Tropp, J. A.: Algorithms for simultaneous sparse approximation Part II: Convex relaxation. Signal Processing. 86, 589–602 (2006). Article MATH Google Scholar Gorodnitsky, I. F. and George, J. S. and Rao, B. D.: Neuromagnetic source imaging with focuss: a recursive weighted minimum ...
4. Filter Design and Spectral Reconstruction In this section, the details on the end-to-end network for simultaneous filter response design and spectral reconstruc- tion will be given. We will start with the spectral recon- struction network, and later append a special convolution layer onto it...
Deep learning to obtain simultaneous image and segmentation outputs from a single input of raw ultrasound channel data Single plane wave transmissions are promising for automated imaging tasks requiring high ultrasound frame rates over an extended field of view. However, a ... AA Nair,KN Washington...
Taken together, these data reveal the existence, in A1 L2/3 of mice trained with complex sound(s), of distinct sparse sets of HB/qHB cells that possessed bursting responses to each trained complex sound (chord or BBN) as a whole, with the co-existence of AB cells that possessed enhanced...
However, radioprotection quantities do not take into account fractionation as well as simultaneous irradiation with high doses in a restricted PTV volume and low doses to the rest of the patient body. Moreover, dose equivalent and effective dose use radiation and tissue weighting factors which are...
For example, can the simultaneous use of enriched clinical cohort and the GWAS data provide a novel clinical-genetic readout to predict AAO in non-affected carriers of pathogenic mutations in the LRRK2 gene? LRRK2 mutation carriers are at-risk of PD, but given that penetrance is reduced, ...
Deep learning technology has enabled successful modeling of complex facial features when high-quality images are available. Nonetheless, accurate modeling and recognition of human faces in real-world scenarios “on the wild” or under adverse conditions
VAE allows for the simultaneous performance of VI (with respect to ϕ ) and the model selection (with respect to θ ), and the resulting VAE objective is given by the following equation: max ϕ , θ L VAE ( θ , ϕ ; y ) = E q ϕ ( x | y ) [ log p θ ( y | x...