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An extended description of the implementation of the deep geothermal wells GRT-1 and GRT-2 respectively drilled in 2012 and 2014 in the framework of the ECOGI project is given.
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Homologous gene groups (HGGs) were identified from reciprocal TBLASTX searches (E-value < 1e-30) between libraries and P. graminis f. sp. tritici gene models followed by graph clustering analysis using a TCL implementation of the Deep-First Search algorithm.
We also tested SACA-K [ 26], a recently published memory-efficient successor of SA-IS and included an implementation of the Deep-Shallow algorithm [ 40], which is theoretically not a linear-time algorithm, but has been shown to be fast and lightweight in practice [ 22].
I first present an informal description of the method, followed by an explicit description of implementation using the deep coalescences criterion.
The implementation of the Association Agreement including the Deep and Comprehensive Free Trade Area is going well.
We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed.
The purpose of this paper is to describe the implementation of CECGWEP in deep coal mines and present a systematic summary of the key techniques based on field trials in recent decades.
Compared with Mori's implementation of SA-IS, Deep Shallow performs significantly poorly for the chr22 data set, but it is fairly competitive for the other data sets.
The wide availability of increasing computing power coupled with the development of more efficient training algorithms have allowed the implementation of deep learning principles in a manner and span that had not been previously possible.
The implementation of deep neural training networks powered by GPUs from companies like Advanced Micro Devices (AMD) and NVIDIA and the big data sets that feed them is enabling the explosion of AI and ML.
We use a fast, fully parameterizable GPU implementation of a Deep Neural Network (DNN) that does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com