"The Physics of Algorithms". Discover phase transition in computational problems and their relation to algorithmic hardness

https://youtu.be/ew3xTcEeht0

Several #postdoc positions are available in Budapest in various research groups in discrete mathematics and probability theory.

Information about the research groups are listed here:

https://erdoscenter.renyi.hu/events/drafting-workshop-discrete-mathematics-and-probability-2023

Diffusion Probabilistic Models, Jascha Sohl-Dickstein

https://youtu.be/XCUlnHP1TNM

'Complex systems in Ecology: a guided tour with large Lotka-Volterra models and random matrices" (by Imane Akjouj, Matthieu Barbier, Maxime Clenet, Walid Hachem, Mylène Maïda, François Massol, Jamal Najim, Viet Chi Tran): arxiv.org/abs/2212.06136

"The aim of this review article is to present an overview of the work at the junction of theoretical ecology and large random matrix theory."

At the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 21st-24th March 2023 we will be hosting a school concerning stochastic differential equations and the YUIMA R package (Simulation and Inference for SDEs and Other Stochastic Processes, https://cran.r-project.org/web/packages/yuima/index.html

The lectures will be given by members of the YUIMA team. Below is a nearly final program of the school. The dates are fixed. A similar event took place in summer 2019: https://yuimaproject.com/yss2019/

Please feel free to spread information about the school around. If anyone would be interested in coming, they should e-mail me directly. The registration deadline is 28th February 2023. However, we might end registration early if the amount of interested participants exceeds our capacity.

Students who choose to take the school as a course, can obtain 3credits for it. Examination will be through a hand-in assignment. Please let me know if you would be interested in this option.

Tentative program, can be subject to change but dates 21 March-24 March fixed

We assume the participants are familiar with R.
Laboratory means that this session will contain short exercises (ca 15min) to be done by the participants on their own laptops. The YUIMA Conference sessions will be more advanced research oriented talks, everyone is welcome to attend. If you would have a related research topic to and would be interested in presenting, then please let me know. I will see what possibilities we would have.

DAY 1 (Mar 21)
10:45-11:30 Introduction to stochastic calculus I (stochastic processes, Brownian motion, labo with R)
13:00-14:15 Introduction to stochastic calculus II (stochastic integral and SDE, labo with R)
14:25-14:50 What can we do with YUIMA?
15:00-16;00 Linkoping Statistics Seminar + YUIMA Conference

DAY 2 (Mar 22)
09:00-10:15 Simulation of diffusion processes I (Euler-Maruyama approximation and introduction to YUIMA: yuima object, simulation, plot, Black-Scholes model, labo)
10:30-11:30 Simulation of diffusion processes II (simulation of various models for illustrations)
13:00-14:00 Laboratory (simulation with YUIMA)
14:15-15:30 Poisson process and Compound Poisson processes (introduction, simulation, laboratorory)
15:50-16:50 YUIMA Conference

DAY 3 (Mar 23)
09:00-10:15 Inference for diffusion processes I (QMLE)
10:30-11:30 Inference for diffusion processes II (quasi-Bayes estimation)
13:00-14:45 Model selection for diffusion processes
15:00-16:00 YUIMA GUI
16:10-17:10 YUIMA Conference

DAY 4 (Mar 24)
09:00-10:00 Levy processes and Levy driven SDE I (theoretical background and some examples)
10:30-11:30 Levy processes and Levy driven SDE II (simulation in YUIMA)
13:00-16:00 YUIMA Conference

Two #postdoc jobs on infectious disease dynamics, one math modelling, one experimental with Roland Regoes and me at ETH Zurich. More info here: https://jobs.ethz.ch/job/view/JOPG_ethz_cFnH1vVUIrGOtR06hZ and https://jobs.ethz.ch/job/view/JOPG_ethz_7PJ2CQ0RJzQvkVMh1K.

IFISC offers up to 7 #PhD positions within the Doctoral INPhINIT Fellowships Programme. The opportunity you are looking for!

https://fundacionlacaixa.org/en/inphinit-doctoral-fellowships-incoming

Deadline: 25/01/23

I'm looking for a #PhD student who will work with me at VUamsterdam on mathematics and network science!

The deadline is March 1st. Details on the position can be found at https://workingat.vu.nl/ad/phd-position-in-the-mathematics-of-network-science/g979oo

For bread, think steering wheels. For custard, think toothpaste.

Oxford Christmas Public Lecture

Watch on Tuesday 20 December at 5pm and any time after:
https://www.youtube.com/c/OxfordMathematics

A Mathematical Journey through Scales - Martin Hairer
Oxford Mathematics Public Lecture

The tiny world of particles and atoms and the gigantic world of the entire universe are separated by about forty orders of magnitude. As we move from one to the other, the laws of nature can behave in drastically different ways, sometimes obeying quantum physics, general relativity, or Newton’s classical mechanics, not to mention other intermediate theories.

