Sentence examples for a soft data from inspiring English sources

In 2015, these requirements were "enhanced" to include a few other measures: any and all promotional rates, fees and surcharges, packet loss, data caps, "application-agnostic degradation of service to a particular end user" — for example, throttling your traffic after you hit a "soft" data cap or the like — and a few other odds and ends.

Gaussian mixture model has also been employed in [163] to develop a soft-data-constrained DPF.

de Nazelle et al. (2010) also found better predictive accuracy for the representation of space-time O3 distribution in North Carolina with a BME model based on observed (hard) and modeled (soft) data from a stochastic analysis of an urban-intercontinental-scale atmospheric chemistry transport model, compared with kriging method estimates based on hard data only.

A training pattern is picked up from the selected class in a random manner or conditioned to the available soft data; an inner part of the pattern (called "patch") is then fixed at the simulation grid centered at the current node; their location addresses are then removed from the predetermined random path and the remaining nodes will be replaced by the future evaluated values.

The report that manufacturing activity grew in May, but at its slowest pace in more than a year, followed similarly soft data on retail sales and orders for big-ticket items.

The reduction in MSE from method (b) to (c) is more pronounced when performing cross-validation on points that contain a higher percentage of soft data (SI Table S3).

Although the availability of data has grown exponentially in the last 20 years, the process of valuing a player remains a combination of hard and soft data.

A transition probability-based stochastic model was implemented using hard borehole data and soft data extracted from a 3-D deterministic lithostratigraphic model.

He said that the sharp drop in the average work week was a big surprise and that the soft data on hours worked "point to declines in both industrial production and housing starts" in July.

The combination of these two approaches allows selecting economically relevant explanatory variables among a large data set of hard and soft data.

In parallel, a neural network probability cube was generated based on a set of attributes derived from 3D seismic cube to be applied into the MPS algorithm as a soft conditioning data.