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The algebraic representation of OWA functions in the binomial decomposition framework and its applications in large-scale problems
Full text linkIn the context of multicriteria decision making, the ordered weighted averaging (OWA) functions play a crucial role in aggregating multiple criteria evaluations into an overall assessment to support decision makers reaching a decision. The determination of OWA weights is, therefore, an important task in this process. Solving real-life problems with a large number of OWA weights, however, can be very challenging and time consuming. In this research we recall that OWA functions correspond to the Choquet integrals associated with symmetric capacities. The problem of defining all Choquet capacities on a set of n criteria requires 2^n real coefficients. Grabisch introduced the k-additive framework to reduce the exponential computational burden. We review the binomial decomposition framework with a constraint on k-additivity whereby OWA functions can be expressed as linear combinations of the first k binomial OWA functions and the associated coefficients of the binomial decomposition framework. In particular, we investigate the role of k-additivity in two particular cases of the binomial decomposition of OWA functions, the 2-additive and 3-additive cases. We identify the relationship between OWA weights and the associated coefficients of the binomial decomposition of OWA functions. Analogously, this relationship is also studied for two well-known parametric families of OWA functions, namely the S-Gini and Lorenzen welfare functions. Finally, we propose a new approach to determine OWA weights in large-scale problems by using the binomial decomposition of OWA functions with natural constraints on k-additivity to control the complexity of the OWA weight distributions
Automated Approaches to Community Question Answering
Full text linkSocial Media applications, e.g., forums, social networks, allow users to pose questions about a given topic to a community of expert users. Although successful, these applications suffer from a major drawback: it is rather complex to find similar questions with traditional keyword-based search. Thus, Community Question Answering (cQA), a branch of QA, has been developed with the aim of automatically answering new user questions. Generally, cQA systems answer new user questions by (i) first looking at the questions most similar to the input question and (ii) selecting the best answer for the related question. Such systems require powerful machine learning algorithms that go beyond traditional approaches based on features. In recent years, tree kernels and neural networks have established as the state-of-the-art machine learning algorithms for solving such kinds of problems. Tree kernels are used to compute the similarity between two sentences encoded in form of trees that incorporate syntactic and semantic information. Neural networks map words into informative vectors called embeddings used to learn non-linear transformations of user inputs. In this work, we used these models for solving classification and ranking tasks needed to build automatic cQA systems. As a first step, we conceived structured input models able to automatically extract discriminative syntactic patterns for classifying relatedness between two questions. Then, we extended the previous work by presenting a new model for question similarity that combines semantic information of neural networks with structured information of tree kernels. We assess the performance of the new model on two tasks, i.e. question duplicate detection and question reranking, showing the advantages of injecting syntactic information in neural models. After that, we focus on more challenging tasks such as building a neural network architecture for ranking comments on a forum according to their relevance with respect to a new question. We show that neural models can benefit from being trained in multi-task learning setting, together with auxiliary tasks. This make possible to train cQA systems in an end-to-end fashion, which is convenient for industrial applications that needs to be easily deployed. Furthermore, we developed a novel intent detection model that combines state-of-the-art methods in relational text matching with the latest techniques in supervised clustering to make inference over a set of questions and automatically discover intent clusters. The latter can be used to quickly bootstrap Natural Language Understanding pipelines for dialog systems. To conclude, we study advantages and disadvantages of neural networks and tree kernel models when applied to cQA tasks. We show that neural networks perform effectively when data is abundant. Conversely,tree kernels are more suitable in presence of data scarcity
Experimental and Numerical Investigation of the Micromechanical Behavior of Selective Laser Melted Ti-6Al-4V Cellular Lattices for Biomedical Applications
