Across the stable density stratification of the abyssal ocean, deep dense water is slowly propelled upward by sustained, though irregular, turbulent mixing. The resulting mean upwelling determines large-scale oceanic circulation properties like heat and
waveforms prone to cycle skipping before calculating the cross-correlation and a time-independent criterion for the comparison of the maximum and the second largest peak (sidelobe maximum, CCslm, corresponding to the largest positive value of the cross-correlation function in the case of anti-...
relying on accurate locations and rupture size estimation2,3is challenging. On the other hand, identifying repeaters from waveforms only requires accomplishing certain cross-correlation coefficients and the processing of high frequency signals22, which constrain this type of observation to relatively small...
and show how the Rademacher and gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes.We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector ...
19,64. Data processing: from observations to 2D spectra Adaptive formula for the 2D spectral energy. Due to the large aspect ratio of the horizontal-to-vertical scales of oceanic internal waves, a minimal statistical description of the wavefield—assuming no pre- ferential directionality in the ...
Then we quickly move to converting the text data into vectors usingTF-IDFvectoriser and fitting aRandom Forestclassification model on that. Image by author Now let’s begin the main interest of this blog which is how to interpret different components of LIME. ...
it is a replacement of text rather than run in js, so you may check the generated code to see is the actual value you want use args in docker-compose to pass the variable instead of ENV, it may make you life easier. Seems the process.env is not traditional env. But it similar like...
and paragraphs to a multidimensional dense vector space (i.e., word embedding;215,216, and which achieves state-of-the-art performance on machine learning-tasks related to text understanding217). Such techniques allow us to define the semantic closeness of words, sentences, or paragraphs in an...