Yann Lanoiselée

Mueve tus moléculas Las charlas de esta tarde no tienen demasiado que ver entre sí, pero sí comparten un punto en común: la ciencia como motor del cambio. En la primera ponenc…

I participated in the 10th edition of 'Pint of Science' in Bilbao, an awesome experience!
Thanks to the organisers. @pintofscience.es

Awesome ! I was actually thinking about assembling 8 pieces like the one you just made to have fractal surface on all sides

Could you make one with the cube assembled? 🙂

Are there good methods to reorder adjacency matrices? In particular when the matrices are large?

Nice! You gradually changed the exponent?

My point is that it is probably better to talk in terms of trends and fluctuations: generic terms. Talking about Brownian motion suppose some statistical properties that are not necessarily verified, in the same way that you gave valid arguments against Ornstein-Ulhenbeck process.

Noise is not always Brownian motion. In this image there is clearly a (scary) deterministic trend since the 60s. The noise seems of the 'Ornstein Uhlenbeck' type, stationary and anticorrelated. Now, it is hard to say if deviations in 2024 are due to change in trend or in fluctuations.

I am not sure, but if the underlying process has Markov property, then most probably yes. Open for discussion for sure 🙂

I use FIJI plugin 'Trackmate', which works very well

Actually all cauliflowers have fractal shapes, but romanesco are certainly the most striking ones :)

There's an interesting attempt to reproduce Ingenhousz experiment that seems to show that he didn't observe Brownian motion but rather coal stirring due to alcohol evaporation
www.microscopy-uk.org.uk/mag/indexmag...

It is Jean Perrin who closed the debate on the existence of atoms by computing Avogadro's number in ~6 independent manners. One measurement involved Brownian motion, for which Einstein provided a theory. Jean Perrin got the Nobel prize for closing the debate, not Einstein.

Very nice pattern! However it is most likely a convection pattern that we see, due to differences between liquid and air temperatures, rather than Brownian motion.

Super-resolved anomalous diffusion: deciphering the joint distribution of anomalous exponent and diffusion coefficient The molecular motion in heterogeneous media displays anomalous diffusion by the mean-squared displacement $\langle X^2(t) \rangle = 2 D t^α$. Motivated by experiments reporting populations of the anom...

Check out our new preprint on how to distinguish superstatistics in anomalous diffusion from finite-size measurement error
arxiv.org/abs/2410.18133