Initial Condition and Behavior Patterns in Learning Dynamics: Study of Complexity and Sustainability from Time Series

Learning is an essential part of human life. In it, our sensory organs and neural networks participate and integrate emotional behaviors, indagative and persuasive abilities, along with the ability to selectively acquire information, to mention a fraction of the media used in learning, converge to it. This study presents the results of the observational monitoring of behaviors, displayed by teams of students in learning processes, their interactions, representing them as series of time. These time series contain the dynamics of learning: weak, average, and chaotic, differentiated by the control parameter (connectivity) that is increasing respectively. The exponents of Lyapunov, the entropy of Kolmogorov, the complexity, the loss of information for each series, and the projection horizon of the processes are calculated for each series. The results, approximate, show that the chaotic dynamics propitiate the learning, given that there is an increase of connectivity within the teams breaking patterns or behavioral stereotypes. The entropic character of connectivity allows estimating the complexity of this human activity, exposing its sustainability, which brings irreversible conflicts with nature, given that the universe of nonequilibrium is a connected universe. Finally, the analysis model developed is historically contextualized, in first approximation, in some ancient civilizations

Aesthetic complexity: practice and perception in art & design

My research investigates the aesthetics of visual complexity in the practice and perception of visual art and design. The aim is to understand visual complexity in terms of the relationship between the objective properties of images and subjective properties of perception. I take a computational and empirical approach to this subject, incorporating methods from information theory, computer graphics, complexity theory and experimental psychology. For testing, I create cellular automata programs to generate stimulus images, and borrow other types of visual material from students and professional artists, designers and craftspeople. Visual complexity is measured in two ways: Firstly, an objective measure of complexity is based on the compression of digital image files, which provides an information-based scale of order to randomness. Secondly, psychophysical techniques are employed to measure the subjective complexity of the images and other aesthetic judgements. Research in complex systems theory and experimental aesthetics suggests that we can expect an inverted ‘U’ correlation between the two measures of complexity

Human experience in the natural and built environment : implications for research policy and practice

22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3

Blomlaok : an inquery into audio-visual performance

Master's thesis Fine Arts KF500 - University of Agder 201

Doppler Radar Techniques for Distinct Respiratory Pattern Recognition and Subject Identification.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Ethnomathematical research and drama in education techniques: developing a dialogue in a geometry class of 10th grade students

Ethnomathematical research, together with digital technologies (WebQuest) and Drama-in- Education (DiE) techniques, can create a fruitful learning environment in a mathematics classroom—a hybrid/third space—enabling increased student participation and higher levels of cognitive engagement. This article examines how ethnomathematical ideas processed within the experiential environment established by the Drama-in-Education techniques challenged students‘ conceptions of the nature of mathematics, the ways in which students engaged with mathematics learning using mind and body, and the ̳dialogue‘ that was developed between the Discourse situated in a particular practice and the classroom Discourse of mathematics teaching. The analysis focuses on an interdisciplinary project based on an ethnomathematical study of a designing tradition carried out by the researchers themselves, involving a search for informal mathematics and the connections with context and culture; 10th grade students in a public school in Athens were introduced to the mathematics content via an original WebQuest based on this previous ethnomathematical study; Geometry content was further introduced and mediated using the Drama-in-Education (DiE) techniques. Students contributed in an unfolding dialogue between formal and informal knowledge, renegotiating both mathematical concepts and their perception of mathematics as a discipline

Organic electrochemical networks for biocompatible and implantable machine learning: Organic bioelectronic beyond sensing

