Fri, 21 Jan 2022

14:00 - 15:00
L3

A mechanochemical instability drives vertebrate gastrulation

Prof Mattia Serra
(Department of Physics University of California San Diego)
Abstract

Gastrulation is a critical event in vertebrate morphogenesis, characterized by coordinated large-scale multi-cellular movements. One grand challenge in modern biology is understanding how spatio-temporal morphological structures emerge from cellular processes in a developing organism and vary across vertebrates. We derive a theoretical framework that couples tissue flows, stress-dependent myosin activity, and actomyosin cable orientation. Our model, consisting of a set of nonlinear coupled PDEs, predicts the onset and development of observed experimental patterns of wild-type and perturbations of chick gastrulation as a spontaneous instability of a uniform state. We use analysis and numerics to show how our model recapitulates the phase space of gastrulation morphologies seen across vertebrates, consistent with experiments. Altogether, this suggests that early embryonic self-organization follows from a minimal predictive theory of active mechano-sensitive flows. 

 https://www.biorxiv.org/content/10.1101/2021.10.03.462928v2 

New Representations for all Sporadic Apéry-Like Sequences, With Applications to Congruences
Gorodetsky, O Experimental Mathematics (2021)
Fri, 03 Dec 2021

10:00 - 11:00
L4

Elucidation of chemical reaction mechanisms by covariance-map imaging of product scattering distributions.

Prof. Claire Vallance
(Department of Chemistry, University of Oxford)
Further Information

Claire brought a problem about exploding molecules to the OCCAM Mathematics and Chemistry Study Group in 2013 and those interactions led to important progress on analysing 2D imaging data on molecular Coulomb explosions using covariance map. The challenge she faces now is on formulating a mathematical expression for the covariance map over the relevant 3D distributions. I encourage all interested party to join us and especially those interested in image processing and inverse problem.

Fri, 26 Nov 2021

10:00 - 11:00
L6

Devising an ANN Classifier Performance Prediction Measure

Darryl Hond
(Thales Group)
Further Information

The challenge they will present is on predicting the performance of artificial neural network (ANN) classifiers and understanding their reliability for predicting data that are not presented in the training set. We encourage all interested party to join us and especially those interested in machine learning and data science.

Time evolution of coupled spin systems in a generalized Wigner representation
Koczor, B Zeier, R Glaser, S (20 Dec 2016)
Continuous phase-space representations for finite-dimensional quantum states and their tomography
Koczor, B Zeier, R Glaser, S (21 Nov 2017)
Continuous phase spaces and the time evolution of spins: star products and spin-weighted spherical harmonics
Koczor, B Zeier, R Glaser, S (08 Aug 2018)
Phase Spaces, Parity Operators, and the Born-Jordan Distribution
Koczor, B Ende, F de Gosson, M Glaser, S Zeier, R (14 Nov 2018)
Variational-State Quantum Metrology
Koczor, B Endo, S Jones, T Matsuzaki, Y Benjamin, S (23 Aug 2019)
Quantum natural gradient generalised to noisy and non-unitary circuits
Koczor, B Benjamin, S (18 Dec 2019)
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