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Tin dioxide-based photonic glass-ceramics
Full text linkLooking at state of the art of optical devices, it is evident that glass-based rare-earth-activated optical structures represent the technological pillar of a huge number of photonic applications covering Health and Biology, Structural Engineering, Environment Monitoring Systems, Lighting, Laser sources and Quantum Technologies. Among different glass-based systems, a strategic place is assigned to transparent glass-ceramics, nanocomposite materials, which offer specific characteristics of capital importance in photonics. Following this strategy, this PhD thesis exploits tin dioxide (SnO2)-based glass-ceramic activated by erbium ions (Er3+) to put the basis for the fabrication of solid state and integrated lasers. The research discussed in my PhD thesis gives a possible solution to two crucial and decisive points in the development of an optically pumped rare-earth-based laser: (i) the low absorption cross section of the rare-earth ions; (ii) the writing of channels and mirrors in the case of waveguide integrated laser, thanks to the demonstration of two innovative and unique characteristics of SnO2-based transparent glass-ceramics, i.e. luminescence sensitizing and photorefractivity. The role of SnO2 nanocrystals as rare-earth ion luminescence sensitizers allows to overcome the low absorption cross section of the Er3+ ion. The photorefractivity in range of 10-3 of SiO2-SnO2:Er3+ glass-ceramics allows applying the robust direct laser photoinscription technique on the systems to fabricate Bragg gratings and channel waveguides for waveguide integrated lasers. Based on an application-oriented approach, a comprehensive study on SiO2-SnO2:Er3+ glass-ceramic planar waveguides and monoliths, has been carried out. The work covers different research stages and aspects from the material preparation to a complete assessment of systems for the applications employing a rich number and variety of experimental techniques. The energy transfer from SnO2 to Er3+ and the efficient pumping scheme exploiting SnO2 as Er3+ luminescence sensitizers were demonstrated. The relaxation dynamic of the electronic states as well as the location of the dopant and density of states are discussed, and a specific modeling has been developed to the proof of concept realization of the considered devices. The obtained photorefractivity in range of 10-3 allowed the inscription of gratings on the fabricated SiO2-SnO2:Er3+ planar waveguides using UV laser direct writing technique. Exploiting the robust femtosecond laser micromachining, the optical waveguides were inscribed in the fabricated SiO2-SnO2:Er3+ monolithic squares. Another important outcome of this research is the design of a solid state laser with lateral pumping scheme and of an integrated waveguide laser in two different distributed feedback structures using all the parameters measured during the experimental activity
Network Level Representation of Conceptual Content
Full text linkOur ability to store knowledge and represent the world within our minds has spanned multiple disciplines (philosophy, psychology, neuroscience). Currently, theories of human conceptual knowledge suggest that human representation of the world is widely distributed across the brain. Regions involved in sensory/motor simulation as well as amodal systems contribute to our flexible ability to manipulate knowledge. A detailed account of how the overall human semantic system works at a network level is still lacking. To begin our investigation into how knowledge is distributed across brain networks, we will first consider a specific kind of knowledge - person related knowledge. Chapter 2 will look at the behavioural indicators of person-knowledge organisation. We will ask participants to judge explicit/subjective similarity between different person-knowledge domains: social, physical, biographical, episodic and nominal knowledge. This will allow us to investigate whether these processes are independent or related to each other. We will then compare these judgements to implicit similarity measures to see whether correlated patterns of responses or reaction are informative about cognitive similarity. Chapter 3 will look at how the brains core/extended system for face perception coordinates across the aforementioned person-knowledge domains. We will investigate the representational similarity of different person-knowledge domains in individual regions, and crucially - across the network as a whole. This will allow us to address whether cognitions are localised in individual regions or distributed across the whole network. Chapter 4 will investigate the stability of network organisation when going across modalities. Extended system for face perception has been shown to be recruited during familiar name reading. We will ask whether network-level patterns of activation during person-knowledge remain stable across input modalities. Chapter 5 will generalize the network-level approach to investigate broader semantic categories. We will interrogate how diverse regions activated during semantic processing, interact during processing of naturally occurring conceptual categories. We will use a corpus derived semantic distance model and compare it to individual region activity to that of the network overall. We will ask whether information about conceptual distance between categories is contained within individual regions or arises as a product of coordinated effort across the network.
