Lossless Astronomical Image Compression and the Effects of Noise

We compare a variety of lossless image compression methods on a large sample of astronomical images and show how the compression ratios and speeds of the algorithms are affected by the amount of noise in the images. In the ideal case where the image pixel values have a random Gaussian distribution, the equivalent number of uncompressible noise bits per pixel is given by Nbits =log2(sigma * sqrt(12)) and the lossless compression ratio is given by R = BITPIX / Nbits + K where BITPIX is the bit length of the pixel values and K is a measure of the efficiency of the compression algorithm. We perform image compression tests on a large sample of integer astronomical CCD images using the GZIP compression program and using a newer FITS tiled-image compression method that currently supports 4 compression algorithms: Rice, Hcompress, PLIO, and GZIP. Overall, the Rice compression algorithm strikes the best balance of compression and computational efficiency; it is 2--3 times faster and produces about 1.4 times greater compression than GZIP. The Rice algorithm produces 75%--90% (depending on the amount of noise in the image) as much compression as an ideal algorithm with K = 0. The image compression and uncompression utility programs used in this study (called fpack and funpack) are publicly available from the HEASARC web site. A simple command-line interface may be used to compress or uncompress any FITS image file.Comment: 20 pages, 9 figures, to be published in PAS

Optimal Compression of Floating-point Astronomical Images Without Significant Loss of Information

We describe a compression method for floating-point astronomical images that gives compression ratios of 6 -- 10 while still preserving the scientifically important information in the image. The pixel values are first preprocessed by quantizing them into scaled integer intensity levels, which removes some of the uncompressible noise in the image. The integers are then losslessly compressed using the fast and efficient Rice algorithm and stored in a portable FITS format file. Quantizing an image more coarsely gives greater image compression, but it also increases the noise and degrades the precision of the photometric and astrometric measurements in the quantized image. Dithering the pixel values during the quantization process can greatly improve the precision of measurements in the images. This is especially important if the analysis algorithm relies on the mode or the median which would be similarly quantized if the pixel values are not dithered. We perform a series of experiments on both synthetic and real astronomical CCD images to quantitatively demonstrate that the magnitudes and positions of stars in the quantized images can be measured with the predicted amount of precision. In order to encourage wider use of these image compression methods, we have made available a pair of general-purpose image compression programs, called fpack and funpack, which can be used to compress any FITS format image.Comment: Accepted PAS

Virtual manipulatives: Making effective instructional choices

This article gives three tips for the effective use of virtual manipulatives in mathematics instruction to promote active engagement and student learning: (1) match the virtual manipulative to the learning target; (2) determine the task to pose to students; and (3) encourage discourse

Academic Job Negotiation Experiences, Reflections, and Biases in Counselor Education: A Descriptive Study

This descriptive study explored the job negotiation experiences of 93 counselor educators through an embedded survey design to examine their negotiation experiences, reflections, and potential hiring biases. The most common negotiation preparation strategy was consulting a mentor (80%) and while salary was most regularly negotiated (76%), a list of other benefits was included. Although a majority of participants regretted not making a request (53%), most reported overall positive experiences (63%). These findings support implications for counselor educators including preparing early, using successful negotiation strategies, exploring all potential benefits, and articulating requests for a more positive negotiation experience

Beta-Lactamase Repressor BlaI Modulates Staphylococcus aureus Cathelicidin Antimicrobial Peptide Resistance and Virulence.

BlaI is a repressor of BlaZ, the beta-lactamase responsible for penicillin resistance in Staphylococcus aureus. Through screening a transposon library in S. aureus Newman for susceptibility to cathelicidin antimicrobial peptide, we discovered BlaI as a novel cathelicidin resistance factor. Additionally, through integrational mutagenesis in S. aureus Newman and MRSA Sanger 252 strains, we confirmed the role of BlaI in resistance to human and murine cathelidicin and showed that it contributes to virulence in human whole blood and murine infection models. We further demonstrated that BlaI could be a target for innate immune-based antimicrobial therapies; by removing BlaI through subinhibitory concentrations of 6-aminopenicillanic acid, we were able to sensitize S. aureus to LL-37 killing

The Future is Hera! Analyzing Astronomical Over the Internet

Hera is the data processing facility provided by the High Energy Astrophysics Science Archive Research Center (HEASARC) at the NASA Goddard Space Flight Center for analyzing astronomical data. Hera provides all the pre-installed software packages, local disk space, and computing resources need to do general processing of FITS format data files residing on the users local computer, and to do research using the publicly available data from the High ENergy Astrophysics Division. Qualified students, educators and researchers may freely use the Hera services over the internet of research and educational purposes

Feasibility and performances of compressed-sensing and sparse map-making with Herschel/PACS data

The Herschel Space Observatory of ESA was launched in May 2009 and is in operation since. From its distant orbit around L2 it needs to transmit a huge quantity of information through a very limited bandwidth. This is especially true for the PACS imaging camera which needs to compress its data far more than what can be achieved with lossless compression. This is currently solved by including lossy averaging and rounding steps on board. Recently, a new theory called compressed-sensing emerged from the statistics community. This theory makes use of the sparsity of natural (or astrophysical) images to optimize the acquisition scheme of the data needed to estimate those images. Thus, it can lead to high compression factors. A previous article by Bobin et al. (2008) showed how the new theory could be applied to simulated Herschel/PACS data to solve the compression requirement of the instrument. In this article, we show that compressed-sensing theory can indeed be successfully applied to actual Herschel/PACS data and give significant improvements over the standard pipeline. In order to fully use the redundancy present in the data, we perform full sky map estimation and decompression at the same time, which cannot be done in most other compression methods. We also demonstrate that the various artifacts affecting the data (pink noise, glitches, whose behavior is a priori not well compatible with compressed-sensing) can be handled as well in this new framework. Finally, we make a comparison between the methods from the compressed-sensing scheme and data acquired with the standard compression scheme. We discuss improvements that can be made on ground for the creation of sky maps from the data.Comment: 11 pages, 6 figures, 5 tables, peer-reviewed articl