## Computing convolutions of measures

30 Jul

Originally posted on What's new:

If $latex {f: {\bf R}^n \rightarrow {\bf C}}&fg=000000$ and $latex {g: {\bf R}^n \rightarrow {\bf C}}&fg=000000$ are two absolutely integrable functions on a Euclidean space $latex {{\bf R}^n}&fg=000000$, then the convolution $latex {f*g: {\bf R}^n \rightarrow {\bf C}}&fg=000000$ of the two functions is defined by the formula

$latex \displaystyle f*g(x) := \int_{{\bf R}^n} f(y) g(x-y)\ dy = \int_{{\bf R}^n} f(x-z) g(z)\ dz.&fg=000000$

A simple application of the Fubini-Tonelli theorem shows that the convolution $latex {f*g}&fg=000000$ is well-defined almost everywhere, and yields another absolutely integrable function. In the case that $latex {f=1_F}&fg=000000$, $latex {g=1_G}&fg=000000$ are indicator functions, the convolution simplifies to

$latex \displaystyle 1_F*1_G(x) = m( F \cap (x-G) ) = m( (x-F) \cap G ) \ \ \ \ \ (1)&fg=000000$

where $latex {m}&fg=000000$ denotes Lebesgue measure. One can also define convolution on more general locally compact groups than $latex {{\bf R}^n}&fg=000000$, but we will restrict attention to the Euclidean case in this post.

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## An improved Type I estimate

30 Jul

Originally posted on What's new:

As in all previous posts in this series, we adopt the following asymptotic notation: $latex {x}&fg=000000$ is a parameter going off to infinity, and all quantities may depend on $latex {x}&fg=000000$ unless explicitly declared to be “fixed”. The asymptotic notation $latex {O(), o(), \ll}&fg=000000$ is then defined relative to this parameter. A quantity $latex {q}&fg=000000$ is said to be of polynomial size if one has $latex {q = O(x^{O(1)})}&fg=000000$, and bounded if $latex {q=O(1)}&fg=000000$. We also write $latex {X \lessapprox Y}&fg=000000$ for $latex {X \ll x^{o(1)} Y}&fg=000000$, and $latex {X \sim Y}&fg=000000$ for $latex {X \ll Y \ll X}&fg=000000$.

The purpose of this (rather technical) post is both to roll over the polymath8 research thread from this previous post, and also to record the details of the latest improvement to the Type I estimates (based on exploiting additional averaging and using Deligne’s proof of the Weil conjectures) which lead to a slight improvement in the numerology.

In order to obtain this new Type I estimate, we need to strengthen the previously used properties of “dense divisibility” or “double dense divisibility” as follows.

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30 Jul

## Spaun brain simulation

30 Jul

Spaun brain simulation

# Spaun brain simulation

http://www.nature.com/news/simulated-brain-scores-top-test-marks-1.11914

30 Jul

## Tetrapod Quantum Dots Light the Way to Stronger Polymers

30 Jul

Tetrapod Quantum Dots Light the Way to Stronger Polymers

http://newscenter.lbl.gov/feature-stories/2013/07/29/tetrapod-quantum-dots-light-the-way-to-stronger-polymers/

## The Riemann hypothesis in various settings

28 Jul

Originally posted on What's new:

[Note: the content of this post is standard number theoretic material that can be found in many textbooks (I am relying principally here on Iwaniec and Kowalski); I am not claiming any new progress on any version of the Riemann hypothesis here, but am simply arranging existing facts together.]

The Riemann hypothesis is arguably the most important and famous unsolved problem in number theory. It is usually phrased in terms of the Riemann zeta function $latex {\zeta}&fg=000000$, defined by

$latex \displaystyle \zeta(s) = \sum_{n=1}^\infty \frac{1}{n^s}&fg=000000$

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