Seminar

Seminar

Recent progress on dualities in W-superalgebras

January 28 at 16:00 - 18:00, 2022

Dr. Shigenori Nakatsuka (JSPS Fellow, Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), The University of Tokyo)

Vertex superalgebras are algebras which describe the chiral part of two dimensional superconformal field theory. A rich and fundamental class is provided by the affine vertex superalgebras associated with simple Lie superalgebras and the W-superalgebras obtained from them by cohomology parametrized by nilpotent orbits. Historically, the W-algebras associated with simple Lie algebras and principal nilpotent orbit have been studied intensively and are well-known to play an essential role in the quantum geometric Langlands program. In particular, they enjoy a duality, called the Feigin-Frenkel duality, which is a chiral analogue of the isomorphism between centers of the enveloping algebras of simple Lie algebras in Langlands duality. Recently, physicists found a suitable generalization for other types of nilpotent orbits from study on four dimensional supersymmetric gauge theory. In this talk, I will report the recent progress on our understanding of dualities in W-superalgebras since then in terms of several aspects: algebras, modules, and fusion rules.

Venue: via Zoom

Event Official Language: English

Seminar

On Flow and Form at Low Reynolds Number

January 27 at 10:00 - 11:00, 2022

Prof. Kenta Ishimoto (Associate Professor, Research Institute for Mathematical Sciences (RIMS), Kyoto University)

Cell locomotion is mechanically restricted by surrounding viscous fluids. With a focus on swimming cells in a low-Reynolds-number flow, I will give a brief introduction to microbiological fluid dynamics and present a 'hydrodynamic shape' theory at the cellular scale.

Venue: via Zoom

Event Official Language: English

Seminar

Bethe ansatz and quantum computing

January 26 at 22:00 - 23:15, 2022

Prof. Rafael I. Nepomechie (Professor, Physics Department, University of Miami, Florida, USA)

We begin with a brief review of the Heisenberg quantum spin chain and its remarkable solution found by Bethe. We then review a probabilistic algorithm for preparing exact eigenstates of this model on a quantum computer. An exact formula for the success probability is presented, and the computation of correlation functions is discussed. A generalization of the algorithm to open chains with boundaries is also noted.

Venue: via Zoom

Event Official Language: English

Seminar

A simple XY model for cascade transfer

January 20 at 13:30 - 15:00, 2022

Mr. Tomohiro Tanogami (Graduate School of Science, Kyoto University)

Cascade transfer is the phenomenon that an inviscid conserved quantity, such as energy or enstrophy, is transferred conservatively from large (small) to small (large) scales. As a consequence of this cascade transfer, the distribution of the transferred quantity obeys a universal scaling law independent of the details of large (small) scales. For example, in the energy cascade in fluid turbulence, the energy spectrum follows Kolmogorov's power law [1]. Such behavior is observed even in systems different from ordinary fluids, such as quantum fluid, elastic body, and spin systems. Here, we aim to establish the concept of a universality class for cascade transfer. As a first step toward this end, we propose a simple model representing one universality class [2]. In doing so, we regard cascade transfer as a cooperative phenomenon of unidirectional transport across scales and ask how it emerges from spatially local interactions. The constructed model is a modified XY model with amplitude fluctuations, in which the spin is regarded as the “velocity” of a turbulent field in d dimensions. We show that the model exhibits an inverse energy cascade with the non-Kolmogorov energy spectrum. We also discuss the relation to spin turbulence [3,4] and atmospheric turbulence [5].

Venue: via Zoom

Event Official Language: English

Seminar

A study of biological systems from topological point of view

January 20 at 10:00 - 11:00, 2022

Dr. Hiroyasu Miyazaki (Senior Research Scientist, iTHEMS)

A biological body can be regarded as a complicated network of chemical reactions. The chemical reaction network (CRN) is a (hyper)graph-theoretic model of such biological networks. Recently, in the joint work with Yuji Hirono, Takashi Okada and Yoshimasa Hidaka, we applied a topological method to the study of CRNs, and found a suitable way to simplify the networks. Since Professor Hirono has already explained our work in this seminar, I will try to explain it from a slightly different point of view. In the first half of the talk, I will review the entire work. In the second half, I will try to give a rough sketch of the mathematical method we used in the work.

Venue: via Zoom

Event Official Language: English

Seminar

Axion-like particles from core-collapse supernovae

January 17 at 11:00 - 12:00, 2022

Dr. Kanji Mori (Research Institute of Stellar Explosive Phenomena (REISEP), Fukuoka University)

Axion-like particles (ALPs) are a class of hypothetical pseudoscalar particles which feebly interact with ordinary matter. The hot plasma of stars and core-collapse supernovae is a possible laboratory to explore physics beyond the standard model including ALPs. Once produced in a supernova, some of the ALPs can be absorbed by the supernova matter and affect energy transfer. We recently calculated the ALP emission in core-collapse supernovae and the backreaction on supernova dynamics consistently. It is found that the stalled bounce shock can be revived if the coupling between ALPs and photons is as high as g_{a gamma} ~ 10^{-9} GeV^{-1} and the ALP mass is 40-400 MeV. In this talk, I will briefly review stellar and supernova constraints on ALPs and then discuss our recent results.

