In the 1980s and 1990s, Joyal and Street developed a graphical notation for various flavours of monoidal category using graphs drawn in the plane, commonly known as string diagrams. In particular, their work comprised a rigorous topological foundation of the notation. In 2007, Harmer, Hyland and Melliès gave a formal mathematical foundation for game semantics using a notions they called ⊸-schedules, ⊗-schedules and heaps. Schedules described interleavings of plays in games formed using ⊸ and ⊗, and heaps provided pointers used for backtracking. Their definitions were combinatorial in nature, but researchers often draw certain pictures when working in practice. In this thesis, we extend the framework of Joyal and Street to give a formal account of the graphical methods already informally employed by researchers in game semantics. We give a geometric formulation of ⊸-schedules and ⊗-schedules, and prove that the games they describe are isomorphic to those described in Harmer et al.’s terms, and also those given by a more general graphical representation of interleaving across games of multiple components. We further illustrate the value of the geometric methods by demonstrating that several proofs of key properties (such as that the composition of ⊸-schedules is associative) can be made straightforward, reflecting the geometry of the plane, and overstepping some of the cumbersome combinatorial detail of proofs in Harmer et al.’s terms. We further extend the framework of formal plane diagrams to account for the heaps and pointer structures used in the backtracking functors for O and P.

Modern computer systems are complex. Even in the best of conditions, it can be difficult to understand the behavior of the system and identify why certain actions are occurring. Existing systems attempt to provide insight by reviewing the effects of actions on the system and estimating their cause. As computer systems are strongly driven by actions of the user, we propose an approach to identify processes which have interacted with the user and provide data to which system behaviors were caused by the user. We implement three sensors within the graphical user interface capable of extracting the necessary information to identify these processes. We show our instrumentation is effective in characterizing applications with an on-screen presence, and provide data towards the determination of user intentions. We prove that our method for obtaining the information from the user interface can be done in an efficient manner with minimal overheads.

Creating for the first time GUI (graphical user interface) applications is an endeavor that can be most challenging. This article introduces the general concepts of GUIs and the need to interact with GUI elements only on the so called "GUI thread". The concepts pertain to GUI applications written for Windows, Linux and MacOS alike. Using Java libraries for creating Rexx GUI applications makes these Rexx GUI applications totally platform independent. Taking advantage of BSF4ooRexx even the powerful JavaFX GUI libraries can be exploited by pure Rexx, allowing Rexx programmers to create the most demanding and complex GUI applications in an unparalleled easiness in an astonishing short period of time. The introduced GUI concepts will be demonstrated with short nutshell examples exploiting the JavaFX GUI libraries, empowering the Rexx programmers with the ability to create stable and error free GUI applications in Rexx.

The mixing desk metaphor found in Digital Audio Workstations (DAW) is built upon a specialised and technical knowledge of signal flow and audio engineering. However, since their inception the DAW has gained a far wider and less technically specialised user-base. Furthermore, the limited screen space of laptop and tablet computers, combined with potentially limitless tracks in current DAWs has resulted in the need for complex interface navigation during mixing which may inhibit a fluid and intuitive approach to mixing. The research outlined in this thesis explores novel designs for Graphical User Interfaces (GUIs) for mixing, which acknowledge the changing role of the user, the limited space of tablet and mobile computers screens and the limitations of human perception during cross modal activities (aural and visual). The author designs and conducts several experiments using non-expert participants drawn from several music technology courses, to assess and quantify the extent to which current DAW designs might influence mixing workflow, aiming our research especially at beginner and non-expert users. The results of our studies suggest that GUIs which load visual working memory, or force the user to mentally integrate visual information across the interface, can reduce the ability to hear subtle simultaneous changes to the audio. We use the analysis of these experiments to propose novel GUI designs that are better suited to human cross-modal perceptual limitations and which take into account the specific challenges and opportunities afforded by screen-based audio mixers. By so doing, we aim to support the user in achieving a more fluid and focused interaction while mixing, where the visual feedback supports and enhances the primary goal of attending to and modifying the audio content of the mix. In turn, it is hoped this will facilitate the artistic and creative approaches required by music computer users.