Combined, evidence presented in this thesis speak to the distributed nature of cognitive representation. Different kinds of person-knowledge and object categories are highly linked and rely on overlapping neural substrates. We demonstrate that instead of being specialised for particular tasks, brain areas involved in meaning extraction tend to be involved in most kinds of conceptual processing. Individually regions have slight cognitive tunings and can be geared towards specific cognitions. Differences in person- knowledge and object categories emerge as a product of the coordinated interplay between multiple brain regions
Coactive Learning Algorithms for Constructive Preference Elicitation
Full text linkPreference-based decision problems often involve choosing one among a large set of options, making common tasks like buying a car or a domestic appliance very challenging for a customer to handle on her own. This is especially true when
buying online, where the amount of available options is humongous, and expert advice is yet limited. Recommender systems have become essential computational tools for aiding users in this endeavor. Recommender systems represent one of the most successful applications of artificial intelligence.
In the last decades, several recommendation approaches have been proposed for different types of applications, from assisted browsing of product catalogs to personalization of results in search engines. Depending on the application, the
job of the recommender system may be to recommend a satisfying option for the given context, as in finding the next best song to play, as opposed to helping the user in finding an optimal instance, e.g. when looking for an
apartment. The former is generally handled by data-driven approaches, such as collaborative filtering and contextual bandits, while in the latter case data is usually scarce, making it necessary to employ specialized algorithms for
preference elicitation. Preference elicitation algorithms interactively build a utility model of the user preferences and then recommend the instances with the highest utility. Preference elicitation is especially effective when recommending infrequently purchased items, such as professional work tools, electronic devices and other products that can be explicitly stored e.g. in the database of an e-commerce website. Standard preference elicitation
algorithms, however, struggle when the options are so numerous that cannot even be explicitly enumerated, and instead need to be represented implicitly as a collection of variables and constraints. Indeed, when a customer wants to
configure a product by putting several components together, e.g. for a custom personal computer, the option space is combinatorial and grows exponentially with the number of components, making it impractical to store every single
feasible combination explicitly. This is an example of constructive decision problem, in which an object has to be synthesized on the basis of the preferences of the customer and the constraints over the configuration domain.
Constructive problems such as product configuration have traditionally been addressed by specialized configurator systems, which guide the user through the configuration process component by component and check whether the user choices are consistent with the set of feasibility constraints. Over the years, however, the limitations of product configurators for mass customization have become
apparent. With the growing scale of configuration problems, product configurators have become more difficult for non-experts to use and ultimately do not provide relief against the "mass confusion" caused by the sheer amount
of choice. Research in this field has progressively been integrating recommendation technologies into configuration systems, in order to make them more flexible and easy to use. Preference elicitation in product configuration
has been attempted as well but still remains a challenge.
We propose a generic framework for preference elicitation in constructive domains, that is able to scale to large combinatorial problems better than existing techniques. Our constructive preference elicitation framework is based
on online structured prediction, a machine learning technique that deals with sequential decision problems over structured objects. By combining online structured prediction and state-of-the-art constraint solvers we can efficiently learn user utility models and make increasingly better recommendations for complex preference-based constructive problems such as product configuration. In
particular, we favor the use of coactive learning, an online structured prediction framework for preference learning. Coactive learning is particularly well suited for constructive preference elicitation as it may be seen as a
cooperation between the user and the system. The user and the systems interact through "coactive" feedback: after each recommendation, the user provides a modification that makes it slightly better for her preferences. This type of
feedback is very flexible and can be acquired both explicitly and implicitly from the user actions. Coactive learning also comes with theoretical convergence
guarantees and a set of ready-made extensions for many related problems such as learning in a multi-user setting and learning with approximate constraint solvers.
