Reader's Reviews: Fabio, Quantum Imaging, Dick Hamming's Notes and Around the webs in 78 hours

As some of you have heard, Google will stop Google Reader on July 1st. Google reader replacements can be found here. In the meantime, you can do like Andrew and feature your blogroll or like I do, feature the best relevant entries you found on the web. 

You probably recall this and this entry on the use of the Fabio image as a replacement for Lena in an image processing paper (and some theoretical guarantees for TV). Well after being featured on Wikipedia in Lena's entry, Nuit Blanche is now being featured in press releases, such as this one: Every Picture Tells A Story. From the story:

The agent called them back the next day. “I spoke to Fabio,” the agent exclaimed, “and he’s thrilled. He’s excited to be a part of this.”

At long last, a willing participant to eternity. I had heard Fabio was smart. This confirms it.

Last Friday, I pointed to the slew of videos by Dick Hamming in It's Friday, It's Hamming's Time: Learning to Learn by Richard Hamming, "You Get What You Measure" / "Error-Correcting Codes". One reader kindly pointed to the notes that Dick was reading from:

Ross B. said...

If you watched these videos and wondered what the notes in Hamming's big folder look like, I believe that they are all scanned here:

http://www.movesinstitute.org/~mcgredo/HammingLearningToLearn/chapters/

There is also a book "Art of Doing Science and Engineering: Learning to Learn"Thursday, May 2, 2013 at 5:49:00 AM CDT

Thanks Ross !

In Toward real-time quantum imaging with a single pixel camera I expressed the concern that I might not underrstand everything about quantum imaging and in particular why they were able to go below shot noise. Curt commented that:

Curt said...

It is a quantum optics trick:

"The standard quantum limit for the noise of some optical measurement scheme usually refers to the minimum level of quantum noise which can be obtained without the use of squeezed states of light."

http://www.rp-photonics.com/standard_quantum_limit.html

But then Raphael Pooser the author of the paper added:

Hi Igor,

I did not notice that you had blogged about this until now. I'm the PI on this project (Raphael Pooser). Curt is basically right - though I might call it more than a trick ;)

As he said we used quantum noise reduction to get below the Poisson noise. The thing about quantum noise reduction is, you have to use some kind of quantum effect to obtain it. In this case it's the nonlinear interaction that occurs in the vapor cell. Using those techniques you can do quantum imaging as you can see in some of the references in the OE paper and in the OE paper itself. So, what we think is cool about this work is the potential combination of quantum imaging and compressive imaging. Please let me know if you have any questions (you have my contact info from my site or via ORNL)! (sorry if this is a double post)

Thanks Rapheel, I think I have a better picture. To be continued.

Since the last around the webs we have had quite a few interesting and very high quality blog entries out there, here is a sample in no particular order:

Terry

• Quasirandom groups and a cheap version of the Brauer-Fowler theorem

Danny

• GraphLab Challenge @ SC13
• Recsys 2013: Yelp! Business Prediction Contest
• Incremental SVD
• ACM KDD CUP 2013
• Presto: distributed R framework from HP Labs
• Distributed Dual Decomposition (DDD) in GraphLab
• CLiMF Algorithm in GraphChi

Fabian

• Logistic Ordinal Regression

Hein

• TED: “The key to growth? Race with the machines”
• “Is chess the drosophila of artificial intelligence?”
• Corey Chivers’ Introduction to Bayesian Methods
• “Introduction To Monte Carlo Algorithms”
• Ted Dunning on Bandits
• What is probabilistic programming and Why it Matters

Cam

• The Multi-Armed Bandit Problem
• ML Counterexamples Pt.2 - Regression Post-PCA

Andrew:

• 7 ways to separate errors from statistics
• “Tragedy of the science-communication commons”
• The blogroll

Rajarshi

• Predictive models – Implementation vs Specification
• Visual pairwise comparison of distributions

Bob

• Paper of the Day (Po'D): Evaluating music emotion recognition: Lessons from music genre recognition? edition
• Optimum path forest at ISMIR 2011
• Paper of the Day (Po'D): Music genre classification risk and rejection edition
• Paper of the Day (Po'D): Music genre classification via compressive sampling edition

