5,164 research outputs found
Dewpoint temperature inversions analyzed
Dewpoint temperature inversion, with regard to other simultaneous meteorological conditions, was examined to establish the influence of meteorological variables on the variation of dewpoint temperature with height. This report covers instrumentation and available data, all the climatological features of dewpoint inversions, and specific special cases
Intrinsic adaptation in autonomous recurrent neural networks
A massively recurrent neural network responds on one side to input stimuli
and is autonomously active, on the other side, in the absence of sensory
inputs. Stimuli and information processing depends crucially on the qualia of
the autonomous-state dynamics of the ongoing neural activity. This default
neural activity may be dynamically structured in time and space, showing
regular, synchronized, bursting or chaotic activity patterns.
We study the influence of non-synaptic plasticity on the default dynamical
state of recurrent neural networks. The non-synaptic adaption considered acts
on intrinsic neural parameters, such as the threshold and the gain, and is
driven by the optimization of the information entropy. We observe, in the
presence of the intrinsic adaptation processes, three distinct and globally
attracting dynamical regimes, a regular synchronized, an overall chaotic and an
intermittent bursting regime. The intermittent bursting regime is characterized
by intervals of regular flows, which are quite insensitive to external stimuli,
interseeded by chaotic bursts which respond sensitively to input signals. We
discuss these finding in the context of self-organized information processing
and critical brain dynamics.Comment: 24 pages, 8 figure
On the micro mechanics of one-dimensional normal compression
Discrete-element modelling has been used to investigate the micro mechanics of one-dimensional compression. One-dimensional compression is modelled in three dimensions using an oedometer and a large number of particles, and without the use of agglomerates. The fracture of a particle is governed by the octahedral shear stress within the particle due to the multiple contacts and a Weibull distribution of strengths. Different fracture mechanisms are considered, and the influence of the distribution of fragments produced for each fracture on the global particle size distribution and the slope of the normal compression line is investigated. Using the discrete-element method, compression is related to the evolution of a fractal distribution of particles. The compression index is found to be solely a function of the strengths of the particles as a function of size
The impact of category separation on unsupervised categorization
Most previous research on unsupervised categorization has used unconstrained tasks in which no instructions are provided about the underlying category structure or the stimuli are not clustered into categories. Few studies have investigated constrained tasks in which the goal is to learn pre-defined stimulus clusters in the absence of feedback. These studies have generally reported good performance when the stimulus clusters could be separated by a one-dimensional rule. The present study investigated the limits of this ability. Results suggest that even when two stimulus clusters are as widely separated as in previous studies, performance is poor if within-category variance on the relevant dimension is nonnegligible. In fact, under these conditions many participants failed even to identify the single relevant stimulus dimension. This poor performance is generally incompatible with all current models of unsupervised category learning
The Effects of Category Overlap on Information-Integration and Rule-Based Category Learning
Three experiments investigate whether the amount of category overlap constrains the decision strategies used in category learning, and whether such constraints depend on the type of category structures used. Experiments 1 and 2 used a category learning task requiring perceptual integration of information from multiple dimensions (information-integration task) and Experiment 3 used a task requiring the application of an explicit strategy (rule-based task). In the information-integration task, participants used perceptual-integration strategies at moderate levels of category overlap, but explicit strategies at extreme levels of overlap – even when such strategies were sub-optimal. In contrast, in the rule-based task, participants used explicit strategies regardless of the level of category overlap. These data are consistent with a multiple systems view of category learning, and suggest that categorization strategy depends on the type of task that is used, and on the degree to which each stimulus is probabilistically associated with the contrasting categories
A new creep law for crushable aggregates
The authors have recently proposed a new equation for the one-dimensional (1D) normal compression line, which contains a parameter controlling the size effect on average strength. They showed that the equation held for a wide range of discrete-element modelling (DEM) simulations of crushable aggregates. This paper incorporates the time-dependence of particle strength. A new equation is proposed and examined using DEM of 1D creep. The simulations show that while the plots may seem linear on a plot of voids ratio against the logarithm of time in the traditional way, the new proposed law, which is linear when the voids ratio is also plotted on a logarithmic scale, is more appropriate. The simulations examine the influence of the size effect hardening law, the time dependence on strength and stress level. It is shown that the new equation holds for each case
Synthetic LISA: Simulating Time Delay Interferometry in a Model LISA
We report on three numerical experiments on the implementation of Time-Delay
Interferometry (TDI) for LISA, performed with Synthetic LISA, a C++/Python
package that we developed to simulate the LISA science process at the level of
scientific and technical requirements. Specifically, we study the laser-noise
residuals left by first-generation TDI when the LISA armlengths have a
realistic time dependence; we characterize the armlength-measurements
accuracies that are needed to have effective laser-noise cancellation in both
first- and second-generation TDI; and we estimate the quantization and
telemetry bitdepth needed for the phase measurements. Synthetic LISA generates
synthetic time series of the LISA fundamental noises, as filtered through all
the TDI observables; it also provides a streamlined module to compute the TDI
responses to gravitational waves according to a full model of TDI, including
the motion of the LISA array and the temporal and directional dependence of the
armlengths. We discuss the theoretical model that underlies the simulation, its
implementation, and its use in future investigations on system characterization
and data-analysis prototyping for LISA.Comment: 18 pages, 14 EPS figures, REVTeX 4. Accepted PRD version. See
http://www.vallis.org/syntheticlisa for information on the Synthetic LISA
software packag
Unsupervised Category Learning with Integral-Dimension Stimuli
Despite the recent surge in research on unsupervised category learning, the majority of studies have focused on unconstrained tasks in which no instructions are provided about the underlying category structure. Relatively little research has focused on constrained tasks in which the goal is to learn pre-defined stimulus clusters in the absence of feedback. The few studies that have addressed this issue have focused almost exclusively on stimuli for which it is relatively easy to attend selectively to the component dimensions (i.e., separable dimensions). In the present study, we investigated the ability of participants to learn categories constructed from stimuli for which it is difficult, if not impossible, to attend selectively to the component dimensions (i.e., integral dimensions). The experiments demonstrate that individuals are capable of learning categories constructed from the integral dimensions of brightness and saturation, but this ability is generally limited to category structures requiring selective attention to brightness. As might be expected with integral dimensions, participants were often able to integrate brightness and saturation information in the absence of feedback – an ability not observed in previous studies with separable dimensions. Even so, there was a bias to weight brightness more heavily than saturation in the categorization process, suggesting a weak form of selective attention to brightness. These data present an important challenge for the development of models of unsupervised category learning
A Bivariate Measure of Redundant Information
We define a measure of redundant information based on projections in the
space of probability distributions. Redundant information between random
variables is information that is shared between those variables. But in
contrast to mutual information, redundant information denotes information that
is shared about the outcome of a third variable. Formalizing this concept, and
being able to measure it, is required for the non-negative decomposition of
mutual information into redundant and synergistic information. Previous
attempts to formalize redundant or synergistic information struggle to capture
some desired properties. We introduce a new formalism for redundant information
and prove that it satisfies all the properties necessary outlined in earlier
work, as well as an additional criterion that we propose to be necessary to
capture redundancy. We also demonstrate the behaviour of this new measure for
several examples, compare it to previous measures and apply it to the
decomposition of transfer entropy.Comment: 16 pages, 15 figures, 1 table, added citation to Griffith et al 2012,
Maurer et al 199
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