Moving forward in the Euro-Mediterranean research and innovation partnership : the experience of the MIRA project

Research and innovation offer significant opportunities for Mediterranean Partner Countries (MPCs) to develop and exploit their assets for the benefit of their economies and of their peoples, as drivers of economic and social development. In this spirit, this book presents the main outcomes of the MIRA project, a coordination and support action acting as a think-tank and an implementation actor of the Euro-Mediterranean Cooperation in Science and Innovation in the Mediterranean area. The book presents the efforts, analysis, reflections on the past and future of EU - MPC cooperation in research and technology development, as well as models and challenges of structuring this cooperation, and a compilation of the lessons learnt along the development of the project. It contains a reflection on policy aspects, analysis and concrete proposals to support the implementation of a future road map of scientific and innovation cooperation for the mutual benefits . The book reflects the internal and external dialogue of the MIRA project consortium on the targeted objective of supporting the EU-MPC dialogue on scientific and innovation cooperation

Layered XY-Models, Anyon Superconductors, and Spin-Liquids

The partition function of the double-layer $XY$ model in the (dual) Villain form is computed exactly in the limit of weak coupling between layers. Both layers are found to be locked together through the Berezinskii-Kosterlitz-Thouless transition, while they become decoupled well inside the normal phase. These results are recovered in the general case of a finite number of such layers. When re-interpreted in terms of the dual problems of lattice anyon superconductivity and of spin-liquids, they also indicate that the essential nature of the transition into the normal state found in two dimensions persists in the case of a finite number of weakly coupled layers.Comment: 10 pgs, TeX, LA-UR-94-394

Plant disease diagnosis trivia: The old, the new and the ugly

Every year the plant and insect diagnostic clinic receives many samples of crop disease and insect problems. Some problems can be readily diagnosed in the field or clinic, but there are always those difficult look-a-like diseases, unique disease symptoms on some hybrids and environmental stressors that can be vexing to us all and make accurate diagnosis more difficult. This presentation will help you identify some of these issues in the field and know when it is best to submit a sample to ISU. A majority of the presentation will consist of clicker question activities followed by discussion. We will also discuss how to use the Plant and Insect Diagnostic Clinic to your advantage

Attenuation of Ultrasonic Waves Generated from Laser Ultrasound during Annealing of Steel; a Comparison between Theory and Experiment and Potential Application to Additive Manufacturing

The advancement of additive manufacturing methods for the production of metallic parts has initiated the potential development of materials with tailored microstructures to enhance their material properties. To help facilitate the development, methods based on ultrasonic grain scattering are proposed to provide in-situ monitoring of the microstructure’s evolution during the build process. In this work, the longitudinal attenuation coefficient is considered, theoretically and experimentally, as a function of temperature during an annealing process of steel. Theoretically, an iterative solution to the attenuation model of Stanke and Kino is given. The theory is compared against experimental measurements of the longitudinal attenuation coefficient for a steel sample taken at various stages of annealing. Laser ultrasound was employed because it is a remote technique that minimizes unwanted temperature related effects. The annealing process brought the sample from room temperature to 950 o C. A phase transformation from ferrite to austenite occurred at 800 o C, which caused a significant drop in the measured attenuation coefficient. The theoretical attenuation model borrowed previously measured temperature-dependent single-crystal elastic constants of pure iron as model inputs. A mixing formula that considers the volume fraction of ferrite to austenite was applied near the 800 o C mark where the drop in attenuation appeared. Remarkably, the theoretical attenuation model almost exactly reproduced the experimental data points. This concurrence supports: (1) the employment of laser ultrasound for measurement of the attenuation during heating of materials, (2) the suitability of theoretical ultrasonic grain scattering models during highly transient temperature behavior, and (3) the ability of the theoretical attenuation model to represent the effect of a phase transformation

NASA GIBS and Worldview: Visualizing NASA's Earth Science Data for All to Explore

For more than 20 years, the NASA Earth Observing System (EOS) has operated dozens of remote sensing satellites collecting nearly 15 Petabytes of data that span thousands of science parameters. Within these observations are keys the Earth Scientists have used to unlock many discoveries that we now understand about our planet. Also contained within these observations are a myriad of opportunities for learning and education. The challenge is making them accessible to educators and students in intuitive and simple ways so that effort can be spent on lesson enrichment and not overcoming technical hurdles.The NASA Global Imagery Browse Services (GIBS) system and NASA Worldview interactive mapping site provide a unique view into EOS data through daily full resolution visualizations of hundreds of Earth science parameters. For many of these parameters, visualizations are available within hours of acquisition from the satellite. For others, visualizations are available for the entire mission of the satellite. Accompanying the visualizations are visual aids such as color legends, place names, and orbit tracks. By using these visualizations, educators and students can observe natural phenomena that enrich a scientific education.This presentation will provide an overview of the visualizations available in NASA GIBS and Worldview and how they are accessed. Specific attention will be given to the newer capabilities and accomplishments, including: Support for geostationary sub-daily visualizations, Enhanced support for vector-based visualizations, Improved Worldview tour and snapshot capabilities, New imagery products across a growing set of scientific areas

Far infrared giant dipole resonances in neutral quantum dots

A resonance behaviour of the far infrared absorption probability at a frequency \sim N^{1/4} is predicted for clusters of N electron-hole pairs (2\le N\le 110) confined in disk-shaped quantum dots. For radially symmetric dots, the absorption is dominated by a Giant Dipole Resonance, which accounts for more than 98 % of the energy-weighted photoabsorption sum rule.Comment: final versio

Large genetic diversity for fine-flavor traits unveiled in cacao (Theobroma cacao L.) with special attention to the native Chuncho variety in Cusco, Peru

The fine-flavor cocoa industry explores mainly six chocolate sensory traits from four traditional cocoa (Theobroma cacao L.) varieties. The importance of cocoa pulp flavors and aromas has been ignored until we recently showed that they migrate into beans and into chocolates. Pulp sensory traits are strongly genotype dependent and correlated to human preference. Growers of the native Chuncho variety from Cusco, Peru, which is the cocoa that the Incas consumed, make pulp juices from preferred trees (genotypes). Evaluations of 226 preferred trees evidenced presence of 64 unique mostly multi-trait sensory profiles. Twenty nine of the 40 flavors and aromas identified mimic those of known fruit and flower or spice species such as mandarin, soursop, custard apple, cranberry, peach, banana, inga, mango, nut, mint, cinnamon, jasmine, rose and lily. Such large sensory diversity and mimicry is unknown in other commercial fleshy fruit species. So far, 14 Chuncho-like pulp sensory traits have been identified among different cocoa varieties elsewhere suggesting that Chuncho is part of the “centre of origin” for cocoa flavors and aromas. Stable expression of multi-trait Chuncho sensory profiles suggest pleiotropic dominant inheritance, favoring selection for quality traits, which is contrasting with the complex sensory trait determination in other fleshy fruit species. It is inferred that the large sensory diversity of Chuncho cocoa can only be explained by highly specialized sensory trait selection pressure exerted by frugivores, during evolution, and by the indigenous “Matsigenkas”, during domestication. Chuncho beans, still largely employed as a bulk cocoa source, deserve to become fully processed as an extra-fine cocoa variety. The valorization of the numerous T. cacao sensory profiles in chocolates, raw beans and juices should substantially diversify and boost the fineflavor cocoa industry, this time based on the Matsigenka/Inca and not anymore on the Maya cocoa traditions

Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu

Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu