357 research outputs found
Transizione ecologica e dividendi ai confini opposti della finanza
No full textAll’alba della transizione ecologica, le imprese statunitensi devono scegliere tra il benessere degli azionisti oggi e quello del pianeta doman
Italian urban tourism predictions using the holiday Climate Index
No full textThe tourism sector is a source of sustenance for local communities, a driver of fiscal revenues and a way to connect local sites to international guests. Its dependence on climate change exposes it to chronic risks such as slowly varying climate patterns. In this paper, we predicted tourism intensity (as the number of beds per square kilometres) according to three Representative Concentration Pathways (RCP) at the municipal level in Italy: 2.6, 4.5 and 8.5. We first estimated a statistical model of tourism intensity using the Holiday Climate Index (HCI) and other drivers. Then, we used the prediction of beds per Km2 to infer changes between 2004 and 2050 according to each RCP scenario of the HCI. We find complex heterogeneous patterns in exposure and a moderate positive effect in the RCP2.6 scenario. However, delayed (RCP4.5) or no climate policy at all (RCP8.5) scenarios present dire consequences for the tourism sector
EU Emissions Trading System Installations Entries and Exits
No full textThis dataset aims to provide a list of installation entries and exits into and from the EU ETS. To the extent possible, we also specify the reason for an identified entry/exit. The dataset includes a list of EU ETS installations, specifying the sector and the firm's Orbis BvD identifier, along with entry year and reason, and, if applicable, exit year and reason.
Almost twenty years have passed since the EU ETS started operating and keeping track of firm dynamics in the EU ETS – which the system itself could potentially affect – has become increasingly important. Knowing whether an observed entry reflects new production capacity, increased production capacity, a change in EU ETS legislation, or a mere administrative decision would allow studies on the impact of the EU ETS on firm dynamics and, more generally, studies on economic performance (risk of estimation bias)
EU Emissions Trading System sectoral environmental and economic indicators
No full textThis dataset is composed of five indicators which provide information on the economic and environmental performance of sectors covered by the European Union’s Emissions Trading Scheme (EU ETS). The focus of the dataset is to have an overview of trends across sectors by tracking the evolution of the economic and environmental scope of the EU ETS.
Doing so generates new insights into the coverage of the EU ETS, a key EU climate policy aimed at reducing Greenhouse Gas Emissions (GHG) cost-effectively. . The indicators were produced within the framework of the project LIFE COASE, co-financed by the EU LIFE Programme. The project aims to support EU and Member State policymakers in the implementation and development of the EU ETS. It aims at creating lasting cooperation between policymakers, academia and stakeholders and raising public awareness in the field of emissions trading
Ion acceleration by superintense laser-plasma interaction
No full textIon acceleration driven by superintense laser pulses is attracting an impressive and steadily increasing effort. Motivations can be found in the applicative potential and in the perspective to investigate novel regimes as available laser intensities will be increasing. Experiments have demonstrated, over a wide range of laser and target parameters, the generation of multi-MeV proton and ion beams with unique properties such as ultrashort duration, high brilliance, and low emittance. An overview is given of the state of the art of ion acceleration by laser pulses as well as an outlook on its future development and perspectives. The main features observed in the experiments, the observed scaling with laser and plasma parameters, and the main models used both to interpret experimental data and to suggest new research directions are described
Data set for anomaly detection on a HPC system
No full text<p>This data set contains the data collected on the DAVIDE HPC system (CINECA & E4 & University of Bologna, Bologna, Italy) in the period March-May 2018.</p>
<p>The data set has been used to train a autoencoder-based model to automatically detect anomalies in a semi-supervised fashion, on a real HPC system.</p>
<p>This work is described in:</p>
<p>1) "Anomaly Detection using Autoencoders in High Performance Computing Systems", <a href="https://arxiv.org/search/cs?searchtype=author&query=Borghesi%2C+A">Andrea Borghesi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bartolini%2C+A">Andrea Bartolini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lombardi%2C+M">Michele Lombardi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Milano%2C+M">Michela Milano</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benini%2C+L">Luca Benini,</a> IAAI19 (proceedings in process) -- https://arxiv.org/abs/1902.08447</p>
<p>2) "Online Anomaly Detection in HPC Systems", <a href="https://arxiv.org/search/cs?searchtype=author&query=Borghesi%2C+A">Andrea Borghesi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Libri%2C+A">Antonio Libri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benini%2C+L">Luca Benini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bartolini%2C+A">Andrea Bartolini, </a>AICAS19 (proceedings in process) -- https://arxiv.org/abs/1811.05269</p>
<p>See the git repository for usage examples & details --> https://github.com/AndreaBorghesi/anomaly_detection_HPC</p>
Using Temporal Convolutional Networks to estimate ball possession in soccer games
No full textThe use of tracking data in the field of sport analytics has increased in the last years as a starting point for in-depth tactical analyses. This work investigates the use of Temporal Convolutional Networks (TCNs), a powerful architecture for sequential data analysis, to extract ball possession information from tracking data. This task is a crucial step for many tactical analyses and is nowadays carried out manually by a human operator in the stadium, which is costly, difficult to implement, and prone to errors. In this work, several classification approaches are explored to classify the game state as dead, ball owned by the home team, or by the away team: as a single-branch, ternary prediction, or as two binary predictions, first detecting whether the game is dead or alive and then which team owns the ball. TCNs are exploited to create independent trajectory embeddings from tracking data of each object; since there is no semantic ordering among the tracked objects, we investigate different permutation-invariant layers to combine the embeddings, namely, an element-wise sum over the embeddings, a self-attention module, and the use of 2D convolutions. Performance evaluation on tracking data from professional soccer games shows that the proposed method outperforms state-of-the-art rule-based methods, achieving 86.2% accuracy in possession estimation (+7.3% compared to the state of the art) and 89.2% accuracy in dead-alive classification (+33.2% compared to the state of the art). Extensive ablation studies were conducted to investigate how different input data concur to the final prediction
Immunohistochemical Prostate-specific Membrane Antigen (PSMA) Expression Patterns of Primary Prostate Cancer Tissue as a Determining Factor for Prostate Cancer Staging with PSMA Positron Emission Tomography/Computed Tomography
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