Phase transformations in 40-60-GPa shocked gneisses from the Haughton Crater (Canada): An Analytical Transmission Electron Microscopy (ATEM) study

In order to better understand phase transformations, chemical migration, and isotopic disequilibrium in highly shocked rocks, we have performed a microprobe and an ATEM study on gneisses shocked up to 60 GPa from the Haughton Crater. This study reveals the following chemical and structural characteristics: (1) SiO2 dominant areas are formed by a mixture of pure SiO2 polycrystalline quartz identified by electron diffraction pattern and chemical analysis and a silica-rich amorphous phase containing minor amounts of aluminium, potassium, and iron; (2) Areas with biotitelike composition are formed by less than 200-nm grains of iron-rich spinels embedded in a silica-rich amorphous phase that is very similar to the one described above; (3) Layers with feldsparlike composition are constituted by 100-200-nm-sized alumina-rich grains (the indexation of the crystalline structure is under progress) and the silica-rich amorphous phase; (4) Zones characterized by the unusual Al/Si ratio close to 1 are formed by spinel grains (200-nm-sized) embedded in the same silica-rich amorphous phase; and (5) The fracturated sillimanites contain domains with a lamellar structure, defined by the intercalation of 100-nm-wide lamellae of mullite crystals and of a silica-rich amorphous phase. These mullite crystals preserved the crystallographical orientation of the preshock sillimanite. All compositional domains, identified at the microprobe scale, can thus be explained by a mixture in different proportion between the following phases: (1) a silica-rich amorphous phase, with minor Al and K; (2) quartz crystals; (3) spinel crystals and alumina-rich crystals; (4) sillimanite; and (5) mullite. Such mixtures of amorphous phases and crystals in different proportions explain disturbed isotope systems in these rocks and chemical heterogeneities observed on the microprobe

MONALISA 2.0 and the sea traffic management - a concept creating the need for new maritime information standards and software solutions

Postprint (published version

Residential Segregation, Neighborhood Social and Physical Context in Obesity Disparities in Hispanic Preschoolers: A Conceptual Model

During the last decade, there has been a significant inclusion in obesity prevention studies from individual characteristics to household factors then neighborhood factors. The study of place in the context of early childhood obesity studies has been limited to the food and physical built environment. With the persistent disparities in the prevalence of childhood obesity, and Hispanic minorities being increasingly affected, there is a need to reexamine existing models and develop new model conceptual frameworks to examine the role of place and residential segregation in the context of race, ethnicity, social position, and socioeconomic disparities. In the context of place as a relational space linked to where young children live, play and learn, this paper conceptualizes the role of the neighborhood social and physical factors as well as organizational, household and/or individual factors as mediators of the correlation between residential segregation and obesity in Hispanic preschoolers. In the model, we also attempted to include the role of policies and programs in moderating the negative effects of racial residential segregation and resource inequalities and their interactions with the multiple factors that may contribute to childhood obesity. Recommendations for future research need are identified

A Security Monitoring Framework For Virtualization Based HEP Infrastructures

High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumption, logs and sequence of system calls for detecting anomalies that indicate the presence of a malicious agent. They should also be able to perform automated reactions to attacks without administrator intervention. We describe a novel framework that accomplishes these requirements, with a proof of concept implementation for the ALICE experiment at CERN. We show how we achieve a fully virtualized environment that improves the security by isolating services and Jobs without a significant performance impact. We also describe a collected dataset for Machine Learning based Intrusion Prevention and Detection Systems on Grid computing. This dataset is composed of resource consumption measurements (such as CPU, RAM and network traffic), logfiles from operating system services, and system call data collected from production Jobs running in an ALICE Grid test site and a big set of malware. This malware was collected from security research sites. Based on this dataset, we will proceed to develop Machine Learning algorithms able to detect malicious Jobs.Comment: Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, 10-14 October 2016, San Francisco. Submitted to Journal of Physics: Conference Series (JPCS

Adaptive perception: learning from sensory predictions to extract object shape with a biomimetic fingertip

In this work, we present an adaptive perception method to improve the performance in accuracy and speed of a tactile exploration task. This work extends our previous studies on sensorimotor control strategies for active tactile perception in robotics. First, we present the active Bayesian perception method to actively reposition a robot to accumulate evidence from better locations to reduce uncertainty. Second, we describe the adaptive perception method that, based on a forward model and a predicted information gain approach, allows to the robot to analyse `what would have happened' if a different decision `would have been made' at previous decision time. This approach permits to adapt the active Bayesian perception process to improve the performance in accuracy and reaction time of an exploration task. Our methods are validated with a contour following exploratory procedure with a touch sensor. The results show that the adaptive perception method allows the robot to make sensory predictions and autonomously adapt, improving the performance of the exploration task