Speculative document evaluation

Optimisation of real world Variable Data printing (VDP) documents is a dicult problem because the interdependencies between layout functions may drastically reduce the number of invariant blocks that can be factored out for pre-rasterisation. This paper examines how speculative evaluation at an early stage in a document-preparation pipeline, provides a generic and effective method of optimising VDP documents that contain such interdependencies. Speculative evaluation will be at its most effective in speeding up print runs if sets of layout invariances can either be discovered automatically, or designed into the document at an early stage. In either case the expertise of the layout designer needs to be supplemented by expertise in exploiting potential invariances and also in predicting the effects of speculative evaluation on the caches used at various stages in the print production pipeline.

Modelling and analysing human behaviour in a department store using discrete event and agent based simulation

Discrete Event Simulation (DES) is a very popular simulation technique in Operational Research. Recently, there has been the emergence of another technique, namely Agent Based Simulation (ABS). Although there is a lot of literature relating to DES and ABS, we have found less that focuses on exploring the capabilities of both in tackling human behaviour issues. In order to understand the gap between these two simulation techniques, therefore, our aim is to understand the distinctions between DES and ABS models with the real world phenomenon in modelling and simulating human behaviour. In achieving the aim, we have carried out a case study at a department store. Both DES and ABS models will be compared using the same problem domain which is concerning on management policy in a fitting room. The behaviour of staffs while working and customers’ satisfaction will be modelled for both models behaviour understanding.

A survey on multi-output regression

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This paper provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks.

An investigation of imputation methods for discrete databases and multi-variate time series

When it comes to information sets in real life, often pieces of the whole set may not be available. This problem can find its origin in various reasons, describing therefore different patterns. In the literature, this problem is known as Missing Data. This issue can be fixed in various ways, from not taking into consideration incomplete observations, to guessing what those values originally were, or just ignoring the fact that some values are missing. The methods used to estimate missing data are called Imputation Methods. The work presented in this thesis has two main goals. The first one is to determine whether any kind of interactions exists between Missing Data, Imputation Methods and Supervised Classification algorithms, when they are applied together. For this first problem we consider a scenario in which the databases used are discrete, understanding discrete as that it is assumed that there is no relation between observations. These datasets underwent processes involving different combina- tions of the three components mentioned. The outcome showed that the missing data pattern strongly influences the outcome produced by a classifier. Also, in some of the cases, the complex imputation techniques investigated in the thesis were able to obtain better results than simple ones. The second goal of this work is to propose a new imputation strategy, but this time we constrain the specifications of the previous problem to a special kind of datasets, the multivariate Time Series. We designed new imputation techniques for this particular domain, and combined them with some of the contrasted strategies tested in the pre- vious chapter of this thesis. The time series also were subjected to processes involving missing data and imputation to finally propose an overall better imputation method. In the final chapter of this work, a real-world example is presented, describing a wa- ter quality prediction problem. The databases that characterized this problem had their own original latent values, which provides a real-world benchmark to test the algorithms developed in this thesis.

Edge centrality via the Holevo quantity

In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we propose a novel edge centrality index rooted in quantum information. More specifically, we measure the importance of an edge in terms of the contribution that it gives to the Von Neumann entropy of the graph. We show that this can be computed in terms of the Holevo quantity, a well known quantum information theoretical measure. While computing the Von Neumann entropy and hence the Holevo quantity requires computing the spectrum of the graph Laplacian, we show how to obtain a simplified measure through a quadratic approximation of the Shannon entropy. This in turns shows that the proposed centrality measure is strongly correlated with the negative degree centrality on the line graph. We evaluate our centrality measure through an extensive set of experiments on real-world as well as synthetic networks, and we compare it against commonly used alternative measures.

Effectively positioning water loss event in smart water networks

With the eye-catching advances in sensing technologies, smart water networks have been attracting immense research interest in recent years. One of the most overarching tasks in smart water network management is the reduction of water loss (such as leaks and bursts in a pipe network). In this paper, we propose an efficient scheme to position water loss event based on water network topology. The state-of-the-art approach to this problem, however, utilizes the limited topology information of the water network, that is, only one single shortest path between two sensor locations. Consequently, the accuracy of positioning water loss events is still less desirable. To resolve this problem, our scheme consists of two key ingredients: First, we design a novel graph topology-based measure, which can recursively quantify the "average distances" for all pairs of senor locations simultaneously in a water network. This measure will substantially improve the accuracy of our positioning strategy, by capturing the entire water network topology information between every two sensor locations, yet without any sacrifice of computational efficiency. Then, we devise an efficient search algorithm that combines the "average distances" with the difference in the arrival times of the pressure variations detected at sensor locations. The viable experimental evaluations on real-world test bed (WaterWiSe@SG) demonstrate that our proposed positioning scheme can identify water loss event more accurately than the best-known competitor.