How can the brain be such a good computer? Part of the answer lies in the astonishing number of neurons and synapses that process electrical impulses in parallel. Part of it must be found in the ability of the nervous system to evolve in response to external stimuli and grow, sharpen, and depress synaptic connections. However, we are far from understanding even the basic mechanisms that allow us to think, be aware, recognize patterns, and imagine. The brain can do all this while consuming only around 20 Watts, out-competing any human-made processor in terms of energy-efficiency. This question is of particular interest in a historical era and technological stage where phrases like machine learning and artificial intelligence are more and more widespread, thanks to recent advances produced in the field of computer science. However, brain-inspired computation is today still relying on algorithms that run on traditional silicon-made, digital processors. Instead, the making of brain-like hardware, where the substrate itself can be used for computation and it can dynamically update its electrical pathways, is still challenging. In this work, I tried to employ organic semiconductors that work in electrolytic solutions, called organic mixed ionic-electronic conductors (OMIECs) to build hardware capable of computation. Moreover, by exploiting an electropolymerization technique, I could form conducting connections in response to electrical spikes, in analogy to how synapses evolve when the neuron fires. After demonstrating artificial synapses as a potential building block for neuromorphic chips, I shifted my attention to the implementation of such synapses in fully operational networks. In doing so, I borrowed the mathematical framework of a machine learning approach known as reservoir computing, which allows computation with random (neural) networks. I capitalized my work on demonstrating the possibility of using such networks in-vivo for the recognition and classification of dangerous and healthy heartbeats. This is the first demonstration of machine learning carried out in a biological environment with a biocompatible substrate. The implications of this technology are straightforward: a constant monitoring of biological signals and fluids accompanied by an active recognition of the presence of malign patterns may lead to a timely, targeted and early diagnosis of potentially mortal conditions. Finally, in the attempt to simulate the random neural networks, I faced difficulties in the modeling of the devices with the state-of-the-art approach. Therefore, I tried to explore a new way to describe OMIECs and OMIECs-based devices, starting from thermodynamic axioms. The results of this model shine a light on the mechanism behind the operation of the organic electrochemical transistors, revealing the importance of the entropy of mixing and suggesting new pathways for device optimization for targeted applications

Determining Criteria for Distinguishing States of Consciousness

Even though there are many views on consciousness theory in the pertinent literature, there remains a need for a unifying framework for specifying the features of specific states of consciousness. In order to know what kinds of experiences conscious states have in common, researchers need to elicit testimony that is more direct and finer-grained than has been previously available. This dissertation endeavors to fill a gap in current research by addressing concepts and methods for making requisite distinctions. This research illuminates the question of whether specific states of consciousness can be reliably and validly distinguished from each other. In order to do this, 41 individuals, who had experienced significant peak or ecstatic states from a variety of induction methods (most prominently by ingestion of psychedelic substances), were invited to be interviewed. The interview was designed as a conversational-type synthesis of 5 well-known questionnaires pertinent to states of consciousness, but without their explicit and implicit assumptions; that is, the volunteers\u27 responses would not conform to predetermined questions. Encoding their responses allowed me to develop a model that helped to answer the research question ( Are there identifiable features that can reliably and validly distinguish among states of consciousness thought to be distinct from each other? ) by formulating a model in which any given conscious state can be catalogued in terms of its component factors (background, resistances, setting, induction, tradition, energies, and breakthrough events). The results of this study provide much-needed insights into people\u27s internal experiences of their various states, thus forming a basis for improved treatments and analyses. Better understanding of these states can be an impetus for social change by allowing for more incisive analyses and treatments, and also enabling more understanding of other people\u27s inner perspectives

Colour and Naming in Healthy and Aphasic People

Abstract The purpose of this study was to create a paradigm suitable for people with aphasia and healthy subjects to evaluate the influence of colour on naming pictures of objects. We designed a completely new stimulus set based on images of 140 common real objects that were inspired by the Snodgrass and Vanderwart picture set (1980). We were especially interested whether there is a difference in performance between the aphasic patients and the group of healthy controls. Adding chromatic information to pictures of objects shows only a small effect in verification and categorisation tasks. However, when observers are required to name objects, colour speeds performance and enhances accuracy (Rossion & Pourtois, 2004). The present study contrasts two different claims as to why colour may benefit object naming. The first is that colour simply aids the segmentation of the object from its background (Wichmann et al., 2002). The second is that colour may help to elicit a wider range of associations with the object, thereby enhancing lexical access (Bisiach, 1966). To distinguish between these processes an equal number of pictures containing high and low colour diagnostic objects were presented against either fractal noise or uniform backgrounds in a naming task to aphasic subjects with anomia and to healthy controls. Performance for chromatic stimuli was compared with that for monochrome stimuli equated in luminance. Results show that colour facilitates naming significantly in both subject groups and there was no significant difference between objects with high or low colour diagnostic values. We also found that object segmentation and the lexical access seem to occur in parallel processes, rather than in an additive way

Spatial models of metapopulations and benthic communities in patchy environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2000The distribution of organisms in space has important consequences for the function and structure of ecological systems. Such distributions are often referred to as patchy, and a patch-based approach to modeling ecosystem dynamics has become a major research focus. These models have been used to explore a wide range of questions concerning population, metapopulation, community, and landscape ecology, in both terrestrial and aquatic systems. In this dissertation I develop and analyze a series of spatial models to study the dynamics of metapopulations and marine benthic communities in patchy environments. All the models have the form of a discrete-time Markov chain, and assume that the landscape is composed of discrete patches, each of which is in one of a number of possible states. The state of a patch is determined by the presence of an individual of a given species, a local population, or a group of species, depending on the spatial scale of the model. The research is organized into two main parts as follows. In the first part, I present an analysis of the effects of habitat destruction on metapopulation persistence. Theoretical studies have already shown that a metapopulation goes extinct when the fraction of suitable patches in the landscape falls below a critical threshold (the so called extinction threshold). This result has become a paradigm in conservation biology and several models have been developed to calculate extinction thresholds for endangered species. These models, however, generally do not take into account the spatial arrangement of habitat destruction, or the actual size of the landscape. To investigate how the spatial structure of habitat destruction affects persistence, I compare the behavior of two models: a spatially implicit patch-occupancy model (which recreates the extinction patterns found in other models) and a spatially explicit cellular automaton (CA) model. In the CA, I use fractal arrangements of suitable and unsuitable patches to simulate habitat destruction and show that the extinction threshold depends on the fractal dimension of the landscape. To investigate how habitat destruction affects persistence in finite landscapes , I develop and analyze a chain-binomial metapopulation (CBM) model. This model predicts the expected extinction time of a metapopulation as a function of the number of patches in the landscape and the number of those patches that are suitable for the population. The CBM model shows that the expected time to extinction decreases greater than exponentially as suitable patches are destroyed. I also describe a statistical method for estimating parameters for the CBM model in order to evaluate metapopulation viability in real landscapes. In the second part, I develop and analyze a series of Markov chain models for a rocky subtidal community in the Gulf of Maine. Data for the model comes from ten permanent quadrats (located on Ammen Rock Pinnacle at 30 meters depth) monitored over an 8-year period (1986-1994). I first parameterize a linear (homogenous) Markov chain model from the data set and analyze it using an array of novel techniques, including a compression algorithm to classify species into functional groups, a set of measures from stochastic process theory to characterize successional patterns, sensitivity analyses to predict how changes in various ecological processes effect community composition, and a method for simulating species removal to identify keystone species. I then explore the effects of time and space on successional patterns using log-linear analysis, and show that transition probabilities vary significantly across small spatial scales and over yearly time intervals. I examine the implications of these findings for predicting equilibrium species abundances and for characterizing the transient dynamics of the community. Finally, I develop a nonlinear Markov chain for the rocky subtidal community. The model is parameterized using maximum likelihood methods to estimate density-dependent transition probabilities. I analyze the best fitting models to study the effects of nonlinear species interactions on community dynamics, and to identify multiple stable states in the subtidal system.This work was supported by the Office of Naval Research and the National Science Foundation through the following grants to Hal Caswell: ONR-URIP Grant NOOOl492- J-1527, NSF Grants DEB-9119420, DEB-95-27400, OCE-981267 and OCE-9302238