Correlations among ‘special’ and ‘general’ grain boundaries are studied on two-dimensional networks, by examining the configurational entropy of boundary structures as well as percolation thresholds. Consideration of crystallographic constraints at various length scales reveals that higher-order constraints play a role in boundary connectivity and network structure. Implications for grain bound...

The Shannon entropy is a standard measure for the order state of symbol sequences, such as, for example, DNA sequences. In order to incorporate correlations between symbols, the entropy of n-mers (consecutive strands of n symbols) has to be determined. Here, an assay is presented to estimate such higher order entropies (block entropies) for DNA sequences when the actual number of observations i...

We introduced the concept of C-space entropy recently in [1–3] as a measure of knowledge of C-space for sensor-based path planning and exploration for general robot-sensor systems. The robot plans the next sensing action to maximally reduce the expected C-space entropy, also called the maximal expected entropy reduction, or MER criterion. The expected C-space entropy computation, however, made ...

We have recently introduced the concept of C-space entropy as a measure of knowledge of C-space for sensor-based path planning and exploration for general robot-sensor systems [1, 2, 3, 5]. The robot plans the next sensing action to maximally reduce the expected C-space entropy, also called the maximal expected entropy reduction, or MER criterion. The expected C-space entropy computation, howev...

We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers [10] and [4] who proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows to properly handle memory effects in spike statistics, for large sized neura...

We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers [10] and [5] who proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows to properly handle memory effects in spike statistics, for large sized neura...

We propose a position-based approach for largescale simulations of rigid bodies at interactive frame-rates. Our method solves positional constraints between rigid bodies and therefore integrates nicely with other position-based methods. Interaction of particles and rigid bodies through common constraints enables two-way coupling with deformables. The method exhibits exceptional performance and ...

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We derive a rst-order approximation of the density of maximum entropy for a continuous 1-D random variable, given a number of simple constraints. This results in a density expansion which is somewhat similar to the classical polynomial density expansions by Gram-Charlier and Edgeworth. Using this approximation of density, an approximation of 1-D diierential entropy is derived. The approximation...