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Complexity in Infinite Games on Graphs and Temporal Constraint Networks
No full textThis dissertation deals with a number of algorithmic problems motivated by automated temporal planning and formal verification of reactive and finite state systems. Particularly, we shall focus on game theoretical methods in order to obtain improved complexity bounds and faster algorithms for the following models: Hyper Temporal Networks, Conditional Simple/Hyper Temporal Networks, Conditional Simple Temporal Networks with Instantaneous Reaction Time, Update Games, Explicit McNaughton-Muller Games, Mean Payoff Games
La modulazione temporale degli effetti della sentenza di annullamento del giudice amministrativo. Un'indagine comparata.
No full textLa ricerca analizza il fenomeno della dissociazione tra accertamento dell’illegittimità del provvedimento amministrativo e annullamento retroattivo dello stesso e, più in generale, il mutamento del ruolo tradizionalmente assegnato alla tutela costitutiva nell’ambito della giustizia amministrativa. Partendo da un esame dei rimedi elaborati per evitare annullamenti sostanzialmente inutili o dagli effetti pregiudizievoli, lo studio si sofferma sull’analisi della tecnica della modulazione temporale degli effetti della sentenza di annullamento che attribuisce al giudice amministrativo un potere di accertamento dell’invalidità dell’atto che prescinde dalla sua caducazione, consentendogli di conoscere dell’illegittimità di un provvedimento senza che da tale accertamento scaturiscano effetti costitutivi tipici o con esclusione e limitazione della portata retroattiva. L’analisi è condotta in un’ottica comparata, attraverso lo studio delle applicazioni pratiche della tecnica modulatoria nell’ordinamento italiano, francese e comunitario. Brevi cenni sono poi dedicati al fenomeno dell’annullamento flessibile nell’esperienza tedesca, inglese e statunitense, in quanto ordinamenti che condividono l’istituto o che comunque riconoscono al giudice amministrativo poteri e strumenti analoghi, tesi ad adeguare gli effetti della pronuncia di annullamento alle specificità dei casi giudicati. L’obiettivo perseguito è verificare, attraverso una comparazione tra le diverse esperienze, l’esistenza di possibili convergenze nell’uso dello strumento con particolare riferimento alle ragioni che inducono le corti a farne uso, agli interessi a tutela dei quali il potere è esercitato e alle ricadute che il ricorso alla tecnica ha comportato sul sistema di giustizia amministrativa.
L’indagine, condotta attraverso un approccio per lo più casistico, dimostra come il potere di modulazione - che condivide nella quasi generalità delle esperienze esaminate la natura pretoria - venga per lo più utilizzato a tutela di interessi di rilevanza pubblicistica, sebbene non trascuri le esigenze di tutela del ricorrente, specie nei casi in cui si tratti di esigenze sostanziali che non trovano nell’annullamento una piena ed efficace soddisfazione. Consente altresì di provare l’utilità della tecnica, che si pone come meccanismo di flessibilità del sistema, garantendo l’adeguamento della pronuncia di annullamento alle esigenze effettive del caso concreto, oltre che il contenimento delle conseguenze disastrose altrimenti ingenerate dalla naturale portata retroattiva di tale pronuncia. Perviene, infine, all’affermazione dell’emergenza di un nuovo modello di azione di annullamento, a carattere non necessariamente costitutivo e retroattivo, in grado di contemperare l’esigenza di certezza e sicurezza giuridica con il rigore del principio di legalità e del rinnovato ruolo assolto dal giudice amministrativo chiamato a farsi carico delle conseguenze economiche e sociali delle proprie pronunce
Dynamical models for diabetes: insights into insulin resistance and type 1 diabetes
No full textThis thesis summarizes my work in systems biology as a PhD student at The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI) and at the University of Trento, department of Mathematics.
Systems biology is an interdisciplinary field that aims at integrating biology with computational and mathematical methods to gain a better understanding of biological phenomena [5, 6]. Among these methods, mathematical and dy- namical modeling have driven the discovery of mechanistic insights from the static representations of phenomena, that is, data. As a result, mathematical and dynamical models have now become standard tools to support new discoveries in biology and in public health issues. For example, models assist governments in determining the policies to contain the spreading of the diseases and in decisions such as vaccine purchases [7]. Similarly, complex and accurate models of the cardio-vascular systems guide surgeons during many procedures on pa- tients [8]. Furthermore, dynamical models of signaling cascades help researchers in identifying new potential drug targets and therapies for many diseases [9]. We used these modeling techniques to address biological questions related to diabetes and insulin resistance. Within this framework, this thesis contains two articles I contributed to, that focus on diabetes. These works are published in the journal of Nature Scientific Reports and are included in Chapters 3 and 4.
