Thursday, October 14, 2021

2021-10-18 / 16:30 ~ 18:00

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The next few talks will be more like learning than research: I will explain some preparation material, which is considered "well-known" by the experts, but which I didn't find a reference for in the form I need. My next goal is to explain the proof that the Picard group of the so-called quotient of a torsor of a simply connected simple split algebraic group modulo a Borel subgroup does not change under field extension. In the first talk I will explain the basic machinery to prove this fact, namely Galois descent theory. Given a variety X over a non-algebraically closed field F with no or "not enough" rational points, Galois descent theory allows one to work with an extension K of F and with X_K and study the properties of the original X. If there is enough time, I will also define torsors and show how to construct them using Galois descent.

2021-10-21 / 11:00 ~ 12:00

IBS-KAIST 세미나 - 수리생물학:

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Within a given species, fluctuations in egg or embryo size is unavoidable. Despite this, the gene expression pattern and hence the embryonic structure often scale in proportion with the body length. This scaling phenomenon is very common in development and regeneration and has long fascinated scientists. I will first discuss a generic theoretical framework to show how scaling gene expression pattern can emerge from non-scaling morphogen gradients. I will then demonstrate that the Drosophila gap gene system achieves scaling in a way that is entirely consistent with our theory. Remarkably, a parameter-free model based on the theory quantitatively accounts for the gap gene expression pattern in nearly all morphogen mutants. Furthermore, the regulation logic and the coding/decoding strategy of the gap gene system can be revealed. Our work provides a general theoretical framework on a large class of problems where scaling output is induced by non-scaling input, as well as a unified understanding of scaling, mutants’ behavior and regulation in the Drosophila gap gene and related systems.

2021-10-15 / 10:00 ~ 11:00

SAARC 세미나 - SAARC 세미나:

by 김건우(포항공과대학교 수학과)

Partial differential equations such as heat equations have traditionally been our main tool to study physical systems. However, physical systems are affected by randomness (noise). Thus, stochastic partial differential equations have gained popularity as an alternative. In this talk, we first consider what “noise” means mathematically and then consider stochastic heat equations perturbed by space-time white noise such as parabolic Anderson model and stochastic reaction-diffusion equations (e.g., KPP or Allen-Cahn equations). Those stochastic heat equations have similar properties as heat equations, but exhibit different behavior such as intermittency and dissipation, especially as time increases. We investigate in this talk how the long-time behaviors of the stochastic heat equations are different from heat equations.

2021-10-15 / 16:00 ~ 17:00

학과 세미나/콜로퀴엄 - PDE 세미나:

by 이기암(서울대학교)

In this talk, we are going to discuss boundary regularities of various degenerate local equation and nonlocal equations. Diffusion rates deform undefined geometry related to diffusion and the corresponding distance function makes important role in the theory of regularity. And then we will also discuss the possible applications.

2021-10-14 / 16:15 ~ 17:15

학과 세미나/콜로퀴엄 - 콜로퀴엄: Brain’s solutions to temporal credit assignment

by 이상완(KAIST 바이오및뇌공학과)

The temporal credit assignment, the problem of determining which actions in the past are responsible for the current outcome (long-term cause and effect), is difficult to solve because one needs to backpropagate the error signal through space and time. Despite its computational challenges, humans are very good at solving this problem. Our lab uses reinforcement learning theory and algorithms to explore the nature of computations underlying the brain’s ability to solve the temporal credit assignment. I will outline two-fold approaches to this issue: (1) training a computational model from human behavioral data without underfitting and overfitting (Brain → AI) and (2) using the trained model to manipulate the way the human brain solves the temporal credit assignment problem (AI → brain).

Events for the 취소된 행사 포함