4,208 research outputs found
Local Causal States and Discrete Coherent Structures
Coherent structures form spontaneously in nonlinear spatiotemporal systems
and are found at all spatial scales in natural phenomena from laboratory
hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary
climate dynamics. Phenomenologically, they appear as key components that
organize the macroscopic behaviors in such systems. Despite a century of
effort, they have eluded rigorous analysis and empirical prediction, with
progress being made only recently. As a step in this, we present a formal
theory of coherent structures in fully-discrete dynamical field theories. It
builds on the notion of structure introduced by computational mechanics,
generalizing it to a local spatiotemporal setting. The analysis' main tool
employs the \localstates, which are used to uncover a system's hidden
spatiotemporal symmetries and which identify coherent structures as
spatially-localized deviations from those symmetries. The approach is
behavior-driven in the sense that it does not rely on directly analyzing
spatiotemporal equations of motion, rather it considers only the spatiotemporal
fields a system generates. As such, it offers an unsupervised approach to
discover and describe coherent structures. We illustrate the approach by
analyzing coherent structures generated by elementary cellular automata,
comparing the results with an earlier, dynamic-invariant-set approach that
decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht
Computation in Finitary Stochastic and Quantum Processes
We introduce stochastic and quantum finite-state transducers as
computation-theoretic models of classical stochastic and quantum finitary
processes. Formal process languages, representing the distribution over a
process's behaviors, are recognized and generated by suitable specializations.
We characterize and compare deterministic and nondeterministic versions,
summarizing their relative computational power in a hierarchy of finitary
process languages. Quantum finite-state transducers and generators are a first
step toward a computation-theoretic analysis of individual, repeatedly measured
quantum dynamical systems. They are explored via several physical systems,
including an iterated beam splitter, an atom in a magnetic field, and atoms in
an ion trap--a special case of which implements the Deutsch quantum algorithm.
We show that these systems' behaviors, and so their information processing
capacity, depends sensitively on the measurement protocol.Comment: 25 pages, 16 figures, 1 table; http://cse.ucdavis.edu/~cmg; numerous
corrections and update
Density Classification Quality of the Traffic-majority Rules
The density classification task is a famous problem in the theory of cellular
automata. It is unsolvable for deterministic automata, but recently solutions
for stochastic cellular automata have been found. One of them is a set of
stochastic transition rules depending on a parameter , the
traffic-majority rules.
Here I derive a simplified model for these cellular automata. It is valid for
a subset of the initial configurations and uses random walks and generating
functions. I compare its prediction with computer simulations and show that it
expresses recognition quality and time correctly for a large range of
values.Comment: 40 pages, 9 figures. Accepted by the Journal of Cellular Automata.
(Some typos corrected; the numbers for theorems, lemmas and definitions have
changed with respect to version 1.
Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems
Most current methods for identifying coherent structures in
spatially-extended systems rely on prior information about the form which those
structures take. Here we present two new approaches to automatically filter the
changing configurations of spatial dynamical systems and extract coherent
structures. One, local sensitivity filtering, is a modification of the local
Lyapunov exponent approach suitable to cellular automata and other discrete
spatial systems. The other, local statistical complexity filtering, calculates
the amount of information needed for optimal prediction of the system's
behavior in the vicinity of a given point. By examining the changing
spatiotemporal distributions of these quantities, we can find the coherent
structures in a variety of pattern-forming cellular automata, without needing
to guess or postulate the form of that structure. We apply both filters to
elementary and cyclical cellular automata (ECA and CCA) and find that they
readily identify particles, domains and other more complicated structures. We
compare the results from ECA with earlier ones based upon the theory of formal
languages, and the results from CCA with a more traditional approach based on
an order parameter and free energy. While sensitivity and statistical
complexity are equally adept at uncovering structure, they are based on
different system properties (dynamical and probabilistic, respectively), and
provide complementary information.Comment: 16 pages, 21 figures. Figures considerably compressed to fit arxiv
requirements; write first author for higher-resolution version
From Models to Simulations
This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s.
Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations
Multiple verification in computational modeling of bone pathologies
We introduce a model checking approach to diagnose the emerging of bone
pathologies. The implementation of a new model of bone remodeling in PRISM has
led to an interesting characterization of osteoporosis as a defective bone
remodeling dynamics with respect to other bone pathologies. Our approach allows
to derive three types of model checking-based diagnostic estimators. The first
diagnostic measure focuses on the level of bone mineral density, which is
currently used in medical practice. In addition, we have introduced a novel
diagnostic estimator which uses the full patient clinical record, here
simulated using the modeling framework. This estimator detects rapid (months)
negative changes in bone mineral density. Independently of the actual bone
mineral density, when the decrease occurs rapidly it is important to alarm the
patient and monitor him/her more closely to detect insurgence of other bone
co-morbidities. A third estimator takes into account the variance of the bone
density, which could address the investigation of metabolic syndromes, diabetes
and cancer. Our implementation could make use of different logical combinations
of these statistical estimators and could incorporate other biomarkers for
other systemic co-morbidities (for example diabetes and thalassemia). We are
delighted to report that the combination of stochastic modeling with formal
methods motivate new diagnostic framework for complex pathologies. In
particular our approach takes into consideration important properties of
biosystems such as multiscale and self-adaptiveness. The multi-diagnosis could
be further expanded, inching towards the complexity of human diseases. Finally,
we briefly introduce self-adaptiveness in formal methods which is a key
property in the regulative mechanisms of biological systems and well known in
other mathematical and engineering areas.Comment: In Proceedings CompMod 2011, arXiv:1109.104
L’INTELLECT INCARNÉ: Sur les interprétations computationnelles, évolutives et philosophiques de la connaissance
Modern cognitive science cannot be understood without recent developments in computer science, artificial intelligence (AI), robotics, neuroscience, biology, linguistics, and psychology. Classic analytic philosophy as well as traditional AI assumed that all kinds of knowledge must eplicitly be represented by formal or programming languages. This assumption is in contradiction to recent insights into the biology of evolution and developmental psychology of the human organism. Most of our knowledge is implicit and unconscious. It is not formally represented, but embodied knowledge which is learnt by doing and understood by bodily interacting with ecological niches and social environments. That is true not only for low-level skills, but even for high-level domains of categorization, language, and abstract thinking. Embodied cognitive science, AI, and robotics try to build the embodied mind in an artificial evolution. From a philosophical point of view, it is amazing that the new ideas of embodied mind and robotics have deep roots in 20th-century philosophy.Die moderne Kognitionswissenschaft kann nicht verstanden werden ohne Einbeziehung der neuesten Errungenschaften aus der Computerwissenschaft, künstlichen Intelligenz (AI), Robotik, Neurowissenschaft, Biologie, Linguistik und Psychologie. Die klassische analytische Philosophie, wie auch die traditionelle AI, setzten voraus, dass alle Arten des Wissens explizit durch formale oder Programmsprachen dargestellt werden müssen. Diese Annahme steht im Widerspruch zu den rezenten Einsichten in die Evolutionsbiologie und Entwicklungspsychologie des menschlichen Organismus. Der größte Teil unseres Wissens ist implizit und unbewusst. Es ist kein formal repräsentiertes, sondern ein verkörpertes Wissen, das durch Handeln gelernt und durch körperliche Interaktion mit ökologischen Nischen und gesellschaftlichen Umgebungen verstanden wird. Dies gilt nicht nur für niedere Fertigkeiten, sondern auch für höher gestellte Domänen: Kategorisierung, Sprache und abstraktes Denken. Die verkörperte Erkenntniswissenschaft, AI und Robotik versuchen, den verkörperten Geist in einer artifiziellen Evolution zu bilden. Vom philosophischen Standpunkt gesehen ist es erstaunlich, wie tief die neuen Ideen des verkörperten Geistes und der Robotik in der Philosophie des 20. Jahrhunderts verankert sind.La science cognitive moderne ne peut être comprise sans les progrès récents en informatique, intelligence artificielle, robotique, neuroscience, biologie, linguistique et psychologie. La philosophie analytique classique et l’intelligence artificielle traditionnelle présumaient que toutes les sortes de savoir devaient être représentées explicitement par des langages formels ou programmatiques. Cette thèse est en contradiction avec les découvertes récentes en biologie de l’évolution et en psychologie évolutive de l’organisme humain. La majeure partie de notre savoir est implicite et inconsciente. Elle n’est pas représentée formellement, mais constitue un savoir incarné, qui s’acquiert par l’action et se comprend en interaction corporelle avec nos niches écologiques et nos environnements sociaux. Cela n’est pas seulement vrai pour nos aptitudes élémentaires, mais aussi pour nos facultés supérieures de catégorisation, de langage et de pensée abstraite. Science cognitive incarnée, l’intelligence artificielle, ainsi que la robotique, tentent de construire un intellect incarné en évolution artificielle. Du point de vue philosophique, il est admirable de voir à quel point les nouvelles idées d’intellect incarné et de robotique sont ancrées dans la philosophie du XXe siècle
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