Individual and Collective Stop-Based Adaptive Trajectory Segmentation

Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspec tive, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results

City Indicators for Geographical Transfer Learning: An Application to Crash Prediction

The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users’ mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution

City Indicators for Mobility Data Mining

Classifying cities and other geographical units is a classical task in urban geography, typically carried out through manual analysis of specific characteristics of the area. The primary objective of this paper is to contribute to this process through the definition of a wide set of city indicators that capture different aspects of the city, mainly based on human mobility and automatically computed from a set of data sources, including mobility traces and road networks. The secondary objective is to prove that such set of characteristics is indeed rich enough to support a simple task of geographical transfer learning, namely identifying which groups of geographical areas can share with each other a basic traffic prediction model. The experiments show that similarity in terms of our city indicators also means better transferability of predictive models, opening the way to the development of more sophisticated solutions that leverage city indicators

Search for H->mu mu in the VBF production channel with the CMS experiment at LHC

Understanding the mechanism that breaks the electroweak symmetry and generates the masses of the known elementary particles has been one of the fundamental endeavors in particle physics. The breaking of the electroweak symmetry is allowed if at least one new particle with well defined properties is added to the ensemble of the elementary particles. Such a particle has long been know as the Higgs boson. Its discovery at the Large Hadron Collider (LHC) at Cern in 2012 by the ATLAS and CMS collaborations represents therefore a major achievement in the field. Starting in 2012, the properties of the Higgs boson have been measured in many of the accessible final states originating from its decay. The mass of the Higgs boson has been determined to be 125.09 ± 0.21 (stat) ±0.11 (syst) GeV, from a combination of the ATLAS and CMS measurements. Several results from both experiments established that its measured properties, including its spin, CP properties, and coupling strengths to fermions and bosons, are consistent with the Standard Model (SM) expectations. As new data is collected, the properties of the Higgs boson can be measured with increasing precision and rarer decay modes become accesible. Such measurements are interesting because any deviation from the prediction of the theory might be a hint of new physics beyond the SM. Among the rare decay modes currently under investigation, the Higgs boson decay into two muons (H → μμ) is the object of study of this thesis. For a Higgs boson with mass of approximately 125 GeV, the probability to decay into a muon pair is expected to be B(H → μμ) = 2.2 × 10 −4, making it one of the smallest accessible at the LHC. On the other hand, the H → μμ signature is one of the cleanest to detect experimentally. Higgs boson decays in two muons are of particular importance because they extend the study of its couplings from the third generation to the second generation of fermions, where deviations from the SM predictions, due to new physics are predicted to be larger. The search for H → μμ presented in this work is performed selecting the vector- boson fusion (VBF) production mode. The cross section is about 10% of the cross section for the gluon-gluon fusion, which is the most important production mode. However, the VBF process gives a cleaner experimental signature. In fact, in the VBF process, a quark coming from each colliding proton radiates a W or Z bosons vvi that subsequently interacts. The two quarks therefore slightly deviated from their original flight direction and typically fall inside the detector acceptance, while a Higgs is emitted. Restricting the scope of the search to the VBF production mode, makes the process even rarer but the peculiar signature of the VBF production mode can be exploited to effectively reduce the experimental backgrounds. The VBF quarks are revealed as jets: two back to back high momentum narrow cones of hadrons and other particles produced by the hadronization of a quark or gluon. Generally the two VBF jets are expected to have high pseudorapidity and large invariant mass while the Higgs decay products are expected to be in the central region of the detector. Imposing the constraints to the invariant mass and the rapidity of the jets as additional cut one reaches an impressive improvement of the signal-to-background ratio. The data used for this search were collected using proton-proton collision at sqrt(s) = 13 TeV by the CMS experiment in 2016, corresponding to an integrated luminosity of 35.9 fb −1 . Only 30 event are expected during the entire data taking period. It is therefore essential to have a high signal efficiency, both in the online and the offline selections, while greatly reducing the backgrounds. The dominant sources of background in these studies are production of top quark pairs (tt) and Drell-Yan leptons with associated jets (referred to as DY+jets). These have a good probability to decay into muons, whose tracks risk to be misclassified as coming from a Higgs decay. The DY+jets background is the hardest to discriminate because it is characterized by two real prompt leptons from a virtual Z or γ boson in addition to two jets, either from initial state radiation. A multivariate approach is used to further discriminate signal from background. As background processes are many orders of magnitude larger than the signal, a Machine Learning (ML) classifier with an extremely good signal acceptance versus background rejection performance is required. For this purpose two different ma- chine learning techniques are used: Boosted Decision Trees (BDTs) and Deep Neural Networks (DNNs). Such systems "learn" (i.e. progressively improve performance on tasks) by considering examples, generally without task-specific programming. The toolkits used in this thesis to implement the multivariate classifier algorithm are TMVA [6] for the BDT method and the Keras library, running on top of Theano, for the NN one. Both are integrated into the ROOT analysis framework. My personal contribution has been the development of these dedicated multivariate techniques, including the search and selection of the most discriminant variables. In order to improve the suppression of the background sources and to obtain the maximum sensitivity a particular attention was given to the choice of the variables starting with the definition of an extensive set of kinematic observables. Several tests were made to search the best discriminant variables checking also the correlation between all the features. Seven variables are considered as the inputs of the BDT. The same input variables are sent to the NN with the addition of other five. After several network configurations the best one results using a pretraining step without the muon invariant mass mll (that is the most discriminant variable) and then a training with the previous weights with the complete features set. In this way is possible to exploit the discriminating power of all the selected variables. The expected final goal is an improvement of the branching ratio upper limit of the process. The current results are still preliminary but encouraging: for a Higgs boson decaying to two muons, the upper limit on the decay rate at 95% confidence level (CL) is expected to be approximately 2.5 times the SM value