In this thesis we detail our coactive learning approach to constructive preference elicitation, and propose extensions for scaling up to very large constructive problems and personalizing the utility model. We also applied our framework to two important classes of constructive
preference elicitation problems, namely layout synthesis and product bundling. The former is a design process for arranging objects into a given space, while the latter is a kind of product configuration problem in which the object to
configure is a package of different products and services. Within the product bundling application, we also performed an extensive validation involving real participants, which highlights the practical benefits of our approach
Variational and convex approximations of 1-dimensional optimal networks and hyperbolic obstacle problems
Full text linkIn this thesis we investigate variational problems involving 1-dimensional sets (e.g., curves, networks) and variational inequalities related to obstacle-type dynamics from a twofold prospective. On one side, we provide variational approximations and convex relaxations of the relevant energies and dynamics, moving mainly within the framework of Gamma-convergence and of convex analysis. On the other side, we thoroughly investigate the numerical optimization of the corresponding approximating energies, both to recover optimal 1-dimensional structures and to accurately simulate the actual dynamics
Second order nonlinearities in silicon photonics
Full text linkIn this thesis, second order optical nonlinearities in silicon waveguides are studied. At the beginning, the strained silicon platform is investigated in detail. In recent years, second order nonlinearities have been demonstrated on this platform. However, the origin of these nonlinearities was not clear. This thesis offers a clear answer to this question, demonstrating that this nonlinearity does not originate on the applied strain, but on the presence of trapped charges that induce a static electric field inside the waveguide. Based on this outcome, a way to induce larger electric fields in silicon waveguide is studied. Using lateral p-n junctions, strong electric fields are introduced in the waveguides, demonstrating both electro-optic effects and second-harmonic generation. These results, together with a detailed modeling of the system, pave the way through the demonstration of spontaneous parametric down-conversion in silicon
Enabling modeling framework with surrogate modeling capabilities and complex networks
Full text linkConceptual and physically based environmental simulation models as products of research environments efforts became complex software over time in order to allow describing the behaviour of natural phenomena more accurately. Results from these models are considered accurate but often require to operate an entire system of modeling resources with dedicated knowledge, an extensive set up, and sometimes significant computational time. Model complexity limits wide model adaptation among consultants because of lower available technical resources and capabilities. However, models should be ubiquitous to use in both research and consulting environments. This dissertation aims to address and alleviate two aspects of research model complexity: 1) for researchers, the model design complexity with respect to its internal software structure and 2) for consultants, the model application complexity with respect to data and parameter setup, runtime requirements, and proper model infrastructure setup. The first contribution provides modeling design and implementation support by managing interacting modeling solutions as “Directed Acyclic Graph”, while the second one helps to create surrogate models of complex physical models as a streamlined process. Both contributions are implemented within the OMS/CSIP modeling framework and infrastructure and were applied in various studies. First, a machine learning (ML)-based surrogate model approach is presented to respond to field application requirements to get quick but “accurate enough” model results with limited input and limited a-priori knowledge of the internal physical processes involved. The surrogate model aims to capture the behaviour of a physical model as an ensemble system of artificial neural networks (ANN). Here, the NeuroEvolution of Augmenting Topology (NEAT) technique has been leveraged because of its integration of a genetic approach to build and evolve its ANNs during supervised training. Throughout this phase, the thorough design of the services facilitate seamless monitoring of structural mutations of the artificial neural network and its performances with respect to behavioural emulation of the original model response. This results in a streamlined surrogate model generation. Furthermore, the stochasticity inherent to the evolutionary genetic algorithm combined with a specially designed cross-validation approach allows for straightforward use of the ensemble application. Several, slightly different artificial neural networks are concurrently trained. The ensemble system is built upon the selection of the utmost performant surrogate models and is used collectively to provide uncertainty quantified results when applied against new data. Secondly, a Directed Acyclic Graph (DAG) modeling structure NET3 was developed. NET3 provides appropriate data structures to represent modeling states interactions as relationships based on network topologies. The inherent structure of the DAG commands the execution of modeling tasks. NET3 implicitly manages the parallel computation depending on the network topology. A node of a NET3 modeling structure encapsulates any sort of modeling solution such as a system of ordinary differential equations, a set of statistical rules, or a system of partial differential equations. Each link connects these modeling solutions by handling their data flow. As a result, NET3 simplifies 1) the translation of physical mathematical concepts into model components, and 2) the management of complex interactions of modeling solutions. NET3 also pushes forward the idea of separating concerns between software architecture and scientific model codebase. It