Development of Optimal Energy Management Strategies for a Hybrid Boat

The present work proposes different approaches to extend the mathematical methods of supervisory energy management used in terrestrial environments to the maritime sector, that diverges in constraints, variables and disturbances. The aim is to find the optimal real-time solution that includes the minimization of a defined track time, while maintaining the classical energetic approach. Starting from analyzing and modelling the powertrain and boat dynamics, the energy economy problem formulation is done, following the mathematical principles behind the optimal control theory. Then, an adaptation aimed in finding a winning strategy for the Monaco Energy Boat Challenge endurance trial is performed via ECMS and A-ECMS control strategies, which lead to a more accurate knowledge of energy sources and boat’s behaviour. The simulations show that the algorithm accomplishes fuel economy and time optimization targets, but the latter adds huge tuning and calculation complexity. In order to assess a practical implementation on real hardware, the knowledge of the previous approaches has been translated into a rule-based algorithm, that let it be run on an embedded CPU. Finally, the algorithm has been tuned and tested in a real-world race scenario, showing promising results.

A framework for risk analysis in automotive cybersecurity

We address the problem of automotive cybersecurity from the point of view of Threat Analysis and Risk Assessment (TARA). The central question that motivates the thesis is the one about the acceptability of risk, which is vital in taking a decision about the implementation of cybersecurity solutions. For this purpose, we develop a quantitative framework in which we take in input the results of risk assessment and define measures of various facets of a possible risk response; we then exploit the natural presence of trade-offs (cost versus effectiveness) to formulate the problem as a multi-objective optimization. Finally, we develop a stochastic model of the future evolution of the risk factors, by means of Markov chains; we adapt the formulations of the optimization problems to this non-deterministic context. The thesis is the result of a collaboration with the Vehicle Electrification division of Marelli, in particular with the Cybersecurity team based in Bologna; this allowed us to consider a particular instance of the problem, deriving from a real TARA, in order to test both the deterministic and the stochastic framework in a real world application. The collaboration also explains why in the work we often assume the point of view of a tier-1 supplier; however, the analyses performed can be adapted to any other level of the supply chain.

Learning representations for graph-structured socio-technical systems

The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism. The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased emphasis on addressing the limitations of interpretability in graph representation learning. This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.

Laser Printer Attribution: Exploring New Features And Beyond.

With a huge amount of printed documents nowadays, identifying their source is useful for criminal investigations and also to authenticate digital copies of a document. In this paper, we propose novel techniques for laser printer attribution. Our solutions do not need very high resolution scanning of the investigated document and explore the multidirectional, multiscale and low-level gradient texture patterns yielded by printing devices. The main contributions of this work are: (1) the description of printed areas using multidirectional and multiscale co-occurring texture patterns; (2) description of texture on low-level gradient areas by a convolution texture gradient filter that emphasizes textures in specific transition areas and (3) the analysis of printer patterns in segments of interest, which we call frames, instead of whole documents or only printed letters. We show by experiments in a well documented dataset that the proposed methods outperform techniques described in the literature and present near-perfect classification accuracy being very promising for deployment in real-world forensic investigations.

Pedagogia do esporte e o atletismo : considerações acerca da iniciação e da especialização esportiva precoce

Universidade Estadual de Campinas . Faculdade de Educação Física

Modelos da Química Quântica no espaço de momento: diferentes representações de um mesmo sistema

The conventional approach to simple quantum chemistry models is contrasted with that known as momentum representation, where the wavefunctions are momentum dependent. Since the physical interactions are the same, state energies should not change, and whence the energy differences correlating with the real world as spectral lines or bands. We emphasize that one representation is not more fundamental than the other, and the choice is a matter of mathematical convenience. As spatial localization is rooted in our brains, to think in terms of the momentum present us a great mental challenge that can lead to complementary perspectives of a model.

Characterization of subgraph relationships and distribution in complex networks

A network can be analyzed at different topological scales, ranging from single nodes to motifs, communities, up to the complete structure. We propose a novel approach which extends from single nodes to the whole network level by considering non-overlapping subgraphs (i.e. connected components) and their interrelationships and distribution through the network. Though such subgraphs can be completely general, our methodology focuses on the cases in which the nodes of these subgraphs share some special feature, such as being critical for the proper operation of the network. The methodology of subgraph characterization involves two main aspects: (i) the generation of histograms of subgraph sizes and distances between subgraphs and (ii) a merging algorithm, developed to assess the relevance of nodes outside subgraphs by progressively merging subgraphs until the whole network is covered. The latter procedure complements the histograms by taking into account the nodes lying between subgraphs, as well as the relevance of these nodes to the overall subgraph interconnectivity. Experiments were carried out using four types of network models and five instances of real-world networks, in order to illustrate how subgraph characterization can help complementing complex network-based studies.

Generalized connectivity between any two nodes in a complex network

This article focuses on the identification of the number of paths with different lengths between pairs of nodes in complex networks and how these paths can be used for characterization of topological properties of theoretical and real-world complex networks. This analysis revealed that the number of paths can provide a better discrimination of network models than traditional network measurements. In addition, the analysis of real-world networks suggests that the long-range connectivity tends to be limited in these networks and may be strongly related to network growth and organization.