This week we had quite a few interesting threads on the interwebs about signals acquisition and making sense of them. While the former seems to be feasible thanks to Moore's law, the latter is, in view of the different results mentioned here and elsewhere, likely substandard. The real issue is: do policymakers understand there is a difference between acquiring data and making sense of them in the same way that there is a difference between a rental car company taking a reservation and making that reservation a reality by delivering a car ?

If you are reading this blog and happen to be coming to (or living in) Paris and want to make a presentation in front of a technical audience, there is the possibility of doing so at a variety of Meet-ups. Lately, a good resource to find (or create) Meet-ups in Paris (and elsewhere) is Meetup.com. At the end of this entry, you'll find some of the meet-ups that I tend to attend to when time permits. In the past few weeks, there were some talks of interest:

But I am sure I missed some. There are no technical meetup groups in College Station as far as I can tell. There ought to be one, I am sure there are enough hardware hackers around there.

Frank Nielsen wants me to mention the Geometric Science of Information conference (www.gsi2013.org ) that will have an 800 pages proceedings and will take place at the end of August in Paris! The page of the conference reads:

The objective of this SEE Conference hosted by MINES ParisTech, is to bring together pure/applied mathematicians and engineers, with common interest for Geometric tools and their applications for Information analysis, with active participation of young researchers for deliberating emerging areas of collaborative research on “Information Geometry Manifolds and Their Advanced Applications”.Current and ongoing uses of Information Geometry Manifolds in applied mathematics are the following: Advanced Signal/Image/Video Processing, Complex Data Modeling and Analysis, Information Ranking and Retrieval, Coding, Cognitive Systems, Optimal Control, Statistics on Manifolds, Machine Learning, Speech/sound recognition, natural language treatment, etc., which are also substantially relevant for the industry....This conference will be an interdisciplinary event and will federate skills from Geometry, Probability and Information Theory to address the following topics among others

- Computational Information Geometry

- Hessian/Symplectic Information Geometry

- Optimization on Matrix Manifolds

- Probability on Manifolds

- Optimal Transport Geometry

- Divergence Geometry & Ancillarity

- Machine/Manifold/Topology Learning

- Tensor-Valued Mathematical Morphology

- Differential Geometry in Signal Processing

- Geometry of Audio Processing

- Geometry for Inverse Problems

- Shape Spaces: Geometry and Statistic

- Geometry of Shape Variability

- Relational Metric

- Discrete Metric Spaces

Of related interest is a recently published book on Matrix Information Geometry.

Here what you could read around the interwebs this past week:

Greg

Brian

2physics

- Quantum experiment preludes the endgame for local realism – photonic Bell violation closes the fair-sampling loophole

Dustin

Patrick

- Post-Prism Data Science Venn Diagram
- State of the OpenStreetMap [Watching the Watchers?]
- NYU Large Scale Machine Learning Class Notes
- Easy mapping with Map Stack
- The Pragmatic Haskeller, Episode 5 – Let’s Write a DSL!
- The Filtering vs. Clustering Dilemma
- Updated Database Landscape map – June 2013
- SSNs: Close Enough for a Drone Strike?
- Beyond NSA, the intelligence community has a big technology footprint [Funding]

John

- Singular Value Consulting, LLC
- Computing skewness and kurtosis in one pass
- A statistical problem with “nothing to hide”
- The weight of code

Gregory:

Sebastien

Roger

John

Ivan

While on Nuit Blanche,

The meet-ups of interest around Paris are:

John

Ivan

While on Nuit Blanche,

- CSJob: PostDoc, Research Associate in Compressive Sensing and Distributed Compressive Sensing for Energy Harvesting Wireless Sensor Networks, London
- PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities = implementation-
- Quantized Iterative Hard Thresholding: Bridging 1-bit and High-Resolution Quantized Compressed Sensing - implementation -
- Simple and Deterministic Matrix Sketches and Near-optimal Distributions for Data Matrix Sampling
- Sparse Recovery of Streaming Signals Using l_1-Homotopy - implementation -
- Saturday Morning Videos: Robot Programming through demonstration, Using Chrome to talk to an Arduino, Orbit Imagery, DYI Raman spectroscopy, Radar data of asteroid 1998 QE2
- This week in Review: COxSwAIN, Machine Learning and Sensors MeetUps, SigFox, Lensless Single Pixel camera, Around the Blogs in 78 hours

The meet-ups of interest around Paris are:

credit: wikipedia,

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