Ownership variables and capital structure: Evidence from Chile

The relationship between ownership structure and capital structure is one of the less understood areas within the corporate finance literature. This study attempts to address this issue within a unique; organizational and institutional framework that may help explain the intricacies of such a relationship. The Chilean corporate scene with its high ownership concentration levels, industrial group structure, and familial control provides a rich testing ground to analyze how ownership variables define a firm\u27s leverage policy. Research has supported both a positive and a negative relationship between ownership concentration and leverage levels. On the one hand, firms characterized by high levels of ownership concentration are expected to prefer debt to equity financing in order to avoid ownership dilution. However, high levels of ownership concentration imply lower levels of diversification on the part of managers/owners and, consequently, lower tolerance to high levels of debt in order to reduce the risk of the firm. Within Chile, several variables are hypothesized to impact or moderate this relationship. The hypotheses developed in this study explore how family ownership, the business group structure, the issuance of dual-class shares, the use of pyramiding structures, and the ensuing effects on the agency costs of debt and equity help define the interaction between ownership variables, leverage, and debt maturity. The empirical analysis follows 102 non-financial, non-utilities Chilean companies. After controlling for several determinants of capital structure, results reveal that family-controlled firms employ higher levels of debt than their non-family counterparts. Further analysis shows that debt is sought for its control function. In the presence of alternative control mechanisms, namely pyramiding structures, family firms utilize less debt. The agency perspective, which rests on the premise that family-owned businesses have lower incentive-related agency costs of debt, is strongly supported within the context of debt maturity choice; managerial involvement by family members results in less reliance on short-term debt. Other results show that group membership leads to lower levels of debt unless a bank is present within the group. No support is found for debt\u27s governance role. Informational asymmetries significantly affect a firm\u27s capital structure

Partitioning Optimization for Massively Parallel Transport Sweeps on Unstructured Grids

The field of radiation transport studies the distribution of radiation throughout a seven-dimensional phase-space consisting of time, space, energy, and direction. Radiation transport is described by the Boltzmann equation that can be solved stochastically or deterministically. The work presented in this dissertation utilizes the deterministic method known as the transport sweep, a popular technique that has been the subject of a large amount of research. We specifically focus on the parallel implementations of the transport sweep, and predicting the time it takes to sweep across a structured or unstructured mesh given a set of partitioning parameters, achieved through a time-to-solution estimator, written in Python. The time-to-solution estimator is tested against PDT, Texas A&M’s massively deterministic transport code. The time-to-solution estimator’s sweep time is within 10% of PDT’s sweep time for the majority of problems tested. We use the time-to-solution estimator as the objective function in an optimization scheme to attempt to get the partitions that lead to the fastest sweep time for a given problem and partitioning scheme. Two optimization methods are discussed: using a black box tool (scipy’s optimize library) and an intuitive method that prioritizes placing partitions in mesh locations that does not increase the number of cells (which we chose to name the CDF method). The time-to-solution estimator proved to not be smooth enough for a black box tool to work, so the CDF optimization method became the primary method. The CDF method proved effective for the majority of problems run, improving the time to solution over previously used partitioning scheme

Leveraging distant supervision for improved named entity recognition

Les techniques d'apprentissage profond ont fait un bond au cours des dernières années, et ont considérablement changé la manière dont les tâches de traitement automatique du langage naturel (TALN) sont traitées. En quelques années, les réseaux de neurones et les plongements de mots sont rapidement devenus des composants centraux à adopter dans le domaine. La supervision distante (SD) est une technique connue en TALN qui consiste à générer automatiquement des données étiquetées à partir d'exemples partiellement annotés. Traditionnellement, ces données sont utilisées pour l'entraînement en l'absence d'annotations manuelles, ou comme données supplémentaires pour améliorer les performances de généralisation. Dans cette thèse, nous étudions comment la supervision distante peut être utilisée dans un cadre d'un TALN moderne basé sur l'apprentissage profond. Puisque les algorithmes d'apprentissage profond s'améliorent lorsqu'une quantité massive de données est fournie (en particulier pour l'apprentissage des représentations), nous revisitons la génération automatique des données avec la supervision distante à partir de Wikipédia. On applique des post-traitements sur Wikipédia pour augmenter la quantité d'exemples annotés, tout en introduisant une quantité raisonnable de bruit. Ensuite, nous explorons différentes méthodes d'utilisation de données obtenues par supervision distante pour l'apprentissage des représentations, principalement pour apprendre des représentations de mots classiques (statistiques) et contextuelles. À cause de sa position centrale pour de nombreuses applications du TALN, nous choisissons la reconnaissance d'entité nommée (NER) comme tâche principale. Nous expérimentons avec des bancs d’essai NER standards et nous observons des performances état de l’art. Ce faisant, nous étudions un cadre plus intéressant, à savoir l'amélioration des performances inter-domaines (généralisation).Recent years have seen a leap in deep learning techniques that greatly changed the way Natural Language Processing (NLP) tasks are tackled. In a couple of years, neural networks and word embeddings quickly became central components to be adopted in the domain. Distant supervision (DS) is a well-used technique in NLP to produce labeled data from partially annotated examples. Traditionally, it was mainly used as training data in the absence of manual annotations, or as additional training data to improve generalization performances. In this thesis, we study how distant supervision can be employed within a modern deep learning based NLP framework. As deep learning algorithms gets better when massive amount of data is provided (especially for representation learning), we revisit the task of generating distant supervision data from Wikipedia. We apply post-processing treatments on the original dump to further increase the quantity of labeled examples, while introducing a reasonable amount of noise. Then, we explore different methods for using distant supervision data for representation learning, mainly to learn classic and contextualized word representations. Due to its importance as a basic component in many NLP applications, we choose Named-Entity Recognition (NER) as our main task. We experiment on standard NER benchmarks showing state-of-the-art performances. By doing so, we investigate a more interesting setting, that is, improving the cross-domain (generalization) performances

Energy-Efficient Service Placement for Latency-Sensitive Applications in Edge Computing

Edge computing is a promising solution to host artificial intelligence (AI) applications that enable real-time insights on user-generated and device-generated data. This requires edge computing resources (storage and compute) to be widely deployed close to end devices. Such edge deployments require a large amount of energy to run as edge resources are typically overprovisioned to flexibly meet the needs of time-varying user demand with a low latency. Moreover, AI applications rely on deep neural network (DNN) models that are increasingly larger in size to support high accuracy. These DNN models must be efficiently stored and transferred, so as to minimize their energy consumption. In this article, we model the problem of energy-efficient placement of services (namely, DNN models) for AI applications as a multiperiod optimization problem. The formulation jointly places services and schedules requests such that the overall energy consumption is minimized and latency is low. We propose a heuristic that efficiently solves the problem while taking into account the impact of placing services across time periods. We assess the quality of the proposed heuristic by comparing its solution to a lower bound of the problem, obtained by formulating and solving a Lagrangian relaxation of the original problem. Extensive simulations show that our proposed heuristic outperforms baseline approaches in achieving a low energy consumption by packing services on a minimal number of edge nodes, while at the same time keeping the average latency of served requests below a configured threshold in nearly all time periods.Peer reviewe