Avoiding Aliasing in Allan Variance: an Application to Fiber Link Data Analysis

Optical fiber links are known as the most performing tools to transfer ultrastable frequency reference signals. However, these signals are affected by phase noise up to bandwidths of several kilohertz and a careful data processing strategy is required to properly estimate the uncertainty. This aspect is often overlooked and a number of approaches have been proposed to implicitly deal with it. Here, we face this issue in terms of aliasing and show how typical tools of signal analysis can be adapted to the evaluation of optical fiber links performance. In this way, it is possible to use the Allan variance as estimator of stability and there is no need to introduce other estimators. The general rules we derive can be extended to all optical links. As an example, we apply this method to the experimental data we obtained on a 1284 km coherent optical link for frequency dissemination, which we realized in Italy

Inter-provincial migration in Italy: a comparison between Italians and foreigners

Internal migration in Italy increased in the 2000s due to foreigners residing in the country. Foreigners have changed the characteristics of Italy’s internal migration. Extended gravity models were run to highlight the differences between the migratory behaviours of Italians and foreigners. The model was implemented to detect the different effects of the Italian and foreign populations, and the distances between the provinces of origin and destinations of the inter-provincial migration of Italians and foreigners. Estimations obtained for the years 1995, 2000, 2005, 2010, and 2015 highlight the different evolutions of the phenomenon

Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks

Information garnered from activity on location-based social networks can be harnessed to characterize urban spaces and organize them into neighborhoods. In this work, we adopt a data-driven approach to the identification and modeling of urban neighborhoods using location-based social networks. We represent geographic points in the city using spatio-temporal information about Foursquare user check-ins and semantic information about places, with the goal of developing features to input into a novel neighborhood detection algorithm. The algorithm first employs a similarity metric that assesses the homogeneity of a geographic area, and then with a simple mechanism of geographic navigation, it detects the boundaries of a city's neighborhoods. The models and algorithms devised are subsequently integrated into a publicly available, map-based tool named Hoodsquare that allows users to explore activities and neighborhoods in cities around the world. Finally, we evaluate Hoodsquare in the context of a recommendation application where user profiles are matched to urban neighborhoods. By comparing with a number of baselines, we demonstrate how Hoodsquare can be used to accurately predict the home neighborhood of Twitter users. We also show that we are able to suggest neighborhoods geographically constrained in size, a desirable property in mobile recommendation scenarios for which geographical precision is key.Comment: ASE/IEEE SocialCom 201

Social and place-focused communities in location-based online social networks

Thanks to widely available, cheap Internet access and the ubiquity of smartphones, millions of people around the world now use online location-based social networking services. Understanding the structural properties of these systems and their dependence upon users' habits and mobility has many potential applications, including resource recommendation and link prediction. Here, we construct and characterise social and place-focused graphs by using longitudinal information about declared social relationships and about users' visits to physical places collected from a popular online location-based social service. We show that although the social and place-focused graphs are constructed from the same data set, they have quite different structural properties. We find that the social and location-focused graphs have different global and meso-scale structure, and in particular that social and place-focused communities have negligible overlap. Consequently, group inference based on community detection performed on the social graph alone fails to isolate place-focused groups, even though these do exist in the network. By studying the evolution of tie structure within communities, we show that the time period over which location data are aggregated has a substantial impact on the stability of place-focused communities, and that information about place-based groups may be more useful for user-centric applications than that obtained from the analysis of social communities alone.Comment: 11 pages, 5 figure

Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement

The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.Comment: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, 2013, Pages 793-80

Applications of Temporal Graph Metrics to Real-World Networks

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.Comment: 25 page

Supervised Feature Compression based on Counterfactual Analysis

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, a smaller, therefore more interpretable Decision Tree can be trained, which, in addition, enhances the stability and robustness of the baseline Decision Tree. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity compared to the baseline Decision Tree.Comment: 29 pages, 12 figure

Minorities internal migration in Italy: an analysis based on gravity models

ABBSTRACT. Italians and foreigners internal migration assume different behaviour in terms of intensity, geography, type. The levels of the mobility, the propensity to move in a short or in a long range, the propensity to cluster or to disseminate in the host country represent important differential characteristics between the two population. Actually the foreign population seems as a mosaic made up of minorities showing different propensities. This is the reason why an analysis considering foreign population as a whole could reach biased outcome. In the paper some gravity models applied to migratory movements among the 110 Italian provinces concerning the most consistent minorities groups are used. The Poissonian effects (regarding various typologies of masses and distance) show in a synthetic way the main differences among the minorities mobility. Moreover, the interpretation of these parameters allows an original interpretation of the minorities mobility structure inside Italy: the sign and level of the estimates derived from the gravity model can permit to better illustrate the residential model of the minorities reflecting how different theories in this domain act. RIASSUNTO. Stranieri ed italiani si muovono all’interno dell’Italia con intensità, forme, percorsi spesso differenti. Livelli di mobilità, tendenza a preferire spostamenti di breve o di lungo raggio, propensione a raccogliersi in determinate aree o al contrario a diffondersi sul territorio sono elementi che agiscono in modo differente tra le due popolazioni. Inoltre, i gruppi che compongono il mosaico etnico nel nostro paese mostrano a loro volta tendenze differenti per cui un’analisi limitata a considerare la popolazione straniera nel suo insieme potrebbe ricostruire una realtà media che non trova corrispondenza nel comportamento di nessun gruppo etnico. Nel lavoro si applica un modello gravitazionale ai movimenti migratori tra le province italiane di alcuni tra i più consistenti gruppi presenti nel Paese. La lettura dei parametri del modello ottenuti attraverso stime di tipo poissoniano consente non solo di acquisire solidi elementi esplorativi sul comportamento differenziale delle varie collettività, ma anche di interpretare la mobilità migratoria dei diversi gruppi alla luce delle teorie più convincenti che inquadrano il modello di insediamento residenziale delle minoranze all’interno del paese ospite

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

: The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics