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Smart Landscape. The architecture of the "micro smart grid" as a resilience strategy for landscape
Get PDF“Smart Landscape”, starting from energy devices for the management and distribution of electricity resources, tends to define a possible vision of landscape.
The main structure and process are based on the architecture of a “micro smart grid”, which is generally associated with urban energy grids and districts, but may become a figurative reference for new forms of landscape, such as “Smart Landscape”.
The output of the research would be to show how the main strategies of “Smart Landscape” and its development could be applied in different context. The outcomes deriving from the theoretical framework and case study prototypes are: strategy (Interoperability and Accountability), structure (smart grid), and process (main case study). The prototype is the island of Venice Lido, to which the concept and structure of the “micro smart grid” would be applied, trying to follow analyses and pilot projects aimed at creating a research project called “L.I.D.O. – Venice: Learning Island Design Opportunities – Venezia. Sustainable scenarios for Venice Lido”.
Smart Landscape is a reflection on development of an urban and landscape design typology linked to the changes brought by the continuous evolution of technologies and the increasingly pressing need for resilience of anthropized contexts, and not only
More than consultation: Civil society organisations mainstreaming fundamental rights in EU border management policies. The case of Frontex and its Consultative Forum
Get PDFWith the growing importance of agencies in the EU executive space (i.e., agencification), civil society organisations (CSOs) have increasingly direct their advocacy efforts towards EU agencies. Currently, CSOs are represented in several consultative bodies of EU agencies (e.g., FRA, EASO, and Frontex). In general, the role of these bodies and platforms is to “merely” assist EU agencies on fundamental rights matters. However, access to EU agencies gives CSOs a privileged position to push their claims forward.
Frontex (or European Coast and Border Guard) is peculiar among EU agencies for its operative competences, and growing resources. Moreover, Frontex has repeatedly raised concerns on its accountability on fundamental rights matters at the EU borders. Therefore, in 2011, Frontex revised Regulation introduced a Fundamental Rights Strategy, and two new bodies: the Fundamental Rights Officer and the Consultative Forum on fundamental rights (CF).
Aim of this research is to establish whether and to what extent CSOs influence Frontex “from within” and what are the outcomes of this interaction in terms of both fundamental rights mainstreaming and agency accountability. These issues are addressed using the literature on CSOs’ participation to EU governance, CSOs’ mainstreaming of fundamental rights, and CSOs’ potential for the accountability/legitimacy of EU agencies. Empirically, this study analyses CSOs’ strategy choice to lobby Frontex from within and questions it in light of the outcomes of this lobbying activity. Even though findings are mixed, due to the absence of CSOs’ clear advocacy goals within the CF, the relationship between CSOs, members of the CF, and Frontex remains unique in terms of mutual learning and potential for the establishment of an effective accountability relationship on fundamental rights matters. Collection of data and analysis have been carried out through expert interviews and by applying an interpretive approach to the study of Frontex official and unofficial documents
Il ghetto di Verona e la sua sinagoga: trasformazioni architettonico-urbane fra XIX e XX secolo
Get PDFLa tesi è dedicata alla Sinagoga di Verona e alle vicende architettonico-urbane che hanno interessato il Ghetto ebraico fra XIX e XX secolo; è organizzata in sette capitoli che, tranne per il primo capitolo, seguono un ordine cronologico. Al fine di poter meglio comprendere le vicende e l’architettura dell’ex Ghetto veronese e delle sue sinagoghe, sono dapprima chiariti i concetti chiave del patrimonio architettonico ebraico anche attraverso confronti stilistici fra gli arredi cultuali appartenenti alle varie regioni nord-italiane, con alcuni rinvii alle testimonianze europee. Nel corso dei capitoli successivi vengono approfondite le vicende di istituzione del Ghetto veronese e delle sue prime sinagoghe, la costruzione del Nuovo Tempio Israelitico, i primi tentativi compiuti alle soglie del Novecento di demolizione del Ghetto, a favore dapprima della realizzazione di un politeama, moderno teatro polifunzionale, e poi a favore della costruzione di una nuova sede per la locale Cassa di Risparmio.
Il quinto capitolo tratta il piano definitivo di demolizione del Ghetto, articolato in tre fasi fra il 1924 e il 1928, soffermandosi sui progetti dell’architetto Aldo Goldschmiedt per l’alzamento del porticato di via Portici e sui tentativi di tutela della casa Pincherli compiuti dal Soprintendente Giuseppe Gerola. Gli ultimi due capitoli analizzano l’intervento dell’architetto Ettore Fagiuoli nel Tempio Israelitico, completato a settembre 1929, con un rapido excursus sulla figura del progettista, e sulla ricostruzione avvenuta fra il 1927 e il 1938 dell’area dell’ex Ghetto ebraico, in cui furono coinvolti sia Ettore Fagiuoli che Francesco Banterle. Del Supercinema, del Superpalazzo, dell’Albergo Touring e della Banca Nazionale del Lavoro sono indagati progetti e realizzazioni.
In Appendice è riportato il regesto dei contratti di demolizione del Ghetto, conservati in 146 buste nell’Archivio del Comune di Verona e analizzati per la stesura del quinto capitolo, mentre una seconda Appendice riguarda i cimiteri ebraici veronesi, le cui vicende si intrecciano cronologicamente con le fasi di sistemazione del Ghetto e sono per questo significative
Il sindacato tra immunità e istanze di eteroregolazione
Get PDFLa ricerca si propone di contribuire a rimettere a tema la democrazia nel sindacato come problema giuridico, confrontandosi con la questione dell’ammissibilità di una regolazione eteronoma dell’ordinamento sindacale e, di conseguenza, con gli approdi della dottrina immunitaria.
A tale fine verranno, innanzitutto, ripercorse le riflessioni dottrinali che hanno portato alla costruzione del principio libertà-immunità, in virtù del quale viene affermata l’inammissibilità di interventi dello Stato e l’impossibilità di predisporre modelli organizzativi sindacali eteroregolati. Le argomentazioni della dottrina italiana verranno quindi discusse in chiave comparata, attraverso un confronto con le teoriche sul rapporto tra controllo democratico e autonomia sindacale provenienti dai sistemi giuridici tedesco e inglese.
Le riflessioni dottrinali emerse dal contesto comparato, unitamente ai modelli organizzativi sindacali inglesi e tedeschi, saranno prese a termine di confronto nella successiva disamina sul funzionamento delle attuali associazioni sindacali italiane, al fine di verificare la qualità democratica del sistema interno risultato dell’autonomia riconosciuta dalla teoria immunitaria.
Dallo studio in chiave comparata e dall’approfondimento delle dinamiche interne al sindacato emergono istanze di eteroregolazione che inducono a ridiscutere il ruolo dello Stato nei confronti delle istanze di tutela dei diritti dei membri. Per questa via si perviene a proporre una diversa interpretazione del dettato costituzionale, fondata sul bilanciamento dell’autonomia sindacale con la tutela degli iscritti dell’organizzazione, diretta a consentire una maggiore protezione delle posizioni giuridiche endoassociative.
Nuovi modelli di regole che, in una prospettiva non paralizzante dell’autonomia collettiva, siano sintonici con la complessiva modificazione strutturale della fattispecie sindacale, potrebbero rafforzare la legittimazione e la funzione sociale del sindacato quale preziosa componente delle democrazie moderne
Remote Sensing-based Channel Modeling and Deployment Planning for Low-power Wireless Networks
Get PDFThe deployment of low power wireless networks is notoriously effort-demanding, as costly in-field campaigns are required to assess the connectivity properties of the target location and understand where to place the wireless nodes. The characteristics of the environment, both static (e.g., obstacles obstructing the link line of sight) and dynamic (e.g., changes in weather conditions) cause variability in the communication performance, thus affecting the network operation quality and reliability. This translates into difficulties in effectively deploy, plan and manage these networks in real-world scenarios, especially outdoor. Despite the large literature on node placement, existing approaches make over-simplifying assumptions neglecting the complexity of the radio environment.
Airborne and satellite Remote Sensing (RS) systems acquire data and images over wide areas, thus enabling one to derive information about these areas at large scale. In this dissertation, we propose to leverage RS systems and related data processing techniques to i) automatically derive the static characteristics of the deployment environment that affect low power wireless communication; ii) model the relation between such characteristics and the communication quality; and iii) exploit this knowledge to support the deployment planning. We focus on two main scenarios: a) the deployment of Wireless Sensor Networks (WSNs) in forests; and b) the communication performance of Internet of Things (IoT) networks based on Long Range (LoRa) wireless technology in the presence of mixed environments.
As a first major contribution, we propose a novel WSN node placement approach (LaPS) that integrates remote sensing data acquired by airborne Light Detection and Ranging (LiDAR) instruments, a specialized path loss model and evolutionary computation to identify (near-)optimal node position in forests, automatically and prior to the actual deployment. When low-power WSNs operating at 2.4 GHz are deployed in forests, the presence of trees greatly affects communication. We define a processing architecture that automatically derives local forest attributes (e.g., tree density) from LiDAR data acquired over the target forest. This information is incorporated into a specialized path loss model, which is validated in deployments in a real forest, enabling fine-grained, per-link estimates of the radio signal attenuation induced by trees. Combining the forest attributes derived from LiDAR data with the specialized path loss model and a genetic algorithm, LaPS provides node placement solutions with higher quality than approaches based on a regular placement or on a standard path loss model, while satisfying the spatial and network requirements provided by the user. In addition, LaPS enables the exploration of the impact of changes in the user requirements on the resulting topologies in advance, thus reducing the in-field deployment effort.
Moreover, to explore a different low-power wireless technology with starkly different trade-offs, we consider a LoRa-based IoT network operating in i) a free space like communication environment, i.e., the LoRa signal is transmitted from an high altitude weather balloon, traverses a free-of-obstacles space and is received by gateways on the ground; and ii) a mixed environment that contains built-up areas, farming fields and groups of trees, with both LoRa transmitters and receiving gateways close to the ground. These scenarios show a huge gap in terms of communication range, thus revealing to which extent the presence of objects affects the coverage that LoRa gateways can provide. To characterize the mixed environment we exploit detailed land cover maps (i.e., with spatial grain 10x10m2) derived by automatically classifying multispectral remote sensing satellite images. The land cover information is jointly analyzed with LoRa connectivity traces, enabling us to observe a correlation between the land cover types involved in LoRa links and the trend of the signal attenuation with the distance. This analysis opens interesting research venues aimed at defining LoRa connectivity models that quantitatively account for the type of environment involved in the communication by leveraging RS data
AMuse: A theoretical framework and technology for extending the museum boundaries in the physical world
Get PDFThe use of intelligent presentation systems within a museum is a well-established practice. This thesis deals with connecting the museum experience with novel cultural experiences in the outside world, with attention to the individual in recognizing opportunities and delivering
tailored presentations. Our goal is to help keep the user connected to the cultural experience and to help develop further knowledge and intellectual pleasure after the museum visit. The specific goal of this research is to explore the potential of technology 1) to define contextual opportunities”, 2) to identify these contextual opportunities, 3) to select relevant material, and 4) to deliver it, given the right context, in the most appropriate way for the specific user. We review the field of personalized cultural heritage experience and technology, and related research areas, needed to serve as a grounded basis for ideas developed in the framework. We examine user preferences by reviewing data from two surveys, we conducted, in order to develop additional (from those in the background) inputs (points) for the theoretical framework model. We then describe our theoretical frameworks, both finding the next place to go, and connecting back to previous experiences. We describe the System Architecture and give three concrete examples of use cases. We report on an initial evaluation of the system (and the underlying theoretical framework) by a visitor study, followed by a discussion of possible implications
Real-time adaptation of stimulation protocols for neuroimaging studies
No full textNeuroimaging techniques allow to acquire images of the brain involved in cognitive tasks. In traditional neuroimaging studies, the brain response to external stimulation is
