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Finally, numerical results are shown to carefully reproduce experimental data on template devices with different gate stack compositions, validating the physical assumptions and making the model a valuable tool for nitride memories investigation and design.
During the construction of the ANN model, the training part, probably better ANN model can be realized by increasing the training events and making the model meets every patient's condition more precisely.
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A module was created for each local population of neurons with common intrinsic properties and connectivity to simplify the connection process and make the model more flexible.
Eliminating the rate constants and the power functions can be physiologically justified and makes the model easier to handle, especially in context with experimental data.
When you go wide, you find the essential elements to model and make the model much larger and more useful.
The weighted TV norm can better smooth the level set function and makes the model detect boundaries more easily.
This assures that a wider range of model parameters can be searched and makes the model development process more efficient as compared to manual tweaking of parameters.
To simplify the analysis and make the model further accurate, we assumed it accurately knew the likelihood's variance.
The reduced columnar receptive field that we introduced served this illustrative purpose and made the model conceptually and computationally simpler.
Simple categorized ECG amplitude data were included, which allows automated ECG reading, makes ACS prediction stable, and makes the model useful for personnel inexperienced in ECG reading.
Moreover, the full sequentialization of the phases inhibits the early detection of badly tuned simulations and makes the model tuning spiral annoyingly slow.
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