Based on these observations and analysis, we propose a joint discriminative probabilistic framework to optimize all relevant subtasks simultaneously. This framework defines a joint probability distribution for both segmentations in sequence data and relations of segments in the form of an exponential family. This model allows tight interactions between segmentations and relations of segments and it offers a natural way for IE tasks. Since exact parameter estimation and inference are prohibitively intractable, a structured variational inference algorithm is developed to perform parameter estimation approximately. For inference, we propose a strong bi-directional MH approach to find the MAP assignments for joint segmentations and relations to explore mutual benefits on both directions, such that segmentations can aid relations, and vice-versa. / Information Extraction (IE) aims at identifying specific pieces of information (data) in a unstructured or semi-structured textual document and transforming unstructured information in a corpus of documents or Web pages into a structured database. There are several representative tasks in IE: named entity recognition (NER), which aims at identifying phrases that denote types of named entities, entity relation extraction, which aims at discovering the events or relations related to the entities, and the task of coreference resolution, aims at determining whether two extracted mentions of entities refer to the same object. IE is useful for a wide variety of applications. / The end-to-end performance of high-level IE systems for compound tasks is often hampered by the use of cascaded frameworks. The integrated model we proposed can alleviate some of these problems, but it is only loosely coupled. Parameter estimation is performed independently and it only allows information to flow in one direction. In this top-down integration model, the decision of the bottom sub-model could guide the decision of the upper sub-model, but not vice-versa. Thus, deep interactions and dependencies between different tasks can hardly be well captured. / We have investigated and developed a cascaded framework in an attempt to consider entity extraction and qualitative domain knowledge based on undirected, discriminatively-trained probabilistic graphical models. This framework consists of two stages and it is the combination of statistical learning and first-order logic. As a pipeline model, the first stage is a base model and the second stage is used to validate and correct the errors made in the base model. We incorporated domain knowledge that can be well formulated into first-order logic to extract entity candidates from the base model. We have applied this framework and achieved encouraging results in Chinese NER on the People's Daily corpus. / We perform extensive experiments on three important IE tasks using real-world datasets, namely Chinese NER, entity identification and relationship extraction from Wikipedia's encyclopedic articles, and citation matching, to test our proposed models, including the bidirectional model, the integrated model, and the joint model. Experimental results show that our models significantly outperform current state-of-the-art probabilistic models, such as decoupled and joint models, illustrating the feasibility and promise of our proposed approaches. (Abstract shortened by UMI.) / We present a general, strongly-coupled, and bidirectional architecture based on discriminatively trained factor graphs for information extraction, which consists of two components---segmentation and relation. First we introduce joint factors connecting variables of relevant subtasks to capture dependencies and interactions between them. We then propose a strong bidirectional Markov chain Monte Carlo (MCMC) sampling inference algorithm which allows information to flow in both directions to find the approximate maximum a posteriori (MAP) solution for all subtasks. Notably, our framework is considerably simpler to implement, and outperforms previous ones. / Yu, Xiaofeng. / Adviser: Zam Wai. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 109-123). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Uma interface com o usuário é composta por duas "vias" de comunicação, uma que vai do usuário até o sistema e outra que vai do sistema até o usuário. Cada uma destas "vias" possui um formalismo que define a comunicação associado. Neste trabalho, estes formalismos são descritos com uma gramática de atributos. Esta gramática foi expandida de forma a permitir a definição dos elementos que compõe a interface do usuário, e da estrutura que irá controlar a seqüência de execução das tarefas oferecidas pelos sistemas de aplicação. Ao longo do trabalho são discutidas algumas técnicas de descrição do formalismo de comunicação entre interface e sistema, são abordados os estilos de interação e apresentada as expansões aplicadas sobre gramáticas de atributos. Um exemplo auxilia a compreensão do uso da linguagem proposta, e um protótipo permite a validação das definições. / A user interface is composed by two "ways" of communication, one from the user to the system and the other linking the system to the user. Each of these "ways" has it's own mechanism. In this work, these mechanisms are described through an attribute grammar. This grammar has been expanded to allow the definition of the structure of the interface elements and the control of the execution of the tasks that the application system implements. Through this work, technics that describe the communication between the interface and the system, interaction styles and the extensions made on attribute grammar are discussed. An example is given to explain the use of the proposed mechanism and a prototype validates ideas discussed.

This thesis is concerned with two main areas: approximate inference in discrete graphical models, and random embeddings for dimensionality reduction and approximate inference in kernel methods. Approximate inference is a fundamental problem in machine learning and statistics, with strong connections to other domains such as theoretical computer science. At the same time, there has often been a gap between the success of many algorithms in this area in practice, and what can be explained by theory; thus, an important research effort is to bridge this gap. Random embeddings for dimensionality reduction and approximate inference have led to great improvements in scalability of a wide variety of methods in machine learning. In recent years, there has been much work on how the stochasticity introduced by these approaches can be better controlled, and what further computational improvements can be made. In the first part of this thesis, we study approximate inference algorithms for discrete graphical models. Firstly, we consider linear programming methods for approximate MAP inference, and develop our understanding of conditions for exactness of these approximations. Such guarantees of exactness are typically based on either structural restrictions on the underlying graph corresponding to the model (such as low treewidth), or restrictions on the types of potential functions that may be present in the model (such as log-supermodularity). We contribute two new classes of exactness guarantees: the first of these takes the form of particular hybrid restrictions on a combination of graph structure and potential types, whilst the second is given by excluding particular substructures from the underlying graph, via graph minor theory. We also study a particular family of transformation methods of graphical models, uprooting and rerooting, and their effect on approximate MAP and marginal inference methods. We prove new theoretical results on the behaviour of particular approximate inference methods under these transformations, in particular showing that the triplet relaxation of the marginal polytope is unique in being universally rooted. We also introduce a heuristic which quickly picks a rerooting, and demonstrate benefits empirically on models over several graph topologies. In the second part of this thesis, we study Monte Carlo methods for both linear dimensionality reduction and approximate inference in kernel machines. We prove the statistical benefit of coupling Monte Carlo samples to be almost-surely orthogonal in a variety of contexts, and study fast approximate methods of inducing this coupling. A surprising result is that these approximate methods can simultaneously offer improved statistical benefits, time complexity, and space complexity over i.i.d. Monte Carlo samples. We evaluate our methods on a variety of datasets, directly studying their effects on approximate kernel evaluation, as well as on downstream tasks such as Gaussian process regression.

