Spurious, Emergent Laws in Number Worlds

We study some aspects of the emergence of logos from chaos on a basal model of the universe using methods and techniques from algorithmic information and Ramsey theories. Thereby an intrinsic and unusual mixture of meaningful and spurious, emerging laws surfaces. The spurious, emergent laws abound, they can be found almost everywhere. In accord with the ancient Greek theogony one could say that logos, the Gods and the laws of the universe, originate from "the void," or from chaos, a picture which supports the unresolvable/irreducible lawless hypothesis. The analysis presented in this paper suggests that the "laws" discovered in science correspond merely to syntactical correlations, are local and not universal.Comment: 24 pages, invited contribution to "Contemporary Natural Philosophy and Philosophies - Part 2" - Special Issue of the journal Philosophie

Metasemantics and fuzzy mathematics

The present thesis is an inquiry into the metasemantics of natural languages, with a particular focus on the philosophical motivations for countenancing degreed formal frameworks for both psychosemantics and truth-conditional semantics. Chapter 1 sets out to offer a bird's eye view of our overall research project and the key questions that we set out to address. Chapter 2 provides a self-contained overview of the main empirical findings in the cognitive science of concepts and categorisation. This scientific background is offered in light of the fact that most variants of psychologically-informed semantics see our network of concepts as providing the raw materials on which lexical and sentential meanings supervene. Consequently, the metaphysical study of internalistically-construed meanings and the empirical study of our mental categories are overlapping research projects. Chapter 3 closely investigates a selection of species of conceptual semantics, together with reasons for adopting or disavowing them. We note that our ultimate aim is not to defend these perspectives on the study of meaning, but to argue that the project of making them formally precise naturally invites the adoption of degreed mathematical frameworks (e.g. probabilistic or fuzzy). In Chapter 4, we switch to the orthodox framework of truth-conditional semantics, and we present the limitations of a philosophical position that we call "classicism about vagueness". In the process, we come up with an empirical hypothesis for the psychological pull of the inductive soritical premiss and we make an original objection against the epistemicist position, based on computability theory. Chapter 5 makes a different case for the adoption of degreed semantic frameworks, based on their (quasi-)superior treatments of the paradoxes of vagueness. Hence, the adoption of tools that allow for graded membership are well-motivated under both semantic internalism and semantic externalism. At the end of this chapter, we defend an unexplored view of vagueness that we call "practical fuzzicism". Chapter 6, viz. the final chapter, is a metamathematical enquiry into both the fuzzy model-theoretic semantics and the fuzzy Davidsonian semantics for formal languages of type-free truth in which precise truth-predications can be expressed

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

Featured Application: Selection of most diverse light spectra from a larger set of possible candidates to be used in subject studies or for machine learning to find correlations between photometric and other parameters such as psychological, physiological, or preference-based outcome measures. Abstract: Current subject studies and data-driven approaches in lighting research often use manually selected light spectra, which usually exhibit a large bias due to the applied selection criteria. This paper, therefore, presents a novel approach to minimize this bias by using a data-driven framework for selecting the most diverse candidates from a given larger set of possible light spectra. The spectral information per wavelength is first reduced by applying a convolutional autoencoder. The relevant features are then selected based on Laplacian Scores and transformed to a two-dimensional embedded space for subsequent clustering. The low dimensional embedding, from which the required diversity follows, is done with respect to the locality of the features. In a second step, photometric parameters are considered and a second clustering is performed. As a result of this algorithmic pipeline, the most diverse selection of light spectra complying with a given set of relevant photometric parameters can be extracted and used for further experiments or applications

Investigating Human Interaction through Mathematical Analysis

Investigating Human Interaction through Mathematical Analysis offers a new and unique approach to social intragroup interaction by using mathematics and psychophysics to create a mathematical model based on social psychological theories. It draws on the work of Dr. Stanley Milgram, Dr. Bibb Latane, and Dr. Bernd Schmitt to develop an algebraic expression and applies it to quantitatively model and explain various independent social psychology experiments taken from refereed journals involving basic social systems with underlying queue-like structures. It is then argued that the social queue as a resource system, containing common-pool resources, meets the eight design principles necessary to support stability within the queue. Making this link provides a means to advance to more complex social systems. It is envisioned that if basic social systems as presented can be modeled, then, with further development, more complex social systems may eventually be modeled for the purpose of identifying and validating social structures that might eventually support stable governments in our common environment called Earth. This is a fascinating reading for academics and advanced students interested in political theory, detection theory, social psychology, organizational behavior, psychophysics, and applied mathematics in the social and information sciences

Deep learning and localized features fusion for medical image classification

Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible. This dissertation tries to tackle these problems, first by developing a localized feature-based classification system for medical images and using these features and to give a classification for the entire image, and second, by improving the computational complexity of feature analysis to make it viable as a diagnostic aid system in practical clinical situations. For local feature development, a new approach based on combining the rising deep learning paradigm with the use of handcrafted features is developed to classify cervical tissue histology images into different cervical intra-epithelial neoplasia classes. Using deep learning combined with handcrafted features improved the accuracy by 8.4% achieving 80.72% exact class classification accuracy compared to 72.29% when using the benchmark feature-based classification method --Abstract, page iv

Fuzzy Inference Systems for Risk Appraisal in Military Operational Planning

Advances in computing and mathematical techniques have given rise to increasingly complex models employed in the management of risk across numerous disciplines. While current military doctrine embraces sound practices for identifying, communicating, and mitigating risk, the complex nature of modern operational environments prevents the enumeration of risk factors and consequences necessary to leverage anything beyond rudimentary risk models. Efforts to model military operational risk in quantitative terms are stymied by the interaction of incomplete, inadequate, and unreliable knowledge. Specifically, it is evident that joint and inter-Service literature on risk are inconsistent, ill-defined, and prescribe imprecise approaches to codifying risk. Notably, the near-ubiquitous use of risk matrices (along with other qualitative methods), are demonstrably problematic at best, and downright harmful at worst, due to misunderstanding and misapplication of their quantitative implications. The use of fuzzy set theory is proposed to overcome the pervasive ambiguity of risk modeling encountered by today’s operational planners. Fuzzy logic is adept at addressing the problems caused by imperfect and imprecise knowledge, entangled causal relationships, and the linguistic input of expert opinion. To this end, a fuzzy inference system is constructed for the purpose of risk appraisal in military operational planning