Digging Deep Into Urban Mobility Data Through Machine Learning Techniques

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An explainable data-driven approach to web directory taxonomy mapping

5noThe spread of e-commerce and web applications has fostered the integration of cross-domain business activities. To efficiently retrieve products and services, web directories allow customers to browse multiple-level taxonomies to find specific products or services according to a predefined categorization. Providers need to periodically update web directory lists by aligning in-house taxonomies to domain-specific hierarchies coming from external sources. However, such taxonomy mapping procedures are often semi-automatic and rely on traditional word disambiguation techniques to capture the semantics behind categories and products descriptions. Hence, the flexibility and explainability of the underlying models are quite limited. This paper proposes an automated, explainable approach to web directory taxonomy mapping based on text categorization. It exploits two complementary word-based text representations: a frequency-based representation, which captures syntactic text similarities, and an embedding one, which highlights the underlying semantic relationships among words. Since the proposed solution is purely data-driven, it can be successfully applied to business domains where there is a lack of semantic models. The frequency-based text representation has shown to be particularly suitable for driving the automated taxonomy mapping procedure, whereas the embedding space has been profitably used to provide local explanations of the category assignments.partially_openopenElena Daraio, Luca Cagliero, Silvia Anna Chiusano, Paolo Garza, Giuseppe RicuperoDaraio, Elena; Cagliero, Luca; Chiusano, SILVIA ANNA; Garza, Paolo; Ricupero, Giusepp

Predicting Car Availability in Free Floating Car Sharing Systems: Leveraging Machine Learning in Challenging Contexts

5Free-Floating Car Sharing (FFCS) services are currently available in tens of cities and countries spread all over the worlds. Depending on citizens’ habits, service policies, and road conditions, car usage profiles are rather variable and often hardly predictable. Even within the same city, different usage trends emerge in different districts and in various time slots and weekdays. Therefore, modeling car availability in FFCS systems is particularly challenging. For these reasons, the research community has started to investigate the applicability of Machine Learning models to analyze FFCS usage data. This paper addresses the problem of predicting the short-term level of availability of the FFCS service in the short term. Specifically, it investigates the applicability of Machine Learning models to forecast the number of available car within a restricted urban area. It seeks the spatial and temporal contexts in which nonlinear ML models, trained on past usage data, are necessary to accurately predict car availability. Leveraging ML has shown to be particularly effective while considering highly dynamic urban contexts, where FFCS service usage is likely to suddenly and unexpectedly change. To tailor predictive models to the real FFCS data, we study also the influence of ML algorithm, prediction horizon, and characteristics of the neighborhood of the target area. The empirical outcomes allow us to provide system managers with practical guidelines to setup and tune ML models.openopenDaraio, Elena; Cagliero, Luca; Chiusano, Silvia; Garza, Paolo; Giordano, DaniloDaraio, Elena; Cagliero, Luca; Chiusano, Silvia; Garza, Paolo; Giordano, Danil

Treatment-Free Remission in Chronic Myeloid Leukemia Harboring Atypical BCR-ABL1 Transcripts

Discontinuation of tyrosine kinase inhibitors (TKI) is the main goal today in the field of Philadelphia positive chronic myeloid leukemia (Ph + CML) and the criteria to attempt the interruption of therapy are well defined and rely on the possibility to regularly monitor the BCR-ABL1 transcript. Patients harboring atypical transcripts are automatically excluded from protocols due to the absence of a standardized method of quantification of their minimal residual disease (MRD). We report here the outcome of 6 patients with atypical transcripts with a long follow up whose MRD was followed in three cases with digital PCR during their treatment free remission (TFR)

CarPredictor: forecasting the number of free floating car sharing vehicles within restricted urban areas