Full text linkCellular materials are characterized by a complex interconnected structure of struts or plates and shells which make up the cells edges and faces. Their structure can be advantageously engineered in order to tailor their properties according to the specific application. This aspect makes them particularly attractive for the manufacturing of bone prosthetics since, compared to traditional fully dense implants, although more complex to produce and with less predictable properties, implants with a highly porous structure can be manufactured to match the bone stiffness and at the same time favor bone ingrowth and regeneration. The development of Selective Laser Melting (SLM) made possible to obtain metallic cellular materials with highly complex structures characterized by a wide range of cell morphologies that allow to finely tune the mechanical properties of the implant to the patient needs. Titanium alloys such as Ti-6Al-4V have shown excellent biocompatibility combined with good mechanical properties and have also been successfully used in the manufacturing of lattice structures with minute details via SLM. Nevertheless, there are still several issues to consider. For instance, despite the static mechanical properties of such lattices being addressed by many studies, the fatigue behavior still remains little investigated, even though it is a critical aspect in load-bearing biomedical implants (consider, for example, the periodic nature of human gait in the case of hip implants). In this regard, increasing the fatigue resistance of cellular lattices by finely adjusting the geometry, for instance by adding fillets at the cell-wall joints, is a new interesting opportunity made possible by additive manufacturing technologies. On the other hand, a discrepancy between the as-designed and the as-built geometry in SLM parts is an issue that can be critically important for lattices with pore size and strut thicknesses of a few hundred microns, such as biomedical lattices. Indeed, any geometrical imperfection introduces a degree of uncertainty that can alter the mechanical properties of the as-built lattice.
This work represents an attempt in the direction of building a deeper understanding of the effect of the fine geometrical details, such as the fillet radius at the joints and the thickness of the struts, on the elastic constants and on the fatigue resistance of Ti-6Al-4V SLM lattices, with the aim to develop analytical predictive models of the mechanical properties. Moreover, this work also aims at investigating the as-built/as-designed morphological discrepancy in lattices in relation to the their as-designed geometry and its effects on the elastic modulus and the fatigue resistance. In this regard, the purpose is to develop quantitative relationships between the as-designed and the as-built geometry in order to obtain design tools to predict the final morphology of the lattice by taking into account the manufacturing errors.
This thesis covers a wide range of topics, therefore, in the interest of a better presentation, the results of the research have been devided into three independent Chapters. Each of them has been provided of an abstract and an Introduction and divided into a Materials and Methods (or Modelling) section, a Results and Discussion section and finally Conclusions and References. Naturally, the chapters are logically connected and coherent with the frame defined by the title of the thesis. Therefore, this thesis is organized into five chapters. In the first Chapter the backrground to the topics discussed in the subsequent chapters is provided and the relevant literature is reviewed, while in the fifth and last Chapter some conclusions are drawn, and future perspectives are discussed. The core of the work is contained in the three central chapters.
In Chapter II, analytical models developed to predict the elastic constants and the stress concentration factors (SCF) of 2D lattices with variously arranged square cells and filleted junctions are presented. The effect of stretching and bending actions on the elastic constants of a single cell is identified by devising an analytical model based on classical beam theory and and periodic boundary conditions. Specifically, two spatial arrangements are considered: a honeycomb with regular square cells and a honeycomb with square cells staggered by a prescribed offset of half of the cell wall length. The theoretical beam model is fitted to the results of a 2D Finite Elements (FE) model based on plane elements via an extensive parametric analysis. In this way, semi-analytical formulas are proposed to calculate the stiffness in large domains of the geometric parameters (strut thickness t0 and fillet radius R). A numerical method is also proposed to estimate the SCFs at the cell wall junctions of a 2D regular square cellular lattice. The aim is to obtain a model capable of calculating the values of the SCF as a function of the unit cell geometrical parameters and consequently assess the stress state in the lattice, which is one of the main factors determining fatigue resistance. This was achieved by applying the FE method to the unit cell for wide intervals of t0 and R to calculate the SCF for each couple of the parameters. The values of the SCFs were then fitted with functions. The models developed in this Chapter are then used in the subsequent chapters as a support in the design of 3D regular square lattices and in the interpretation of the mechanical characterization.
