We use mathematical methods from the theory of tailored random graphs to study systematically the effects of sampling on topological features of large biological signalling networks. Our aim in doing so is to increase our quantitative understanding of the relation between true biological networks and the imperfect and often biased samples of these networks that are reported in public data repos...

Array detector-based instruments are now fundamental to measurements of ozone and other atmospheric trace gases from space in the ultraviolet, visible, and infrared. The present generation of such instruments suffers, to a greater or lesser degree, from undersampling of the spectra, leading to difficulties in the analysis of atmospheric radiances. We provide extended analysis of the undersampli...

Machine learning models have gained popularity nowadays for their potential to solve real-life issues when trained on pertinent data. In many cases, the data are class imbalanced and hence corresponding machine tend perform poorly metrics like precision, recall, AUC, F1, G-mean score. Since imbalance issue poses serious challenges performance of models, a multitude research works addressed this...

Introduction Compressed sensing (CS) can be used with data driven parallel imaging (PI) (1) in an integrated approach, such as L1 SPIR-iT (2), or a serial approach where CS is the second step (3). Likewise, the combination with model driven PI can be integrated (4,5) or serial (6) but, in the serial case, CS is the first step. A serial approach has the advantage that acceleration can be clearly...

This paper provides statistical analysis for efficient detection of signal components when missing data samples are present. This analysis is important for both the areas of Lstatistics and compressive sensing. In both cases, few samples are available due to either noisy sample elimination or random undersampling signal strategies. The analysis enables the determination of the sufficient number...

Undersampling in the detector array degrades the performance of iris-recognition imaging systems. We find that an undersampling of 8 x 8 reduces the iris-recognition performance by nearly a factor of 4 (on CASIA iris database), as measured by the false rejection ratio (FRR) metric. We employ optical point spread function (PSF) engineering via a Zernike phase mask in conjunction with multiple su...

PROPELLER MRI (periodically rotated overlapping parallel lines with enhanced reconstruction) provides images with significantly fewer B(0)-related artifacts than echo-planar imaging (EPI), as well as reduced sensitivity to motion compared to conventional multiple-shot fast spin-echo (FSE). However, the minimum imaging time in PROPELLER is markedly longer than in EPI and 50% longer than in conve...

Introduction: Machine learning (ML) applications for studying asteroid resonant dynamics are a relatively new field of study. Results from several different approaches currently available asteroids interacting with the z 2 , 1 M1:2, and ν 6 resonances. However, one challenge when using ML to databases produced by these studies is that there often severe imbalance ratio between number in librati...

We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD OCT) and studied its effectiveness. We tested the CS reconstruction by randomly undersampling the k-space SD OCT signal. We achieved this by applying pseudo-random masks to sample 62.5%, 50%, and 37.5% of the CCD camera pixels. OCT images are reconstructed by solving an optimization problem that minimizes the...