13,804 research outputs found

    A framework for Thinking about Distributed Cognition

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    As is often the case when scientific or engineering fields emerge, new concepts are forged or old ones are adapted. When this happens, various arguments rage over what ultimately turns out to be conceptual misunderstandings. At that critical time, there is a need for an explicit reflection on the meaning of the concepts that define the field. In this position paper, we aim to provide a reasoned framework in which to think about various issues in the field of distributed cognition. We argue that both relevant concepts, distribution and cognition, must be understood as continuous. As it is used in the context of distributed cognition, the concept of distribution is essentially fuzzy, and we will link it to the notion of emergence of system-level properties. The concept of cognition must also be seen as fuzzy, but for different a reason: due its origin as an anthropocentric concept, no one has a clear handle on its meaning in a distributed setting. As the proposed framework forms a space, we then explore its geography and (re)visit famous landmarks

    Bayesian inference and non-linear extensions of the CIRCE method for quantifying the uncertainty of closure relationships integrated into thermal-hydraulic system codes

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    Uncertainty Quantification of closure relationships integrated into thermal-hydraulic system codes is a critical prerequisite in applying the Best-Estimate Plus Uncertainty (BEPU) methodology for nuclear safety and licensing processes.The purpose of the CIRCE method is to estimate the (log)-Gaussian probability distribution of a multiplicative factor applied to a reference closure relationship in order to assess its uncertainty. Even though this method has been implemented with success in numerous physical scenarios, it can still suffer from substantial limitations such as the linearity assumption and the difficulty of properly taking into account the inherent statistical uncertainty. In the paper, we will extend the CIRCE method in two aspects. On the one hand, we adopt the Bayesian setting putting prior probability distributions on the parameters of the (log)-Gaussian distribution. The posterior distribution of the parameters is then computed with respect to an experimental database by means of Markov Chain Monte Carlo (MCMC) algorithms. On the other hand, we tackle the more general setting where the simulations do not move linearly against the multiplicative factor(s). MCMC algorithms then become time-prohibitive when the thermal-hydraulic simulations exceed a few minutes. This handicap is overcome by using Gaussian process (GP) emulators which can yield both reliable and fast predictions of the simulations. The GP-based MCMC algorithms will be applied to quantify the uncertainty of two condensation closure relationships at a safety injection with respect to a database of experimental tests. The thermal-hydraulic simulations will be run with the CATHARE 2 computer code.Comment: 37 pages, 5 figure

    Majority voting with stochastic preferences : The whims of a committee are smaller than the whims of its members.

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    We study the volatility of the policy chosen by a committee whose members have volatile preferences. It is smaller than if it was chosen by a single member, smaller the larger the size of the committee, and smaller the volatility of members' preferences.Committee, majority voting, uncertainty, volatility.

    The Making of International Environmental Agreements

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    We examine in this paper the formation and the stability of international environmental agreements when cooperation means to commit to a minimum abatement level. Each country decides whether to ratify the agreement and this latter enters into force only if it is ratified by a number of countries at least equal to some ratification threshold. We analyze the role played by ratification threshold rules and provide conditions for international environmental agreements to enter into force. We show that a large typology of agreements can enter into force among the one constituted by the grand coalition.International Environmental Agreement,

    Global well-posedness of a conservative relaxed cross diffusion system

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    We prove global existence in time of solutions to relaxed conservative cross diffusion systems governed by nonlinear operators of the form ui→∂tui−Δ(ai(u~)ui)u_i\to \partial_tu_i-\Delta(a_i(\tilde{u})u_i) where the ui,i=1,...,Iu_i, i=1,...,I represent II density-functions, u~\tilde{u} is a spatially regularized form of (u1,...,uI)(u_1,...,u_I) and the nonlinearities aia_i are merely assumed to be continuous and bounded from below. Existence of global weak solutions is obtained in any space dimension. Solutions are proved to be regular and unique when the aia_i are locally Lipschitz continuous

    New Equations for Neutral Terms: A Sound and Complete Decision Procedure, Formalized

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    The definitional equality of an intensional type theory is its test of type compatibility. Today's systems rely on ordinary evaluation semantics to compare expressions in types, frustrating users with type errors arising when evaluation fails to identify two `obviously' equal terms. If only the machine could decide a richer theory! We propose a way to decide theories which supplement evaluation with `Μ\nu-rules', rearranging the neutral parts of normal forms, and report a successful initial experiment. We study a simple -calculus with primitive fold, map and append operations on lists and develop in Agda a sound and complete decision procedure for an equational theory enriched with monoid, functor and fusion laws

    Splitting methods with variable metric for KL functions

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    We study the convergence of general abstract descent methods applied to a lower semicontinuous nonconvex function f that satisfies the Kurdyka-Lojasiewicz inequality in a Hilbert space. We prove that any precompact sequence converges to a critical point of f and obtain new convergence rates both for the values and the iterates. The analysis covers alternating versions of the forward-backward method with variable metric and relative errors. As an example, a nonsmooth and nonconvex version of the Levenberg-Marquardt algorithm is detailled

    Accretion Dynamics on Wet Granular Materials

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    Wet granular aggregates are common precursors of construction materials, food, and health care products. The physical mechanisms involved in the mixing of dry grains with a wet substrate are not well understood and difficult to control. Here, we study experimentally the accretion of dry grains on a wet granular substrate by measuring the growth dynamics of the wet aggregate. We show that this aggregate is fully saturated and its cohesion is ensured by the capillary depression at the air-liquid interface. The growth dynamics is controlled by the liquid fraction at the surface of the aggregate and exhibits two regimes. In the viscous regime, the growth dynamics is limited by the capillary-driven flow of liquid through the granular packing to the surface of the aggregate. In the capture regime, the capture probability depends on the availability of the liquid at the saturated interface, which is controlled by the hydrostatic depression in the material. We propose a model that rationalizes our observations and captures both dynamics based on the evolution of the capture probability with the hydrostatic depression

    Geodesics for a class of distances in the space of probability measures

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    In this paper, we study the characterization of geodesics for a class of distances between probability measures introduced by Dolbeault, Nazaret and Savar e. We first prove the existence of a potential function and then give necessary and suffi cient optimality conditions that take the form of a coupled system of PDEs somehow similar to the Mean-Field-Games system of Lasry and Lions. We also consider an equivalent formulation posed in a set of probability measures over curves

    A Bayesian alternative to mutual information for the hierarchical clustering of dependent random variables

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    The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering
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