In Chapter III, the results of the mechanical and morphological characterization of different regular cubic open-cell cellular structures produced via SLM of Ti-6Al-4V alloy, all with the same nominal elastic modulus of 3 GPa that matches that of human trabecular bone, are presented. The fully reversed fatigue strength at 106 cycles and the elastic modulus were measured and an attempt was made to link them to the manufacturing defects (porosity and geometrical inaccuracies). Half of the specimens was subjected to a stress relief thermal treatment while the other half to Hot Isostatic Pressing (HIP), and the effect of the treatments on porosity and on the mechanical properties was assessed. The results of fatigue and quasi-static tests on regular cubic lattices were compared with FE calculations based on the as-designed geometry and on the as-built geometry reconstructed from micro X-ray computed tomography (µCT) scans. It was observed that the fatigue strength and, to a lesser extent, the elastic modulus are correlated with the number and severity of defects and that predictions on the mechanical properties based on the as-designed geometry are not accurate. The fatigue strength seems to be highly dependent on the surface irregularities and on the notches introduced during the manufacturing process. In fully reversed fatigue tests, the high performances of stretching dominated structures compared to bending dominated structures are not found. In fact, with thicker struts, such structures proved to be more resistant, even if bending actions were present. Given the small size of the unit cells (the unit cell size is 1.5 mm and the strut thickness is 0.26 mm) and the limitations in accuracy of the printer, the fillet radii at the junctions were highly irregular and somewhat hard to recognize. In order to investigate the real benefit of filleted junctions on the stress concentration effects at the junctions and to assess the manufacturability of such minute geometrical detail, a new experimental campaign was set up. In Chapter IV, a set of cubic lattice specimens with filleted junctions was designed and produced via SLM. The size of the unit cell is considerably larger than that of the previous specimens, being 8 mm, 6 mm and 4 mm with the rest of the geometrical parameters scaled accordingly. Thus, nine combinations of the geometrical parameters of the unit cell and three orientations with respect to the printing direction are considered. The aim is to investigate the relationship between the as-designed and the as-built geometry and to find the smallest radius which can be accurately reproduced by the printer. Moreover, a compensation strategy of the morphological defects is devised using the mathematical relationships obtained between the as-designed and the as-built strut thickness. This strategy consists in modifying the input CAD to compensate for the deviations introduced by the SLM process
A Dynamic Model for Optimal Covenants in Loan Contracts
Full text linkCovenants are an important part of financial contracts, that are used for resolving the conflicts of interest between borrowers and lenders. In more formal way covenants can be determined as special provisions in loans that give lenders the possibility of putting certain actions in force (normally early repayment) when covenants are violated. For instance, a covenant may restrict the company in taking additional credit, or require a firm to maintain certain financial ratios, such as leverage, coverage, liquidity ratios, etc. Our study develops a theoretical framework that allows to determine the covenant strength index that should be included in a debt contract in a way that minimizes expected losses for a bank subject to the rising restructuring costs. This optimal covenant is found in order to better allocate control rights ex ante and to minimize the costs of renegotiations for both parties.
The approach that explores dynamic contingent claim models is applied to the prob- lem. This approach was pioneered by Black and Scholes (1973) and Merton (1974), and extended by Black and Cox (1976). The dynamics of the optimal covenant strength with respect to various model parameters is investigated. Different modifications to the initial model are considered which are important in exploring more realistic model setting. First of all, we introduce the concept of deadweight costs of distress or firesale price. The concept of deadweight losses imply that the debt holder gets some fraction of the asset value on default instead of the fundamental asset value (Das and Kim, 2015). Along with the concept of deadweight costs, the notion of firesale price is used, that represents the price at which the asset can be sold before the contract maturity. We explore how this extension of our baseline dynamic model influence the optimal level of covenant strictness in debt contracts.