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 2010

Orientador: Lucia Galvão de Albuquerque / Resumo: Registros de características quantitativas e informações genotípicas cole- tadas para cada animal são utilizados para identificar regiões do genoma associadas à variação fenotípica. No entanto, essas investigações são, geralmente, realizadas com base em testes estatísticos de correlação ou associação, que não implicam em causalidade. A fim de explorar amplamente essas informações, métodos poderosos de inferência causal foram desenvolvidos para estimar os efeitos causais entre as variáveis estudadas. Apesar do progresso significativo neste campo, inferir os efeitos causais entre variáveis aleatórias contínuas ainda é um desafio e poucos estudos têm explorado as relações causais em genética quantitativa e no melhoramento animal. Neste contexto, dois estudos foram realizados com os seguintes objetivos: 1) Buscar as relações causais entre as características de carcaça e qualidade de carne usando um modelo de equação estrutural (MEE), sob modelo linear misto em bovinos da raça Nelore, e 2) Reconstruir redes de genes-fenótipos e realizar análise de rede causal por meio da integração de dados fenotípicos, genotípicos e transcriptômicos em bovinos da raça Nelore. Para o primeiro estudo, um total de 4.479 animais com informação fenotípica para o peso da carcaça quente (PCQ), área de olho lombo (AOL), espessura de gordura subcutânea (EGS), força de cisalhamento (FC) e marmoreio (MAR) foram usados. Os animais foram genotipados usando os painéis BovineHD Bead- Chip e GeneSeek Genomic Pro... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Quantitative traits and genotypes information have been collected for each animal and used to identify genome regions related to phenotypes variation. However, these investigations are, usually, performed based on correlation or association statistical tests, which do not imply in causation. In order to fully explore these information, powerful causal inference methods have been developed to estimate causal effects among the variables under study. Despite significant progress in this field infer causal effect among random variables remains a challenge and some few studies have explored causal relationships in quantitative genetics and animal breeding. In this context, two studies were performed with the following objectives: 1) Search for the causal relationship among carcass yield and meat quality traits using a structural equation model (SEM), under linear mixed model context in Nelore cattle, and 2) Reconstruct gene-phenotype networks and perform causal network analysis through the integrating of phenotypic, genotypic, and transcriptomic data in Nelore cattle. For the first study, a total of 4,479 animals with phenotypic information for hot carcass weight (HCW), longissimus muscle area (LMA), backfat thickness (BF), Warner-Bratzler shear force (WBSF), and marbling score (MB) traits were used. Animals were genotyped using BovineHD BeadChip and GeneSeek Genomic Profiler Indicus HD - GGP75Ki. For causal inference using SEM a multistep procedure methodology was used as follow:... (Complete abstract click electronic access below) / Doutor

According to NHTSA Traffic Safety Facts [9], bicyclist deaths and injuries in 2013 are recorded as 732 and 48,000, respectively. In the State of Florida the safety of bicyclists is of particular concern as the bicycle fatality rates are nearly triple the national average. Further Florida ranks #1 on bicycle fatality rate in the nation for several years. To determine the cause of near-misses and crashes, a detailed study of bicyclist behavior and environmental conditions is needed. In a Florida Department of Transport (FDOT) funded project, USF CUTR has proposed naturalistic bicycle study based on ride data collected from 100 bicyclists for 3000 hrs. To this end, Bicycle Data Acquisition System (BDAS) is being researched and developed. The main objective of this thesis work is to design and implement low power video and audio subsystems of BDAS as specified by domain experts (USF CUTR researchers). This work also involves design of graphical user interface (Windows application) to visualize the data in a synchronized manner. Selection of appropriate hardware to capture and store data is critical as it should meet several criteria like low power consumption, low cost, and small form factor. Several Camera controllers were evaluated in terms of their performance and cost. The major challenges in this design are synchronization between collected data, storage of the video and sensor data, and design of low power embedded subsystems.