Call & response -- Winter 2016

Call & Response is the newsletter of the South Carolina African American Heritage Commission, whose mission is to identify and promote the preservation of historic sites, structures, buildings, and culture of the African American experience in South Carolina. This is volume X number 2 and includes a message from the chair, list of board members, spotlight on Vitoria A. Smalls and the agenda for the annual conference and awards ceremony.

Méthodes de segmentation du ventricule gauche basée sur l'algorithme graph cut pour les images par résonance magnétique et échocardiographiques

L’échocardiographie et l’imagerie par résonance magnétique sont toutes deux des techniques non invasives utilisées en clinique afin de diagnostiquer ou faire le suivi de maladies cardiaques. La première mesure un délai entre l’émission et la réception d’ultrasons traversant le corps, tandis que l’autre mesure un signal électromagnétique généré par des protons d’hydrogène présents dans le corps humain. Les résultats des acquisitions de ces deux modalités d’imagerie sont fondamentalement différents, mais contiennent dans les deux cas de l’information sur les structures du coeur humain. La segmentation du ventricule gauche consiste à délimiter les parois internes du muscle cardiaque, le myocarde, afin d’en calculer différentes métriques cliniques utiles au diagnostic et au suivi de différentes maladies cardiaques, telle la quantité de sang qui circule à chaque battement de coeur. Suite à un infarctus ou autre condition, les performances ainsi que la forme du coeur en sont affectées. L’imagerie du ventricule gauche est utilisée afin d’aider les cardiologues à poser les bons diagnostics. Cependant, dessiner les tracés manuels du ventricule gauche requiert un temps non négligeable aux cardiologues experts, d’où l’intérêt pour une méthode de segmentation automatisée fiable et rapide. Ce mémoire porte sur la segmentation du ventricule gauche. La plupart des méthodes existantes sont spécifiques à une seule modalité d’imagerie. Celle proposée dans ce document permet de traiter rapidement des acquisitions provenant de deux modalités avec une précision de segmentation équivalente au tracé manuel d’un expert. Pour y parvenir, elle opère dans un espace anatomique, induisant ainsi une forme a priori implicite. L’algorithme de Graph Cut, combiné avec des stratégies telles les cartes probabilistes et les enveloppes convexes régionales, parvient à générer des résultats qui équivalent (ou qui, pour la majorité des cas, surpassent) l’état de l’art ii Sommaire au moment de la rédaction de ce mémoire. La performance de la méthode proposée, quant à l’état de l’art, a été démontrée lors d’un concours international. Elle est également validée exhaustivement via trois bases de données complètes en se comparant aux tracés manuels de deux experts et des tracés automatisés du logiciel Syngovia. Cette recherche est un projet collaboratif avec l’Université de Bourgogne, en France.

Prevalencia y caracterización de la disfonía, en operadores de un call center en Bogotá Colombia.

Resumen Introducción: El uso de la voz profesional requiere de una técnica y medidas de conservación para no verse afectada. Un inadecuado esquema corporal en el profesional de la voz, ocasiona alteración en los parámetros respiratorios y vocales manifestándose como disfonía. Objetivo: Determinar la prevalencia y caracterización de disfonía en 200 tele operadores de un call Center en Bogotá Colombia. Métodos: Estudio de corte transversal con datos secundarios provenientes de una base de datos con registros de una población de 200 tele operadores de un call center en Bogotá, Colombia, a los cuales se les aplicó evaluación de respiración y de voz durante el año 2003. La estimación de la prevalencia de la disfonía se realizó a través de la distribución de frecuencias relativas. Se realizó caracterización de la población estudiada según variables sociodemográficas, ocupacionales y parámetros respiratorios y vocales a través de métodos estadísticos según la naturaleza de estas variables. Se determinó la asociación entre factores ambientales, síntomas asociados, síntomas vocales, perfil vocal de Wilson y disfonía mediante la prueba de Chi Cuadrado de Pearson. Resultados: la prevalencia de disfonía fue del 73% (n= 146), el 34% presentó grado de disfonía moderado. Los resultados obtenidos en los parámetros de evaluación vocal se encontraron dentro del rango de normalidad, analizados en forma individual (tono, rango intensidad) y se relacionan con los resultados obtenidos con la prevalencia disfonía. El 95,5% de los tele operadores los parámetros respiratorios se encontraron alterados. Los tele operadores con disfonía en comparación a los sin disfonía tuvieron mayor frecuencia de presentación de los siguientes factores ambientales: ruido (68% vs 50,9% p=0,03) y vapores (27,2% vs 11,3% p= 0,02), síntomas corporales y de la voz respectivamente: cuello (69,4% vs 54,7% p= 0,05), dolor en la laringe (19,7% vs 7,5% p= 0,04). Conclusión: La prevalencia de disfonía encontrada en este call center fue alta Lo que requiere la implementación de medidas de prevención como tamizajes acústicos, para hacer seguimiento a las cualidades de la voz más afectadas, entrenamiento respiratorio y vocal, pausas vocales y medidas de conservación de la voz, para contribuir a que el tele operador desarrolle un mejor manejo de sus cualidades vocales acorde con su uso y disminuir la prevalencia de disfonía.

Synthetic portfolio for event studies: Estimating the effects of volatility call auctions

We propose a method denoted as synthetic portfolio for event studies in market microstructure that is particularly interesting to use with high frequency data and thinly traded markets. The method is based on Synthetic Control Method and provides a robust data driven method to build a counterfactual for evaluating the effects of the volatility call auctions. We find that SMC could be used if the loss function is defined as the difference between the returns of the asset and the returns of a synthetic portfolio. We apply SCM to test the performance of the volatility call auction as a circuit breaker in the context of an event study. We find that for Colombian Stock Market securities, the asynchronicity of intraday data reduces the analysis to a selected group of stocks, however it is possible to build a tracking portfolio. The realized volatility increases after the auction, indicating that the mechanism is not enhancing the price discovery process.

Simplicial Complexes From Graphs Toward Graph Persistence

Persistent homology is a branch of computational topology which uses geometry and topology for shape description and analysis. This dissertation is an introductory study to link persistent homology and graph theory, the connection being represented by various methods to build simplicial complexes from a graph. The methods we consider are the complex of cliques, of independent sets, of neighbours, of enclaveless sets and complexes from acyclic subgraphs, each revealing several properties of the underlying graph. Moreover, we apply the core ideas of persistence theory in the new context of graph theory, we define the persistent block number and the persistent edge-block number.

Stochastic models and graph theory for Zipf's law

In questo elaborato ci siamo occupati della legge di Zipf sia da un punto di vista applicativo che teorico. Tale legge empirica afferma che il rango in frequenza (RF) delle parole di un testo seguono una legge a potenza con esponente -1. Per quanto riguarda l'approccio teorico abbiamo trattato due classi di modelli in grado di ricreare leggi a potenza nella loro distribuzione di probabilità. In particolare, abbiamo considerato delle generalizzazioni delle urne di Polya e i processi SSR (Sample Space Reducing). Di questi ultimi abbiamo dato una formalizzazione in termini di catene di Markov. Infine abbiamo proposto un modello di dinamica delle popolazioni capace di unificare e riprodurre i risultati dei tre SSR presenti in letteratura. Successivamente siamo passati all'analisi quantitativa dell'andamento del RF sulle parole di un corpus di testi. Infatti in questo caso si osserva che la RF non segue una pura legge a potenza ma ha un duplice andamento che può essere rappresentato da una legge a potenza che cambia esponente. Abbiamo cercato di capire se fosse possibile legare l'analisi dell'andamento del RF con le proprietà topologiche di un grafo. In particolare, a partire da un corpus di testi abbiamo costruito una rete di adiacenza dove ogni parola era collegata tramite un link alla parola successiva. Svolgendo un'analisi topologica della struttura del grafo abbiamo trovato alcuni risultati che sembrano confermare l'ipotesi che la sua struttura sia legata al cambiamento di pendenza della RF. Questo risultato può portare ad alcuni sviluppi nell'ambito dello studio del linguaggio e della mente umana. Inoltre, siccome la struttura del grafo presenterebbe alcune componenti che raggruppano parole in base al loro significato, un approfondimento di questo studio potrebbe condurre ad alcuni sviluppi nell'ambito della comprensione automatica del testo (text mining).

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly larger than seen during training.

Edge-aware Graph Attention Networks: Joint Reasoning On Text and Knowledge Graphs for Biomedical Question Answering

Much of the real-world dataset, including textual data, can be represented using graph structures. The use of graphs to represent textual data has many advantages, mainly related to maintaining a more significant amount of information, such as the relationships between words and their types. In recent years, many neural network architectures have been proposed to deal with tasks on graphs. Many of them consider only node features, ignoring or not giving the proper relevance to relationships between them. However, in many node classification tasks, they play a fundamental role. This thesis aims to analyze the main GNNs, evaluate their advantages and disadvantages, propose an innovative solution considered as an extension of GAT, and apply them to a case study in the biomedical field. We propose the reference GNNs, implemented with methodologies later analyzed, and then applied to a question answering system in the biomedical field as a replacement for the pre-existing GNN. We attempt to obtain better results by using models that can accept as input both node and edge features. As shown later, our proposed models can beat the original solution and define the state-of-the-art for the task under analysis.

