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

VizieR Online Data Catalog: TrES-4b RV and Ic curves (Sozzetti+, 2015)

The TrES-4 system was observed with HARPS-N on 17 individual epochs between March 2013 and July 2014. We carried out Ic-band precision photometric observations of two complete transit events of TrES-4 b with the CAHA 1.23-m on UT 2013 July 6 and UT 2014 June 30. (2 data files)

COVID-19 Severity in Multiple Sclerosis: Putting Data Into Context

Background and objectives: It is unclear how multiple sclerosis (MS) affects the severity of COVID-19. The aim of this study is to compare COVID-19-related outcomes collected in an Italian cohort of patients with MS with the outcomes expected in the age- and sex-matched Italian population. Methods: Hospitalization, intensive care unit (ICU) admission, and death after COVID-19 diagnosis of 1,362 patients with MS were compared with the age- and sex-matched Italian population in a retrospective observational case-cohort study with population-based control. The observed vs the expected events were compared in the whole MS cohort and in different subgroups (higher risk: Expanded Disability Status Scale [EDSS] score > 3 or at least 1 comorbidity, lower risk: EDSS score ≤ 3 and no comorbidities) by the χ2 test, and the risk excess was quantified by risk ratios (RRs). Results: The risk of severe events was about twice the risk in the age- and sex-matched Italian population: RR = 2.12 for hospitalization (p < 0.001), RR = 2.19 for ICU admission (p < 0.001), and RR = 2.43 for death (p < 0.001). The excess of risk was confined to the higher-risk group (n = 553). In lower-risk patients (n = 809), the rate of events was close to that of the Italian age- and sex-matched population (RR = 1.12 for hospitalization, RR = 1.52 for ICU admission, and RR = 1.19 for death). In the lower-risk group, an increased hospitalization risk was detected in patients on anti-CD20 (RR = 3.03, p = 0.005), whereas a decrease was detected in patients on interferon (0 observed vs 4 expected events, p = 0.04). Discussion: Overall, the MS cohort had a risk of severe events that is twice the risk than the age- and sex-matched Italian population. This excess of risk is mainly explained by the EDSS score and comorbidities, whereas a residual increase of hospitalization risk was observed in patients on anti-CD20 therapies and a decrease in people on interferon

VizieR Online Data Catalog: 51 Eri b SPHERE/IFS spectra & atmosphere models (Samland+, 2017)

One fits file for each spectrum of 51 Eridani b (SPHERE IFS-YJ, IFS-YH, Samland et al., 2017, this work; GPI-H band, Macintosh et al., 2015, Cat. J/other/Sci/350.64). The first extension of the file contains the spectrum used in the paper (fits-table). The second extension contains the correlation matrix for the uncertainty of the spectral points (fits-image). The petitCODE (a self-consistent 1d radiative-convective equilibrium code, see Molliere et al., 2015ApJ...813...47M, 2017A&A...600A..10M) atmospheric model grids (cloudy and clear) as used and described in Samland et al. 2017, this work, are provided as fits-files. The first extension contains the wavelength sampling of the model cube at a resolution of 1000 (same for all models). The second extension contains the table of all model parameter combinations (each row one model, columns represent parameters). The third extension contains the flattened model cube as 2D-fits image (index of row of table in 2nd ext. corresponds to index of model in 3rd extension). The header of the 3rd extension gives the dimensions of the model cube prior to flattening to make it easy to restore the non-flattened shape if necessary. Units and descriptions can always be found in the respective headers. (2 data files)

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon

VizieR Online Data Catalog: HD 284149 SPHERE/IFS spectrum (Bonavita+, 2017)

HD 284149 was observed with SPHERE in IRDIFS mode on 2015-10-25 and with IRDIFS_EXT mode on 2015-11-27 as part of the SHINE (SpHere INfrared survey for Exoplanet) GTO campaign. (1 data file)