We further develop a model of an optimal covenant in bank loans with information asymmetry. Asymmetric information as a source of agency problems is very important in studying control rights in financial contracting. The conclusions of the papers on information asymmetry regarding control rights allocation and covenant strictness are often ambiguous. Different papers demonstrate more or less control rights of lenders or greater or lesser strictness of covenants depending on the setting and model parameters. Our model is unique in a sense that it unites different implications of empirical and theoretical models with information asymmetry and reflects both perspectives. We also introduce a framework for accessing the consequences of covenant violation in Monte Carlo simulation. Our simulation model allows us to measure different risk- parameters of a project, such as the probability of covenant violation and the probability of repayment of the loan. The measurements (average number of covenant violations per contract, frequency of covenant violation, frequency of loan repayment) can be used in implementation of different rules for a bank that extends the traditional risk-analysis of a project. Moreover, we implement a recursive technique for determining the level of covenant strength that allows the bank to maintain the performance of a specific risk- parameter. We employ a dynamic approach in the spirit of Borgonovo and Gatti (2013); Chang and Lee (2013); Liang et al. (2014) by simulating project value paths over time
Effectively Encoding SAT and Other Intractable Problems into Ising Models for Quantum Computing
Full text linkQuantum computing theory posits that a computer exploiting quantum mechanics can be strictly more powerful than classical models. Several quantum computing devices are under development, but current technology is limited by noise sensitivity. Quantum Annealing is an alternative approach that uses a noisy quantum system to solve a particular optimization problem. Problems such as SAT and MaxSAT need to be encoded to make use of quantum annealers. Encoding SAT and MaxSAT problems while respecting the constraints and limitations of current hardware is a difficult task. This thesis presents an approach to encoding SAT and MaxSAT problems that is able to encode bigger and more interesting problems for quantum annealing. A software implementation and preliminary evaluation of the method are described
Alberico da Rosciate (c. 1290-1360) lettore e commentatore dell'Inferno dantesco. Esegesi letteraria e tradizione giuridica.
Full text linkIl presente studio affronta il problema del rapporto tra letteratura e diritto con specifico riferimento al giurista bergamasco Alberico da Rosciate (c. 1290-1360) ed al suo commento alla Commedia. In quest'opera, composta nel quarto decennio del XIV secolo, Alberico inserisce numerosi riferimenti di carattere giuridico utili a spiegare il testo poetico dantesco: il patrimonio concettuale della tradizione giuridica diviene perciò parte integrante del lavorio esegetico sulla Commedia proprio nel modo in cui, nell'opera dottrinale di Alberico, il pensiero di Dante si fa fonte autoritativa a sostegno del discorso del giurista sulle più spinose questioni del dibattito politico, giuridico ed ecclesiologico del suo tempo.
Il lavoro si compone di due parti. L'Introduzione, dopo aver ripercorso sinteticamente la vicenda umana ed intellettuale di Alberico, si concentra sul commento alla Commedia discutendo le problematiche relative alla sua datazione ed al rapporto con l'opera di Iacomo della Lana ed evidenziandone i principali tratti di originalità . Si offre un elenco dei manoscritti contenenti il commento albericiano e si descrive dettagliatamente quello utilizzato per il presente lavoro (BERGAMO, BIBLIOTECA CIVICA "ANGELO MAI", Cassaforte 6.1, noto come Codice Grumelli). Si propone infine la trascrizione interpretativa del commento all'Inferno corredata da un apparato di note che segnala fonti e ipotesti, espressi e no, utilizzati da Alberico
Construction and characterization of proteome-minimized OMVs from E. coli and their exploitation in infectious disease and cancer vaccines
Full text linkBacterial Outer Membrane Vesicles (OMVs) are naturally produced by all Gram-negative bacteria and play a key role in their biology and pathogenesis. Over the last few years, OMVs have become an increasingly attractive vaccine platform for three main reasons. First, they contain several Microbe-Associated-Molecular Patterns (MAMPs), crucial for stimulating innate immunity and promoting adaptive immune responses. Second, they can be easily purified from the culture supernatant, thus making their production process inexpensive and scalable. Third, OMVs can be engineered with foreign antigens. However, the OMV platform requires some optimization for a full-blown exploitation. First, OMVs carry a number of endogenous proteins that would be useful to eliminate to avoid possible interference of immune responses toward the vaccine antigens. Second, OMVs carry abundant quantities of lipopolysaccharide (LPS). LPS is a potent stimulator of the immune