Pattern recognition in narrative: Tracking emotional expression in context

Using geometric data analysis, our objective is the analysis of narrative, with narrative of emotion being the focus in this work. The following two principles for analysis of emotion inform our work. Firstly, emotion is revealed not as a quality in its own right but rather through interaction. We study the 2-way relationship of Ilsa and Rick in the movie Casablanca, and the 3-way relationship of Emma, Charles and Rodolphe in the novel {\em Madame Bovary}. Secondly, emotion, that is expression of states of mind of subjects, is formed and evolves within the narrative that expresses external events and (personal, social, physical) context. In addition to the analysis methodology with key aspects that are innovative, the input data used is crucial. We use, firstly, dialogue, and secondly, broad and general description that incorporates dialogue. In a follow-on study, we apply our unsupervised narrative mapping to data streams with very low emotional expression. We map the narrative of Twitter streams. Thus we demonstrate map analysis of general narratives

Financial Ratios, Size, Industry and Interest Rate Issues in Company Failure: An Extended Multidimensional Scaling Analysis

Three-way multidimensional scaling methods are used to study the differences between UK failed and continuing companies from 1993 to 2001. The technique allows for visual representations of the results, so that qualitative information can be brought to bear when judging the health of a company. It is shown that it is important to take into account company size and area of activity. Results also suggest that the ratio structure of the companies varies between years in response to changes in the interest rates, suggesting that the frontier between failing and continuing firms moves in response to the economic cycle

A PLS PATH MODEL TO INVESTIGATE THE RELATIONS BETWEEN INSTITUTIONS AND HUMAN DEVELOPMENT

The paper studies the relations between types of institutions on different components of human development. A role of aggregate demand in determining the material components of human development is assumed. We thus divide institutions into those that create demand and those that are determined by the whole process of development. Similarly we divide human development in its three traditional components (economic development, health, knowledge). Both human development and institutions are assumed as multidimensional constructs; all the main components of these constructs are defined as latent variables, and the relations between them as structural relations. A Partial Least Squares (PLS) path model is developed: it is the aggregation (and simultaneous estimation) of an outer model relating observed or manifest variables to their own latent variable and of a structural model (inner model) relating some endogenous latent variable to other latent variables. From the goodness of fit point of view, our results seem to validate our theoretical assumptions.Structural Equations Models, Institutions, Human Development

Bayesian Nonparametric Learning of Cloth Models for Real-time State Estimation

Robotic solutions to clothing assistance can significantly improve quality of life for the elderly and disabled. Real-time estimation of the human-cloth relationship is crucial for efficient learning of motor skills for robotic clothing assistance. The major challenge involved is cloth-state estimation due to inherent nonrigidity and occlusion. In this study, we present a novel framework for real-time estimation of the cloth state using a low-cost depth sensor, making it suitable for a feasible social implementation. The framework relies on the hypothesis that clothing articles are constrained to a low-dimensional latent manifold during clothing tasks. We propose the use of manifold relevance determination (MRD) to learn an offline cloth model that can be used to perform informed cloth-state estimation in real time. The cloth model is trained using observations from a motion capture system and depth sensor. MRD provides a principled probabilistic framework for inferring the accurate motion-capture state when only the noisy depth sensor feature state is available in real time. The experimental results demonstrate that our framework is capable of learning consistent task-specific latent features using few data samples and has the ability to generalize to unseen environmental settings. We further present several factors that affect the predictive performance of the learned cloth-state model