The performance of IP mobility protocols is highly dependent on the change of mobile nodes’ (MNs’) mobility and traffic-related characteristics. Therefore, it is essential to investigate the effects of these characteristics and to conduct an in-depth performance study of these protocols. In this paper, we introduce a novel analytical approach using a continuous-time Markov chain model and hiera...

BACKGROUND Understanding the complex hierarchical topology of functional brain networks is a key aspect of functional connectivity research. Such topics are obscured by the widespread use of sparse binary network models which are fundamentally different to the complete weighted networks derived from functional connectivity. NEW METHODS We introduce two techniques to probe the hierarchical com...

We adapt the hierarchical attention network for the task of genre classification using lyrics. Utilising a large dataset of intact song lyrics we apply a recurrent neural network model which tries to learn importance of words, lines and sections in the genre classification task. This hierarchical structure attempts to replicate the structure of lyrics and enable learning of which sections, line...

Accurate short-term forecasting of intensive rainfall has high practical value but remains difficult to achieve. Based on deep learning and spatial–temporal sequence predictions, this paper proposes a hierarchical dynamic graph network. To fully model the correlations among data, uses dynamically constructed convolution operator spatial correlation, recurrent structure time architecture built w...

Hierarchical application of Triple-Modular Redundancy (TMR) increases fault tolerance of digital Integrated Circuit (IC). In this paper, a simple probabilistic model was proposed for analysis of fault masking performance of hierarchical TMR networks. Performance improvements obtained by second order TMR network were theoretically compared with first order TMR network.

The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov model (HMM) that models sequences with structure at many length/time scales [FST98]. Unfortunately, the original inference algorithm is rather complicated, and takes time, where is the length of the sequence, making it impractical for many domains. In this paper, we show how HHMMs are a special kind of dynamic ...