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In the total matching problem, one is given a graph G with weights on the vertices and edges. The goal is to find a maximum weight set of vertices and edges that is the non-incident union of a stable set and a matching. We consider the natural formulation of the problem as an integer program (IP), with variables corresponding to vertices and edges. Let M = M(G) denote the constraint matrix of this IP. We define Delta(G) as the maximum absolute value of the determinant of a square submatrix of M. We show that the total matching problem can be solved in strongly polynomial time provided Delta(G) <= Delta for some constant Delta is an element of Z(>= 1). We also show that the problem of computing Delta(G) admits an FPT algorithm. We also establish further results on Delta(G) when G is a forest.

Urban computing techniques harnessing digital mobility traces can assume a pivotal role in comprehending travel patterns and behavioral dynamics within an urban context. These methodologies yield invaluable insights for transportation planners, enabling them to make informed decisions and augment the overall efficiency of urban transportation systems. The goal of this research is to analyze the parking phenomenon in urban areas and develop predictive models for parking-related indicators. Specifically, we introduce two quantitative metrics: the Average Parking Time and the Average Number of Simultaneously Parked Vehicles, aimed at characterizing parking activities from temporal and volumetric perspectives. A large collection of raw GPS traces from a sample of private cars in the metropolitan areas of Rome was used to investigate and model the spatiotemporal dynamics of parking demand and saturation on public streets. Our investigation entailed the application of a diverse array of Machine Learning and Deep Learning techniques, encompassing statistical models, Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs), for the prediction of these indicator values. In our experiments, we found that the 3D-CLoST model excelled in accurately predicting parking indicators compared to other techniques. Interestingly, we also observed that statistical models were able to achieve performance levels that were similar to those of more complex models.

We introduce QuaterGCN, a spectral Graph Convolutional Network (GCN) with quaternion-valued weights at whose core lies the Quaternionic Laplacian, a quaternion-valued Laplacian matrix by whose proposal we generalize two widely-used Laplacian matrices: the classical Laplacian (defined for undirected graphs) and the complex-valued Sign-Magnetic Laplacian (proposed to handle digraphs with weights of arbitrary sign). In addition to its generality, our Quaternionic Laplacian is the only Laplacian to completely preserve the topology of a digraph, as it can handle graphs and digraphs containing antiparallel pairs of edges (digons) of different weights without reducing them to a single (directed or undirected) edge as done with other Laplacians. Experimental results show the superior performance of QuaterGCN compared to other state-of-the-art GCNs, particularly in scenarios where the information the digons carry is crucial to successfully address the task at hand.

In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel autoencoder architecture featuring time-distributed convolutions, cascade hierarchical units and two distinct attention mechanisms (one spatial and one temporal) that effectively captures and exploits complex spatial and temporal patterns in mobility data for short-term flow prediction problem. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task.

Recent years have witnessed the emerging of novel shared mobility solutions that provide diffused on-demand access to transportation. The widespread adoption of these solutions, particularly electric mopeds (e-mopeds), is expected to bring important benefits such as the reduction of noise and atmospheric pollution, and road congestion, with extensive repercussions on liveability and quality of life in urban areas. Currently, almost no effort has been devoted to exploring the adoption patterns of e-moped sharing services, therefore, optimal management and allocation of vehicles appears to be a problem for service managers. In this study, we tried to demonstrate the validity of the hypothesis that the adoption of electric mopeds depends on the built environment and demographic aspects of each neighbourhood. In detail, we singled out three features concerning the area characteristics (distance from centre, walkability, concentration of places) and one about the population (education index). The results obtained on a real world case study show the strong impact these factors have in determining the adoption of e-moped sharing services. Finally, an analysis was conducted on the possible role that the electric moped sharing can play in social equalization by studying the interactions between rich and poor neighbourhoods. The results of the analyses conducted indicate that communities within a city tend to aggregate by wealth and isolate themselves from one another (social isolation): very few interactions, in terms of trajectories, have been observed between the richest and poorest areas of the city under study.