Understanding the transformations that take place from one scale to another is one of the great classical questions in mathematics and theoretical physics, one that still hasn't been fully resolved. In this lecture, we will explore how these questions still inform and motivate interesting problems in probability theory and why so-called toy models, despite their superficially playful character, can sometimes lead to certain quantitative predictions.

Professor Martin Hairer is Professor of Pure Mathematics at Imperial College London. He was awarded the Fields Medal in 2014.

https://youtu.be/TOY52LF_ZTA

Sixth Groningen Spring School on Cognitive Modeling
– ACT-R, Nengo, PRIMs –

Date: 27-31 March 2023
Location: Groningen, the Netherlands
Fee: € 305 (late fee after February 26 will be € 355)
More information and registration: www.cognitive-modeling.com/springschool
____________________

Dear colleagues and students,

We are excited to announce the sixth Spring School on Cognitive Modeling in Groningen, from 27-31 March 2023!

This time, the Spring School will cover three different modeling paradigms: ACT-R, Nengo, and PRIMs. Each of these topics consists of a series of lectures, as well as a number of hands-on exercises (tutorials).

Past years have shown that students get most out of the spring school if they really emerge themselves into one modeling paradigm. We therefore recommend you choose one topic for which you will attend both the lectures as well as the tutorials. In addition, you can select a second paradigm, for which you attend the lectures only.

To give students a broader picture, there will also be three guest lectures throughout the week. These lectures each give an introduction to yet another modeling paradigm: accumulator models (Leendert van Maanen), error-driven learning models (Jacolien van Rij), and dynamical systems (Herbert Jäger). Everyone is encouraged to attend those lectures.

To round of the program, there will be a poster session, where students present themselves and their research, as well as a city tour, and our (in)famous spring school dinner.

Registration is now open.

Please feel free to forward the information to anyone who might be interested in the Spring School.

We are looking forward to welcoming you (again) in Groningen,

The Spring School team
springschool@rug.nl

Up to 9 #PhD contracts will be offered soon IFISC mallorca, including one contract to work with me and Miguel Cornelles at the interface of complex systems and machine learning. Please spread the word !! Details here 👉 lnkd.in/dEPy6fNq

اینجا کدهای کتاب
An Introduction to Modeling Neuronal Dynamics, Christoph Börgers 2017
را به صورت یک پکیج پایتون پیاده سازی کردم که میشه کدها رو به صورت آنلاین و بدون نصب پکیجی و مستقل از سیستم عاملی که استفاده می کنید اجرا کرد.
کتاب خوبی برای یادگیری هست. پکیج میتونه هنگام تدریس استفاده شود.

ده فصل از کتاب آماده شده. باقی فصل ها به زودی اضافه می شود.

https://github.com/Ziaeemehr/mndynamics/tree/main/mndynamics/examples

Defining physicists’ relationship with AI

As physicists are increasingly reliant on artificial intelligence (AI) methods in their research, we ponder the role of human beings in future scientific discoveries. Will we be guides to AI, or be guided by it?

https://www.nature.com/articles/s42254-022-00544-1

If you (or your students) are interested in #PhD or #postdoc positions at Aalto University on topics related to the Web & its impact on individuals/society, contact me!

http://www.juhikulshrestha.com/

How did complex systems emerge from chaos? Physicist Sean Carroll explains.

When you cast a visible light shadow you also cast a thermal shadow. But while the former disappear when you walk away, an infrared thermography shows you the latter staying on the wall.

[source: https://buff.ly/3RsB6r4]

"A Compact Introduction to Fractional Calculus" (by Alexander I. Zhmakin): https://arxiv.org/abs/2301.00037

"A Compact Introduction to Fractional Calculus is presented including basic definitions, fractional differential equations and special functions."

"Information content of note transitions in the music of J. S. Bach"
Suman Kulkarni, Sophia U. David, Christopher W. Lynn, Dani S. Bassett https://arxiv.org/abs/2301.00783

Music has a complex structure that expresses emotion and conveys information. Humans process that information through imperfect cognitive instruments that produce a gestalt, smeared version of reality. What is the information that humans see? And how does their perception relate to (and differ from) reality? To address these questions quantitatively, we analyze J. S. Bach's music through the lens of network science and information theory. Regarded as one of the greatest composers in the Western music tradition, Bach's work is highly mathematically structured and spans a wide range of compositional forms, such as fugues and choral pieces. Conceptualizing each composition as a network of note transitions, we quantify the information contained in each piece and find that different kinds of compositions can be grouped together according to their information content. Moreover, we find that Bach's music is structured for efficient communication; that is, it communicates large amounts of information while maintaining small deviations of the inferred network from reality. We probe the network structures that enable this rapid and efficient communication of information -- namely, high heterogeneity and strong clustering. Taken together, our findings shed new light on the information and network properties of Bach's compositions. More generally, we gain insight into features that make networks of information effective for communication.

Excellent video on Chaotic Dynamical Systems
https://www.youtube.com/watch?v=PDeN3iCtyNY

Playlist
Course Website: http://faculty.washington.edu/sbrunton/me564/