system, therefore is essential for OMV adjuvaticity, but such adjuvanticity has to be modulated to avoid reactogenicity. In this study, we have addressed the two issues by creating a strain releasing OMVs with a minimal amount of endogenous proteins and containing a detoxified LPS. In particular, we first developed a CRISPR/Cas9-based genome editing tool which allows the inactivation of any “dispensable” gene in two working days. The efficacy and robustness of this tool was validated on 78 “dispensable genes”. Using our CRISPR/Cas9 protocol, an OMV proteome-minimized E. coli strain, named E. coli BL21(DE3)Δ58, deprived of 58 OMV associated proteins was created. We demonstrated that E. coli BL21(DE3)Δ58 had growth kinetics similar to the progenitor strain and featured a remarkable increase in OMV production. Two additional genes involved in the LPS biosynthetic pathway (msbB and pagP) were subsequently inactivated creating E. coli BL21(DE3)Δ60 which released OMVs with a substantially reduced reactogenicity. The exploitation of the two strains in vaccine applications was finally validated. We successfully engineered E. coli BL21(DE3)Δ58 and E. coli BL21(DE3)Δ60 with several different antigens, demonstrating that such antigens compartmentalized with high efficiency in the OMVs. We also demonstrated that the engineered OMVs from E. coli BL21(DE3)Δ58 and E. coli BL21(DE3)Δ60-derived OMVs elicited high antigen-specific antibody and T cell responses
Progressive Collapse Assessment of Steel and Concrete Composite Structures Subjected to Extreme Loading Conditions
Full text linkAccidental events, such as impact loading and explosions, are rare events with a very low probability of occurrence, but their effects often lead to very high human losses and economical consequences. Vulnerability of structures to the effects of local damages and its mitigation are issues widely discussed inside the scientific community. The structural property associated with such a vulnerability is named robustness. Depending on the type of the structural system and on the importance of consequences, specific design strategies can be adopted in order to ensure a robust structural response. Among them, the system redundancy, the joints and members ductility and the alternate load paths are the ones commonly adopted in case of multi-storey framed buildings. The present work focuses on the study of the behaviour of steel-concrete composite structures subjected to a column loss, and proposes a global overview to quantify the robustness of such systems subjected to this hazard scenario. The description of validated finite element models and of a new analytical tool to predict the response of flat concrete slabs subjected to large displacement are reported in this dissertation. Furthermore, important design hints for composite buildings are proposed. The starting point of the research is an experimental campaign conducted at the University of Trento. Two tests on 3D full-scale one storey composite steel-concrete frames, extracted from five storeys frames designed in accordance to the Eurocodes, were performed simulating the central column removal. The role of the beam-to-column connections and of the concrete slab for the force redistribution was investigated. The experimental data have been then taken as reference for the calibration of finite element models that allowed to conduct further numerical analyses on different structural configurations and design scenarios. In particular, it was studied the influence of the location of the removed column on the structural behaviour. The collapse of central, lateral and corner columns were investigated in order to understand the load transfer mechanism, the requirement of joint ductility and the influence of the concrete slab on the development of alternate load paths. Both experimental and numerical results showed that the concrete slab plays a key role on the load transfer mechanism within the structure: it can hence contribute significantly to the robustness of the system preventing progressive collapse. The knowledge of the response of reinforced concrete slabs subjected to large displacements, as in the case of a column loss, allows quantifying the contribution to the resistance of the building to collapse associated with activation of membrane forces. Regarding this aspect, a new analytical simplified method, based on the principle of virtual works, was developed to predict the load-deflection response of simply supported reinforced concrete slabs with planar edge restraints subjected to large displacement. In conclusion, the present work provides a significant contribution to the knowledge of composite steel-concrete structures subjected to extreme loading conditions and open the way to extend results to different structural configurations and loading scenarious
Mechanical and physical characterization of graphene composites
Full text linkDuring my PhD activities, I studied the introduction of carbon-based nanofillers in materials at different scales, while focusing primarily on fibres and fibrillar materials. Several production techniques were exploited.