A significant contribution to the development of these models, and models in general, is given by optimization. Optimization is often used in modeling to determine certain unknown values or factors in a way that allow the model to optimally reproduce the experimental data. Moreover, the parameters of a model that correctly describe the undergoing dynamics may be used as diagnostic tools [10–13]. To this end, this thesis contains a methodological appendix that includes a review of optimization algorithms that has been submitted to the journal of Frontiers in Applied Mathematics and Statistics, special topic Optimization. The content of this article is reported in Appendix A
Concept challenge game: a game used to find errors from a multilangual linguistic resource
No full textMultilingual semantic linguistic resource is critical for many applications in Natural Language Processing (NLP). While, building large-scale lexico-semantic resources manually from scratch is extremely expensive, which promoted the applications of automatic extraction or merger algorithms. These algorithms did benefit us in creation of large-scale resources, but introduced many kinds of errors as the side effect. For example, Chinese WordNet follows the WordNet structure and is generated via several algorithms. This automatic generation of resources introduces many kinds of errors such as wrong translation, typos and false mapping between multilingual terms. The quality of a linguistic resource influences the performance of the further applications direct- ly, which means the quality of a linguistic resource should be the higher the better. Thus, finding errors is inevitable. However, till now, there is not any efficient method to find errors from a large-scale and multi- lingual resource. Validating manually by experts could be a solution, but it is very expensive, where the obstacles come from not only the large-scale dataset, but also multilingual. Even though crowdsourcing is a method for solving large-scale and tedious task, it is still costly. By thinking in this scenario, we plan to find an effective method that can help us finding errors in low cost.
We use games as our solution and adopt Universal Knowledge Core (UKC) with respect to Chinese language as our case study. UKC is a multi-layered multilingual lexico-semantic resource where a common lexical element from a different language is mapped to a formal concept. In this dissertation, we present a non-immersive game named Concept Challenge Game to find the errors that exist in English-Chinese lexico-semantic resource. In this game, people will face challenges in English synsets and have to choose the most appropriate option from the listed Chinese synsets. The players are unaware when finding errors in the lexico-semantic resource. Our evaluation shows that people are spending a significant amount of time playing and able to find differ- ent erroneous mappings. Moreover, we further extended our game to Italian version, the result is promising as well, indicating that our game has the ability to figure out errors in multilingual linguistic resources
Constitutive Modeling of the Densification Process of Ceramic Powders Subjected to Cold, Quasi-Static Pressing
No full textThe consistent, uniform pressing of green bodies is a necessary part of producing high-quality, high-performance ceramics with predictable qualities and behavior. Undesirable density variation in the compacted ceramic powder causes variability in performance, failure to meet quality control standards, and, possibly, complete piece failure during successive processing. These issues contribute directly to a decrease in production efficiency through lost time and an increase in energy and material use. The careful control of the green body density field is of the utmost importance to consistently producing high-performance ceramics. Current methods for minimizing heterogeneity of the density field are often based on trial-and-error to optimize mold geometry and forming pressure, which is both expensive and prolongs development. The present research presents a continuum-level constitutive model for accurately modeling the densification of ceramic powders into green bodies and outlines the numerical implimentation of said model. The constitutive model incorporates nonlinear elasticity, elatic-plastic coupling, cap evolution, pressure- and Lode angle-dependent plasticity, and hardening. To evaluate the constitutive model, a new method for measuring density in green bodies has been developed. This method utilizes readily-available laboratory equipment to produce density projection data for the sample and subsequently processes that data to produce a 3D density field using well-developed tomographic reconstruction techniques. Finally, a green body is produced from alumina powder (Martoxid KMS-96) and the density field is evaluated and compared to that of a numerical simulation. They are shown to agree within the error of the density measurements. These comparisons demonstrate the performance of the developed constitutive model and the potential utility for companies and research institutions that are in the ceramics production field
