Hopcroft, John Edward

HopcroftJohn Edward Hopcroft
Cornell University
426 Gates Hall
Ithaca, NY 14853

ArXiv
Key Concept: Singular value decomposition
Our Primary Reference: FOUNDATIONS OF DATA SCIENCE (2017)
Wikipedia: Cinderella Book – Introduction to Automata Theory, Languages, and Computation
YouTube: Computer Science in the Information Age (Use the CC; it helps!)

First email: September 21, 2017
1. Foundations of Data Science (2017) is being sourced for conceptual frameworks now.
2. Updated the John Hopcroft page within Wikipedia to re-direct to the online document at Cornell. Navin Goyal (Microsoft) had once posted the document, but it is “No Longer Found.”

Dear Prof. Dr. John Hopcroft:

I am looking at data structure from a rather idiosyncratic place.

We are exploring applications beginning at the smallest possible scale (Planck’s) and going to largest (Age of the Universe, Observable Universe) all within just 202 base-2 exponential notations or doublings. I am most interested in your conceptual framework for singular value decomposition and its application using continuity and symmetry within the Planck scale to follow its progressive expansion into the large-scale universe and to the present time.

Silliness perhaps. Idiosyncratic for sure.
It all started in a high school geometry class in December 2011.

I would be pleased to hear your initial reactions to such a construct:
•  https://81018.com/chart contains all the numbers.
•  https://81018.com/planck_universe is an analysis of six groups of numbers out of the 202.
• I openly share my naiveté about your work within my introduction to hypostatic structure: https://81018.com/hypostatic  (seventh paragraph)

Who am I? Although I have done a fair amount of work with IBM over the years,
my focus has been on first principles within information, knowledge, insight, and wisdom.

Thank you.

Most sincerely,
Bruce

PS. PS. Email has been a wonderful way for me to begin to collect references (to your work) currently being  studied.

Key concepts:
“As an application of the Law of Large Numbers, let z be a d-dimensional random point whose coordinates are each selected from a zero mean, 1/2π variance Gaussian. We set the variance to 1/2π so the Gaussian probability density equals one at the origin and is bounded below throughout the unit ball by a constant.” Page 13ff
2.4 Properties of the Unit Ball
2.5 Generating Points Uniformly at Random from a Ball
Context-free language:  Hopcroft & Ullman 1979, p. 100, Theorem 4.7.
DFA Minimization
Minimal Supersymmetric Standard Model:
•  https://en.wikipedia.org/wiki/Minimal_Supersymmetric_Standard_Model
•  https://en.wikipedia.org/wiki/Supersymmetry_nonrenormalization_theorems
•  https://en.wikipedia.org/wiki/Theory_of_computation
•  Joseph L. Walsh, Walsh matrix

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