An entropic feature selection method in perspective of Turing formula

Health data are generally complex in type and small in sample size. Such domain-specific challenges make it difficult to capture information reliably and contribute further to the issue of generalization. To assist the analytics of healthcare datasets, we develop a feature selection method based on the concept of Coverage Adjusted Standardized Mutual Information (CASMI). The main advantages of the proposed method are: 1) it selects features more efficiently with the help of an improved entropy estimator, particularly when the sample size is small, and 2) it automatically learns the number of features to be selected based on the information from sample data. Additionally, the proposed method handles feature redundancy from the perspective of joint-distribution. The proposed method focuses on non-ordinal data, while it works with numerical data with an appropriate binning method. A simulation study comparing the proposed method to six widely cited feature selection methods shows that the proposed method performs better when measured by the Information Recovery Ratio, particularly when the sample size is small

Are there new models of computation? Reply to Wegner and Eberbach

Wegner and Eberbach[Weg04b] have argued that there are fundamental limitations to Turing Machines as a foundation of computability and that these can be overcome by so-called superTuring models such as interaction machines, the [pi]calculus and the $-calculus. In this paper we contest Weger and Eberbach claims

Information processing in biology

To survive, organisms must respond appropriately to a variety of challenges posed by a dynamic and uncertain environment. The mechanisms underlying such responses can in general be framed as input-output devices which map environment states (inputs) to associated responses (output. In this light, it is appealing to attempt to model these systems using information theory, a well developed mathematical framework to describe input-output systems. Under the information theoretical perspective, an organism’s behavior is fully characterized by the repertoire of its outputs under different environmental conditions. Due to natural selection, it is reasonable to assume this input-output mapping has been fine tuned in such a way as to maximize the organism’s fitness. If that is the case, it should be possible to abstract away the mechanistic implementation details and obtain the general principles that lead to fitness under a certain environment. These can then be used inferentially to both generate hypotheses about the underlying implementation as well as predict novel responses under external perturbations. In this work I use information theory to address the question of how biological systems generate complex outputs using relatively simple mechanisms in a robust manner. In particular, I will examine how communication and distributed processing can lead to emergent phenomena which allow collective systems to respond in a much richer way than a single organism could

Understanding Learning through the Lens of Dynamical Invariants

This paper proposes a novel perspective on learning, positing it as the pursuit of dynamical invariants -- data combinations that remain constant or exhibit minimal change over time as a system evolves. This concept is underpinned by both informational and physical principles, rooted in the inherent properties of these invariants. Firstly, their stability makes them ideal for memorization and integration into associative networks, forming the basis of our knowledge structures. Secondly, the predictability of these stable invariants makes them valuable sources of usable energy, quantifiable as kTln2 per bit of accurately predicted information. This energy can be harnessed to explore new transformations, rendering learning systems energetically autonomous and increasingly effective. Such systems are driven to continuously seek new data invariants as energy sources. The paper further explores several meta-architectures of autonomous, self-propelled learning agents that utilize predictable information patterns as a source of usable energy.Comment: 19 page

Aesthetics at its very limits: Art History meets cognition

The aim with this master thesis is to prove that prehistoric art is worth the Westerners attention, not the least the attention of art historians. I am interested in placing prehistoric art/cave art in the spotlight, by reminding readers about the stunning craftsmanship and timeless beauty these paintings convey. I will do this by participating in an on- going scientific discourse, which reflects the wide range of scientists participating in the mystery we are facing: who painted this and why? I am interested in how our species started creating images, and also how our ancestors, who had never seen a painting before, were able to paint beautiful murals. The challenge alone in converting three-dimensional motifs to two- dimensional images is impressive. In terms of brain development, such a skill proves that these early Homo sapiens had a fully developed parietal cortex, the part of the brain perceiving 3D, perspective etc. My approach differs substantially from what is common in art history, quite simply by the fact that there is no common agreement as to whether my material is classified as art or not, at least in a Western sense of the word art, and all theoretical ways to explore art derives from western philosophical Aesthetics. I therefore prefer the word artification, as Ellen Dissanayake codes it. I am particularly interested in art in the perspective of cognitive development because findings within this research area are claiming that aesthetic experiences arise from the same neurophysiological processes that comprise the rest of our cognitive-perceptual-emotional life.Master i KunsthistorieMAHF-KUNKUN35

The Quantum Adiabatic Algorithm applied to random optimization problems: the quantum spin glass perspective

Among various algorithms designed to exploit the specific properties of quantum computers with respect to classical ones, the quantum adiabatic algorithm is a versatile proposition to find the minimal value of an arbitrary cost function (ground state energy). Random optimization problems provide a natural testbed to compare its efficiency with that of classical algorithms. These problems correspond to mean field spin glasses that have been extensively studied in the classical case. This paper reviews recent analytical works that extended these studies to incorporate the effect of quantum fluctuations, and presents also some original results in this direction.Comment: 151 pages, 21 figure

From Decline of the West to Dawn of Day

This paper subjects Dan Brown’s most recent novel Origin to a philosophical reading. Origin is regarded as a literary window into contemporary technoscience, inviting us to explore its transformative momentum and disruptive impact, focusing on the cultural significance of artificial intelligence and computer science: on the way in which established world-views are challenged by the incessant wave of scientific discoveries made possible by super-computation. While initially focusing on the tension between science and religion, the novel’s attention gradually shifts to the increased dependence of human beings on smart technologies and artificial (or even “synthetic”) intelligence. Origin’s message, I will argue, reverberates with Oswald Spengler’s The Decline of the West, which aims to outline a morphology of world civilizations. Although the novel starts with a series of oppositions, most notably between religion and science, the eventual tendency is towards convergence, synthesis and sublation, exemplified by Sagrada Família as a monumental symptom of this transition. Three instances of convergence will be highlighted, namely the convergence between science and religion, between humanity and technology and between the natural sciences and the humanities