Self-Adapting Trajectory Segmentation

Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results

City indicators for geographical transfer learning: an application to crash prediction

The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users’ mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution

Whole-brain propagation delays in multiple sclerosis, a combined tractography - magnetoencephalography study

Two structurally connected brain regions are more likely to interact, with the lengths of the structural bundles, their widths, myelination, and the topology of the structural connectome influencing the timing of the interactions. We introduce an in vivo approach for measuring functional delays across the whole brain in humans (of either sex) using magneto/electroencephalography and integrating them with the structural bundles. The resulting topochronic map of the functional delays/velocities shows that larger bundles have faster velocities. We estimated the topochronic map in multiple sclerosis patients, who have damaged myelin sheaths, and controls, demonstrating greater delays in patients across the network and that structurally lesioned tracts were slowed down more than unaffected ones. We provide a novel framework for estimating functional transmission delays in vivo at the single-subject and single-tract level.SIGNIFICANCE STATEMENT:This manuscript provides a straightforward way to estimate patient-specific delays and conduction velocities in the central nervous system, at the individual level, in healthy and diseased subjects. To do so, it uses a principled way to merge M/EEG and tractography

Dalla competizione all’integrazione nel Medio Oriente-Nord Africa. L’impatto degli Accordi di Abramo sugli equilibri regionali

In che modo sta mutando l’equilibrio regionale in Medio Oriente-Nord Africa? Quale impatto la firma degli Accordi di Abramo sta avendo sulla regione? Prendendo le mosse dalla firma degli accordi di normalizzazione tra Israele e quattro Paesi arabi – Bahrain, Emirati Arabi Uniti, Marocco, Sudan – avvenuta nel 2020, il volume riflette sul mutamento dell’equilibrio regionale innescato dal processo di integrazione tra Israele e alcuni Paesi del mondo arabo. A tale proposito, esamina da una prospettiva teorica la strategia negoziale dietro gli Accordi di Abramo per poi analizzare tre dimensioni chiave entro cui può essere suddivisa la nascente cooperazione tra gli attori coinvolti: piano regionale, geo-economico e socio-culturale. Energia, transizione ecologica, turismo e dialogo interreligioso sono solo alcuni dei settori di cooperazione che l’opera si prefigge di indagare, per comprendere se, almeno parzialmente, in Medio Oriente-Nord Africa la logica della competizione stia lasciando il passo a quella dell’integrazione. Infine, il volume prende in esame gli effetti indiretti degli Accordi di Abramo su alcuni attori regionali ed extra-regionali non firmatari degli stessi