Open Education and OER - A guide and call to action for policy makers

Executive Summary ALT has produced this call to action to highlight to education policy makers and professionals how Open Education and OER can expand inclusive and equitable access to education and lifelong learning, widen participation, and create new opportunities for the next generation of teachers and learners, preparing them to become fully engaged digital citizens. Open Education can also promote knowledge transfer while enhancing quality and sustainability, supporting social inclusion and creating a culture of inter-institutional collaboration and sharing. One of ALT’s three strategic aims is to increase the impact of Learning Technology for the wider community and we are issuing this call to action for policy makers to mandate that publicly funded educational resources are released under open licence to ensure that they reside in the public domain and are freely and openly available to all. This will be of wide benefit, but in particular will enable education providers and learning technology professionals to: Keep up to date with the rapid pace of technological innovation Develop critical, informed approaches to the implementation of Learning Technology and the impact on learners Scale up knowledge sharing and its benefits across sectors.

Network assimilation methodology and sectorization of water distribution systems by graph mining and optimization algorithm

Water Distribution Networks (WDNs) play a vital importance rule in communities, ensuring well-being band supporting economic growth and productivity. The need for greater investment requires design choices will impact on the efficiency of management in the coming decades. This thesis proposes an algorithmic approach to address two related problems:(i) identify the fundamental asset of large WDNs in terms of main infrastructure;(ii) sectorize large WDNs into isolated sectors in order to respect the minimum service to be guaranteed to users. Two methodologies have been developed to meet these objectives and subsequently they were integrated to guarantee an overall process which allows to optimize the sectorized configuration of WDN taking into account the needs to integrated in a global vision the two problems (i) and (ii). With regards to the problem (i), the methodology developed introduces the concept of primary network to give an answer with a dual approach, of connecting main nodes of WDN in terms of hydraulic infrastructures (reservoirs, tanks, pumps stations) and identifying hypothetical paths with the minimal energy losses. This primary network thus identified can be used as an initial basis to design the sectors. The sectorization problem (ii) has been faced using optimization techniques by the development of a new dedicated Tabu Search algorithm able to deal with real case studies of WDNs. For this reason, three new large WDNs models have been developed in order to test the capabilities of the algorithm on different and complex real cases. The developed methodology also allows to automatically identify the deficient parts of the primary network and dynamically includes new edges in order to support a sectorized configuration of the WDN. The application of the overall algorithm to the new real case studies and to others from literature has given applicable solutions even in specific complex situations.

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.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.

Poset associahedra as sections of graph associahedra

Poset associahedra are a family of convex polytopes recently introduced by Pavel Galashin in 2021. The associahedron An is an (n-2)-dimensional convex polytope whose facial structure encodes the ways of parenthesizing an n-letter word (among several equivalent combinatorial objects). Associahedra are deeply studied polytopes that appear naturally in many areas of mathematics: algebra, combinatorics, geometry, topology... They have many presentations and generalizations. One of their incarnations is as a compactification of the configuration space of n points on a line. Similarly, the P-associahedron of a poset P is a compactification of the configuration space of order preserving maps from P to R. Galashin presents poset associahedra as combinatorial objects and shows that they can be realized as convex polytopes. However, his proof is not constructive, in the sense that no explicit coordinates are provided. The main goal of this thesis is to provide an explicit construction of poset associahedra as sections of graph associahedra, thus solving the open problem stated in Remark 1.5 of Galashin's paper.

Graph theory and geometric deep learning

La seguente tesi propone un’introduzione al geometric deep learning. Nella prima parte vengono presentati i concetti principali di teoria dei grafi ed introdotta una dinamica di diffusione su grafo, in analogia con l’equazione del calore. A seguire, iniziando dal linear classifier verranno introdotte le architetture che hanno portato all’ideazione delle graph convolutional networks. In conclusione, si analizzano esempi di alcuni algoritmi utilizzati nel geometric deep learning e si mostra una loro implementazione sul Cora dataset, un insieme di dati con struttura a grafo.

Graph-based Vision-Language Model for Multi-Modal Retrieval of Fashion Products

Artificial Intelligence is reshaping the field of fashion industry in different ways. E-commerce retailers exploit their data through AI to enhance their search engines, make outfit suggestions and forecast the success of a specific fashion product. However, it is a challenging endeavour as the data they possess is huge, complex and multi-modal. The most common way to search for fashion products online is by matching keywords with phrases in the product's description which are often cluttered, inadequate and differ across collections and sellers. A customer may also browse an online store's taxonomy, although this is time-consuming and doesn't guarantee relevant items. With the advent of Deep Learning architectures, particularly Vision-Language models, ad-hoc solutions have been proposed to model both the product image and description to solve this problems. However, the suggested solutions do not exploit effectively the semantic or syntactic information of these modalities, and the unique qualities and relations of clothing items. In this work of thesis, a novel approach is proposed to address this issues, which aims to model and process images and text descriptions as graphs in order to exploit the relations inside and between each modality and employs specific techniques to extract syntactic and semantic information. The results obtained show promising performances on different tasks when compared to the present state-of-the-art deep learning architectures.