200 research outputs found
Dominantnost jezika dvojezicnih govornika talijanskog I hrvatskog jezika [Language dominance in bilingual speakers of Italian and Croatian language]
Because of the high variability in any bilingual population, it is of a great importance to control for language dominance in both research and language assessment. This control is crucial in research in order to form unified groups of participants according to language dominance. In the language assessment of bilingual children, determining language dominance should be a priority. Children exposed to two languages from an early age may acquire them at a slower rate when compared to their monolingual piers. While this lag is hardly noticeable in some children, for others it is significant. Without knowledge about the child\u2019s language skills in the other, non-assessed language, it is impossible to determine if the results of language assessment point to the dominance of one language over another or general language difficulties. In bilingual areas of Croatia, such as Rijeka and Istria, this can be quite a challenge. While language dominance has generally been measured using a large number of different methods, there is no universally accepted procedure. This research uses the results of the Italian and Croatian versions of the TROG test to determine language dominance. Participants were 56 preschool-aged children attending kindergartens with an Italian language programme in Rijeka and Istria. Participants were preselected by their kindergarten teachers as children that might be balanced bilinguals. Using the differences in results between both TROG tests, approximately 70% of children were placed in a balanced bilinguals group. No differences were shown between groups of participants from Rijeka and Istria
Generalized Integer Partitions, Tilings of Zonotopes and Lattices
In this paper, we study two kinds of combinatorial objects, generalized
integer partitions and tilings of two dimensional zonotopes, using dynamical
systems and order theory. We show that the sets of partitions ordered with a
simple dynamics, have the distributive lattice structure. Likewise, we show
that the set of tilings of zonotopes, ordered with a simple and classical
dynamics, is the disjoint union of distributive lattices which we describe. We
also discuss the special case of linear integer partitions, for which other
dynamical systems exist. These results give a better understanding of the
behaviour of tilings of zonotopes with flips and dynamical systems involving
partitions.Comment: See http://www.liafa.jussieu.fr/~latapy
Majority Dynamics and Aggregation of Information in Social Networks
Consider n individuals who, by popular vote, choose among q >= 2
alternatives, one of which is "better" than the others. Assume that each
individual votes independently at random, and that the probability of voting
for the better alternative is larger than the probability of voting for any
other. It follows from the law of large numbers that a plurality vote among the
n individuals would result in the correct outcome, with probability approaching
one exponentially quickly as n tends to infinity. Our interest in this paper is
in a variant of the process above where, after forming their initial opinions,
the voters update their decisions based on some interaction with their
neighbors in a social network. Our main example is "majority dynamics", in
which each voter adopts the most popular opinion among its friends. The
interaction repeats for some number of rounds and is then followed by a
population-wide plurality vote.
The question we tackle is that of "efficient aggregation of information": in
which cases is the better alternative chosen with probability approaching one
as n tends to infinity? Conversely, for which sequences of growing graphs does
aggregation fail, so that the wrong alternative gets chosen with probability
bounded away from zero? We construct a family of examples in which interaction
prevents efficient aggregation of information, and give a condition on the
social network which ensures that aggregation occurs. For the case of majority
dynamics we also investigate the question of unanimity in the limit. In
particular, if the voters' social network is an expander graph, we show that if
the initial population is sufficiently biased towards a particular alternative
then that alternative will eventually become the unanimous preference of the
entire population.Comment: 22 page
Non-maximal sensitivity to synchronism in periodic elementary cellular automata: exact asymptotic measures
In [11] and [13] the authors showed that elementary cellular automata rules
0, 3, 8, 12, 15, 28, 32, 34, 44, 51, 60, 128, 136, 140, 160, 162, 170, 200 and
204 (and their conjugation, reflection, reflected-conjugation) are not maximum