Free floating car sharing is a popular rental model for cars in shared use. In urban environments, it has become particularly attractive for users who make short trips or who make occasional use of the car. Since cars are not uniformly distributed across city areas, monitoring the number of cars available within restricted urban areas is crucial for both shaping service provision and improving the user experience. To address these issues, the application of machine learning techniques to analyze car mobility data has become more and more appealing. This paper focuses on forecasting the number of cars available in a restricted urban area in the short term (e.g., in the next 2 hours). It applies regression techniques to train multivariate models from heterogeneous data including the occupancy levels of the target and neighbor areas, weather and temporal information (e.g., season, holidays, daily time slots). To contextualize occupancy level predictions according to the target time and location, we generate models tailored to specific profiles of areas according to the prevalent category of Points-of-Interest in the area. Furthermore, to avoid bias due to presence of uncorrelated features we perform feature selection prior to regression model learning. As a case study, the prediction system is applied to data acquired from a real car sharing system. The results show promising system performance and leave room for insightful extensions

Machine Learning methods to forecast public transport demand based on Smart Card validations

This paper explores the forecasting of public transport demand using mobility data obtained from electronic tickets and smart cards. The research aims to estimate the demand for a selected route at a specific bus stop on a given day and time slot. The study utilizes a large dataset of historical demand data, including approximately 10 million validations collected in 2019 by the Piedmont transport operator Granda Bus, and combines it with additional information such as weather conditions, anonymized user data, and temporal segmentation of the yearly calendar. To identify the peculiarities in demand forecasting for each bus route and stop, a clustering analysis is performed, resulting in the identification of six cohesive and homogeneous clusters. Various machine learning models are tested and compared to determine the most suitable model for forecasting public transport demand at each stop within one-hour time slots. The results demonstrate that machine learning algorithms consistently outperform average-based techniques: the machine learning algorithms exhibit a significant improvement (up to 50% compared to the baseline) when demand uncertainty is greater. The proposed methodology framework is replicable and transferable to other areas, providing a valuable tool for optimizing resource allocation and network planning, while enhancing user satisfaction by accurately forecasting passenger demand at each stop and desired time slot

The European university landscape: a micro characterization based on evidence from the Aquameth Project

This paper provides a new and systematic characterization of 488 universities, from 11 European countries: Finland, France, Germany, Hungary, Italy, Netherlands, Norway, Portugal, Spain, Switzerland and UK. Using micro indicators built on the integrated Aquameth database, we characterize the European university landscape according to the following dimensions: history/foundation of university, dynamics of growth, specialization pattern, subject mix, funding composition, offer profile and productivity

The European university landscape: A micro characterization based on evidence from the Aquameth project

This paper provides a new and systematic characterization of 488 universities, from 11 European countries: Finland, France, Germany, Hungary, Italy, Netherlands, Norway, Portugal, Spain, Switzerland and UK. Using micro indicators built on the integrated Aquameth database, we characterize the European university landscape according to the following dimensions: history/foundation of university, dynamics of growth, specialization pattern, subject mix, funding composition, offer profile and productivity.Universities Size Growth Productivity Specialization Differentiation

