AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to extract features from EEG signals. Link to documentation Installation AntroPy can be installed with pip ...
This function is used to initialize the runtime environment of the specified device in the current system. The flag indicates different types of devices. For example, when the flag is set to 1, the real device is initialized. When the flag is 0, the fake device can be initialized, which...
Anybody writing software that has to work in more than one geographic area must at some point think about how to handle time zones. Many developers have an incomplete picture of how time zones work, and this post is written in an attempt to describe this
This library provides a decorator that disassembles the function's bytecode, checks if it calculates linear recurrences, and tries to reduce the algorithm's time complexity from O(n) to O(log n) using the fast matrix exponentiation.Detailed description: Russian, English.Inspired by the Alexander...
of high inference time complexity. By using gradient-boosted regression trees as a predictor of the labels obtained from nearest neighbor analysis, we demonstrate a significant increase in inference speed, improving by several orders of magnitude. We validate the effectiveness of our approach on a ...
Ideally, uncertainties in field-estimates and lidar-estimates are propagated to the satellite-based estimates, which is notoriously difficult either because such information is not existent, or unavailable for large areas, or the complexity of inventory designs prohibits analytical solutions. ...
It not only overcomes the computational complexity, training inefficiency, and difficulty of the practical application of RNN but also avoids the problem of locally optimal solutions. ESN mimics the structure of recursively connected neuron circuits in the brain and consists of an input layer, an ...
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Techniques such as deconvolutional networks48 make it possible to map the learned space of a deep learning algorithm back onto the original temporal dataset. This allows one to visualise the features that the algorithm is using to make its decision, which could serve as a starting point for an...
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing. This is an adaptation to take RRI or peak times from fetal and maternal heart rate data as input and output 60+ HRV measures including optimal time delay-based complexity measures wi