In this talk, we present a short history of Lp theories for (stochastic) partial differential equations. In particular, we introduce recent developments handling degenerate equations in weighted Sobolev spaces. It is well known that there exist probabilistic representations of solutions to second order (stochastic) partial equations, which enables us to use many interesting probabilistic theories to investigate solutions. Recently, by applying probabilistic tools, we obtain interesting new type weighted estimates to second order degenerate (stochastic) partial differential equations.

A family of surfaces is called mean curvature flow (MCF) if the velocity of surface is equal to the mean curvature of the surface at that point. Even starting from smooth surface, the MCF typically encounters some singularities and various generalized notions of MCF have been proposed to extend the existence past singularities. They are level set flow, Brakke flow and BV flow, just to name a few. In my talk I explain a recent global-in-time existence result of a particular generalized solution which has some desirable properties. I describe a basic outline of how to construct the solution.

A family of surfaces is called mean curvature flow (MCF) if the velocity of surface is equal to the mean curvature of the surface at that point. Even starting from smooth surface, the MCF typically encounters some singularities and various generalized notions of MCF have been proposed to extend the existence past singularities. They are level set flow, Brakke flow and BV flow, just to name a few. In my talk I explain a recent global-in-time existence result of a particular generalized solution which has some desirable properties. I describe a basic outline of how to construct the solution.

Over recent years, data science and machine learning have been the center of attention in both the scientific community and the general public. Closely tied to the ‘AI-hype’, these fields are enjoying expanding scientific influence as well as a booming job market. In this talk, I will first discuss why mathematical knowledge is important for becoming a good machine learner and/or data scientist, by covering various topics in modern deep learning research. I will then introduce my recent efforts in utilizing various deep learning methods for statistical analysis of mathematical simulations and observational data, including surrogate modeling, parameter estimation, and long-term trend reconstruction. Various scientific application examples will be discussed, including ocean diffusivity estimation, WRF-hydro calibration, AMOC reconstruction, and SIR calibration.

Polarization is a technique in algebra which provides combinatorial tools to study algebraic invariants of monomial ideals. Depolarization of a square free monomial ideal is a monomial ideal whose polarization is the original ideal. In this talk, we briefly introduce the depolarization and related problems and introduce the new method using hyper graph coloring.

It is challenging to perform a multiscale analysis of mesoscopic systems exhibiting singularities at the macroscopic scale. In this paper, we study the hydrodynamic limit of the Boltzmann equations \begin{equation} \mathrm{St} \partial_t F + v \cdot \nabla_x F = \frac{1}{\mathrm{Kn} } Q(F, F) \end{equation} toward the singular solutions of 2D incompressible Euler equations whose vorticity is unbounded \begin{equation} \partial_t u + u \cdot \nabla_x u + \nabla_x p = 0, \quad \mathrm{div} u = 0. \end{equation} We obtain a microscopic description of the singularity through the so-called kinetic vorticity and understand its behavior in the vicinity of the macroscopic singularity. As a consequence of our new analysis, we settle affirmatively an open problem of convergence toward Lagrangian solutions of the 2D incompressible Euler equation whose vorticity is unbounded ($\omega \in L^{\mathfrak{p} }$ for any fixed $1 \le \mathfrak{p} < \infty$). Moreover, we prove the convergence of kinetic vorticities toward the vorticity of the Lagrangian solution of the Euler equation. In particular, we obtain the rate of convergence when the vorticity blows up moderately in $L^{\mathfrak{p} }$ as $\mathfrak{p} \rightarrow \infty$ (localized Yudovich class).

We will discuss on large time behavior of the one dimensional barotropic compressible Navier-Stokes equations with initial data connecting two different constant states. When the two constant states are prescribed by the Riemann data of the associated Euler equations, the Navier-Stokes flow would converge to a viscous counterpart of Riemann solution. This talk will present the latest result on the cases where the Riemann solution consist of two shocks, and introduce the main idea for using to prove.

Deep neural networks have proven to work very well on many complicated tasks. However, theoretical explanations on why deep networks are very good at such tasks are yet to come. To give a satisfactory mathematical explanation, one recently developed theory considers an idealized network where it has infinitely many nodes on each layer and an infinitesimal learning rate. This simplifies the stochastic behavior of the whole network at initialization and during the training. This way, it is possible to answer, at least partly, why the initialization and training of such a network is good at particular tasks, in terms of other statistical tools that have been previously developed. In this talk, we consider the limiting behavior of a deep feed-forward network and its training dynamics, under the setting where the width tends to infinity. Then we see that the limiting behaviors can be related to Bayesian posterior inference and kernel methods. If time allows, we will also introduce a particular way to encode heavy-tailed behaviors into the network, as there are some empirical evidences that some neural networks exhibit heavy-tailed distributions.

