Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta ($\Sigma\Delta$) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201

Restricted Isometries for Partial Random Circulant Matrices

In the theory of compressed sensing, restricted isometry analysis has become a standard tool for studying how efficiently a measurement matrix acquires information about sparse and compressible signals. Many recovery algorithms are known to succeed when the restricted isometry constants of the sampling matrix are small. Many potential applications of compressed sensing involve a data-acquisition process that proceeds by convolution with a random pulse followed by (nonrandom) subsampling. At present, the theoretical analysis of this measurement technique is lacking. This paper demonstrates that the $s$th order restricted isometry constant is small when the number $m$ of samples satisfies $m \gtrsim (s \log n)^{3/2}$, where $n$ is the length of the pulse. This bound improves on previous estimates, which exhibit quadratic scaling

qpMerge: Merging different peptide isoforms using a motif centric strategy

Accurate quantification and enumeration of peptide motifs is hampered by redundancy in peptide identification. A single phosphorylation motif may be split across charge states, alternative modifications (e.g. acetylation and oxidation), and multiple miss-cleavage sites which render the biological interpretation of MS data a challenge. In addition motif redundancy can affect quantitative and statistical analysis and prevent a realistic comparison of peptide numbers between datasets. In this study, we present a merging tool set developed for the Galaxy workflow environment to achieve a non-redundant set of quantifications for phospho-motifs. We present a Galaxy workflow to merge three exemplar dataset, and observe reduced phospho-motif redundancy and decreased replicate variation. The qpMerge tools provide a straightforward and reusable approach to facilitating phospho-motif analysis. The source-code and wiki documentation is publically available at http://sourceforge.net/projects/ppmerge. The galaxy pipeline used in the exemplar analysis can be found at http://www.myexperiment.org/workflows/4186

Electron Microscopy Of Wood Of Callixylon And Cordaites

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141638/1/ajb210693.pd

Universal and efficient compressed sensing by spread spectrum and application to realistic Fourier imaging techniques

We advocate a compressed sensing strategy that consists of multiplying the signal of interest by a wide bandwidth modulation before projection onto randomly selected vectors of an orthonormal basis. Firstly, in a digital setting with random modulation, considering a whole class of sensing bases including the Fourier basis, we prove that the technique is universal in the sense that the required number of measurements for accurate recovery is optimal and independent of the sparsity basis. This universality stems from a drastic decrease of coherence between the sparsity and the sensing bases, which for a Fourier sensing basis relates to a spread of the original signal spectrum by the modulation (hence the name "spread spectrum"). The approach is also efficient as sensing matrices with fast matrix multiplication algorithms can be used, in particular in the case of Fourier measurements. Secondly, these results are confirmed by a numerical analysis of the phase transition of the l1- minimization problem. Finally, we show that the spread spectrum technique remains effective in an analog setting with chirp modulation for application to realistic Fourier imaging. We illustrate these findings in the context of radio interferometry and magnetic resonance imaging.Comment: Submitted for publication in EURASIP Journal on Advances in Signal Processin

Adipocyte-derived extracellular vesicles increase insulin secretion through transport of insulinotropic protein cargo

Adipocyte-derived extracellular vesicles (AdEVs) are membranous nanoparticles that convey communication from adipose tissue to other organs. Here, to delineate their role as messengers with glucoregulatory nature, we paired fluorescence AdEV-tracing and SILAC-labeling with (phospho)proteomics, and revealed that AdEVs transfer functional insulinotropic protein cargo into pancreatic β-cells. Upon transfer, AdEV proteins were subjects for phosphorylation, augmented insulinotropic GPCR/cAMP/PKA signaling by increasing total protein abundances and phosphosite dynamics, and ultimately enhanced 1st-phase glucose-stimulated insulin secretion (GSIS) in murine islets. Notably, insulinotropic effects were restricted to AdEVs isolated from obese and insulin resistant, but not lean mice, which was consistent with differential protein loads and AdEV luminal morphologies. Likewise, in vivo pre-treatment with AdEVs from obese but not lean mice amplified insulin secretion and glucose tolerance in mice. This data suggests that secreted AdEVs can inform pancreatic β-cells about insulin resistance in adipose tissue in order to amplify GSIS in times of increased insulin demand

Second Language Tutoring Using Social Robots: A Large-Scale Study

We present a large-scale study of a series of seven lessons designed to help young children learn English vocabulary as a foreign language using a social robot. The experiment was designed to investigate 1) the effectiveness of a social robot teaching children new words over the course of multiple interactions (supported by a tablet), 2) the added benefit of a robot's iconic gestures on word learning and retention, and 3) the effect of learning from a robot tutor accompanied by a tablet versus learning from a tablet application alone. For reasons of transparency, the research questions, hypotheses and methods were preregistered. With a sample size of 194 children, our study was statistically well-powered. Our findings demonstrate that children are able to acquire and retain English vocabulary words taught by a robot tutor to a similar extent as when they are taught by a tablet application. In addition, we found no beneficial effect of a robot's iconic gestures on learning gains