PURPOSE To assess the wavelet entropy for the characterization of intrinsic aberrant temporal irregularities in the time series of resting-state blood-oxygen-level-dependent (BOLD) signal fluctuations. Further, to evaluate the temporal irregularities (disorder/order) on a voxel-by-voxel basis in the brains of children with Rolandic epilepsy. MATERIALS AND METHODS The BOLD time series was deco...

As research expands in multiagent intelligent systems, investigators need new tools for evaluating the artificial societies they study. It is impossible, for example, to correlate heterogeneity with performance in multiagent robotics without a quantitative metric of diversity. Currently diversity is evaluated on a bipolar scale with systems classified as either heterogeneous or homogeneous, dep...

In this paper, we introduce an assumption which makes it possible to extend the learning ability of discriminative model to unsupervised setting. We propose an informationtheoretic framework as an implementation of the low-density separation assumption. The proposed framework provides a unified perspective of Maximum Margin Clustering (MMC), Discriminative k -means, Spectral Clustering and Unsu...

In this paper, the moments of nearest neighbor distance distributions are examined. While the asymptotic form of such moments is wellknown, the boundary effect has this far resisted a rigorous analysis. Our goal is to develop a new technique that allows a closed-form high order expansion, where the boundaries are taken into account up to the first order. The resulting theoretical predictions ar...

Rényi divergence is related to Rényi entropy much like information divergence (also called Kullback-Leibler divergence or relative entropy) is related to Shannon’s entropy, and comes up in many settings. It was introduced by Rényi as a measure of information that satisfies almost the same axioms as information divergence. We review the most important properties of Rényi divergence, including it...

Recently, entropy measures have shown a significant promise in detecting diverse set of network anomalies. While many different forms of entropy exist, only a few have been studied in the context of network anomaly detection. In the paper, results of our case study on entropy-based IP traffic anomaly detection are prestented. Besides the well-known Shannon approach and counter-based methods, va...

It has long been appreciated that Shannon information can in principle be used to measure the informational content of sensory signals. But the lack of a general, context-sensitive method for modelling signal probability distributions has obstructed practical applications. Addressing this problem, the paper introduces an approach based on dependency models. Using Shannon’s concept of conditiona...

Automated analysis and grading of the neonatal EEG has a potential to assist clinical decision making for neonates with hypoxic-ischemic encephalopathy. This paper proposes a method to grade the degree of abnormality in hour-long segments of neonatal EEG. The HMM-based speaker diarization approach is employed to segment and cluster the neonatal EEG into homogeneous states. Several features are ...

Approaches based on generative adversarial networks for imitation learning are promising because they sample efficient in terms of expert demonstrations. However, training a generator requires many interactions with the actual environment model-free reinforcement is adopted to update policy. To improve efficiency using model-based learning, we propose Entropy-Regularized Imitation Learning (MB-...

Precisely describing the formation and evolution rules of coal-rock fracturing crack have great value on reservoir fracturing improvement and highly efficient mining of coal bed methane well. In this paper, a non-linear dynamic method is used to study crack damage evolution behavior of coal-rock fracturing. Considering distribution characteristics of natural cracks in coal-rock, and based on da...