manages aspects that relate to the architectural design of the graph modeling structure and lets research scientist focus on their model’s domain. NET3 improves encapsulation and reusability of scientific/mathematical concepts. It avoids code duplication by allowing the same modeling solution to be adopted in different nodes and finely adapted to specific requirements. In summary, NET3 enables a new level of modeling flexibility by allowing to quickly change model representations to explore new modeling solutions. The two presented contributions were integrated into the Object Modeling System/Cloud Services Integrated Platform (OMS/CSIP) environmental modeling framework (EMF). EMFs are standard practice in environmental modeling because they represent a software solution of separating the burden of software architectural design management from scientific research. Here, OMS/CSIP has been identified “advanced” in terms of EMFs design. It offers high flexibility, low language invasiveness, fine and thorough architectural design, and innovative cloud computing deployment infrastructure. These aspects make OMS/CSIP infrastructure the suitable platform to host NEAT based surrogate modeling and NET3 extensions. Framework-enabled NEAT based Surrogate modeling (FeNS) results from the full integration of NEAT based surrogate modeling approach with OMS/CSIP platform. Here, the surrogate model approach was developed as CSIP services to help transitioning from research models to “field models” by enabling the modeling framework to interact with CSIP services, ML libraries, and a NoSQL database to emerge model surrogates for a(ny) modelling solution. OMS/CSIP was extended to harvest data from each model run and automatically derive the surrogate model at the modeling framework level. NET3 extends OMS modeling simulations to run as a graph network of interconnected modeling solutions. Furthermore, it enhances available OMS calibration algorithms to become multi-site calibration procedures. OMS already provided implicit parallel computation of independent components in a modeling solution. NET3 now adds a further layer of implicit parallelism by concurrently running independent modeling solutions. Two studies were carried out to develop and test FeSN while three applications supported the development and testing of NET3. Surrogate models of the Revised Universal Soil Loss Equation, Version 2 (R2) were generated to scale up from simple test cases with a constrained input space to more generic applications including a larger variety of input parameters. The main goal of the surrogate model was to streamline and simplify access to the R2 model behaviour. We performed sensitivity analysis of R2 to limit the input space to only relevant parameters (e.g. soil properties, climate parameter, field geometries, crop rotation description). The main study area was the State of Iowa starting from a single county (Clay county) ending up to four counties (Buena Vista, Cherokee, Clay, and Wright). Clustering methodologies were applied to improve surrogate model accuracy and to accelerate the training process by reducing the dataset size. The overall “goodness-of-fit” against the testing dataset estimated on the median of the uncertainty quantified result of the surrogate models ensemble was always above 0.95 Nash-Sutcliffe (NS), root mean squared error (RMSE) between 0.13 and 0.36, and bias between -0.07 and 0.02. In many cases, accuracy of the surrogate model with respect to testing dataset was above 0.98 NS. Surrogate models of the AgroEcoSystem (AgES) were generated to apply and test FeNS methodology to a semi-distributed hydrologic model. The main goal of the surrogate model was to streamline and simplify access to the AgES model behaviour. Only relevant lumped parameters on watershed centroid were used to train the surrogate models and limit the input space to only relevant parameters (e.g. precipitation, groundwater level, LAI, and potential evapotranspiration). The main study area was the South Fork Iowa River (SFIR) watershed in the State of Iowa across Wright, Franklin, Hamilton, and Hardin counties. The overall “goodness-of-fit” against the testing dataset estimated on the median of the uncertainty quantified result of the surrogate models ensemble was above 0.97 Nash-Sutcliffe (NS), root mean squared error (RMSE) of 2.24, and bias of -0.0794. With respect to NET3, the first application is the real-time modeling of flood forecasting through GEOframe system for the Civil Protection of Regione Basilicata implemented by PhD Bancheri. To scale the computation and finely tune calibration parameters, the Basilicata river basins were split into subcatchments where each was represented by a different NET3 node. The second application was part of Mr. Dalla Torre’s master thesis where the computational core of the rainfall-runoff model of Storm Water Management Model (SWMM by EPA) was componentized. NET3 now allows for reimplementing a concise and lightweight SWMM modeling core and highly parallel model runs. Software architectural design of rainfall-runoff, routing and sewer pipe design components targeted separation of concerns, single responsibility, and encapsulation principles. It resulted in clean and minimized code base. NET3 manages component connections and scalable computation by hosting rainfall-runoff modeling solution into separated nodes from routing and sewer pipe design modeling solution. It also enables each node of the modeling structure to 1) access a shared data structure to fetch input data from and push results to (SWMMobject), and 2) internally analyze the upstream subtree in order to adjust sewer pipe design parameters. The third test case is the application of a “system of systems” of urban models where each node of the graph modeling structure encapsulates a single responsibility system of models. Because of the stochasticity involved in each system of models, the entire graph modeling solution was required to run several times and generate independent realizations. Hence, NET3 was enabled to run a “graph of graphs” modeling structure
Knowledge Network Structures and Dynamics in Local Systems: Evidence from the Wine Industry.