sensitive to synchronism, i.e. they do not have a different dynamics for each
(non-equivalent) block-sequential update schedule (defined as ordered
partitions of cell positions). In this work we present exact measurements of
the sensitivity to synchronism for these rules, as functions of the size. These
exhibit a surprising variety of values and associated proof methods, such as
the special pairs of rule 128, and the connection to the bissection of Lucas
numbers of rule 8
Attraction Basins as Gauges of Robustness against Boundary Conditions in Biological Complex Systems
One fundamental concept in the context of biological systems on which researches have flourished in the past decade is that of the apparent robustness of these systems, i.e., their ability to resist to perturbations or constraints induced by external or boundary elements such as electromagnetic fields acting on neural networks, micro-RNAs acting on genetic networks and even hormone flows acting both on neural and genetic networks. Recent studies have shown the importance of addressing the question of the environmental robustness of biological networks such as neural and genetic networks. In some cases, external regulatory elements can be given a relevant formal representation by assimilating them to or modeling them by boundary conditions. This article presents a generic mathematical approach to understand the influence of boundary elements on the dynamics of regulation networks, considering their attraction basins as gauges of their robustness. The application of this method on a real genetic regulation network will point out a mathematical explanation of a biological phenomenon which has only been observed experimentally until now, namely the necessity of the presence of gibberellin for the flower of the plant Arabidopsis thaliana to develop normally
Finding robust solutions for constraint satisfaction problems with discrete and ordered domains by coverings
Constraint programming is a paradigm wherein relations between variables are
stated in the form of constraints. Many real life problems come from uncertain and dynamic
environments, where the initial constraints and domains may change during its execution.
Thus, the solution found for the problem may become invalid. The search forrobustsolutions
for constraint satisfaction problems (CSPs) has become an important issue in the ¿eld of
constraint programming. In some cases, there exists knowledge about the uncertain and
dynamic environment. In other cases, this information is unknown or hard to obtain. In
this paper, we consider CSPs with discrete and ordered domains where changes only involve
restrictions or expansions of domains or constraints. To this end, we model CSPs as weighted
CSPs (WCSPs) by assigning weights to each valid tuple of the problem constraints and
domains. The weight of each valid tuple is based on its distance from the borders of the
space of valid tuples in the corresponding constraint/domain. This distance is estimated by
a new concept introduced in this paper: coverings. Thus, the best solution for the modeled
WCSP can be considered as a most robust solution for the original CSP according to these
assumptionsThis work has been partially supported by the research projects TIN2010-20976-C02-01 (Min. de Ciencia e Innovacion, Spain) and P19/08 (Min. de Fomento, Spain-FEDER), and the fellowship program FPU.Climent Aunés, LI.; Wallace, RJ.; Salido Gregorio, MA.; Barber Sanchís, F. (2013). Finding robust solutions for constraint satisfaction problems with discrete and ordered domains by coverings. Artificial Intelligence Review. 1-26. https://doi.org/10.1007/s10462-013-9420-0S126Climent L, Salido M, Barber F (2011) Reformulating dynamic linear constraint satisfaction problems as weighted csps for searching robust solutions. In: Ninth symposium of abstraction, reformulation, and approximation (SARA-11), pp 34–41Dechter R, Dechter A (1988) Belief maintenance in dynamic constraint networks. In: Proceedings of the 7th national conference on, artificial intelligence (AAAI-88), pp 37–42Dechter R, Meiri I, Pearl J (1991) Temporal constraint networks. Artif Intell 49(1):61–95Fargier H, Lang J (1993) Uncertainty in constraint satisfaction problems: a probabilistic approach. In: Proceedings of the symbolic and quantitative approaches to reasoning and uncertainty (EC-SQARU-93), pp 97–104Fargier H, Lang J, Schiex T (1996) Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge. In: Proceedings of the 13th national conference on, artificial intelligence, pp 175–180Fowler D, Brown K (2000) Branching constraint satisfaction problems for solutions robust under likely changes. In: Proceedings of the international conference on principles and practice of constraint programming (CP-2000), pp 500–504Goles E, Martínez S (1990) Neural and automata networks: dynamical behavior and applications. Kluwer