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An Overview of Models for Response Times and Processes in Cognitive Tests.
Response times (RTs) are a natural kind of data to investigate cognitive processes underlying cognitive test performance. We give an overview of modeling approaches and of findings obtained with these approaches. Four types of models are discussed: response time models (RT as the sole dependent variable), joint models (RT together with other variables as dependent variable), local dependency models (with remaining dependencies between RT and accuracy), and response time as covariate models (RT as independent variable). The evidence from these approaches is often not very informative about the specific kind of processes (other than problem solving, information accumulation, and rapid guessing), but the findings do suggest dual processing: automated processing (e.g., knowledge retrieval) vs. controlled processing (e.g., sequential reasoning steps), and alternative explanations for the same results exist. While it seems well-possible to differentiate rapid guessing from normal problem solving (which can be based on automated or controlled processing), further decompositions of response times are rarely made, although possible based on some of model approaches
Estimating a pressure dependent thermal conductivity coefficient with applications in food technology
In this paper we introduce a method to estimate a pressure dependent thermal
conductivity coefficient arising in a heat diffusion model with applications in
food technology. To address the known smoothing effect of the direct problem,
we model the uncertainty of the conductivity coefficient as a hierarchical
Gaussian Markov random field (GMRF) restricted to uniqueness conditions for the
solution of the inverse problem established in Fraguela et al. Furthermore, we
propose a Single Variable Exchange Metropolis-Hastings algorithm to sample the
corresponding conditional probability distribution of the conductivity
coefficient given observations of the temperature. Sensitivity analysis of the
direct problem suggests that large integration times are necessary to identify
the thermal conductivity coefficient. Numerical evidence indicates that a
signal to noise ratio of roughly 1000 suffices to reliably retrieve the thermal
conductivity coefficient
A curve shortening flow rule for closed embedded plane curves with a prescribed rate of change in enclosed area
Motivated by a problem from fluid mechanics, we consider a generalization of the standard curve shortening flow problem for a closed embedded plane curve such that the area enclosed by the curve is forced to decrease at a prescribed rate. Using formal asymptotic and numerical techniques, we derive possible extinction shapes as the curve contracts to a point, dependent on the rate of decreasing area; we find there is a wider class of extinction shapes than for standard curve shortening, for which initially simple closed curves are always asymptotically circular. We also provide numerical evidence that self-intersection is possible for non-convex initial conditions, distinguishing between pinch-off and coalescence of the curve interior
Fast non-parametric Bayesian inference on infinite trees
Given i.i.d. data from an unknown distribution,
we consider the problem of predicting future items.
An adaptive way to estimate the probability density
is to recursively subdivide the domain to an appropriate
data-dependent granularity. A Bayesian would assign a
data-independent prior probability to "subdivide", which leads
to a prior over infinite(ly many) trees. We derive an exact, fast,
and simple inference algorithm for such a prior, for the data
evidence, the predictive distribution, the effective model
dimension, and other quantities
Magnetic models on Apollonian networks
Thermodynamic and magnetic properties of Ising models defined on the
triangular Apollonian network are investigated. This and other similar networks
are inspired by the problem of covering an Euclidian domain with circles of
maximal radii. Maps for the thermodynamic functions in two subsequent
generations of the construction of the network are obtained by formulating the
problem in terms of transfer matrices. Numerical iteration of this set of maps
leads to exact values for the thermodynamic properties of the model. Different
choices for the coupling constants between only nearest neighbors along the
lattice are taken into account. For both ferromagnetic and anti-ferromagnetic
constants, long range magnetic ordering is obtained. With exception of a size
dependent effective critical behavior of the correlation length, no evidence of
asymptotic criticality was detected.Comment: 21 pages, 5 figure
Feedback stabilization of dynamical systems with switched delays
We analyze a classification of two main families of controllers that are of
interest when the feedback loop is subject to switching propagation delays due
to routing via a wireless multi-hop communication network. We show that we can
cast this problem as a subclass of classical switching systems, which is a
non-trivial generalization of classical LTI systems with timevarying delays. We
consider both cases where delay-dependent and delay independent controllers are
used, and show that both can be modeled as switching systems with unconstrained
switchings. We provide NP-hardness results for the stability verification
problem, and propose a general methodology for approximate stability analysis
with arbitrary precision. We finally give evidence that non-trivial design
problems arise for which new algorithmic methods are needed
Fast Non-Parametric Bayesian Inference on Infinite Trees
Given i.i.d. data from an unknown distribution, we consider the problem of
predicting future items. An adaptive way to estimate the probability density is
to recursively subdivide the domain to an appropriate data-dependent
granularity. A Bayesian would assign a data-independent prior probability to
"subdivide", which leads to a prior over infinite(ly many) trees. We derive an
exact, fast, and simple inference algorithm for such a prior, for the data
evidence, the predictive distribution, the effective model dimension, and other
quantities.Comment: 8 twocolumn pages, 3 figure
A Critique of Current Magnetic-Accretion Models for Classical T-Tauri Stars
Current magnetic-accretion models for classical T-Tauri stars rely on a
strong, dipolar magnetic field of stellar origin to funnel the disk material
onto the star, and assume a steady-state. In this paper, I critically examine
the physical basis of these models in light of the observational evidence and
our knowledge of magnetic fields in low-mass stars, and find it lacking.
I also argue that magnetic accretion onto these stars is inherently a
time-dependent problem, and that a steady-state is not warranted.
Finally, directions for future work towards fully-consistent models are
pointed out.Comment: 2 figure
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