Max Stirner's relevance to contemporary philosophy: a critical analysis

The aim of this dissertation is to revive the 19th-century thinker Max Stirner’s thought through a critical reexamination of his mistaken legacy as a ‘political’ thinker. The reading of Stirner that I present is one of an ontological thinker, spurred on as much—if not more—by the contents of Hegel’s Phenomenology of Spirit as it is the radical roots that Hegel unintentionally planted. In the first chapter, the role of language in Stirner’s thought is examined, and the problems to which his conception of language seem to give rise are addressed. The second chapter looks at Stirner’s purportedly ‘anarchistic’ politics and finds the ‘anarchist’ reading of Stirner misguided. Rather than being a ‘political’ anarchist, it is argued that we ought to understand Stirner as advocating a sort of ‘ontological’ anarchism in which the very existence of authority is questioned. In the third chapter, I look at the political ramifications of Stirner’s ontology as well as the critique of liberalism contained within it, and argue that the politics implicit in his philosophy shares more in common with the tradition of political realism than it does anarchism. The fourth chapter is dedicated to an examination of Stirner’s anti-humanism, which is concluded to be much different than the ‘anti-humanisms’ associated with other, more famous thinkers, such as Foucault and Heidegger. In the fifth and final chapter, I provide an answer to the question(s) of how, if, and to what extent Friedrich Nietzsche was influenced by Stirner. It is concluded that the complete lack of evidence that Nietzsche ever read Stirner is proof enough to dismiss accusations of plagiarism on Nietzsche’s part, thus emphasizing the originality and singularity of both thinkers.

Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.