Growth of the Brownian forest

Trees in Brownian excursions have been studied since the late 1980s. Forests in excursions of Brownian motion above its past minimum are a natural extension of this notion. In this paper we study a forest-valued Markov process which describes the growth of the Brownian forest. The key result is a composition rule for binary Galton--Watson forests with i.i.d. exponential branch lengths. We give elementary proofs of this composition rule and explain how it is intimately linked with Williams' decomposition for Brownian motion with drift.Comment: Published at http://dx.doi.org/10.1214/009117905000000422 in the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Audiomomma Music Recommendation System

We design and implement a system that recommends musicians to listeners. The basic idea is to keep track of what artists a user listens to, to find other users with similar tastes, and to recommend other artists that these similar listeners enjoy. The system utilizes a client-server architecture, a web-based interface, and an SQL database to store and process information. We describe Audiomomma-0.3, a proof-of-concept implementation of the above ideas

Multi-year mapping of water demand at crop level:An end-to-end workflow based on high-resolution crop type maps and meteorological data

This article presents a novel system that produces multiyear high-resolution irrigation water demand maps for agricultural areas, enabling a new level of detail for irrigation support for farmers and agricultural stakeholders. The system is based on a scalable distributed deep learning (DL) model trained on dense time series of Sentinel-2 images and a large training set for the first year of observation and fine tuned on new labeled data for the consecutive years. The trained models are used to generate multiyear crop type maps, which are assimilated together with the Sentinel-2 dense time series and the meteorological data into a physically based agrohydrological model to derive the irrigation water demand for different crops. To process the required large volume of multiyear Copernicus Sentinel-2 data, the software architecture of the proposed system has been built on the integration of the Food Security thematic exploitation platform (TEP) and the data-intensive artificial intelligence Hopsworks platform. While the Food Security TEP provides easy access to Sentinel-2 data and the possibility of developing processing algorithms directly in the cloud, the Hopsworks platform has been used to train DL algorithms in a distributed manner. The experimental analysis was carried out in the upper part of the Danube Basin for the years 2018, 2019, and 2020 considering 37 Sentinel-2 tiles acquired in Austria, Moravia, Hungary, Slovakia, and Germany

Emergence of studies

info:eu-repo/semantics/publishedVersio

Field Demonstration of a Real-time Non-intrusive Monitoring System for Condition-based Maintenance

The performance of important electrical loads on mission critical systems like warships or off-shore platforms is often tracked by dedicated monitoring equipment. Individual monitoring of each load is expensive and risky. Expense occurs because of the need for individual sensors and sensor wiring for every load of interest. Reliability is compromised because detected failures or fault conditions might legitimately be due to load failure, but might also be due to errors or failure in the sensor network or recording instruments. The power distribution network on a warship could be pressed into “dual-use” service, providing not only power distribution but also a diagnostic monitoring capability based on observations of the way in which loads draw power from the distribution service. This paper describes field tests of a prototype system that monitors multiple loads using existing electrical wiring. Initial results are presented from a device that monitors a small collection of motors and two other devices that monitor an entire engine room.Grainger Foundation; National Science Foundation (U.S.); United States. National Aeronautics and Space Administration; United States. Coast Guard; United States. Office of Naval Research. Electric Ship Research and Development Consortium; NAVSEA; University of North Carolin

5G-XHaul:a converged optical and wireless solution for 5G transport networks

This is the pre-peer reviewed version of the following article: Gutiérrez-Terán, J., Maletic, N., Camps, D., Garcia-Villegas, E., Berberana, I., Anastasopoulos, M., Tzanakaki, A., Kalokidou, V., Flegkas, P., Syrivelis, D., Korakis, T., Legg, P., Markovic, D., Limperopoulos, G., Bartelt, J., Chaudhary, J.K., Grieger, M., Vucic, N., Zou, J., Grass, E. 5G-XHaul: a converged optical and wireless solution for 5G transport networks. "Transactions on emerging telecommunications technologies", 8 Juliol 2016, vol. 27, núm. 9, p. 1187-1195, which has been published in final form at http://onlinelibrary.wiley.com.recursos.biblioteca.upc.edu/doi/10.1002/ett.3063/epdf. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.The common European Information and Communications Technology sector vision for 5G is that it should leverage on the strengths of both optical and wireless technologies. In the 5G context, a wide spectra of radio access technologies—such as millimetre wave transmission, massive multiple-input multiple-output and new waveforms—demand for high capacity, highly flexible and convergent transport networks. As the requirements imposed on future 5G networks rise, so do the challenges in the transport network. Hence, 5G-XHaul proposes a converged optical and wireless transport network solution with a unified control plane based on software defined networking. This solution is able to support the flexible backhaul and fronthaul—X-Haul—options required to tackle the future challenges imposed by 5G radio access technologies. 5G-XHaul studies the trade-offs involving fully or partially converged backhaul and fronthaul functions, with the aim of maximising the associated sharing benefits, improving efficiency in resource utilisation and providing measurable benefits in terms of overall cost, scalability and sustainabilityPeer ReviewedPostprint (published version

From Copernicus Big Data to Extreme Earth Analytics

Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019

ExtremeEarth meets satellite data from space

Bringing together a number of cutting-edge technologies that range from storing extremely large volumesof data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner, and having them operate over the same infrastructure poses unprecedentedchallenges. One of these challenges is the integration of European Space Agency (ESA)s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, that enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this paper, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in thispaper are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we presentthe integration of Hopsworks with the Polar and Food Securityuse cases and the flow of events for the products offered through the TEPs

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data