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Aspects of emergent cyclicity in language and computation
This thesis has four parts, which correspond to the presentation and development of a theoretical
framework for the study of cognitive capacities qua physical phenomena, and a case study of locality conditions over natural languages.
Part I deals with computational considerations, setting the tone of the rest of the thesis, and introducing and defining critical concepts like âgrammarâ, âautomatonâ, and the relations between them
. Fundamental questions concerning the place of formal language theory in
linguistic inquiry, as well as the expressibility of linguistic and computational concepts in
common terms, are raised in this part.
Part II further explores the issues addressed in Part I with particular emphasis on how
grammars are implemented by means of automata, and the properties of the formal languages
that these automata generate. We will argue against the equation between effective computation
and function-based computation, and introduce examples of computable procedures which are
nevertheless impossible to capture using traditional function-based theories. The connection
with cognition will be made in the light of dynamical frustrations: the irreconciliable tension
between mutually incompatible tendencies that hold for a given dynamical system. We will
provide arguments in favour of analyzing natural language as emerging from a tension between
different systems (essentially, semantics and morpho-phonology) which impose orthogonal
requirements over admissible outputs. The concept of level of organization or scale comes to
the foreground here; and apparent contradictions and incommensurabilities between concepts
and theories are revisited in a new light: that of dynamical nonlinear systems which are
fundamentally frustrated. We will also characterize the computational system that emerges from
such an architecture: the goal is to get a syntactic component which assigns the simplest
possible structural description to sub-strings, in terms of its computational complexity. A
system which can oscillate back and forth in the hierarchy of formal languages in assigning
structural representations to local domains will be referred to as a computationally mixed
system.
Part III is where the really fun stuff starts. Field theory is introduced, and its applicability to
neurocognitive phenomena is made explicit, with all due scale considerations. Physical and
mathematical concepts are permanently interacting as we analyze phrase structure in terms of
pseudo-fractals (in Mandelbrotâs sense) and define syntax as a (possibly unary) set of
topological operations over completely Hausdorff (CH) ultrametric spaces. These operations, which makes field perturbations interfere, transform that initial completely Hausdorff
ultrametric space into a metric, Hausdorff space with a weaker separation axiom. Syntax, in this
proposal, is not âgenerativeâ in any traditional sense âexcept the âfully explicit theoryâ one-:
rather, it partitions (technically, âparametrizesâ) a topological space. Syntactic dependencies are
defined as interferences between perturbations over a field, which reduce the total entropy of
the system per cycles, at the cost of introducing further dimensions where attractors
corresponding to interpretations for a phrase marker can be found.
Part IV is a sample of what we can gain by further pursuing the physics of language approach,
both in terms of empirical adequacy and theoretical elegance, not to mention the unlimited
possibilities of interdisciplinary collaboration. In this section we set our focus on island
phenomena as defined by Ross (1967), critically revisiting the most relevant literature on this
topic, and establishing a typology of constructions that are strong islands, which cannot be
violated. These constructions are particularly interesting because they limit the phase space of
what is expressible via natural language, and thus reveal crucial aspects of its underlying
dynamics. We will argue that a dynamically frustrated system which is characterized by
displaying mixed computational dependencies can provide straightforward characterizations of
cyclicity in terms of changes in dependencies in local domains
Investing in biological diversity: economic valuation and priorities for development
By all informed scientific accounts the world's biological diversity is currently in a critical condition.
Biodiversity is vital for the continued existence of the global biosphere and, by extension, human
wellbeing and development. It is inconceivable that a discipline predicated on the issues of scarcity
and choice has nothing to contribute in terms of an understanding of either the causes and
consequences of biodiversity loss, or in proposing solutions to the crisis. This thesis examines some
of the economic parameters of the issue. Alongside the acknowledged root problems of market and
institutional failure lies the question of economic valuation. Valuation of biodiversity puts conservation
on a more level playing field with the economic forces which threaten its demise. Provided economic
values can be appropriated (i.e. converted to flows of real economic resources) it becomes worthwhile
for countries to invest in valuable biological assets. But the practice of economic valuation and the
quantification of biodiversity are in their infancy and the complexity of the latter hinders the precise
application of the former. Much of this thesis focuses on the use and development of the contingent
valuation method (CV) as a flexible approach to valuing biodiversity. The method has a useful role
to play in resource allocation, and, for valuing biological resources. Faced by the irreducible
complexity of life which is the essence of biodiversity, CV does have its limitations. It is possible to
conclude that existing valuation methods are a vital part of a "holding operation" alongside other
surrogate approaches to setting priorities for global conservation. Nevertheless, the development of
an interface between economic (preference-based) values, and biological values, which together can
comprehensively inform conservation decisions remains the objective for the future
Evolutionary genomics : statistical and computational methods
This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward
Evolutionary Genomics
This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward
On the Informational Asymmetry between Upper and Lower Bounds for Ultrametric Evolutionary Trees
. This paper addresses the informational asymmetry for constructing an ultrametric evolutionary tree from upper and lower bounds on pairwise distances between n given species. We show that the tallest ultrametric tree exists and can be constructed in O(n 2 ) time, while the existence of the shortest ultrametric tree depends on whether the lower bounds are ultrametric. The tallest tree construction algorithm gives a very simple solution to the construction of an ultrametric tree. We also provide an efficient O(n 2 )-time algorithm for checking the uniqueness of an ultrametric tree, and study a query problem for testing whether an ultrametric tree satisfies both upper and lower bounds. 1 Introduction Constructing the evolutionary tree of a species set is a fundamental problem in computational biology. Such trees describe how species are related to one another in terms of common ancestors. A useful computational problem for constructing evolutionary tree is that given an n \Theta n d..
Complexity in Economic and Social Systems
There is no term that better describes the essential features of human society than complexity. On various levels, from the decision-making processes of individuals, through to the interactions between individuals leading to the spontaneous formation of groups and social hierarchies, up to the collective, herding processes that reshape whole societies, all these features share the property of irreducibility, i.e., they require a holistic, multi-level approach formed by researchers from different disciplines. This Special Issue aims to collect research studies that, by exploiting the latest advances in physics, economics, complex networks, and data science, make a step towards understanding these economic and social systems. The majority of submissions are devoted to financial market analysis and modeling, including the stock and cryptocurrency markets in the COVID-19 pandemic, systemic risk quantification and control, wealth condensation, the innovation-related performance of companies, and more. Looking more at societies, there are papers that deal with regional development, land speculation, and the-fake news-fighting strategies, the issues which are of central interest in contemporary society. On top of this, one of the contributions proposes a new, improved complexity measure
On the Informational Asymmetry between Upper and Lower Bounds for Ultrametric Evolutionary Trees
Abstract. This paper addresses the informational asymmetry for constructing an ultrametric evolutionary tree from upper and lower bounds on pairwise distances between n given species. We show that the tallest ultrametric tree exists and can be constructed in O(n 2) time, while the existence of the shortest ultrametric tree depends on whether the lower bounds are ultrametric. The tallest tree construction algorithm gives a very simple solution to the construction of an ultrametric tree. We also provide an e cient O(n 2)-time algorithm for checking the uniqueness of an ultrametric tree, and study a query problem for testing whether an ultrametric tree satis es both upper and lower bounds.
A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium
When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its Ï parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available
A Statistical Approach to the Alignment of fMRI Data
Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods