There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

https://caltcm.memberclicks.net/assets/documents/acc_aha_chf_2013_guidelines.pdf

2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Soil Chemical Analysis

Statistics for Spatial Data.

This article and the associated data and programs provided with it are intended to assist color engineers and scientists in correctly implementing the recently developed CIEDE2000 color-difference formula. We indicate several potential implementation errors that are not uncovered in tests performed using the original sample data published with the standard. A supplemental set of data is provided for comprehensive testing of implementations. The test data, Microsoft Excel spreadsheets, and MATLAB scripts for evaluating the CIEDE2000 color difference are made available at the first author's website. Finally, we also point out small mathematical discontinuities in the formula. © 2004 Wiley Periodicals, Inc. Col Res Appl, 30, 21–30, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20070

The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations

We present a novel reversible (lossless) data hiding (embedding) technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known LSB (least significant bit) modification is proposed as the data embedding method, which introduces additional operating points on the capacity-distortion curve. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion, and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditional entropy coder which utilizes static portions of the host as side-information improves the compression efficiency, and thus the lossless data embedding capacity.

Reversible data hiding

We present a novel lossless (reversible) data-embedding technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known least significant bit (LSB) modification is proposed as the data-embedding method, which introduces additional operating points on the capacity-distortion curve. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditional entropy coder which utilizes unaltered portions of the host signal as side-information improves the compression efficiency and, thus, the lossless data-embedding capacity.

Lossless generalized-LSB data embedding

Color Fundamentals for Digital Imaging, Gaurav Sharma, Xerox Corporation Visual Psychophysics and Color Appearance, Garrett M. Johnson and Mark D. Fairchild, Rochester Institute of Technology Physical Models for Color Prediction, Patrick Emmel, Clariant International Color Management for Digital Imaging Systems, Edward J. Giorgianni, Thomas E. Madden and Kevin E. Spaulding, Eastman Kodak Company Device Characterization, Raja Bala, Xerox Corporation Digital Color Halftones, Charles M Hains, Shen-ge Wang, and Keith T. Knox, Xerox Corporation Human Visual Model Based Color Halftoning, A. Ufuk Agar, Hewlett Packard Company, Farhan A. Baqai, Sony Electronics, and Jan P. Allebach, Purdue University Compression of Color Images, Ricardo de Queiroz, Xerox Corporation Color Quantization, Luc Brun, Universite Reims, Champagne Ardenne and Alain Tremeau, Universite Jean Monnet de Saint-Etienne Gamut Mapping, Jan Morovic, University of Derby Efficient Color Transformation Implementation, Raja Bala and R. Victor Klassen, Xerox Corporation Color Image Processing for Digital Cameras, Ken Parulski and Kevin E. Spaulding, Eastman Kodak Company

/pdf/digital-color-imaging-handbook-3pyxl1fjks.pdf

Digital Color Imaging Handbook

Background: Clinician rating scales and patient-reported outcomes are the principal means of assessing motor symptoms in Parkinson disease and Huntington disease.

/pdf/multiple-wearable-sensors-in-parkinson-and-huntington-m5tj0ekk13.pdf

Gaurav Sharma

Papers

The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations

Reversible data hiding

Lossless generalized-LSB data embedding

Digital Color Imaging Handbook

Multiple Wearable Sensors in Parkinson and Huntington Disease Individuals: A Pilot Study in Clinic and at Home