Little is known about the interaction of graphene with electrospun polymeric fibres. Manufacturing composite fibres is complex since fillers have lateral sizes nearing that of the embedding fibre. Indeed, graphene has a direct effect in both the assembly of the electrospun composite fibres and their mechanical performance. Moreover, the tensile behaviour of hollow micrometric electrospun fibres was compared with macroscopic hollow structures such as drinking straws. The acquired insights helped to explain the toughening mechanisms at the micro-scale and develop a model capable of predicting the stress-strain response of such structures.
Among natural materials, wood has the most relevant structural applications even at large scales. Its main structural component is cellulose that has a high resistance and a low light absorption. Several structural modifications of wood derived materials were recently investigated in order to enhance the mechanical and optical properties of cellulose. These enhancements can take place after the internal structure is chemically modified with the removal of lignin and after a structural densification. Potentially, any type of wood-like materials, such as giant reed (that is a fast-growing and invasive species), can be turned into a strong structural composite. Such modifications lead to an open and interconnected internal structure that is the ideal scaffold for nanoparticle intercalation. Graphene oxide and silicon carbide nanoparticles were intercalated into densified reed. They produced an even stiffer, stronger and tougher composite compared to the best up-to-date process available. Moreover, its capabilities to resist fire and water-absorption were tested.
Finally, the previous process was further developed on wood to achieve a combination of improved transparency and electrical conductivity. Graphene and carbon nanotubes were introduced into the structure of wood to foster conductivity and explore the viability of its application as a self-strain sensor
Advanced methods for tree species classification and biophysical parameter estimation using crown geometric information in high density LiDAR data
Full text linkThe ecological, climatic and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high density multi-return small footprint Light Detection and Ranging (LiDAR) data is becoming a cost-effective method to automatic estimation of forest parameters at the Individual Tree Crown (ITC) level.
Individual tree detection and delineation techniques form the basis for ITC level parameter estimation. However SoA techniques often fail to exploit the huge amount of three dimensional (3D) structural information in the high density LiDAR data to achieve accurate detection and delineation of the 3D crown in dense forests, and thus, the first contribution of the thesis is a technique that detects and delineates both dominant and subdominant trees in dense multilayered forests. The proposed method uses novel two dimensional (2D) and 3D features to achieve this goal.
Species knowledge at individual tree level is relevant for accurate forest parameter estimation. Most state-of-the-art techniques use features that represent the distribution of data points within the crown to achieve species classification. However, the performance of such methods is low when the trees belong to the same taxonomic class (e.g., the conifer class). High density LiDAR data contain a huge amount of fine structural information of individual tree crowns. Thus, the second contribution of the thesis is on novel methods for classifying conifer species using both the branch level and the crown level geometric characteristics.
Accurate localization of trees is fundamental to calibrate the individual tree level inventory data, as it allows to match reference to LiDAR data. An important biophysical parameter for precision forestry applications is the Diameter at Breast Height (DBH). SoA methods locate the stem directly below the tree top, and indirectly estimate DBH using species-specific allometric models. Both approaches tend to be inaccurate and depend on the forest type. Thus, in this thesis, a method for accurate stem localization and DBH measurement is proposed. This is the third contribution of the thesis.
Qualitative and quantitative results of the experiments confirm the effectiveness of the proposed methods over the SoA ones