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But in case of data access policy changes, the memories need to be redesigned.
We thus discuss the use of different approaches in order to reduce the number of parameters of the networks in case of data sets with extremely small number of samples.
To overcome this disadvantage, redundant copies of the data are kept, in case of data loss.
In case of data scarcity, we can further reduce the training size to 5k with minimal deterioration in performance.
44% have a response plan in place in case of data breach, 47% track where all data is stored, 43% keep updated list of sensitive data types collected, and 44% train all employees in IT security risks and practices (SMR).
In comparison, the partitioning method of Mai et al. [1] introduces an area overhead of 80.33 % for instruction and 44.64 % in case of data memory.
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The re-calibrated model was a good alternative to onsite weather station data for simulating soil moisture patterns and evapotranspiration fluxes, particularly in cases of data scarcity.
In cases of data normalization, the standard errors were re-calculated using Gaussian error propagation.
Original data collection forms were used for correction in cases of data entry errors identified by the coordinators.
In case of missing data (1,3% of data values), mean values or modal category scores were imputed [ 11].
In case of bimodal data, the correlation was calculated over subsets of data [ 12].
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