Discontinuous Galerkin methods for compressible and incompressible flows on space-time adaptive meshes
No full textIn this work the numerical discretization of the partial differential governing equations for compressible and incompressible flows is dealt within the discontinuous
Galerkin (DG) framework along space-time adaptive meshes. Two main research fields can be distinguished: (1) fully explicit DG methods on collocated grids and (2) semi-implicit DG methods on edge-based staggered grids. DG methods became increasingly popular in the last twenty years mainly because of three intriguing properties: i) non-linear L2 stability has been proven; ii) arbitrary high order of
accuracy can be achieved by simply increasing the polynomial order of the chosen basis functions, used for approximating the state-variables; iii) high scalability properties make DG methods suitable for large-scale simulations on general unstructured meshes. It is a well known fact that a major weakness of high order DG methods lies in the difficulty of limiting discontinuous solutions, which generate spurious oscillations, namely the so-called ’Gibbs phenomenon’. Over the years, several attempts have been made to cope with this problem and different kinds of limiters have been proposed. Among them, a rather intriguing paradigm has been defined in the work of [71], in which the nonlinear stabilization of the scheme is sequentially and locally introduced only for troubled cells on the basis of a multidimensional optimal order detection (MOOD) criterion. In the present work the main benefits of the MOOD paradigm, i.e. the computational robustness even in the presence of strong shocks, are preserved and the numerical diffusion is considerably reduced also for the limited cells by resorting to a proper sub-grid. In practice the method first produces a so-called candidate solution by using a high order accurate unlimited DG scheme. Then, a set of numerical and physical detection criteria is applied to the candidate solution, namely: positivity of pressure and density, absence of floating point errors and satisfaction of a discrete maximum principle in the sense of polynomials. Then, in those cells where at least one of these criteria is violated the computed candidate solution is detected as troubled and is locally rejected. Next, the numerical solution of the previous time step is scattered onto cell averages on
a suitable sub-grid in order to preserve the natural sub-cell resolution of the DG scheme. Then, a more reliable numerical solution is recomputed a posteriori by employing a more robust but still very accurate ADER-WENO finite volume scheme on the sub-grid averages within that troubled cell. Finally, a high order DG polynomial is reconstructed back from the evolved sub-cell averages. Moreover, handling typical multiscale problems, dynamic adaptive mesh refinement (AMR) and adaptive polynomial order methods are probably the two main ways of preserving accuracy and efficiency, and saving computational effort. The here adopted AMRapproach is the so called ’cell by cell’ refinement because of its formally very simple
tree-type data structure. In the here-presented ’cell-by-cell’ AMR every single element is recursively refined, from a coarsest refinement level l0 = 0 to a prescribed finest (maximum) refinement level lmax, accordingly to a refinement-estimator function X that drives step by step the choice for recoarsening or refinement. The combination of the sub-cell resolution with the advantages of AMR allows for an unprecedented ability in resolving even the finest details in the dynamics of the fluid. First, the Euler equations of compressible gas dynamics and the magnetohydrodynamics (MHD) equations have been treated [281]. Then, the presented method has been readily extended to the special relativistic ideal MHD equations [280], but also the the case of diffusive fluids, i.e. fluid flows in the presence of viscosity, thermal
conductivity and magnetic resistivity [116]. In particular, the adopted formalism is quite general, leading to a novel family of adaptive ADER-DG schemes suitable for hyperbolic systems of partial differential equations in which the numerical fluxes also depend on the gradient of the state vector because of the parabolic nature of diffusive terms. The presented results show clearly that the shock-capturing capability of the news schemes are significantly enhanced within the cell-by-cell Adaptive Mesh
Refinement (AMR) implementation together with time accurate local time stepping (LTS). The resolution properties of the new scheme have been shown through a wide number of test cases performed in two and in three space dimensions, from low to high Mach numbers, from low to high Reynolds regimes.
