We investigate the critical behavior of continuous (second-order) phase transitions in the context of (2+1)-dimensional Ginzburg-Landau models with a double-well effective potential. In particular, we show that the recently-proposed configurational entropy (CE)–a measure of the spatial complexity of the order parameter based on its Fourier-mode decomposition–can be used to identify the critical...

This paper considers the joint precoding and transmit antenna selection to reduce the hardware cost such as the radio-frequency (RF) chains associated with antennas in the downlink of multi-user multi-input multi-output (MU-MIMO) systems with limited feedback. The joint precoding and transmit antenna selection algorithm requires an exhaustive search (ES) of all possible combinations and permuta...

Decentralized POMDPs (Dec-POMDPs) are becoming increasingly popular as models for multiagent planning under uncertainty, but solving a Dec-POMDP exactly is known to be an intractable combinatorial optimization problem. In this paper we apply the Cross-Entropy (CE) method, a recently introduced method for combinatorial optimization, to Dec-POMDPs, resulting in a randomized (sampling-based) algor...

MOTIVATION Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contr...

We investigate and extend the cross entropy (CE) approach for parameter estimation for ODE and DDE models introduced in [27]. Software is developed to allow models to be easily specified for use with an existing package for working with DDEs. Our software implements CE and is used to explore potential improvements to the method. A 3-stage optimization procedure is presented. First, an inexpensi...

In this paper, we provide a new algorithm for the problem of prediction in Reinforcement Learning, i.e., estimating the Value Function of a Markov Reward Process (MRP) using the linear function approximation architecture, with memory and computation costs scaling quadratically in the size of the feature set. The algorithm is a multi-timescale variant of the very popular Cross Entropy (CE) metho...

In this paper, we propose a novel technique for direct recognition of multiple speech streams given the single channel of mixed speech, without first separating them. Our technique is based on permutation invariant training (PIT) for automatic speech recognition (ASR). In PIT-ASR, we compute the average cross entropy (CE) over all frames in the whole utterance for each possible output-target as...

This work proposes and compares perceptually motivated loss functions for deep learning based binary mask estimation for speech separation. Previous loss functions have focused on maximising classification accuracy of mask estimation but we now propose loss functions that aim to maximise the hit minus false-alarm (HIT-FA) rate which is known to correlate more closely to speech intelligibility. ...

In this article, we propose a new stopping criterion for turbo codes. This is based on the behaviour of probabilistic values alpha ' α calculated in forward recursion during decoding. We called Sum-α. The simulation results show that Bit Error Rates BER are very close to those Cross-Entropy CE with same average number iterations.

We study optimization methods for source-initiated energyefficient broadcast in all-wireless networks. The minimum energy broadcast (MEB) problem in wireless networks proved to be NP-complete [1,4]. Past studies on MEB problem [1,2,5,8,9] focus on deterministic optimization to construct energy-efficient broadcast tree. In this paper, we present a probabilistic optimization approach, the Randomi...