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biophysical model to engineer inverted genetic sensors with improved EC50 and G∞. To get a baseline for comparison of the performance of the precision engineering approaches, we measured multiple replicate dose ...
regularization (which encourages sparsity in the number of active spatial wavefronts and can be solved with the LASSO), and a linear Gaussian state space model (which encourages temporal smoothness and can ...
sensors , whose response properties are based on experimental measurements from mechanosensory neurons. With a sparsity - promoting optimization method, we solve for the locations of a small, fixed number ...
in seismic data recovery. This model suggests that natural seismic signals are compressible, or well approximated, by a linear combination of only a few atoms from a dictionary. Imposing sparsity constraints ...
any training signal, but corresponds to a bilinear inverse problem whose algorithmic solution is an open issue. We here address blind calibration as a non -convex problem for linear random sensing models ...
reveals an equivalence between sparse coding models and neural networks with linear plasticity mechanisms, where the sparsity constraint is determined by the f-I curve g. While Oja’s rule is commonly ...
model averaging. A clinically relevant and intuitive method for selecting the model averaging weights is to maximize the linear correlation between forecasts and measurements over some time period ...
traditional motor performance measures as well as non -motor features, namely cognitive factors and chronic health. Via a series of regression models and employing variable selection techniques, we identified ...
1 Introduction In many real applications, during the image recording, the measurement of light inevitably leads to the uncertainty of striking particles on the image sensor . In other words ...
is to promote sparsity in the model by forcing some feature coefficients to be exactly zero. This, in turn, aids in feature selection [13]. The application of L1 regularisation to our model resulted in improved ...
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