Self-Stabilizing Wavelets and r-Hops Coordination

We introduce a simple tool called the wavelet (or, r-wavelet) scheme. Wavelets deals with coordination among processes which are at most r hops away of each other. We present a selfstabilizing solution for this scheme. Our solution requires no underlying structure and works in arbritrary anonymous networks, i.e., no process identifier is required. Moreover, our solution works under any (even unfair) daemon. Next, we use the wavelet scheme to design self-stabilizing layer clocks. We show that they provide an efficient device in the design of local coordination problems at distance r, i.e., r-barrier synchronization and r-local resource allocation (LRA) such as r-local mutual exclusion (LME), r-group mutual exclusion (GME), and r-Reader/Writers. Some solutions to the r-LRA problem (e.g., r-LME) also provide transformers to transform algorithms written assuming any r-central daemon into algorithms working with any distributed daemon

Uneven development and insurgency in Turkey: a computational approach

This thesis develops computational techniques to gather, process, and analyze fine- grained data on the war between the Turkish State and the Partiya Karkeren Kurdistan (PKK), a Kurdish insurgent group. In three chapters, I seek to better understand the reasons that lead people join the PKK, assess the impact of development policies aimed at dissuading them from doing so, and explain the group’s structural resilience to military force. The first chapter explores the recruitment of young urban recruits using web scraping, fuzzy matching, and other computational techniques. Leveraging an unprecedentedly detailed research design, militants are compared to random samples of Turkish citizens. I find evidence linking insurgent recruitment and a range factors including birth order and family size, peer effects, and conflict-induced migration. The second chapter explores economic motivations in Turkey’s agrarian Southeast using remote sensing and spatial econometrics. I find that clashes are more frequent following poor harvests, irrigation decouples agricultural income from rainfall, and that conflict was reduced in areas benefiting from a state-sponsored agricultural development program. The third chapter examines the PKK as a whole, focusing on its structural characteristics. I develop a new methodology that leverages deep learning to create a social network graph based on co-appearance in photographs which retains many of the broad structural features of the PKK. Analytical results indicate that the densely interconnected nature of the PKK makes it highly robust to a range of counterinsurgency strategies. Together, substantive findings suggests that development policy is a far more promising avenue for the resolution of the conflict than military policy, while the methodological contributions include the development of forward looking analytical techniques and open source software that enable highly detailed quantitative analysis of civil conflict

Designing and implementing a distributed earthquake early warning system for resilient communities: a PhD thesis

The present work aims to comprehensively contribute to the process, design, and technologies of Earthquake Early Warning (EEW). EEW systems aim to detect the earthquake immediately at the epicenter and relay the information in real-time to nearby areas, anticipating the arrival of the shake. These systems exploit the difference between the earthquake wave speed and the time needed to detect and send alerts. This Ph.D. thesis aims to improve the adoption, robustness, security, and scalability of Earthquake Early Warning systems using a decentralized approach to data processing and information exchange. The proposed architecture aims to have a more resilient detection, remove Single point of failure, higher efficiency, mitigate security vulnerabilities, and improve privacy regarding centralized EEW architectures. A prototype of the proposed architecture has been implemented using low-cost sensors and processing devices to quickly assess the ability to provide the expected information and guarantees. The capabilities of the proposed architecture are evaluated not only on the main EEW problem but also on the quick estimation of the epicentral area of an earthquake, and the results demonstrated that our proposal is capable of matching the performance of current centralized counterparts

Local dominance unveils clusters in networks

Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another perspective built on the notion of local dominance, where low-degree nodes are assigned to the basin of influence of high-degree nodes, and design an efficient algorithm based on local information. Local dominance gives rises to community centers, and uncovers local hierarchies in the network. Community centers have a larger degree than their neighbors and are sufficiently distant from other centers. The strength of our framework is demonstrated on synthesized and empirical networks with ground-truth community labels. The notion of local dominance and the associated asymmetric relations between nodes are not restricted to community detection, and can be utilised in clustering problems, as we illustrate on networks derived from vector data

