Right-angled Artin groups (RAAGs) are defined from finite simplicial graphs.

It is a fundamental question whether or not, given two RAAGs, there is an embedding from one group to the other.

Extension graphs are useful in solving this problem.

In this talk, I will briefly review RAAGs and extension graphs,

and show some results on solving the embeddability problem in RAAGs by using extension graphs.

The spatially varying coefficient process model is a nonstationary approach to explaining spatial heterogeneity by allowing coefficients to vary across space. To accommodate geographically hierarchical data, we develop a methodology for generalizing this model. We consider two-level hierarchical structures and allow for the coefficients of both low-level and high-level units to vary over space. We assume that the spatially varying low-level coefficients follow the multivariate Gaussian process, and the spatially varying high-level coefficients follow the multivariate simultaneous autoregressive model that we develop by extending the standard simultaneous autoregressive model to incorporate multivariate data. We apply the proposed model to transaction data of houses sold in 2014 in a part of the city of Los Angeles. The results show that the proposed model predicts housing prices and fits the data effectively.

빅데이터(Big-Data), 단어는 많이 들어보았는데 과연 빅데이터의 본질은 무엇일까? Volume, Velocity, Variety의 3V로 정의되는 빅데이터는 과연 우리의 미래를 예측할 수 있을까? 이번 강연에서는 우리가 살고 있는 복잡한 사회를 이해하는 도구로서의 빅데이터와 네트워크 과학에 관한 설명과 함께, 그 성공적 응용사례로 구글의 검색엔진을 이용한 선거의 예측, N-gram 프로젝트를 통한 과거의 검색엔진 개발, 알파고의 AI 알고리즘, 대규모 특허분석을 통한 기술예측, 인문학 및 예술에서의 빅데이터 분석사례 등을 소개하고자 합니다. 또한 성공의 이면에 숨겨있던 빅데이터의 한계점과 어두운 그림자까지 빅데이터의 모든 것을 함께 고민하고 이를 통해 우리의 미래의 가능성을 살짝 엿보고자 합니다.

For a motivation of stochastic integrals, we start with stochastic (random) versions of deterministic systems and then we discuss  the Ito integral as a non-anticipating (adapted) stochastic integral.

Based on the quantum decomposition of Brownian motion, we study the Skorohod integral and Stratonovich integral as anticipating (non-adapted) stochastic integrals.

Note that the Skorohod integral is the adjoint action of the Malliavin derivative.