Implementazione automatica di protocolli di interazione AUML mediante reti di Petri

Questa tesi si basa su una serie di lavori precedenti, volti ad analizzare la correlazione tra i modelli AUML e le reti di Petri, per riuscire a fornire una metodologia di traduzione dai primi alle seconde. Questa traduzione permetterà di applicare tecniche di model checking alle reti così create, al fine di stabilire le proprietà necessarie al sistema per poter essere realizzato effettivamente. Verrà poi discussa un'implementazione di tale algoritmo sviluppata in tuProlog ed un primo approccio al model checking utilizzando il programma Maude. Con piccole modifiche all'algoritmo utilizzato per la conversione dei diagrammi AUML in reti di Petri, è stato possibile, inoltre, realizzare un sistema di implementazione automatica dei protocolli precedentemente analizzati, verso due piattaforme per la realizzazione di sistemi multiagente: Jason e TuCSoN. Verranno quindi presentate tre implementazioni diverse: la prima per la piattaforma Jason, che utilizza degli agenti BDI per realizzare il protocollo di interazione; la seconda per la piattaforma TuCSoN, che utilizza il modello A&A per rendersi compatibile ad un ambiente distribuito, ma che ricalca la struttura dell'implementazione precedente; la terza ancora per TuCSoN, che sfrutta gli strumenti forniti dalle reazioni ReSpecT per generare degli artefatti in grado di fornire una infrastruttura in grado di garantire la realizzazione del protocollo di interazione agli agenti partecipanti. Infine, verranno discusse le caratteristiche di queste tre differenti implementazioni su un caso di studio reale, analizzandone i punti chiave.

Morphological and physiological responses of apple trees under photoselective colored nets

In recent years and thanks to innovative technological advances in supplemental lighting sources and photo-selective filters, light quality manipulation (i.e. spectral composition of sunlight) have demonstrated positive effects on plant performance in ornamentals and vegetable crops. However, this aspect has been much less studied in fruit trees due to the difficulty of conditioning the light environment of orchards. The aim of the present PhD research was to study the use of different colored nets with selective light transmission in the blue (400 – 500 nm), red (600 – 700 nm) and near infrared (700 – 1100 nm) wavelengths as a tool to the light quality management and its morphological and physiological effects in field-grown apple trees. Chapter I provides a review the current status on physiological and technological advances on light quality management in fruit trees. Chapter II shows the main effect of colored nets on morpho-anatomical (stomata density, mesophyll structure and leaf mass area index) characteristics in apple leaves. Chapter III provides an analysis about the effect of micro-environmental conditions under colored nets on leaf stomatal conductance and leaf photosynthetic capacity. Chapter IV describes a study approach to evaluate the impact of colored nets on fruit growth potential in apples. Summing up results obtained in the present PhD dissertation clearly demonstrate that light quality management through photo-selective colored nets presents an interesting potential for the manipulation of plant morphological and physiological traits in apple trees. Cover orchards with colored nets might be and alternative technology to address many of the most important challenges of modern fruit growing, such as: the need for the efficient use of natural resources (water, soil and nutrients) the reduction of environmental impacts and the mitigation of possible negative effects of global climate change.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma

We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z-score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b-high/CD10-low/CD221-high) and a second group clustering close to fibroblasts (CD49b-low/CD10-high/CD221-low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes.

Determination of Pelvic Orientation from Ultrasound Images Using Patch-SSMs and a Hierarchical Speed of Sound Compensation Strategy

In the field of computer assisted orthopedic surgery (CAOS) the anterior pelvic plane (APP) is a common concept to determine the pelvic orientation by digitizing distinct pelvic landmarks. As percutaneous palpation is - especially for obese patients - known to be error-prone, B-mode ultrasound (US) imaging could provide an alternative means. Several concepts of using ultrasound imaging to determine the APP landmarks have been introduced. In this paper we present a novel technique, which uses local patch statistical shape models (SSMs) and a hierarchical speed of sound compensation strategy for an accurate determination of the APP. These patches are independently matched and instantiated with respect to associated point clouds derived from the acquired ultrasound images. Potential inaccuracies due to the assumption of a constant speed of sound are compensated by an extended reconstruction scheme. We validated our method with in-vitro studies using a plastic bone covered with a soft-tissue simulation phantom and with a preliminary cadaver trial.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Clinical aspects, diagnostic challenges and management of patients with neuroendocrine tumors (NETs)

Neuroendocrine tumor (NET) entities are rare malignancies. Higher awareness and improved diagnostic methods have led to an increasing incidence of these diseases, and most oncologists deal with such patients in their daily practice. The symposium on NETs that was held in Merano (Italy) in October 2009 was organized by the German-speaking European School of Oncology (dESO) and gathered specialists from different disciplines of transalpine countries to bring together experiences and observations regarding these tumors. The goal of the meeting and of this review was to illustrate both well- and poorly differentiated NETs and to encourage interdisciplinary approaches.

Hierarchical Approach for Survivable Network Design

A central design challenge facing network planners is how to select a cost-effective network configuration that can provide uninterrupted service despite edge failures. In this paper, we study the Survivable Network Design (SND) problem, a core model underlying the design of such resilient networks that incorporates complex cost and connectivity trade-offs. Given an undirected graph with specified edge costs and (integer) connectivity requirements between pairs of nodes, the SND problem seeks the minimum cost set of edges that interconnects each node pair with at least as many edge-disjoint paths as the connectivity requirement of the nodes. We develop a hierarchical approach for solving the problem that integrates ideas from decomposition, tabu search, randomization, and optimization. The approach decomposes the SND problem into two subproblems, Backbone design and Access design, and uses an iterative multi-stage method for solving the SND problem in a hierarchical fashion. Since both subproblems are NP-hard, we develop effective optimization-based tabu search strategies that balance intensification and diversification to identify near-optimal solutions. To initiate this method, we develop two heuristic procedures that can yield good starting points. We test the combined approach on large-scale SND instances, and empirically assess the quality of the solutions vis-à-vis optimal values or lower bounds. On average, our hierarchical solution approach generates solutions within 2.7% of optimality even for very large problems (that cannot be solved using exact methods), and our results demonstrate that the performance of the method is robust for a variety of problems with different size and connectivity characteristics.

A smart multi-hop hierarchical routing protocol for efficient video communication over wireless multimedia sensor networks

For smart applications, nodes in wireless multimedia sensor networks (MWSNs) have to take decisions based on sensed scalar physical measurements. A routing protocol must provide the multimedia delivery with quality level support and be energy-efficient for large-scale networks. With this goal in mind, this paper proposes a smart Multi-hop hierarchical routing protocol for Efficient VIdeo communication (MEVI). MEVI combines an opportunistic scheme to create clusters, a cross-layer solution to select routes based on network conditions, and a smart solution to trigger multimedia transmission according to sensed data. Simulations were conducted to show the benefits of MEVI compared with the well-known Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. This paper includes an analysis of the signaling overhead, energy-efficiency, and video quality.