In particular, concerning MHD equations, the divergence-free character of the magnetic field is taken into account through the so-called hyperbolic ’divergence-cleaning’ approach which allows to artificially transport and spread the numerical spurious ’magnetic monopoles’ out of the computational domain. A special treatment has been followed for the incompressible Navier-Stokes equations. In fact, the elliptic character of the incompressible Navier-Stokes equations introduces an important difficulty in their numerical solution: whenever the smallest physical or numerical perturbation arises in the fluid flow then it will instantaneously affect the entire computational domain. Thus, a semi-implicit approach has been used. The main advantage of making use of a semi-implicit discretization is that the numerical stability can be obtained for large time-steps without leading to an excessive computational demand [117]. In this context, we derived two new families of spectral semi-implicit and spectral space-time DG methods for the solution of the two and three dimensional Navier-Stokes equations on edge-based staggered Cartesian grids [115], following the ideas outlined in [97] for the shallow water equations. The discrete solutions of pressure and velocity are expressed in the form of piecewise polynomials along different meshes. While the pressure is defined on the control volumes of the main grid, the velocity components are defined on edge-based dual control volumes, leading to a spatially staggered mesh. In the first family, high order of accuracy is achieved only in space, while a simple semi-implicit time discretization is derived by introducing an implicitness factor theta in [0.5, 1] for the pressure gradient in the momentum equation. The real advantages of the staggering arise after substituting the discrete momentum equation into the weak form of the continuity equation. In fact, the resulting linear system for the pressure is symmetric and positive definite and either block penta-diagonal (in 2D) or block hepta-diagonal (in 3D). As a consequence, the pressure system can be solved very efficiently by means of a classical matrix-free conjugate gradient method. Moreover, a rigorous theoretical analysis of the condition number of the resulting linear systems and the design of specific preconditioners, using the theory of matrix-valued symbols and Generalized Locally Toeplitz (GLT) algebra has been successfully carried out with promising results in terms of numerical efficiency [102]. The resulting algorithm is stable, computationally very efficient, and at the same time arbitrary high order accurate in both space and time. The new numerical method has been thoroughly validated for approximation polynomials
of degree up to N = 11, using a large set of non-trivial test problems in two and three space dimensions, for which either analytical, numerical or experimental reference solutions exist. Moreover, the here mentioned semi-implicit DG method has been successfully extended to a novel edge-based staggered ’cell-by-cell’ adaptive meshes [114]
Automatic Speech Recognition Quality Estimation
No full textEvaluation of automatic speech recognition (ASR) systems is difficult and costly, since it requires manual transcriptions. This evaluation is usually done by computing word error rate (WER) that is the most popular metric in ASR community. Such computation is doable only if the manual references are available, whereas in the real-life applications, it is a too rigid condition. A reference-free metric to evaluate the ASR performance is \textit{confidence measure} which is provided by the ASR decoder. However, the confidence measure is not always available, especially in commercial ASR usages. Even if available, this measure is usually biased towards the decoder. From this perspective, the confidence measure is not suitable for comparison purposes, for example between two ASR systems.
These issues motivate the necessity of an automatic quality estimation system for ASR outputs. This thesis explores ASR quality estimation (ASR QE) from different perspectives including: feature engineering, learning algorithms and applications. From feature engineering perspective, a wide range of features extractable from input signal and output transcription are studied. These features represent the quality of the recognition from different aspects and they are divided into four groups: signal, textual, hybrid and word-based features. From learning point of view, we address two main approaches: i) QE via regression, suitable for single hypothesis scenario; ii) QE via machine-learned ranking (MLR), suitable for multiple hypotheses scenario. In the former, a regression model is used to predict the WER score of each single hypothesis that is created through a single automatic transcription channel. In the latter, a ranking model is used to predict the order of multiple hypotheses with respect to their quality.
Multiple hypotheses are mainly generated by several ASR systems or several recording microphones.
From application point of view, we introduce two applications in which ASR QE makes salient improvement in terms of WER: i) QE-informed data selection for acoustic model adaptation;
ii) QE-informed system combination. In the former, we exploit single hypothesis ASR QE methods in order to select the best adaptation data for upgrading the acoustic model. In the latter, we exploit multiple hypotheses ASR QE methods to rank and combine the automatic transcriptions in a supervised manner.
The experiments are mostly conducted on CHiME-3 English dataset. CHiME-3 consists of Wall Street Journal utterances, recorded by multiple far distant microphones in noisy environments. The results show that QE-informed acoustic model adaptation leads to 1.8\% absolute WER reduction and QE-informed system combination leads to 1.7% absolute WER reduction in CHiME-3 task.
