Search results
Results from the WOW.Com Content Network
In a spreadsheet, cells can contain formulas referring to the contents of other cells; if the user changes the content of a cell, the values of all its dependent cells are automatically updated. In a similar fashion, the properties of components in a Power Fx program are connected by formulas (whose syntax is very reminiscent of Excel ) and ...
The method is based on the idea of a so-called fraction function . It is a scalar function, defined as the integral of a fluid's characteristic function in the control volume, namely the volume of a computational grid cell. The volume fraction of each fluid is tracked through every cell in the computational grid, while all fluids share a single ...
Cell-based models are mathematical models that represent biological cells as discrete entities. Within the field of computational biology they are often simply called agent-based models [1] of which they are a specific application and they are used for simulating the biomechanics of multicellular structures such as tissues. to study the influence of these behaviors on how tissues are organised ...
Arithmetic if is an unstructured control statement, and is not used in structured programming. In practice it has been observed that most arithmetic IF statements reference the following statement with one or two of the labels. This was the only conditional control statement in the original implementation of Fortran on the IBM 704 computer. On ...
To make comparisons based on dates (e.g., if the current date and time is after some other date and time), first convert the time(s) to the number of seconds after January 1, 1970, using the function {{#time: U }}, then compare (or add, subtract, etc.) those numerical values.
Gradient-based one-side sampling (GOSS) is a method that leverages the fact that there is no native weight for data instance in GBDT. Since data instances with different gradients play different roles in the computation of information gain, the instances with larger gradients will contribute more to the information gain.
The last condition is the key condition; in words it says that for each state x we can find a control u that will reduce the "energy" V. Intuitively, if in each state we can always find a way to reduce the energy, we should eventually be able to bring the energy asymptotically to zero, that is to bring the system to a stop.
In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...