All the ties that bind. A socio-semantic network analysis of Twitter political discussions

Social media play a crucial role in what contemporary sociological reflections define as a ‘hybrid media system’. Online spaces created by social media platforms resemble global public squares hosting large-scale social networks populated by citizens, political leaders, parties and organizations, journalists, activists and institutions that establish direct interactions and exchange contents in a disintermediated fashion. In the last decade, an increasing number of studies from researchers coming from different disciplines has approached the study of the manifold facets of citizen participation in online political spaces. In most cases, these studies have focused on the investigation of direct relationships amongst political actors. Conversely, relatively less attention has been paid to the study of contents that circulate during online discussions and how their diffusion contributes to building political identities. Even more rarely, the study of social media contents has been investigated in connection with those concerning social interactions amongst online users. To fill in this gap, my thesis work proposes a methodological procedure consisting in a network-based, data-driven approach to both infer communities of users with a similar communication behavior and to extract the most prominent contents discussed within those communities. More specifically, my work focuses on Twitter, a social media platform that is widely used during political debates. Groups of users with a similar retweeting behavior - hereby referred to as discursive communities - are identified starting with the bipartite network of Twitter verified users retweeted by nonverified users. Once the discursive communities are obtained, the corresponding semantic networks are identified by considering the co-occurrences of the hashtags that are present in the tweets sent by their members. The identification of discursive communities and the study of the related semantic networks represent the starting point for exploring more in detail two specific conversations that took place in the Italian Twittersphere: the former occured during the electoral campaign before the 2018 Italian general elections and in the two weeks after Election day; the latter centered on the issue of migration during the period May-November 2019. Regarding the social analysis, the main result of my work is the identification of a behavior-driven picture of discursive communities induced by the retweeting activity of Twitter users, rather than determined by prior information on their political affiliation. Although these communities do not necessarily match the political orientation of their users, they are closely related to the evolution of the Italian political arena. As for the semantic analysis, this work sheds light on the symbolic dimension of partisan dynamics. Different discursive communities are, in fact, characterized by a peculiar conversational dynamics at both the daily and the monthly time-scale. From a purely methodological aspect, semantic networks have been analyzed by employing three (increasingly restrictive) benchmarks. The k-shell decomposition of both filtered and non-filtered semantic networks reveals the presence of a core-periphery structure providing information on the most debated topics within each discursive community and characterizing the communication strategy of the corresponding political coalition

Planetary Scale Data Storage

The success of virtualization and container-based application deployment has fundamentally changed computing infrastructure from dedicated hardware provisioning to on-demand, shared clouds of computational resources. One of the most interesting effects of this shift is the opportunity to localize applications in multiple geographies and support mobile users around the globe. With relatively few steps, an application and its data systems can be deployed and scaled across continents and oceans, leveraging the existing data centers of much larger cloud providers. The novelty and ease of a global computing context means that we are closer to the advent of an Oceanstore, an Internet-like revolution in personalized, persistent data that securely travels with its users. At a global scale, however, data systems suffer from physical limitations that significantly impact its consistency and performance. Even with modern telecommunications technology, the latency in communication from Brazil to Japan results in noticeable synchronization delays that violate user expectations. Moreover, the required scale of such systems means that failure is routine. To address these issues, we explore consistency in the implementation of distributed logs, key/value databases and file systems that are replicated across wide areas. At the core of our system is hierarchical consensus, a geographically-distributed consensus algorithm that provides strong consistency, fault tolerance, durability, and adaptability to varying user access patterns. Using hierarchical consensus as a backbone, we further extend our system from data centers to edge regions using federated consistency, an adaptive consistency model that gives satellite replicas high availability at a stronger global consistency than existing weak consistency models. In a deployment of 105 replicas in 15 geographic regions across 5 continents, we show that our implementation provides high throughput, strong consistency, and resiliency in the face of failure. From our experimental validation, we conclude that planetary-scale data storage systems can be implemented algorithmically without sacrificing consistency or performance