Le città sono in continua evoluzione a causa dei cambiamenti e delle interazioni con i loro cittadini. Sono sistemi complessi composti da individui, veicoli e infrastrutture che costituiscono il cuore della vita moderna e dello sviluppo sociale. Un fattore chiave che guida questa crescita è l'avanzamento della mobilità verso uno scenario più intelligente e tecnologico, noto anche come Smart Mobility. Questo concetto implica il passaggio da un sistema di trasporto tradizionale a uno più innovativo, in cui un tessuto infrastrutturale intelligente collega le parti interessate e le entità per fornire soluzioni efficienti e sostenibili. I ricercatori in questo campo pongono una forte enfasi sulla comprensione e sull'identificazione dei modelli di spostamento degli individui, al fine di migliorare i servizi e, più in generale, la vivibilità delle città. L'avvento della Data Science ha rivelato l'importanza dello sviluppo e dell'applicazione di tecniche di Intelligenza Artificiale nella creazione di sistemi urbani automatizzati e intelligenti. Sebbene l'IA sia sempre più utilizzata nella Smart Mobility, ci sono ancora molte sfide da affrontare. Per questo motivo, in questa tesi, l'IA e la Scienza dei Trasporti vengono combinate per risolvere problemi di mobilità attraverso l'estrazione di informazioni dai dati di mobilità e lo sviluppo di algoritmi sofisticati. Tramite un'ampia sperimentazione, dimostriamo che le soluzioni proposte migliorano effettivamente la comprensione e l'applicazione dell'intelligenza artificiale ai problemi di mobilità. In particolare, ci concentriamo inizialmente sull'elaborazione dei dati e sull'estrazione della conoscenza da essi. Per spiegare l'adozione di veicoli per la mobilità condivisa, proponiamo l'uso di caratteristiche geografiche (distanza dal centro, walkability, concentrazione di punti di interesse) e demografiche (indice di istruzione). Inoltre, presentiamo un algoritmo di ottimizzazione multi-obiettivo per identificare, Cities are constantly evolving due to the changes and interactions with their citizens. They are complex systems made up of individuals, vehicles, and infrastructures that form the heart of modern life and social development. A key factor driving this growth is the advancement of mobility towards a more intelligent and technological scenario, also known as Smart Mobility. This concept involves moving from a traditional transportation system to a more advanced one, where an intelligent infrastructure fabric connects stakeholders and entities to provide efficient, smart, and sustainable solutions. Researchers in this field place a strong emphasis on understanding and identifying individuals' displacement patterns, in order to enhance the services and, more in general, the livability of cities. The advent of Data Science has revealed the importance of developing and applying Artificial Intelligence techniques in the creation of automated and intelligent city systems. Although AI is increasingly used in Smart Mobility, there are still many challenges to be addressed. For this reason, in this thesis, AI and Transportation Science are combined to develop solutions to mobility-related problems by extracting knowledge from mobility data and developing sophisticated algorithms. We show, through extensive experimentation, that the proposed solutions effectively improve the understanding and application of Artificial Intelligence in mobility problems. In particular, we initially focus on data processing and knowledge extraction. In order to explain the adoption of shared mobility vehicles, we propose the use of geographic (distance from center, walkability, concentration of places) and demographic features (education index). Additionally, we present a multi-objective optimization algorithm to identify behavioral communities, taking into account shared mobility usage patterns, distance between areas, and structural information (via Map Embeddings). We also explore the theoretic

Mobile applications and portable devices are being used extensively in the healthcare sector due to their rapid development. Wearable devices having sensors can be used to collect, analyze, and transmit the vital signs of the wearer. In this paper, we have proposed a wearable expert system that supports and monitors the skier during his activity. This research work is motivated by the need to provide rapid assistance to skiers, especially during off-piste skiing, where its more dangerous, and seeking help is difficult with mishaps. Our approach mainly focuses on proposing an expert system that integrates wearable devices (helmet, goggles, digital watch) with the skier’s smartphone. We present an architecture model and knowledge artifacts to design a wearable expert system for skiing.

E-scooter sharing lets people rent an e-scooter while the system owner manages the fleet. Relocation is fundamental to increase system utilization and revenues, but it is also an expensive task. In this paper we aim at assessing the benefits of relocation while quantifying its economic costs. For this, we rely on trace driven simulations where we build upon millions of actual rentals from two cities, Austin and Louisville. Firstly, we build prediction models to estimate which areas will present a surplus or a lack of e-scooters. We compare a simple stationary model with a state-of-art deep-learning model. Secondly, we replay the exact same traces to quantify the benefits of a relocation heuristic, comparing different system options. Our results show that relocation is fundamental to maximize the number of trips the system can satisfy. Interestingly, even a light and simple relocation policy with few relocations per hour can improve the percentage of satisfied trips by up to 42%. This can also translate in a fleet size reduction without impacting the performances. However, when projected into the economic benefits, the additional costs of relocation must be carefully considered to avoid wasting its benefits.

Characterizing urban communities is essential for understanding citizens' needs and neighborhood-wise dynamics. Discriminating factors are population mobility patterns, neighborhood structural characteristics, and distance to other areas of the city. Available approaches focus on one aspect and, often, suffer from isolated nodes and excessive geographical fragmentation of solutions. For these reasons, we formulate the problem of urban community clustering considering all three aspects and provide an algorithm that combines hierarchical aggregation with node adjustment and relocation. We evaluate our approach on a real-world data set and the obtained results show its efficacy. Finally, we also show the importance of using map embedding for characterizing neighborhood from the structural standpoint.

The problem of reliably predicting vehicle flows is paramount for traffic management, risk assessment, and public safety. It is a challenging problem as it is influenced by multiple factors, such as spatio-temporal dependencies with external factors (as events and weather conditions). In recent years, with the exponential data growth and technological advancement, deep learning has been adopted to approach urban mobility problems by addressing spatial dependency with convolutional neural networks and the temporal one with recurrent neural networks. We propose a spatio-temporal flow prediction framework, called 3D-CLoST, that exploits the synergy between 3D convolution and long short-term memory (LSTM) networks to jointly learn the characteristics of the space-time correlation from low to high levels. To the best of our knowledge, no method currently proposes such a structure for this problem. The results achieved on the two real-world datasets show that 3D-CLoST can learn behaviors from the data effectively.

Thin-slices multi-detector computed tomography (MDCT) plays a key role in the differential diagnosis of interstitial lung disease (ILD). However, thin-slices MDCT has a limited ability to detect active inflammation, which is an important target of newly developed ILD drug therapy. Magnetic resonance imaging (MRI), thanks to its multi-parameter capability, provides better tissue characterisation than thin-slices MDCT.Our aim was to summarise the current status of MRI applications in ILD and to propose an ILD-MRI protocol. A systematic literature search was conducted for relevant studies on chest MRI in patients with ILD.We retrieved 1246 papers of which 55 original papers were selected for the review. We identified 24 studies comparing image quality of thin-slices MDCT and MRI using several MRI sequences. These studies described new MRI sequences to assess ILD parenchymal abnormalities, such as honeycombing, reticulation and ground-glass opacity. Thin-slices MDCT remains superior to MRI for morphological imaging. However, recent studies with ultra-short echo-time MRI showed image quality comparable to thin-slices MDCT. Several studies demonstrated the added value of chest MRI by using functional imaging, especially to detect and quantify inflammatory changes.We concluded that chest MRI could play a role in ILD patients to differentiate inflammatory and fibrotic changes and to assess efficacy of new ILD drugs.

[Aim]: To study the efficiency of multifunctional polymer-based superparamagnetic iron oxide nanoparticles (bioferrofluids) as a T2 magnetic resonance contrast agent and their uptake and toxicity in liver. [Materials & methods]: Mice were intravenously injected with bioferrofluids and Endorem®. The magnetic resonance efficiency, uptake and in vivo toxicity were investigated by means of magnetic resonance imaging (MRI) and histological techniques. [Results]: Bioferrofluids are a good T2 contrast agent with a higher r2/r1 ratio than Endorem. Bioferrofluids have a shorter blood circulation time and persist in liver for longer time period compared with Endorem. Both bioferrofluids and Endorem do not generate any noticeable histological lesions in liver over a period of 60 days post-injection. [Conclusion]: Our bioferrofluids are powerful diagnostic tool without any observed toxicity over a period of 60 days post-injection.

We solve a 20-year old problem posed by Yannakakis and prove that no polynomial-size linear program (LP) exists whose associated polytope projects to the traveling salesman polytope, even if the LP is not required to be symmetric. Moreover, we prove that this holds also for the cut polytope and the stable set polytope. These results were discovered through a new connection that we make between one-way quantum communication protocols and semidefinite programming reformulations of LPs.

We solve a 20-year old problem posed by Yannakakis and prove that there exists no polynomial-size linear program (LP) whose associated polytope projects to the traveling salesman polytope, even if the LP is not required to be symmetric. Moreover, we prove that this holds also for the cut polytope and the stable set polytope. These results were discovered through a new connection that we make between one-way quantum communication protocols and semidefinite programming reformulations of LPs.

Cortical reorganization occurring in multiple sclerosis(MS)patients isthoughtto playakeyrole in limiting the effect of structural tissuedamage. Conversely, its exhaustionmaycontribute to the irreversible disability that accumulates with disease progression. Several aspects of MS-related cortical reorganization, including the overall functional effect and likely modulation by therapies, still remain to be elucidated. The aim of this work was to assess the extent of functional cortical reorganization and its brain structural/pathological correlates in Dark Agouti rats with experimental autoimmune encephalomyelitis (EAE), a widely accepted preclinical model of chronic MS. Morphological and functional MRI (fMRI) were performed before disease induction and during the relapsing and chronic phases of EAE. During somatosensory stimulation of the right forepaw, fMRI demonstrated that cortical reorganization occurs in both relapsing and chronic phases of EAE with increased activated volume and decreased laterality index versus baseline values. Voxel-based morphometry demonstrated gray matter (GM) atrophy in the cerebral cortex, and both GM and white matter atrophy were assessed by ex vivo pathology of the sensorimotor cortex and corpus callosum. Neuroinflammation persisted in the relapsing and chronic phases, with dendritic spine density in the layer IV sensory neurons inversely correlating with the number of cluster of differentiation 45-positive inflammatory lesions. Our work provides an innovative experimental platform that may be pivotal for the comprehension of key mechanisms responsible for the accumulation of irreversible brain damage and for the development of innovative therapies to reduce disability in EAE/MS.

In this paper, we propose a cognitive semantic approach to represent part-whole relations. We base our proposal on the theory of conceptual spaces, focusing on prototypical structures in part-whole relations. Prototypical structures are not accounted for in traditional mereological formalisms. In our account, parts and wholes are represented in distinct conceptual spaces; parts are joined to form wholes in a structure space. The structure space allows systematic similarity judgments between wholes, taking into consideration shared parts and their configurations. A point in the structure space denotes a particular part structure; regions in the space represent different general types of part structures. We argue that the structural space can represent prototype effects: structural types are formed around typical arrangements of parts. We also show how structure space captures the variations in part structure of a given concept across different domains. In addition, we discuss how some taxonomies of part-whole relations can be understood within our framework. © 2013 Marta Olivetti Belardinelli and Springer-Verlag.