A collapsible section with markdown

• header

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  • Summary

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insert .md

• 8/17 Tuesday Zoom
  Caiyun Ma

  • Feng, D.-J. Lyapunov exponents for products of matrices and multifractal analysis. I. Positive matrices. Israel J. Math., 2003, 138, 353-376.

  • Summary

    To be announced.
    Just random test... hello world blabla... $\Sigma = \{1,\ldots,m\}^{\mathbb{N}}$

    $$\begin{aligned} \pi & = 3 + \frac{\displaystyle 1}{\displaystyle 7 + \frac{\displaystyle 1}{\displaystyle 15 + \frac{\displaystyle 1}{\displaystyle 1 + \frac{\displaystyle 1}{\displaystyle 292 + \frac{\displaystyle 1}{\displaystyle \dots }}}}}\\ f(x) & = \begin{cases} \exp(\frac{1}{(x-a)(x-b)}) & x \in (a, b) \subset\mathbb{R} \\ 0 & otherwise \end{cases} \end{aligned}$$

    

    Let $(X, \mathscr{B}, \mu, T)$ be a measure-preserving system.

• 8/17 Tuesday Zoom
  Caiyun Ma

  • Feng, D.-J. Lyapunov exponents for products of matrices and multifractal analysis. I. Positive matrices. Israel J. Math., 2003, 138, 353-376.

  • // Couldn't find a file when trying to resolve an input request with relative path: `summaries/2021/0817.md`. //

• Summary

  To be announced. Just random test... hello world blabla... $\Sigma = \{1,\ldots,m\}^{\mathbb{N}}$

  $$\begin{aligned} \pi & = 3 + \frac{\displaystyle 1}{\displaystyle 7 + \frac{\displaystyle 1}{\displaystyle 15 + \frac{\displaystyle 1}{\displaystyle 1 + \frac{\displaystyle 1}{\displaystyle 292 + \frac{\displaystyle 1}{\displaystyle \dots }}}}}\\ f(x) & = \begin{cases} \exp(\frac{1}{(x-a)(x-b)}) & x \in (a, b) \subset\mathbb{R} \\ 0 & otherwise \end{cases} \end{aligned}$$

  

  Let $(X, \mathscr{B}, \mu, T)$ be a measure-preserving system.

• Summary

  To be announced. Just random test... hello world blabla... $\Sigma = \{1,\ldots,m\}^{\mathbb{N}}$

  $$\begin{aligned} \pi & = 3 + \frac{\displaystyle 1}{\displaystyle 7 + \frac{\displaystyle 1}{\displaystyle 15 + \frac{\displaystyle 1}{\displaystyle 1 + \frac{\displaystyle 1}{\displaystyle 292 + \frac{\displaystyle 1}{\displaystyle \dots }}}}}\\ f(x) & = \begin{cases} \exp(\frac{1}{(x-a)(x-b)}) & x \in (a, b) \subset\mathbb{R} \\ 0 & otherwise \end{cases} \end{aligned}$$

  

  Let $(X, \mathscr{B}, \mu, T)$ be a measure-preserving system.

To be announced. Just random test... hello world blabla... $\Sigma = \{1,\ldots,m\}^{\mathbb{N}}$

Let $(X, \mathscr{B}, \mu, T)$ be a measure-preserving system. To be announced. Just random test... hello world blabla... $\Sigma = \{1,\ldots,m\}^{\mathbb{N}}$

Let $(X, \mathscr{B}, \mu, T)$ be a measure-preserving system.