An analytical framework to nowcast well-being using mobile phone data

An intriguing open question is whether measurements made on Big Data recording human activities can yield us high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory

PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this paper, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by {\sf PlayeRank} and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank -- i.e. searching players and player versatility --- showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics

The landscape of QCD axion models

We review the landscape of QCD axion models. Theoretical constructions that extend the window for the axion mass and couplings beyond conventional regions are highlighted and classified. Bounds from cosmology, astrophysics and experimental searches are reexamined and updated.Comment: Review article for Physics Reports, 151 pages. Few typos and signs corrected. Matches journal versio

Prazo suplementar, prudencial ou notificação premonitória (Nachfrist): contornos do instituto à luz da resolução por inadimplemento

Este artigo aborda o prazo suplementar, prudencial ou a notificação preminotória, todos nomes de um instituto comum (Nachfrist), a partir da história do direito e do direito comparado para traçar seu regime jurídico no direito brasileiro. Além presente na disciplina de alguns contratos, cláusulas prevendo Nachfrist vem sendo cada vez mais frequentes na prática, sem que se cuide de aclimatá-las ao regime geral da resolução contratual. Conclui-se que pela admissibilidade geral da figura nas cláusulas resolutivas, apresentando seu regime jurídico mais adequado à luz da experiência comparada, e pela relevância da fixação de um prazo máximo para o cumprimento na resolução por inadimplemento prevista no art. 475 do Código Civil

SINTESI DI SISTEMI 1,3-DIIDROBENZOFURANICI E ISOCROMANICI MEDIANTE REAZIONI DI SONOGASHIRA CICLOCARBONILATIVE CATALIZZATE DA PALLADIO

Sistemi O-eterociclici come 1-acililiden-(3H)-isobenzofurani e 1-acililiden-isocromani sono stati sintetizzati con elevate rese e stereoselettività attraverso reazioni di Sonogashira ciclocarbonilative Pd-catalizzate a partire da iodoareni funzionalizzati e alcol benzilici o omobenzilici. Sono stati utilizzati catalizzatori omogenei come PdCl2(PPh3)2 e sistemi eterogenei a base di nanoparticelle di palladio (MVS) supportate su resine tioliche

MELCOR-To-MELCOR Coupling Method in Severe Accident Analysis Involving Core and Spent Fuel Pool

A lot of effort has been spent to prevent the occurrence of SA in nuclear plant and to develop Severe Accidents (SA) Management to mitigate the consequences of a SA. Those consequences are mainly related to limit the release of fission product to the environment. The core in the vessel is not the only source of fission products as the Spent Fuel Pool (SFP) hosting the fuel removed by the core is, in some NPP, inside the containment and SA conditions can also occur. This is especially important in reactors having proximity between the RPV and SFP such as the VVER-1200. This close proximity implies that any SA occurring in the SFP potentially affects the RPV and vice-versa. This potential combination might cause unexpected evolution in the SA progression to whom the safety systems are not able to contain. MELCOR code is a widely used, flexible powerful SA code but it is incapable (due to the uniqueness of the COR package use inside the same input) to reproduce a situation in which both the fuel in vessel core and the fuel in the SFP, inside the same containment, are going to experience a severe accident scenario. The current study presents a MELCOR-to-MELCOR coupling method to simulate simultaneously scenarios with both, core and SFP, as sources capable of H2 generation, fuel damage and FP release in a VVER-1200 NPP. The coupling is performed by running two simulations in parallel and with the data exchange supervised and managed by a dedicated Python coupling supervising script developed at NINE

Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences

Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors