19,988 research outputs found
Phase Changes in the Evolution of the IPv4 and IPv6 AS-Level Internet Topologies
In this paper we investigate the evolution of the IPv4 and IPv6 Internet
topologies at the autonomous system (AS) level over a long period of time.We
provide abundant empirical evidence that there is a phase transition in the
growth trend of the two networks. For the IPv4 network, the phase change
occurred in 2001. Before then the network's size grew exponentially, and
thereafter it followed a linear growth. Changes are also observed around the
same time for the maximum node degree, the average node degree and the average
shortest path length. For the IPv6 network, the phase change occurred in late
2006. It is notable that the observed phase transitions in the two networks are
different, for example the size of IPv6 network initially grew linearly and
then shifted to an exponential growth. Our results show that following decades
of rapid expansion up to the beginning of this century, the IPv4 network has
now evolved into a mature, steady stage characterised by a relatively slow
growth with a stable network structure; whereas the IPv6 network, after a slow
startup process, has just taken off to a full speed growth. We also provide
insight into the possible impact of IPv6-over-IPv4 tunneling deployment scheme
on the evolution of the IPv6 network. The Internet topology generators so far
are based on an inexplicit assumption that the evolution of Internet follows
non-changing dynamic mechanisms. This assumption, however, is invalidated by
our results.Our work reveals insights into the Internet evolution and provides
inputs to future AS-Level Internet models.Comment: 12 pages, 21 figures; G. Zhang et al.,Phase changes in the evolution
of the IPv4 and IPv6 AS-Level Internet topologies, Comput. Commun. (2010
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A Goal-Directed Bayesian Framework for Categorization
Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis
Digital Tectonics as a Morphogenetic Process
p. 938-948Tectonics is a seminal concept that defines the nature of the relationship between
architecture and its structural properties. The changing definition of the symbiotic
relationship between structural engineering and architectural design may be considered one of the formative influences on the conceptual evolution of tectonics in different historical periods. Recent developments in the field of morphogenesis, digital media, theories techniques and methods of digital design have contributed a new models of integration between structure, material and form in digital tectonics.
The objective of this paper is to propose and define tectonics as a model of morphogenetic process. The paper identifies and presents the manner in which theory and emerging concepts of morphogenesis as well as digital models of design are contributing to this new model. The paper first analyzes the historical evolution of tectonics as a concept and characterizes the emergence of theoretical framework reflected in concepts and terms related to morphogenesis.Oxman, R. (2010). Digital Tectonics as a Morphogenetic Process. Editorial Universitat PolitĂšcnica de ValĂšncia. http://hdl.handle.net/10251/695
A modular modelling framework for hypotheses testing in the simulation of urbanisation
In this paper, we present a modelling experiment developed to study systems
of cities and processes of urbanisation in large territories over long time
spans. Building on geographical theories of urban evolution, we rely on
agent-based models to 1/ formalise complementary and alternative hypotheses of
urbanisation and 2/ explore their ability to simulate observed patterns in a
virtual laboratory. The paper is therefore divided into two sections : an
overview of the mechanisms implemented to represent competing hypotheses used
to simulate urban evolution; and an evaluation of the resulting model
structures in their ability to simulate - efficiently and parsimoniously - a
system of cities (the Former Soviet Union) over several periods of time (before
and after the crash of the USSR). We do so using a modular framework of
model-building and evolutionary algorithms for the calibration of several model
structures. This project aims at tackling equifinality in systems dynamics by
confronting different mechanisms with similar evaluation criteria. It enables
the identification of the best-performing models with respect to the chosen
criteria by scanning automatically the parameter along with the space of model
structures (as combinations of modelled dynamics).Comment: 21 pages, 3 figures, working pape
Spontaneous and deliberate future thinking: A dual process account
© 2019 Springer Nature.This is the final published version of an article published in Psychological Research, licensed under a Creative Commons Attri-bution 4.0 International License. Available online at: https://doi.org/10.1007/s00426-019-01262-7.In this article, we address an apparent paradox in the literature on mental time travel and mind-wandering: How is it possible that future thinking is both constructive, yet often experienced as occurring spontaneously? We identify and describe two âroutesâ whereby episodic future thoughts are brought to consciousness, with each of the âroutesâ being associated with separable cognitive processes and functions. Voluntary future thinking relies on controlled, deliberate and slow cognitive processing. The other, termed involuntary or spontaneous future thinking, relies on automatic processes that allows âfully-fledgedâ episodic future thoughts to freely come to mind, often triggered by internal or external cues. To unravel the paradox, we propose that the majority of spontaneous future thoughts are âpre-madeâ (i.e., each spontaneous future thought is a re-iteration of a previously constructed future event), and therefore based on simple, well-understood, memory processes. We also propose that the pre-made hypothesis explains why spontaneous future thoughts occur rapidly, are similar to involuntary memories, and predominantly about upcoming tasks and goals. We also raise the possibility that spontaneous future thinking is the default mode of imagining the future. This dual process approach complements and extends standard theoretical approaches that emphasise constructive simulation, and outlines novel opportunities for researchers examining voluntary and spontaneous forms of future thinking.Peer reviewe
The cybernetic Bayesian brain: from interoceptive inference to sensorimotor contingencies
Is there a single principle by which neural operations can account for perception, cognition, action, and even consciousness? A strong candidate is now taking shape in the form of âpredictive processingâ. On this theory, brains engage in predictive inference on the causes of sensory inputs by continuous minimization of prediction errors or informational âfree energyâ. Predictive processing can account, supposedly, not only for perception, but also for action and for the essential contribution of the body and environment in structuring sensorimotor interactions. In this paper I draw together some recent developments within predictive processing that involve predictive modelling of internal physiological states (interoceptive inference), and integration with âenactiveâ and âembodiedâ approaches to cognitive science (predictive perception of sensorimotor contingencies). The upshot is a development of predictive processing that originates, not in Helmholtzian perception-as-inference, but rather in 20th-century cybernetic principles that emphasized homeostasis and predictive control. This way of thinking leads to (i) a new view of emotion as active interoceptive inference; (ii) a common predictive framework linking experiences of body ownership, emotion, and exteroceptive perception; (iii) distinct interpretations of active inference as involving disruptive and disambiguatoryânot just confirmatoryâactions to test perceptual hypotheses; (iv) a neurocognitive operationalization of the âmastery of sensorimotor contingenciesâ (where sensorimotor contingencies reflect the rules governing sensory changes produced by various actions); and (v) an account of the sense of subjective reality of perceptual contents (âperceptual presenceâ) in terms of the extent to which predictive models encode potential sensorimotor relations (this being âcounterfactual richnessâ). This is rich and varied territory, and surveying its landmarks emphasizes the need for experimental tests of its key contributions
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Bayesian Learning Models of Pain: A Call to Action
Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain
Automated analysis of feature models: Quo vadis?
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de EconomĂa y Competitividad TIN2015-70560-RJunta de AndalucĂa TIC-186
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