Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

An examination of the appropriateness of the contemporary strategic management models for the Greek small manufacturing enterprises

The starting point of the project was the observation that strategic management is absent in small businesses. The first objective of the project was to examine the reasons causing this situation in Greece, the second one, to examine the appropriateness of the contemporary models of strategic planning for the Greek S.M.E.s, and the third to examine the appropriateness of the alternative approaches to strategic management for the Greek S.M.E.s. The term appropriateness includes (a) the ability of managers to use the models and (b) the ability of the models to assist the managers. The results of the research indicate that none of the two above conditions exists, hence, it is suggested that the contemporary models of strategic management are inappropriate for the Greek S.M.E.s. Many previous research projects on the topic suggest that since the strategic decision making process in S.M.E.s is informal, the whole process is absent or ineffective. Current trends in S.M.E.s' strategic management do not consider the informality of the strategic decision making process as a kind of managerial illness, but as a managerial characteristic. The use of sophisticated data collection and analytical methods does not indicate successful strategic decisions, but it indicates the method large firms use to manage their strategy. According to the literature review, the S.M.E.s' managers avoid the use of the contemporary models of strategic management, because they do not have the knowledge, the resources or the time. Another thesis, expressed by some firms' specialists, suggests that small firms are different from large ones, hence their practice of strategic management should not follow the large firm's prototypes.

Effects of oxidative stress and neuroprotection in apoptosis in neuronal cell models

The PC12 and SH-SY5Y cell models have been proposed as potentially realistic models to investigate neuronal cell toxicity. The effects of oxidative stress (OS) caused by both H2O2 and Aβ on both cell models were assessed by several methods. Cell toxicity was quantitated by measuring cell viability using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) viability assay, an indicator of the integrity of the electron transfer chain (ETC), and cell morphology by fluorescence and video microscopy, both of which showed OS to cause decreased viability and changes in morphology. Levels of intracellular peroxide production, and changes in glutathione and carbonyl levels were also assessed, which showed OS to cause increases in intracellular peroxide production, glutathione and carbonyl levels. Differentiated SH-SY5y cells were also employed and observed to exhibit the greatest sensitivity to toxicity. The neurotrophic factor, nerve growth factor (NGF) was shown to cause protection against OS. Cells pre-treated with NGF showed higher viability after OS, generally less apoptotic morphology, recorded less apoptotic nucleiods, generally lower levels of intracellular peroxides and changes in gene expression. The neutrophic factor, brain derived growth factor (BDNF) and ascorbic acid (AA) were also investigated. BDNF showed no specific neuroprotection, however the preliminary data does warrant further investigation. AA showed a 'janus face' showing either anti-oxidant action and neuroprotection or pro-oxidant action depending on the situation. Results showed that the toxic effects of compounds such as Aβ and H2O2 are cell type dependent, and that OS alters glutathione metabolism in neuronal cells. Following toxic insult, glutathione levels are depleted to low levels. It is herein suggested that this lowering triggers an adaptive response causing alterations in glutathione metabolism as assessed by evaluation of glutathione mRNA biosynthetic enzyme expression and the subsequent increase in glutathione peroxidase (GPX) levels.

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.