We can observe that our proposed detection method gives accurate sparsity estimation for low sparsity signal.
Therefore, the average number of iterations for DOMP can be found in the Table 1, which actually matches the signal's sparsity for low sparsity case.
During parcellation, two different λ sequences were tested: the first one ranged from low sparsity (i.e., λ = 0.0001) to high sparsity (i.e., λ = 10) with logarithmic equal steps, and the second one focused on a local range [0.1, 1] with linear equal steps.
Recently, Buetfering and colleagues found that parvalbumin-positive (PV+) interneurons in the medial entorhinal cortex (MEC) integrate input form grid cells with various phases, and exhibited low spatial sparsity and no spatial periodicity (Buetfering et al., 2014. Nat Neurosci 17 5):717 517 5
While there is little data on the spatial firing of interneurons in the MEC, recent evidence indicates that the majority of parvalbumin positive interneurons have firing fields with significant spatial stability, but low spatial sparsity and grid scores compared to excitatory grid cells (Buetfering et al., 2014).
From the second contribution, we showed that our CS-based cryptosystem can be computationally secure in terms of the indistinguishability, as long as the keystream length for each encryption is sufficiently large with low compression and sparsity ratios.
By developing upper bounds on the distance measures, we show that the CS-based cryptosystem can be computationally secure in terms of the indistinguishability, as long as the keystream length for each encryption is sufficiently large with low compression and sparsity ratios.
This may be due to the fact that the SPU scheme is the only one which completely ignores entire subbands when updating the filters, while the other schemes may allocate a few taps to each subband when the reference signal has a low amount of sparsity.
Finally, our simulations show that images with large pixels can be successfully obtained by utilizing only a small number of PMT, due to the temporal and spatial sparsity of low level light.
In this paper, we propose a novel unsupervised feature selection algorithm, which retains the preserving power, and implements high sparsity and low redundancy in a unified manner.
We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity, and low rank of the BES matrix.
