24,529 research outputs found
Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG
This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings
From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning
Video captioning in essential is a complex natural process, which is affected
by various uncertainties stemming from video content, subjective judgment, etc.
In this paper we build on the recent progress in using encoder-decoder
framework for video captioning and address what we find to be a critical
deficiency of the existing methods, that most of the decoders propagate
deterministic hidden states. Such complex uncertainty cannot be modeled
efficiently by the deterministic models. In this paper, we propose a generative
approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which
models the uncertainty observed in the data using latent stochastic variables.
Therefore, MS-RNN can improve the performance of video captioning, and generate
multiple sentences to describe a video considering different random factors.
Specifically, a multi-modal LSTM (M-LSTM) is first proposed to interact with
both visual and textual features to capture a high-level representation. Then,
a backward stochastic LSTM (S-LSTM) is proposed to support uncertainty
propagation by introducing latent variables. Experimental results on the
challenging datasets MSVD and MSR-VTT show that our proposed MS-RNN approach
outperforms the state-of-the-art video captioning benchmarks
Practical implementation of nonlinear time series methods: The TISEAN package
Nonlinear time series analysis is becoming a more and more reliable tool for
the study of complicated dynamics from measurements. The concept of
low-dimensional chaos has proven to be fruitful in the understanding of many
complex phenomena despite the fact that very few natural systems have actually
been found to be low dimensional deterministic in the sense of the theory. In
order to evaluate the long term usefulness of the nonlinear time series
approach as inspired by chaos theory, it will be important that the
corresponding methods become more widely accessible. This paper, while not a
proper review on nonlinear time series analysis, tries to make a contribution
to this process by describing the actual implementation of the algorithms, and
their proper usage. Most of the methods require the choice of certain
parameters for each specific time series application. We will try to give
guidance in this respect. The scope and selection of topics in this article, as
well as the implementational choices that have been made, correspond to the
contents of the software package TISEAN which is publicly available from
http://www.mpipks-dresden.mpg.de/~tisean . In fact, this paper can be seen as
an extended manual for the TISEAN programs. It fills the gap between the
technical documentation and the existing literature, providing the necessary
entry points for a more thorough study of the theoretical background.Comment: 27 pages, 21 figures, downloadable software at
http://www.mpipks-dresden.mpg.de/~tisea
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