Academic Publishers, DordrechtHays W (1973) Statistics for the social sciences, vol 410, 2nd edn. Holt, Rinehart and Winston, New YorkHebrard E (2006) Robust solutions for constraint satisfaction and optimisation under uncertainty. PhD thesis, University of New South WalesHerrmann H, Schneider C, Moreira A, Andrade Jr J, Havlin S (2011) Onion-like network topology enhances robustness against malicious attacks. J Stat Mech Theory Exp 2011(1):P01,027Larrosa J, Schiex T (2004) Solving weighted CSP by maintaining arc consistency. Artif Intell 159:1–26Larrosa J, Meseguer P, Schiex T (1999) Maintaining reversible DAC for Max-CSP. J Artif Intell 107(1):149–163Mackworth A (1977) On reading sketch maps. In: Proceedings of IJCAI’77, pp 598–606Sam J (1995) Constraint consistency techniques for continuous domains. These de doctorat, École polytechnique fédérale de LausanneSchiex T, Fargier H, Verfaillie G (1995) Valued constraint satisfaction problems: hard and easy problems. In: Proceedings of the 14th international joint conference on, artificial intelligence (IJCAI-95), pp 631–637Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64(2):278–285Verfaillie G, Jussien N (2005) Constraint solving in uncertain and dynamic environments: a survey. Constraints 10(3):253–281Wallace R, Freuder E (1998) Stable solutions for dynamic constraint satisfaction problems. In: Proceedings of the 4th international conference on principles and practice of constraint programming (CP-98), pp 447–461Wallace RJ, Grimes D (2010) Problem-structure versus solution-based methods for solving dynamic constraint satisfaction problems. In: Proceedings of the 22nd international conference on tools with artificial intelligence (ICTAI-10), IEEEWalsh T (2002) Stochastic constraint programming. In: Proceedings of the 15th European conference on, artificial intelligence (ECAI-02), pp 111–115William F (2006) Topology and its applications. Wiley, New YorkWiner B (1971) Statistical principles in experimental design, 2nd edn. McGraw-Hill, New YorkYorke-Smith N, Gervet C (2009) Certainty closure: reliable constraint reasoning with incomplete or erroneous data. J ACM Trans Comput Log (TOCL) 10(1):
Relaxation, closing probabilities and transition from oscillatory to chaotic attractors in asymmetric neural networks
Attractors in asymmetric neural networks with deterministic parallel dynamics
were shown to present a "chaotic" regime at symmetry eta < 0.5, where the
average length of the cycles increases exponentially with system size, and an
oscillatory regime at high symmetry, where the typical length of the cycles is
2. We show, both with analytic arguments and numerically, that there is a sharp
transition, at a critical symmetry \e_c=0.33, between a phase where the
typical cycles have length 2 and basins of attraction of vanishing weight and a
phase where the typical cycles are exponentially long with system size, and the
weights of their attraction basins are distributed as in a Random Map with
reversal symmetry. The time-scale after which cycles are reached grows
exponentially with system size , and the exponent vanishes in the symmetric
limit, where . The transition can be related to the dynamics
of the infinite system (where cycles are never reached), using the closing
probabilities as a tool.
We also study the relaxation of the function ,
where is the local field experienced by the neuron . In the symmetric
system, it plays the role of a Ljapunov function which drives the system
towards its minima through steepest descent. This interpretation survives, even
if only on the average, also for small asymmetry. This acts like an effective
temperature: the larger is the asymmetry, the faster is the relaxation of ,
and the higher is the asymptotic value reached. reachs very deep minima in
the fixed points of the dynamics, which are reached with vanishing probability,
and attains a larger value on the typical attractors, which are cycles of
length 2.Comment: 24 pages, 9 figures, accepted on Journal of Physics A: Math. Ge
On the complexity of acyclic modules in automata networks
Modules were introduced as an extension of Boolean automata networks. They
have inputs which are used in the computation said modules perform, and can be
used to wire modules with each other. In the present paper we extend this new
formalism and study the specific case of acyclic modules. These modules prove
to be well described in their limit behavior by functions called output
functions. We provide other results that offer an upper bound on the number of
attractors in an acyclic module when wired recursively into an automata
network, alongside a diversity of complexity results around the difficulty of
deciding the existence of cycles depending on the number of inputs and the size
of said cycle.Comment: 21 page
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