutional neural networks (CNNs) have facilitated the task of analyzing the veracity and authenticity of largely distributed image datasets. We examine in this paper the problem of identifying the camera model or type that was used to take an image and that can be spoofed. Due to the linear nature of CNNs and the high-dimensionality of images, neural networks are vulnerable to attacks with adversarial examples. These examples are imperceptibly different from correctly classified images but are misclassified with high confidence by CNNs. In this paper, we describe a counter-forensic method capable of subtly altering images to change their estimated camera model when they are analyzed by any CNN-based camera model detector. Our method can use both the Fast Gradient Sign Method (FGSM) or the Jacobian-based Saliency Map Attack (JSMA) to craft these adversarial images and does not require direct access to the CNN. Our results show that even advanced deep learning architectures trained to analyze images and obtain camera model information are still vulnerable to our proposed method.Comment: Presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Media Forensic

Fatty acids and sugars in commercial baked goods

In Spain the consumption of bakery products is increasing, while that of bread is decreasing. Baked goods have a high fat and sugar content, and their intake accounts for a high percentage of the food consumed by the population for breakfast, mid-morning and mid-afternoon meals. Twenty products, with and without cream and chocolate, were analysed. The nutrients examined were proteins, fats and fatty acids, carbohydrates, sugars, starch, and fibre. The values for carbohydrates ranged between 36.8% and 62.3%, and for sugars between 9.0% and 33.8%. The fat content ranged from 6.0% to 36.8%, while 76% of the saturated fatty acids (SFA) determined were atherogenic acids. In accordance with daily energy intake recommendations for SFA and sugars, the intake of one serving of the product provides 25% or more of the recommended energy from SFA for nine of the twenty baked goods tested, and more than 15% of the energy recommended from sugars for fourteen of these products

Satellite image forgery detection and localization using GAN and One-Class classifier

Current satellite imaging technology enables shooting highresolution pictures of the ground. As any other kind of digital images, overhead pictures can also be easily forged. However, common image forensic techniques are often developed for consumer camera images, which strongly differ in their nature from satellite ones (e.g., compression schemes, post-processing, sensors, etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound to fail if blindly applied to overhead image analysis. Development of novel forensic tools for satellite images is paramount to assess their authenticity and integrity. In this paper, we propose an algorithm for satellite image forgery detection and localization. Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene. Our algorithm works under the assumption that no forged images are available for training. Using a generative adversarial network (GAN), we learn a feature representation of pristine satellite images. A one-class support vector machine (SVM) is trained on these features to determine their distribution. Finally, image forgeries are detected as anomalies. The proposed algorithm is validated against different kinds of satellite images containing forgeries of different size and shape