2Physics

• Storage and Control of Optical Photons

Rich

• Personalized Learning Workshop @ Rice 2013

Tim

• Answers, results of polls, and a brief description of the program

MathBlogging

• Mathematical Instruments: Gianluigi Filippelli

Christian

• Bayesian brittleness
• R for dummies
• austerity in MCMC land (#2)
• austerity in MCMC land

Sebastien

• ORF523: Optimization with bandit feedback
• ORF523: Acceleration by randomization for a sum of smooth and strongly convex functions
• ORF523: Noisy oracles
• ORF523: Mirror Prox
• COLT 2013 accepted papers
• ORF523: Mirror Descent, part II/II
• ORF523: Mirror Descent, part I/II
• ORF523: ISTA and FISTA

Dick

• A Trillion Dollar Math Trick
• Happy Birthday, Kurt Gödel
• Can We Prove Better Independence Theorems?
• Subset Powers of Graphs
• Measures Are Better
• Zeno Proof Paradox

0xDE

• Pair programming meets group testing
• The Golden Ticket
• A Gray code for involutions
• An early reference on crossing minimization
• Generating Stirling permutations
• Why we still need real peer review
• Universal permutations

Petros

• New position: Compressive sensing for structural health monitoring
• New page: Resources on Quantization

Hal

• What is a sparse difference in probability distributions?

John:

• Rolling dice for normal samples: Python version
• More sides or more dice?
• 2,000th post
• Hacking debt

Jerry

• Pricing for Non-Profit Organizations

Greg:

• Join the Tin Can Radar Forum, what have you been working on? -

Roger

• Introspection in AI

Muthu

• Lighthearted Symmetry
• Blickets and Streams
• New Conference on OSNs
• DIMACS/Stanford/Technicolor workshop on Economics of Info

Franck

• Symmetrical Kullback-Leibler centroid and W-Lambert function
• Non-Linear Book Manifolds: Learning from Associations the Dynamic Geometry of Digital Libraries
• Hypermask, Furhat,and the manifold of multiple homographies!

Michael

• Machine Learning Glossary

Brian

• A sleeping giant wakes?

Vladimir

• CIS Power Analysis
• Spherical Imager
• APAC Touchless & Gesture Recognition Market to Reach $4.18B by 2018
• Aptina Proposes Motion Compensated HDR

Greg

• Physics demonstrations: Chladni patterns
• Physics demonstrations: cloaking device?
• Another video of the Kaye effect
• Physics demonstrations: A short discussion of the Kaye effect

Nuit Blanche

• Blind Sensor Calibration in Sparse Recovery Using Convex Optimization and Analysis Based Blind Compressive Sensing
• CSjob: Research Fellow in Compressive Sensing, London
• Nuit Blanche in Review (April 2013)
• Big data: theoretical and practical challenges May 14-15, 2013, Paris
• Slides: Fête Parisienne in Computation, Inference and Optimization: A Young Researchers' Forum
• Compressive Light Field Photography Using Overcomplete Dictionaries And Optimized Projections
• Sunday Morning Insight: Compressive Sensing, What is it good for ?
• Video Compressive Sensing by Larry Carin ( compressive hyperspectral camera and Compressive video )
• It's Friday, It's Hamming's Time: Learning to Learn by Richard Hamming, "You Get What You Measure" / "Error-Correcting Codes"
• Correcting Errors in Linear Measurements and Compressed Sensing of Multiple Sources - implementation -
• Greedy Approach for Subspace Clustering from Corrupted and Incomplete Data - implementation -
• Greedy Approach for Low-Rank Matrix Recovery - implementation -
• Nuit Blanche Reader's Reviews: GlobalSIP, some preprints, abstracts of the Workshop on Sparse Representation of Functions: Analytic and Computational Aspects
• Robust error correction for real-valued signals via message-passing decoding and spatial coupling
• Compressive Sensing: What Is It Good For ?
• Sparsity Averaging Reweighted Analysis (SARA) - implementation -
• Multiple PhD position announcement at BASP - EPFL
• Bending JL: Efficient Coding of Signal Distances Using Universal Quantized Embeddings
• NACHOS: Nearfield Acoustic Holography using sparsity and compressive sampling principles - implementation -
• Sunday Morning Insight: A review
• Saturday Morning Videos
• The Nuit Blanche Viewership Dataset
• Bayesian methods for gene expression factor analysis - implementation -
• Nuit Blanche Readers' Review: MOUSSE, Google Reader replacement, QuantCS, Supelec

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