Full text linkAmong the advantages of belonging to successful local systems like clusters, the regional economic literature has stressed the critical role played by localized knowledge. For some time, scholars have been arguing that knowledge spreads unevenly among local actors, rather than pervasively and widely, but, its drivers, underlying social structure, and evolution over time remain poorly understood. Particularly, on the one hand, heterogeneity of firms and the way they are perceived are fundamental features to understand evolutionary patterns of clustered firms acting in a world of uncertainty and imperfect information; on the other hand, different ties among the same set of actors simultaneously diffuse specific knowledge.
This PhD thesis aims to go deeper in this debate investigating a framework to study local development in relation to architectures and dynamics of local systems and focussing on a network perspective; particularly, stressing the role of both individual heterogeneity and relational multiplicity; testing the efficacy of the identified framework within the same industry, but, with two different and original databases; implementing two different methodologies of social network analysis for the study of knowledge network structures and dynamics (Exponential Random Graph Models and Stochastic Actors Oriented Models); and identifying a few policy implications.
To organically achieve these aims, the thesis aims to answer the following general research questions: What is the state of the art of knowledge networks within local systems? To what extent do multiple ties as different relational sets through which knowledge diffuses impact on the local exchange of knowledge? To what extent does status as the perceived relative qualities of a firm in a given market or organizational field affect knowledge network evolution over time?
To answer these questions, the first chapter “Knowledge Networks within Local Systems. Their Structures and Dynamics” provides a literature review on knowledge network structures and dynamics within local systems and it offers an original explanation of local systems evolution with a knowledge network perspective. The second chapter “Complementary Inter-Firm Relations of Multiple Knowledge Networks in Industrial Clusters: Evidence from a Growing Wine Cluster in Italy” shows that different kinds of relationships positively impact on the spread of technical knowledge, but they are different in magnitude and they follow complementary patterns rather than substitutive ones. The third chapter “Status and the Assortative Dynamics of Knowledge Networks in Industrial Clusters: Evidence from a Successful Wine Cluster in Italy” shows the presence of an assortative network change, where high-status firms are more likely to interact with other high-status firms but not with low-status firms (and vice-versa). Finally, the last part concludes with a summary of the main findings and it offers a few possible policy implications. Also, the main limitations of the study as well as a few future possible extensions are discussed
International Investments Flows: The Role of Cultural Preferences and Migrants Networks
Full text linkForeign Direct Investments are the most complex form of internationalization. A large part of the recent international trade literature has focused on their determinants on the ground that they spur growth and have a positive impact on development. This thesis examines FDI along two different and understudied lines. The first line of research focuses on cultural factors promoting bilateral investments flows. In chapter 1 and chapter 3, I propose a novel definition of Cultural Proximity wich separates the effect of cultural similarity from the role of perceptions and cultural affiity. I am able to innovate with respect to the existing literature by capturing the effect of time varying and possibly asymmetric patterns in the reciprocal cultural appreciation between two countries. In Chapter 1 I explicitly deal with the potential asymmetry in bilateral cultural appreciation, and test for the emergence of non reciprocal cultural patterns in the analysis of bilateral Greenfield FDI. An example clarifies what I mean: consider South Korea and Latin America. The so called Korean Wave, consisting of soap operas and Korean pop music has become extremely popular in Latin America since the mid 2000s, despite of geographic and cultural distance in terms of language and ethnicity. Yet, there is no evidence of a symmetric rise in popularity of Latin American culture in South Korea. The underlying idea is that the "new" positive perception of Korea enhances bilateral (trade and) FDI. In Chapter 3 I highlight the heterogeneity of FDI and the non-linearities that could emerge in the relationship between cultural affnity and bilateral M&A. In the empirical exercise, I use an econometric model that