investigated. Stimulation categories, the order they are presented to the subject and the presentation duration are dened in the stimulation protocol. The protocol is xed before the beginning of the study and does not change in the course of experiment. Recently, there has been a major rise in the number of real-time neuroscientic experiments where the incoming brain data is analysed in an online mode. Real-time neuroimaging studies open an avenue for approaching a whole new broad range of questions, like, for instance, how the outcome of the cognitive task depends on the current brain state. Real-time experiments need a dierent protocol type that can be exibly and interactively adjusted in line with the experimental scope, e.g. hypotheses testing or optimising design for individual subject's parameters. A plethora of methods is currently deployed for protocol adaptation: information theory, optimisation algorithms, genetic algorithms. What is lacking, however, is the paradigm for interacting with the subject's state, brain state in particular. I am addressing this problem in my research. I have concentrated on two types of real-time experiments: closed-loop stimulation experiments and brain-state dependent stimulation (BSDS). As the rst contribution, I put forward a method for closed-loop stimulation adaptation and apply it in a real-time Galvanic Skin Response (GSR) experimental setting. The second contribution is an unsupervised method for brain state detection and
a real-time functional Magnetic Resonance Imaging (rtfMRI) setup making use of this method.
In a neurofeedback setting the goal is for the subject to achieve a target state. Ideally, the stimulation protocol should be adapted to the subject to better guide them towards that state. One way to do this would be modelling the subject's activity in a way that we can evaluate the eect of various stimulation options and choose the optimised ones, maximising the reward or minimising the error. However, currently developing such models for neuroimaging neurofeedback experiments presents a number of challenges, namely: complex dynamics of a very noisy neural signal and non-trivial mapping of neural and cognitive processes. We designed a simpler experiment as a proof of concept using GSR signal. We showed that if it is possible to model the subject's state and the dynamics of the system, it is also possible to steer the subject towards the desired state.
In BSDS, there is no target state, but the challenge lies in the most accurate identication of the subject state in any given moment. The reference, state-of-the-art method for
determining the current brain state is the use of machine learning classiers, or multivariate decoding. However, running supervised machine learning classiers on neuroimaging data has a number of issues that might seriously limit their application, especially in real-
time scenarios. For BSDS, we show how an unsupervised machine learning algorithm (clustering in real-time) can be employed with fMRI data to determine the onset of the
activated brain state. We also developed a real-time fMRI setup for BSDS that uses this method. In an initial attempt to base BSDS on brain decoding, we encountered a set of issues related to classier use. These issues prompted us to developed a new set of methods based on statistical inference that help address fundamental neuroscientic questions. The methods are presented as the secondary contribution of the thesis
Droplet based synthetic biology: chemotaxis and interface with biology
Get PDFLife-like behaviors such as fission, fusion and movement can be artificially re-created exploiting highly simplified protocell systems. This thesis is mainly focused on chemotaxis protocell systems and their integration with biological systems in order to show potential future applications. 1-Decanol droplets, formed in an aqueous medium containing decanoate at high pH, become chemotactic when a chemical gradient is placed in the external aqueous environment. We investigated the behavior of these droplets, their ability to transport and deposit living and non-living objects and to interface them with biofilms. To make the artificial system compatible with natural living systems we developed a partially hydrophobic alginate capsule as a protective unit that can be precisely embedded in a droplet, transported along chemical gradients and deposited. We developed a system that was able to transport: Escherichia coli, Bacillus subtilis and Saccharomyces cerevisiae. Both bacteria survived the transport. However, yeast survived but not in a consistent and repeatable way. Next, we evolved the system to transport human cell lines. We found that A549 cells survive encapsulation but not the transport. A549 cells are in fact very sensitive to toxic 1-decanol. We however found out that this cell line secretes compounds able to decrease the surface tension and to increase the capsule-droplet affinity. Finally we discuss future solutions for the effective transport of human cells
Learning to Learn Concept Descriptions
No full textThe goal of automatically encoding natural language text
into some formal representation has been pursued in the field of Knowledge Engineering to support the construction of Formal Ontologies. Many \SOA{} methods have been proposed
for the automatic extraction of lightweight Ontologies and to populate them. Only few have tackled the challenge of extracting expressive axioms that formalize the possibly complex semantics of ontological concepts.
