CoSy Seminars Autumn 2020

20 October
Speaker: Davide Faranda, Researcher in Complex Systems at the LSCE laboratory of the University of Paris-Saclay
Title: Computation and characterization of local sub-filter-scale energy transfers in atmospheric flows with a focus on Hurricanes
Place: Häggsalen
Time: 12:15 - 13:00

Abstract: Atmospheric motions are governed by turbulent motions associated to non-trivial energy transfers at small scales (direct cascade) and/or at large scales (inverse cascade). Although it is known that the two cascades coexist, energy fluxes have been previously investigated from the spectral point of view but not on their instantaneous spatial and local structure. Here, we compute local and instantaneous sub-filter scale energy transfers in two sets of reanalyses (NCEP-NCAR and ERA-Interim) in the troposphere and the lower stratosphere for the year 2005. The fluxes are mostly positive (towards subgrid scales) in the troposphere and negative in the stratosphere reflecting the baroclinic and barotropic nature of the motions respectively. The most intense positive energy fluxes are found in the troposphere and are associated with baroclinic eddies or tropical cyclones. The computation of such fluxes can be used to characterize the amount of energy lost or missing at the smallest scale in climate and weather models. We also show that climate extreme events such as tropical cyclones can be identified and characterized by such extreme energy transfers which contribute to the creation and destruction of direct and inverse local energy cascades.

27 October

3 November
Speaker: Attila Szilva, Researcher at Department of Physics and Astronomy, Materials Theory, Uppsala University
Title: Universal Scaling Laws of Organisms and Cities
Place: Å4001
Time: 12:15 - 13:00

Abstract: Animals from rats to the blues whales are built up from cells arranged in networks. The topology of the underlying networks explains the so-called Kleiber’ scaling law, which states that an animal's metabolic rate scales to the ¾ power of the animal's mass (a cat having a mass 100 times that of a mouse will consume only about 32 times the energy the mouse uses). This scaling is sublinear because the power is less than 1. In the presentation, it will be shown that the infrastructure of cities (the length of electric cables or the number of gas stations) also follows universal sublinear scaling law while in socio-economic dimensions (GDP per capita, innovation, crime) cities are superlinear. They are as a result of the individual interactions proven by a large set of mobile phone data. The concept of scaling and universality is originated in statistical physics where a large complex system emerges from simple interactions, and its behavior is almost totally independent of its microscopic structure. The talk is based mostly on the book of Geoffrey West: Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies. In the presentation, a possible generalization for the emergence of political parties (and social movements) will be discussed.

10 November
SpeakerFreddy Bouchet, researcher in climate and statistical mechanics at CNRS, Lyon. 
Title: New mathematical approaches for studying abrupt transitions for turbulent atmosphere and climate dynamics
Place: Zoom
Time: 12:15 - 13:00

Abstract: In the past, the Earth and Jupiter atmospheres have experienced fast and drastic transitions, sometimes associated with abrupt climate changes. Some of these changes of turbulent attractors where not caused by external perturbations, but the result of internal variability. Studying such abrupt transitions, for fully turbulent atmosphere dynamics, requires new numerical and mathematical tools. We have developed mathematical and computational approaches based on large deviation theory and rare event algorithms.

I will present two examples of recent works using these approaches in order to study abrupt transitions. First, I will explain the use of rare event algorithms and large deviation theory to study abrupt changes in Jupiter atmosphere, in the turbulent dynamics of either barotropic or two-layer models. Second, for Earth like atmospheres, I will discuss the conditions for abrupt transitions to climates with superrotating equatorial jets. I will explain the fluid mechanics mechanisms and parameters that determine whether such transitions are discontinuous or continuous.

17 November
Speaker: Emtiyaz Khan, team leader at the RIKEN center for Advanced Intelligence Project (AIP)
Title: Bayesian Principles for Learning Machines
Place: Zoom
Time: 12:15 - 13:00

Abstract and bio: Humans and animals have a natural ability to autonomously learn and quickly adapt to their surroundings. How can we design machines that do the same? In this talk, I will present Bayesian principles to bridge such gaps between humans and machines. I will show that a wide-variety of machine-learning algorithms are instances of a single learning-rule derived from Bayesian principles. The rule unravels a dual-perspective yielding new mechanisms for knowledge transfer in learning machines. My hope is to convince the audience that Bayesian principles are indispensable for an AI that learns as efficiently as we do.

Emtiyaz Khan (also known as Emti) is a team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. He is also a visiting professor at the Tokyo University of Agriculture and Technology (TUAT). Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For the past 10 years, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.

24 November

1 December
Speaker: Rahul Gaurav, researcher/engineer at Center for Neuroimaging Research (CENIR) and Movement, Investigations and Therapeutics (Mov'it) team of Paris Brain Institute (ICM), Pitié Salpêtrière Hospital, Paris 
Title: Investigating Substantia Nigra Damage and Iron Accumulation in REM Sleep Disorder and Parkinson’s Disease Patients using MRI
Place: Häggsalen
Time: 12:15 - 13:00
Zoom link:

Abstract: Parkinson's disease (PD) is characterized by the progressive loss of dopaminergic neurons in the substantia nigra (SN) pars compacta (SNc), which contains neuromelanin pigment, resulting in high T1-weighted signal on MRI. This loss is also associated with an increase in iron deposition in the entire SN including the SNc and the SN pars reticulata in PD. Idiopathic rapid eye movement sleep behavior disorder (iRBD) is considered to be a prodromal stage of Parkinsonism.

Moreover, neuromelanin signal diminishes early in the SNc of PD. Thus, neuromelanin-sensitive MRI could be used to monitor disease progression and response to therapies, which requires characterizing the neuromelanin signal variations by investigating the SNc. Furthermore, the iron overload in the SN can be evaluated using quantitative susceptibility mapping (QSM) or the apparent transverse relaxation rate (R2*).

We scanned the subjects using a 3T whole body PRISMA scanner (Siemens, Erlangen, Germany) and assessed neuromelanin SNc damage using deep neural network and iron accumulation using Advanced Normalization Tools (ANTs) in iRBD and early PD patients compared to their respective age-matched healthy volunteers (HV).

For neuromelanin, SNc Volume, corrected volume using total intracranial volume, signal to noise ratio and contrast to noise ratio were computed. For QSM and R2* maps, quantitative mean susceptibility values and R2* values were computed. One-way general linear model – ANOVA was conducted while adjusting for age and sex.

For neuromelanin, iRBD and PD patients demonstrated a significant reduction for all SNc measurements compared to HV whereas for QSM and R2* maps, iRBD and PD patients showed an increase in SNc iron accumulation compared to HV.

The proposed automatic method for quantifying SN variations showed comparable diagnostic performance with manual method which can possibly help us better understand the abnormalities in SN and could be useful for the diagnosis of PD. Such imaging techniques might allow a direct non rater-dependent noninvasive evaluation of progression of SN cellular loss in PD and potential target for disease modification biomarker in drug trials.

8 December

15 December