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Information Theory

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Scale-Limited Lagrange Stability and Finite-Time Synchronization for Memristive Recurrent Neural Networks on Time Scales.

IEEE transactions on cybernetics
The existed results of Lagrange stability and finite-time synchronization for memristive recurrent neural networks (MRNNs) are scale-free on time evolvement, and some restrictions appear naturally. In this paper, two novel scale-limited comparison pr...

Aging, frailty and complex networks.

Biogerontology
When people age their mortality rate increases exponentially, following Gompertz's law. Even so, individuals do not die from old age. Instead, they accumulate age-related illnesses and conditions and so become increasingly vulnerable to death from va...

Information theory and robotics meet to study predator-prey interactions.

Chaos (Woodbury, N.Y.)
Transfer entropy holds promise to advance our understanding of animal behavior, by affording the identification of causal relationships that underlie animal interactions. A critical step toward the reliable implementation of this powerful information...

Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning.

Computational intelligence and neuroscience
This paper describes a new image generation algorithm based on generative adversarial network. With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the ...

Information-theoretic decomposition of embodied and situated systems.

Neural networks : the official journal of the International Neural Network Society
The embodied and situated view of cognition stresses the importance of real-time and nonlinear bodily interaction with the environment for developing concepts and structuring knowledge. In this article, populations of robots controlled by an artifici...

Learning Domain-Independent Deep Representations by Mutual Information Minimization.

Computational intelligence and neuroscience
Domain transfer learning aims to learn common data representations from a source domain and a target domain so that the source domain data can help the classification of the target domain. Conventional transfer representation learning imposes the dis...

Information flow reveals prediction limits in online social activity.

Nature human behaviour
Modern society depends on the flow of information over online social networks, and users of popular platforms generate substantial behavioural data about themselves and their social ties. However, it remains unclear what fundamental limits exist when...

DNA Steganalysis Using Deep Recurrent Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in convent...

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

Medical image analysis
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised le...

Integrated information in the thermodynamic limit.

Neural networks : the official journal of the International Neural Network Society
The capacity to integrate information is a prominent feature of biological, neural, and cognitive processes. Integrated Information Theory (IIT) provides mathematical tools for quantifying the level of integration in a system, but its computational c...