allows me to disentangle the impact of the different level on M&A. The second line of research explores the role of migrants' flows on bilateral FDI. Borrowing the tools from social network analysis, in Chapter 2 I investigate whether and how the position of a country in the International Migration Network affects a country's bilateral investment flows beyond the direct role of its local emi(immi)-grant population. The empirical application is on Greenfield FDI
Static and dynamic disorder in nanocrystalline materials
Full text linkPeak profiles in X-ray Diffraction (XRD) patterns from nanocrystalline materials are affected by static and dynamic disorder which is specific of the size and shape of the nanocrystalline domains. Owing to their intrinsic differences, the two types of disorder can be separated, providing independent information from the modelling of the XRD patterns. In the present thesis a model for the static strain created by the nanoparticle surface is proposed. The model is built within the frame of the Whole Powder Pattern Modelling (WPPM) approach for XRD line profile analysis, developed at the University of Trento in the past 20 years. The WPPM approach is decribed in details. Based on a complex Fourier Transform of the diffraction profiles, the model leads to general equations to be used with the WPPM approach to represent the distorted atomic configuration with respect to the reference bulk one. The model was also implemented in TOPAS, a commercial and very popular software, developing a specific macro allowing a larger community of users to benefit of this new opportunity of studying nanocrystalline materials. The thesis work also extended to a more traditional and general description of strain
broadening of XRD peak profiles, involving invariant forms under the Laue group symmetry operations of the material under study. As for the dynamic strain, the fundamentals of the Thermal Diffuse Scattering (TDS) contribution to the peak profiles are reviewed. Starting from the original work
of B.E. Warren, the theory is generalized to account for surface effects, leading to a particular model developed recently at the University of Trento. This model was thoroughly reviewed and corrected. To test the model a parallel computer code in C was written, exploiting Molecular Dynamics simulations for obtaining reliable and independent estimates of static and dynamic disorder in nanocrystals
The perception of intonation in native and non-native linguistic contexts and by different individuals: From question-answer categorization to the integration of prosody and discourse structure
Full text linkThis thesis addresses the cognitive foundations of categorization and acquisition of intonational categories in native (L1) and second language (L2). It focuses on the link between the processing of intonational categories and the and pragmatic functions of language. The thesis reports two behavioral psychoacoustic experiments that studied the disambiguation of sentence-modality (statement vs. question) signaled by sentence-final Boundary Tones by manipulating lexical and linguistic status of the underlying segmental information. A third ERPs experiment studied with ERPs the association of specific Pitch Accents with the discourse status of a referent in German and how different processing-correlates of PA violation are processed in L1 and L2 speakers. In all experiments, specific attention has been devoted to individual differences both at the theoretical and empirical level. I showed that perceivers can display variability in processing as a function of biographic factors, in the quantity and quality of training in a second language, and in the presence of variables related to the construct of Theory-of-Mind (ToM). I support the view that the processing of intonational categories, modulated by Fundamental Frequency contours, links with the processing of segmental information, the semantic access at word-level, and the decoding of the information structure within the discourse model. The study of processing of pitch contours is a highly multidisciplinary discipline, but the different theoretical perspectives are not always considered within specific research. I propose to approach the study of pitch processing by trying to integrate the different theoretical and empirical approaches with the aim to use the available knowledge. This broader perspective considers the auditory categorization process, the integration of the sound-domain information with higher-order linguistic structure, and the modeling of individual variability of the perceivers.
I support the view that the presence of individual traits that favor the efficient decoding of the interlocutor’s perspective and intentions correlates with a more efficient processing of the discourse information structure. I propose that this is observable through the manipulation of the associated intonational categories. I think that the adoption of a multidisciplinary perspective, centered on the processing of intonational categories, and the approach developed in this thesis is relevant to develop further the study of specific populations known to display less efficient processing of the pragmatic aspects of discourse, such as individuals with Autism Spectrum Conditions