Voce: Prelievi e analisi di campioni

Con la legge 30.6.2009 n. 85 l'Italia ha ratificato l'adesione al Trattato di Pr\ufcm, in vista del rafforzamento della cooperazione tra Stati nella lotta al terrorismo, alla criminalit\ue0 transfrontaliera e alla migrazione illegale, tramite lo scambio di informazioni genetiche. La novit\ue0 pi\uf9 saliente che l'adesione al Trattato ha importato nell'ordinamento interno concerne l'introduzione di un'inedita disciplina dei prelievi coattivi di materiale biologico, volta alla tutela dei diritti individuali nell'impiego processuale di strumenti tecnico-scientifici che consentano di non disperdere il materiale probatorio relativo ad un fatto criminoso. Il tema rievoca la tradizionale distinzione che attribuisce all'imputato la duplice funzione di \u201corgano\u201d ed \u201coggetto\u201d nella formazione della prova, a seconda del contributo attivo o passivo che lo stesso apporti alla vicenda processuale. Questi \ue8 considerato \u201corgano\u201d di prova nell'espletamento di attivit\ue0 che costituiscono esercizio del diritto di difesa, nelle due componenti, positiva e negativa, del diritto di difendersi provando e del diritto al silenzio. Viceversa, si parla di imputato come \u201coggetto\u201d di prova allorquando gli sia richiesto un mero pati rispetto all'attivit\ue0 di istruzione probatoria, come accade nelle ispezioni, nelle perquisizioni, nelle ricognizioni personali, nonch\ue9, pi\uf9 in generale, negli accertamenti che si espletano sul corpo del giudicabile, il quale viene in rilievo non come parte processuale, ma come mera entit\ue0 fisica. Il regime di nuovo conio \ue8 intervenuto a colmare la lacuna normativa lasciata dalla sentenza n. 238 del 1996 con cui la Corte costituzionale aveva dichiarato l'illegittimit\ue0 dell'art. 224, comma 2, c.p.p. nella parte in cui consentiva interventi peritali sul corpo della persona, in violazione del principio di riserva di legge che presidia, ex art. 13 Cost., la libert\ue0 personale. La pronuncia ha individuato un \u201cnocciolo duro\u201d rappresentato dalla libert\ue0 corporale, indissolubilmente legata ai principi di libert\ue0 morale, integrit\ue0 psico-fisica e salute della persona, non comprimibili a fini processuali. La Carta fondamentale prevede un'unica ipotesi di lesione del diritto alla salute nell'ambito dei trattamenti sanitari obbligatori, per finalit\ue0 estranee all'accertamento penale; la libert\ue0 morale, peraltro, rappresenta il quid pluris che sopravvive alla compressione del potere statale, persino durante la pi\uf9 intensa restrizione della libert\ue0 personale. Se questo \ue8 il quadro costituzionale di riferimento, \ue8 evidente come il previgente regime in materia di prelievi biologici coattivi abbia disatteso le indicazioni provenienti dalla Consulta. Difatti, a distanza di quasi un decennio dal monito del Giudice delle leggi, il legislatore intervenne (con la legge 31 luglio 2005, n. 155) \uabin un modo persino pi\uf9 imbarazzante dell'inerzia sino ad allora mantenuta\ubb, attribuendo un potere di intrusione corporale (attraverso il prelievo di capelli o saliva nel corso delle indagini) alla polizia giudiziaria, previa autorizzazione - anche orale, purch\ue9 confermata per iscritto - del pubblico ministero, a soli fini identificativi e purch\ue9 sussistesse il pericolo di alterazione o dispersione della res. Non era contemplato il potere giudiziale di disporre un prelievo biologico a fini peritali e l\u2019esclusivo orientamento teleologico dell\u2019atto d\u2019indagine ne limitava fortemente l\u2019utilit\ue0. Veniva, pertanto, inopinatamente elusa la doppia riserva, di legge e di giurisdizione, che presidia la materia. La riforma realizza una netta soluzione di continuit\ue0 rispetto alla normativa precedente, attraverso l'individuazione nell'organo giurisdizionale del baricentro del micro-sistema normativo dedicato ai prelievi biologici coattivi. Se la libert\ue0 personale pu\uf2 subire restrizioni per atto motivato dell'autorit\ue0 giudiziaria, pertanto anche del pubblico ministero, l'intrusione nella sfera corporale esige l'egida di un soggetto super partes, indifferente rispetto all'esito del processo. Il legislatore ha costruito una disciplina minuziosa, concernente sia l'an che il quomodo dei prelievi, in ossequio alla riserva di legge dettagliata (nei \u201cmodi\u201d e nei \u201ccasi\u201d) imposta dalla Consulta. Ne \ue8 derivato un apparato \uabmulti-livello\ubb, calibrato sulla sistematica del codice e diversificato in base all'orientamento teleologico dell'accertamento \u2013istituzionale, probatorio, investigativo o identificativo-, in cui ogni tipologia \ue8 rigidamente separata dalle altre