1. Two Tantalizing Concepts Randomness –“Any one who considers arithmetical methods of producing random digits is, of course, in a state of sin” – Von Neumann –There is no such thing as a random number; there are only methods (e.g., arithmetic vs. quantum) to produce random numbers Redundancy –Narrow sense, in information theory –Broad sense, in science and engineering

2. Pseudorandom Number Generator Complexity-based pseudorandomness is a deep concept in theoretical CS (with wide applications in scientific computing such as Monte Carlo methods) Engineers generate pseudorandom numbers by arithmetic algorithms such as linear congruential generators and linear feedback shift registers (LFSR) A maximal LFSR produces an m-sequence (i.e. cycles through all possible 2n − 1 states within the shift register except the state where all bits are zero), unless it contains all zeros, in which case it will never change.

3. AWGN How is AWGN generated under MATLAB? Yes, by “randn” function but how does it work? RANDN('seed') in MATLAB4 vs. RANDN('state',J) in MATLAB5 Subtle implication into denoising experiments

4. Randomness in Nature Waitomo Glow-worm Caves on Lake Roturura Star Constellation

5. Homogeneous PoissonRandom pin-dropping

6. Redundancy What is redundancy in Shannon’s mind? –In source coding, redundancy refers to the gap between the source entropy and the actual bit rate (so we want to eliminate redundancy). –In channel coding, redundancy refers to the “extra bits” used for error correction (so we add redundancy in a controlled fashion). Divide-and-conquer: an engineering solution

7. Redundancy in Nature Redundancy in language –Why are human languages redundant? How is it possible for young kids to learn speaking a language? Redundancy in natural world –Why are natural images compressible? How does human vision systems work? Redundancy in genetics –Why are Chromosomes in pairs?

8. Redundancy in SP Sampling – an artificial tool which we have not yet understood well No signal from the natural world is band- limited –Shannon/Nyquist’s sampling theorem never holds in practice –“Truth is much too complicated to allow anything but approximations.” Uniform sampling- a “sin” operator?

9. Two Contrasting Views Redundancy is bad –Since most natural signals are still so compressible, we should acquire much fewer samples (joint design of sampling and coding) –The emerging “compressive sensing” paradigm Redundancy is good –Redundancy is essential to human intelligence especially the redundancy exploitation hypothesis advocated by H. Barlow –To solve high-level computer vision problems, get low-level (image sampling, feature extraction) done right first.