EULER INTEGRATION OF GAUSSIAN RANDOM FIELDS AND PERSISTENT HOMOLOGY O MER B OBROWSKI & M ATTHEW S TROM B ORMAN Presented by Roei Zohar

T HE EULER I NTEGRAL - REASONING As we know, the Euler characteristic is an additive operator on compact sets: which reminds us of a measure That is why it seems reasonable to define an integral with respect to this “measure”.

T HE EULER I NTEGRAL - DRAWBACK The main problem with this kind of integration is that the Euler characteristic is only finitely additive. This is why under some conditions it can be defined for “constructible functions” as But we can’t go on from here approximating other functions using CF functions

E ULER INTEGRAL - E XTENSIONS We shall define another form of integration that will be more useful for calculations: For a tame function the limits are well defined, but generally not equal.tame This definition enable us to use the following useful proposition

E ULER INTEGRAL - E XTENSIONS The proof appears in [3][3] We will only use the upper Euler integral

E ULER INTEGRAL - E XTENSIONS We continue to derive the following Morse like expression for the integral:

F URTHER HELPFUL DEVELOPMENT

G AUSSIAN R ANDOM F IELDS AND THE G AUSSIAN K INEMATIC F ORMULA Now we turn to show the GK formula which is an explicit expression for the mean value of all Lipschitz-Killing curvatures of excursion sets for zero mean, constant value variance, c², Gaussian random fields. We shall not go into details, you can take a look in [2].[2]

The metric here, under certain conditions is Under this metric the L-K curvatures are computed in the GKF, and in it the manifold M is bounded

When taking we receive and: Now we are interested in computing the Euler integral of a Gaussian random field: Let M be a stratified space and let be a Gaussian or Gaussian related random field. We are interested in computing the expected value of the Euler integral of the field g over M.

Theorem: Let M be a compact d-dimensional stratified space, and let be a k- dimensional Gaussian random field satisfying the GKF conditions. For piecewise c² function let setting, we have

The proof:

The difficulty in evaluating the expression above lies in computing the Minkowski functionals In the article few cases where they have been computed are presented, which allows us to simplify, I will mention one of them Real Valued Fields:

And if G is strictly monotone:

W EIGHTED S UM OF C RITICAL V ALUES Taking in Theorem and using Proposition yields the following compact formula

W EIGHTED S UM OF C RITICAL V ALUES The thing to note about the last result, is that the expected value of a weighted sum of the critical values scales like, a 1-dimensional measure of M and not the volume, as one might have expected. Remark: if we scale the metric by, then scales by

W E ’ LL NEED THIS ONE UP AHEAD The proof relies on

INTRODUCTION : P ERSISTENT H OMOLOGY Have a single topological space, X Get a chain complex For k=0, 1, 2, … compute H k ( X ) H k =Z k /B k C k (X)C 1 (X)C 0 (X)C k-1 (X)0 ∂ ∂∂∂∂∂ The Usual Homology

INTRODUCTION : P ERSISTENT H OMOLOGY Persistent homology is a way of tracking how the homology of a sequence of spaces changes Given a filtration of spaces such that if s < t, the persistent homology of,,consists of families of homology classes that ‘persist’ through time.

INTRODUCTION : P ERSISTENT H OMOLOGY o Explicitly an element ofis a family of homology classes for o The map induced by the inclusion, maps Given a tame functiontame

B ARCODE DEMONSTRATION (1)

B ARCODE DEMONSTRATION (2)

INTRODUCTION : P ERSISTENT H OMOLOGY X 1  X 2  X 3  …  X n a b a b cd a b cd a b cd a b cd a b cd a, bc, d, ab,bccd, adacabcacd t=0t=1t=2t=3t=4t=5 A filtration of spaces (Simplicial complexes example):

G ETTING TO THE POINT … I’ll leave the proof of the second claim to you 1. 2.

T HE E XPECTED E ULER C HARACTERISTIC OF THE P ERSISTENT H OMOLOGY OF A G AUSSIAN R ANDOM F IELD In light of the connection between the Euler integral of a function and the Euler characteristic of the function’s persistent homology in place, we will now reinterpret our computations about the expected Euler integral of a Gaussian random field

1. 2. The proof makes use of the following two expressions we saw before:

Computing is not usually possible, but in the case of a real random Gaussian field we can get around it and it comes out that:

A N APPLICATION – TARGET ENUMERATION

T HE END

D EFINITIONS A “tame” function :tame

R EFERENCES 1. Paper presented here, by Omer Bobrowski & Matthew Strom Borman 2. R.J. Adler and J.E. Taylor. Random Fields and Geometry. Springer, Y. Baryshikov and R. Ghrist. Euler integration over definable functions. In print, 2009.