The outcomes of this thesis are packed in the frame of an open source toolkit named TranscRater -transcription rating toolkit- (https://github.com/hlt-mt/TranscRater) which has been developed based on the aforementioned studies. TranscRater can be used to extract informative features, train the QE models and predict the quality of the reference-less recognitions in a variety of ASR tasks
Detecting Brain Effective Connectivity with Supervised and Bayesian Methods
No full textThe study of causality has drawn the attention of researchers from many different fields for centuries.
In particular, nowadays causal inference is a central question in neuroscience and an entire body of research, called brain effective connectivity, is devoted to detecting causal interactions between distinct brain areas.
Brain effective connectivity is typically studied by the statistical analysis of direct measurements of the neural activity. The main purpose of this work is on methods for studying time series causality. More in details, we focus on a well-establish criterion of causality:
the Granger criterion, which is based on the concepts of temporal precedence and predictability.
Firstly, we consider the standard parametric implementation of the Granger criterion that is based on the multivariate autoregressive model, where we face the problem of model identification. For this purpose, we present a new Bayesian method for linear model identification and we explore its capability of modeling the sparsity structure of the signals. As a second contribution, we look at the causal inference through the lens of machine learning and we propose an approach based on the concept of learning from examples. Thus, given a set of signals, their causal interactions are estimated by a classifier that is trained on a synthetic dataset generated by a parametric model.
This approach, that we call supervised parametric approach, is implemented by adopting the Granger criterion of causality and compared with the standard parametric measure of Granger causality. Moreover, the roles of the feature space and the generative model of the training set are investigated through a simulation study. Additionally, we show an example of application on rat neural recordings.
Finally, we focus on the bias introduced by parametric methods when applied in a real context, i.e. the inability of having a fully realistic generative model.
For this purpose, we analyze how the supervised parametric approach can help in making the inference more application-dependent, by exploiting a physiologically plausible generative model
Il bilinguismo di minoranza come variabile rilevante nell'apprendimento di una terza lingua.
No full textIl campo di studi sull’apprendimento della terza lingua (L3) e sull’influenza interlinguistica è piuttosto recente nel panorama degli studi riguardanti l’acquisizione linguistica e ancora più recente è il focus sull’ambiente plurilingue e sui parlanti plurilingui. Il presente lavoro è stato sviluppato a partire da una ricerca condotta nel 2012 nella scuola primaria di Fierozzo e nelle scuole primarie della valle del Fèrsina e si propone di inserire lo studio sul plurilinguismo mòcheno all’interno del campo di studi della L3 proseguendo la ricerca nella fascia di età 11-14 anni, allo scopo di approfondire tre principali aspetti: l'aspetto sociolinguistico, l'aspetto cognitivo - acquisizionale e l'aspetto educativo
Protection infrastructures and methods for reducing the impacts downstream of hydropower plants
No full textHydropower plants, in particular High-head Hydropower Plants (HPPs), are an important source of energy also for their role in covering the daily peaks of energy demand. However, HPPs, especially storage power plants, have several negative effects on the ecosystems of downstream watercourses inducing unnatural changes in flow regime (hydropeaking).
One way to study ecological implications induced by hydropeaking is represented by the coupling of hydrodynamic models (CFD) with habitat suitability models, in which hydrodynamic parameters are typically used to describe the physical habitat of indicator species. The research activity wanted to investigate possible differences between the use of 2D and 3D CFD approaches to determine the watercourse hydraulic characteristics and their effects on habitat evaluations, performed with CASiMiR software, in complex morphology as usually presents in hydropeaked reaches.
In particular the habitat suitability for the two case studies (Valsura River and Rio Selva dei Molini), is analysed comparing different approaches for the reconstruction of the velocity field (depth-averaged velocities from 2D modelling, bottom velocity field reconstruction with log-law approach from 2D modelling and bottom velocity field from 3D modelling). The results show that the habitat suitability index (HSI) using 2D or 3D hydrodynamic models can be significantly different. Considering the entire flow range of hydropeaking events, the habitat simulations with bottom flow velocities from 3D modelling provide suitable habitats over the entire flow range representing the availability of stable suitable habitats. The results from the hydraulics and habitat analyses are used to investigate the effects of a hydropeaking mitigation project on the Valsura River (realization of a compensation bypass tunnel to decrease the peak flow rate and to remodel the up and down flow ramping rates) and on Rio Selva dei Molini (morphological measures to reduce the hydropeaking effects)