Several years ago, Chi Li introduced the local volume of a klt singularity in his work on K-stability. The local-global analogy between klt singularities and Fano varieties, together with recent study in K-stability lead to the conjecture that klt singularities whose local volumes are bounded away from zero are bounded up to special degeneration. In this talk, I will discuss some recent work on this conjecture through the minimal log discrepancies of Kollár components. * Zoom information will not be provided. Please send an email to Jinhyung Park if you are interested in.

We introduce homotopy coherent nerves of Kan-enriched categories. We discuss homotopy theory of Kan complexes and how composition is performed inside infinity-categories. For this, we introduce the

The Structural Theorem of the Graph Minors series of Robertson and Seymour asserts that, for every $t\in\mathbb{N},$ there exists some constant $c_{t}$ such that every $K_{t}$-minor-free graph admits a tree decomposition whose torsos can be transformed, by the removal of at most $c_{t}$ vertices, to graphs that can be seen as the union of some graph that is embeddable to some surface of Euler genus at most $c_{t}$ and "at most $c_{t}$ vortices of depth $c_{t}$". Our main combinatorial result is a "vortex-free" refinement of the above structural theorem as follows: we identify a (parameterized) graph $H_{t}$, called shallow vortex grid, and we prove that if in the above structural theorem we replace $K_{t}$ by $H_{t},$ then the resulting decomposition becomes "vortex-free". Up to now, the most general classes of graphs admitting such a result were either bounded Euler genus graphs or the so called single-crossing minor-free graphs. Our result is tight in the sense that, whenever we minor-exclude a graph that is not a minor of some $H_{t},$ the appearance of vortices is unavoidable. Using the above decomposition theorem, we design an algorithm that, given an $H_{t}$-minor-free graph $G$, computes the generating function of all perfect matchings of $G$ in polynomial time. This algorithm yields, on $H_{t}$-minor-free graphs, polynomial algorithms for computational problems such as the {dimer problem, the exact matching problem}, and the computation of the permanent. Our results, combined with known complexity results, imply a complete characterization of minor-closed graphs classes where the number of perfect matchings is polynomially computable: They are exactly those graph classes that do not contain every $H_{t}$ as a minor. This provides a sharp complexity dichotomy for the problem of counting perfect matchings in minor-closed classes. This is joint work with Dimitrios M. Thilikos.

We define the notion of infinity-categories and Kan complex using observations from the previous talk. A process, called the nerve construction, producing infinity-categories from usual categories will be introduced and we will set dictionaries between them. Infinity-categories of functors will be introduced as well.

Katona's intersection theorem states that every intersecting family $\mathcal F\subseteq[n]^{(k)}$ satisfies $\vert\partial\mathcal F\vert\geq\vert\mathcal F\vert$, where $\partial\mathcal F=\{F\setminus x:x\in F\in\mathcal F\}$ is the shadow of $\mathcal F$. Frankl conjectured that for $n>2k$ and every intersecting family $\mathcal F\subseteq [n]^{(k)}$, there is some $i\in[n]$ such that $\vert \partial \mathcal F(i)\vert\geq \vert\mathcal F(i)\vert$, where $\mathcal F(i)=\{F\setminus i:i\in F\in\mathcal F\}$ is the link of $\mathcal F$ at $i$. Here, we prove this conjecture in a very strong form for $n> \binom{k+1}{2}$. In particular, our result implies that for any $j\in[k]$, there is a $j$-set $\{a_1,\dots,a_j\}\in[n]^{(j)}$ such that \[ \vert \partial \mathcal F(a_1,\dots,a_j)\vert\geq \vert\mathcal F(a_1,\dots,a_j)\vert.\]A similar statement is also obtained for cross-intersecting families.

The activation of Ras depends upon the translocation of its guanine nucleotide exchange factor, Sos, to the plasma membrane. Moreover, artificially inducing Sos to translocate to the plasma membrane is sufficient to bring about Ras activation and activation of Ras’s targets. There are many other examples of signaling proteins that must translocate to the membrane in order to relay a signal. One attractive idea is that translocation promotes signaling by bringing a protein closer to its target. However, proteins that are anchored to the membrane diffuse more slowly than cytosolic proteins do, and it is not clear whether the concentration effect or the diffusion effect would be expected to dominate. Here we have used a reconstituted, controllable system to measure the association rate for the same binding reaction in 3D vs. 2D to see whether association is promoted, and, if so, how.

