Sunday, April 10, 2022

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2022-04-13 / 16:00 ~ 17:00
IBS-KAIST 세미나 - IBS-KAIST 세미나: 인쇄
by ()
Extension problems through a small singular set appear throughout complex analysis. After a short reminder of some classical results we shall focus on problems of extending (pluri)subharmonic functions. In particular we shall focus on new techniques coming from PDEs that lead to resolutions of several questions in the field. The talk is partially based on joint works with Zywomir Dinew.
2022-04-11 / 16:30 ~ 17:30
IBS-KAIST 세미나 - 이산수학: On the extremal problems related to Szemerédi’s theorem 인쇄
by 김연진(POSTECH)
In 1975, Szemerédi proved that for every real number $\delta > 0 $ and every positive integer $k$, there exists a positive integer $N$ such that every subset $A$ of the set $\{1, 2, \cdots, N \}$ with $|A| \geq \delta N$ contains an arithmetic progression of length $k$. There has been a plethora of research related to Szemerédi's theorem in many areas of mathematics. In 1990, Cameron and Erdős proposed a conjecture about counting the number of subsets of the set $\{1,2, \dots, N\}$ which do not contain an arithmetic progression of length $k$. In the talk, we study a natural higher dimensional version of this conjecture, and also introduce recent extremal problems related to Szemerédi's theorem.
2022-04-11 / 16:30 ~ 17:30
학과 세미나/콜로퀴엄 - 계산수학 세미나: 그래프 신경망 및 추천 시스템으로의 응용 인쇄
by 신원용(연세대학교)
그래프 신경망은 그래프에서 높은 표현 능력과 함께 특징 정보를 추출하는 방법론으로 학계와 산업체에서 최근 폭발적인 관심을 받고 있다. 본 세미나에서는 그래프 신경망의 개요 및 주요 동작 원리를 다룬다. 구체적으로, message passing의 원리를 이해하고 state-of-the-art 알고리즘에서 사용한 다양한 message passing 함수를 소개한다. 그리고, 협업 필터링에 기반한 추천 시스템을 소개하고, 이러한 추천 시스템 설계에 그래프 신경망의 응용에 대해 학습한다. 경량화된 그래프 신경망을 사용한 state-of-the-art 추천 알고리즘을 소개하고, 해당 방법들이 가지는 challenge를 이해한다. 마지막으로, 발표자 연구실에서 제안한 그래프 신경망을 활용한 새로운 추천 시스템 방법을 간단히 소개한다.
2022-04-14 / 16:15 ~ 17:15
학과 세미나/콜로퀴엄 - 콜로퀴엄: 인쇄
by 김항준()
Ordinary differential equations are useful in modeling the periodic behavior of organisms, such as circadian rhythm, based on known biological knowledge and researchers' hypotheses. The theoretical mathematical models are calibrated to the experimental measurements by estimating a set of unknown model parameters. Traditional parameter estimation with mathematical models often focuses only on the point estimation relying on an optimization method such as simulated annealing, but it often neglects the uncertainty in point estimates and suffers from the local trap issue. This talk provides a gentle introduction to Bayesian analysis focusing on its usefulness in uncertainty quantification; introduces a Bayesian computing method with an advanced Markov chain Monte Carlo called the generalized multiset sampler; and illustrates the proposed Bayesian inference with circadian oscillations observed in a model filamentous fungus, Neurospora crassa.
2022-04-14 / 11:00 ~ 12:00
IBS-KAIST 세미나 - 수리생물학: 인쇄
by ()
Tuberculosis (TB) is one of the world’s deadliest infectious diseases. Caused by the pathogen Mycobacterium tuberculosis (Mtb), the standard regimen for treating TB consists of treatment with multiple antibiotics for at least six months. There are a number of complicating factors that contribute to the need for this long treatment duration and increase the risk of treatment failure. The structure of granulomas, lesions forming in lungs in response to Mtb infection, create heterogeneous antibiotic distributions that limit antibiotic exposure to Mtb. We can use a systems biology approach pairing experimental data from non-human primates with computational modeling to represent and predict how factors impact antibiotic regimen efficacy and granuloma bacterial sterilization. We utilize an agent-based, computational model that simulates granuloma formation, function and treatment, called GranSim. A goal in improving antibiotic treatment for TB is to find regimens that can shorten the time it takes to sterilize granulomas while minimizing the amount of antibiotic required. We also created a whole host model, called HOSTSIM, to study Mtb dynamics within a human host. Overall, we use these models to help better understand TB treatment and strengthen our ability to predict regimens that can improve clinical treatment of TB.
2022-04-14 / 10:30 ~ 10:55
IBS-KAIST 세미나 - 수리생물학: 인쇄
by ()
TBA
Events for the 취소된 행사 포함 모두인쇄
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