In this thesis, we address the problem of encoding a natural language sentence expressing the description of a concept into a corresponding Description Logic axiom. In our approach, the encoding happens through a syntactic transformation, so that all the extralogical symbols in the formula are words actually occurring in the input sentence. We followed the recent advances in the field of Deep
Learning in order to design suitable Neural Network architectures capable to learn by examples how to perform this transformation. Since no pre-existing dataset was available to adequately train Neural Networks for this task, we designed a data generation pipeline to produce datasets to train and evaluate the architectures proposed in this thesis. These datasets provide therefore a first reference corpus for the task of learning concept description axioms from text via Machine Learning techniques, and are now
available for the Knowledge Engineering community to fill the pre-existing lack of data.
During our evaluation, we assessed some key characteristics of the approach we propose. First, we evaluated the capability of the trained models to generalize
over the syntactic structures used in the expression of concept descriptions, together with the tolerance to unknown words. The importance of these characteristics is due to the fact that Machine Learning systems are trained on
a statistical sample of the problem space, and they have to learn to generalize over this sample in order to process new inputs. In particular, in our scenario, even an extremely large training set is not able to include all the possible
ways a human can express the definition of a concept. At the same time, part of the human vocabulary is likely to fall out of the training set. Thus, testing these generalization capabilities and the tolerance to unknown words is crucial
to evaluate the effectiveness of the model. Second, we evaluated the improvement in the performance of the model when it is incrementally trained with additional
training examples. This is also a pivotal characteristic of our approach, since Machine Learning-based systems are typically supposed to continuously evolve and improve, on the long term, through iterative repetitions of training set
enlargements and training process runs. Therefore, a valuable model has to show the ability to improve its performance when new training examples are added to
the training set.
To the best of our knowledge, this work represents the first assessment of an approach to the problem of encoding expressive concept descriptions from text that is entirely Machine Learning-based and is trained in a end-to-end fashion starting from raw text. In detail, this thesis proposes the first two Neural Network architectures in literature to solve the problem together with their
evaluation with respect to the above pivotal characteristics, and a first dataset generation pipeline together with concrete datasets
Learning the Meaning of Quantifiers from Language and Vision
Get PDFDefining the meaning of vague quantifiers (‘few’, ‘most’, ‘all’) has been, and still is, the Holy Grail of a mare magnum of studies in philosophy, logic, and linguistics. The way by which they are learned by children has been largely investigated in the realm of language acquisition, and the mechanisms underlying their comprehension and processing have received attention from experimental pragmatics, cognitive psychology, and neuroscience. Very often their meaning has been tied to that of numbers, amounts, and proportions, and many attempts have been made to place them on ordered scales.
In this thesis, I study quantifiers from a novel, cognitively-inspired computational perspective. By carrying out several behavioral studies with human speakers, I seek to answer several questions concerning their meaning and use: Is the choice of quantifiers modulated by the linguistic context? Do quantifiers lie on a mental, semantically-ordered scale? Which are the features of such a scale? By exploiting recent advances in computational linguistics and computer vision, I test the performance of state-of-art neural networks in performing the same tasks and propose novel architectures to model speakers’ use of quantifiers in grounded contexts. In particular, I ask the following questions: Can the meaning of quantifiers be learned from visual scenes? How does this mechanism compare with that subtending comparatives, numbers, and proportions?
The contribution of this work is two-fold: On the cognitive level, it sheds new light on various issues concerning the meaning and use of such expressions, and provides experimental evidence supporting the validity of the foundational theories. On the computational level, it proposes a novel, theoretically-informed approach to the modeling of vague and context-dependent expressions from both linguistic and visual data. By carefully analyzing the performance and errors of the models, I show the effectiveness of neural networks in performing challenging, high-level tasks. At the same time, I highlight commonalities and differences with human behavior