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This talk will be presented online. ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: 1234)

This talk is the very first talk for a semester long series on higher algebra. Higher algerbra is the study of algebraic objects in spaces, correcting abnormal behavior of classical homological algebra and encorporating spheres into algebra. In this talk, we will see a concerete example of a higher-algebraic structure on singular cochains, and what structures are needed to formalize such structures with concrete diagrams. We assume familiarity with algebraic topology, category theory and homological algebra (at the first year graduate level).

We prove that for $n>k\geq 3$, if $G$ is an $n$-vertex graph with chromatic number $k$ but any its proper subgraph has smaller chromatic number, then $G$ contains at most $n-k+3$ copies of cliques of size $k-1$. This answers a problem of Abbott and Zhou and provides a tight bound on a conjecture of Gallai. This is joint work with Jie Ma.

A temporal graph is a graph whose edges are available only at specific times. In this scenario, the only valid walks are the ones traversing adjacent edges respecting their availability, i.e. sequence of adjacent edges whose appearing times are non-decreasing. Given a graph G and vertices s and t of G, Menger’s Theorem states that the maximum number of (internally) vertex disjoint s,t-paths is equal to the minimum size of a subset X for which G-X contains no s,t-path. This is a classical result in Graph Theory, taught in most basic Graph Theory courses, and it holds also when G is directed and when edge disjoint paths and edge cuts are considered instead. A direct translation of Menger’s Theorem to the temporal context has been known not to hold since an example was shown in the seminal paper by Kempe, Kleinberg and Kumar (STOC’00). In this talk, an overview of possible temporal versions of Menger’s Theorem will be discussed, as well as the complexity of the related problems.

In this talk, I will mostly discuss the singularity formation of Burgers equation. It is well-known that, when the initial data has negative gradient at some point, the solutions blow up in a finite time. We shall study the properties of the blow-up profile of Burgers equation by introducing the self-similar variables and the modulations, which can be used to study the blow-up for general nonlinear hyperbolic systems. If time permits, I will also discuss the singularity formation for the 1D compressible Euler equations and the related open questions.

In this talk, I will mostly discuss the singularity formation of Burgers equation. It is well-known that, when the initial data has negative gradient at some point, the solutions blow up in a finite time. We shall study the properties of the blow-up profile of Burgers equation by introducing the self-similar variables and the modulations, which can be used to study the blow-up for general nonlinear hyperbolic systems. If time permits, I will also discuss the singularity formation for the 1D compressible Euler equations and the related open questions.

In this talk, we present a Weisfielier-Leman Isomorphism test algorithm of featured graphs and how it can be used to extract representing features of nodes or entire graphs. This leads to a message passing framework of Aggregate-Combine of node-features which is one of the fundamental procedures to currently uesd graph neural networks. We proceed by showing various basic examples arised in real-world non-standard datasets like social network, knowledge graph and chemical compounds.

In this talk, I will mostly discuss the singularity formation of Burgers equation. It is well-known that, when the initial data has negative gradient at some point, the solutions blow up in a finite time. We shall study the properties of the blow-up profile of Burgers equation by introducing the self-similar variables and the modulations, which can be used to study the blow-up for general nonlinear hyperbolic systems. If time permits, I will also discuss the singularity formation for the 1D compressible Euler equations and the related open questions.

Various plasma phenomena will be discussed using a fundamentalfluid model for plasmas, called the Euler-Poisson system. These include plasma sheaths and plasma soliton. First we will briefly introduce recent results on the stability of plasma sheath solutions, and the quasi-neutral limit of the Euler-Poisson system in the presence of plasma sheaths. Another example of ourinterest is plasma solitary waves, for which we discuss existence, stability, and the time-asymptotic behavior. To study the nonlinear stability of solitary waves, the global existence of smooth solutions must be established, which is completelyopen. As a negative answer for global existence, we look into the finite-time blow-up results for the Euler-Poisson system, and discuss the related open questions.

Abstract: Let S:=S(a_{1}, ..., a_{n}) \subset P^{n} be a smooth rational normal n-fold scroll. Then the dimension of the projective automophism group {rm Aut}(S,``VecP^ {N} ) of S is \dim(Aut(S, P^{N})) = 2+ \frac{n(n+1)}{2}-(n+1)(N-n+1)+2 sum _{ n} ^{ j=1} ja_j + #{(i,j)|i Host: 곽시종     Contact: 김윤옥 (5745)     To be announced     2022-08-13 17:47:33 Detailed