Venue: via Zoom

Event Official Language: English

Seminar

The Ohsawa-Takegoshi $L^2$ extension theorem and variations of Bergman kernels

January 14 at 16:00 - 18:00, 2022

Dr. Genki Hosono (Mathematical Institute, Graduate School of Science, Tohoku University)

In complex analysis and geometry, $L^2$ methods are very important and widely used. Recent studies show that the $L^2$ theory and the variational theory are closely related. In particular, the (optimal) $L^2$ extension theorem can be proved by subharmonicity of variations of Bergman kernels and vice versa. In this talk, I will explain the background, results, and key ideas of the proof. *Please contact Keita Mikami mailing address to get access to the Zoom meeting room.

Venue: via Zoom

Event Official Language: English

Seminar

A comprehensive view of the SARS-CoV-2 infection process

January 13 at 10:00 - 11:00, 2022

Dr. Wataru Nishima (Scientist, New Mexico Consortium, Mexico)

Nishima et al. recently published a paper about a computational model of SARS-CoV-2 Spike Protein [1]. Although it is still a hypothesis due to the lack of direct experimental evidence, the story comprehensively explains the initial infection process of SARS-CoV-2 consistent with most of the empirical evidence. In the presentation, I would like to explain the overview of the infection process for the non-expert audience and how the hypothesis influences the current COVID-19 situation. If time permits, I would like to briefly explain the current plan of the iTHEMS-NMC COVID project, which is going to be the first case of undergoing an interdisciplinary collaboration framework between Japan and the US.

Venue: via Zoom

Event Official Language: English

Seminar

Physics of nuclear bodies

January 6 at 10:00 - 11:00, 2022

Prof. Tetsuya Yamamoto (Specially Appointed Associate Professor, Institute for Chemical Reaction Design and Discovery, Hokkaido University)

Eukaryotic nucleus is not a uniform solution of DNA, but there are a number of nuclear bodies in the interchromatin spaces. There are growing number of experiments that suggest that nuclear bodies are assembled by liquid-liquid phase separation (LLPS). Condensates assembled by LLPS show coarsening or coalescence to decrease the surface energy. However, in some nuclear bodies, such as paraspeckles, nuclear stress bodies, and fibrillar centers in nucleoli, multiple condensates are stably dispersed and are not likely assembled by LLPS. The assembly mechanism of nuclear bodies is relevant to the regulation of the area of condensate surfaces, which are functional in some nuclear bodies, and the mobility of nuclear bodies. Hirose group (Osaka Univ.) has elucidated that nuclear bodies are scaffolded by a class of RNA, called architectural RNA (arcRNA), which forms complexes with RNA binding proteins. This implies that the assembly of nuclear bodies is governed RNA dynamics, such as transcription, degradation, and processing, and the sequence of bases of arcRNA. In the seminar, I will show how the base sequences and the dynamics of RNA are involved in the assembly of paraspeckles and fibrillar centers in nucleoli.

Venue: via Zoom

Event Official Language: English

Seminar

Hidden Markov Models and their applications

December 23 at 10:00 - 11:00, 2021

Dr. Takashi Okada (Senior Research Scientist, iTHEMS)

The Hidden Markov models (HMM) have been used in a variety of fields for different purposes. I am going to review statistical inference methods associated with HMM & related biological problems. As an example of their applications, I'll also present my research on the SARS-CoV-2 evolution.

Venue: via Zoom

Event Official Language: English

Seminar

Quantum metric of topological and non-topological insulators in AMO and other systems

December 20 at 13:30 - 15:00, 2021

Dr. Tomoki Ozawa (Associate Professor, Advanced Institute for Materials Research (AIMR), Tohoku University)

Recently, the concept of quantum geometry is attracting great interests in various areas of condensed matter and AMO physics. Quantum geometry tells how much the quantum states "change" as one moves in a parameter space, and is closely related to the topology of the quantum states. Quantum geometric tensor is often used to characterize the geometry, whose real part is the quantum metric and the imaginary part is the Berry curvature. Although Berry curvature is rather well-studied in the context of topological insulators and superconductors, less has been known about the quantum metric. However, experiments detecting the quantum metric have appeared in the past couple of years and interest in quantum metric is indeed growing. In this talk, I first explain basics of quantum metric and its recent experimental observations. I then discuss various aspects of quantum metric, including its relation to localization, topology, and the Kähler geometry.

Venue: via Zoom

Event Official Language: English

Seminar

Revisiting Standard Methods for Phylogenetic Tree Inference

December 16 at 10:00 - 11:00, 2021

Dr. Motomu Matsui (Research Associate, Graduate School of Science, The University of Tokyo)

Phylogenetic tree inference is the foundation to answer any biological questions, for example, how the living systems were established. However, the existing methods show poor performance to infer the phylogenetic tree when constructing an informative multiple sequence alignment (MSA) is difficult. In this talk, I will first review the current problems in phylogenetics, then introduce the graph splitting (GS), and edge perturbation (EP) method. The GS method rapidly reconstructs a protein superfamily-scale phylogenetic tree using a graph-based approach; evolutionary simulation showed that the GS method can accurately reconstruct phylogenetic trees when sequences substantially diverge. The EP method is the bootstrap-like method using pairwise sequence alignment (PSA) instead of MSA, which can provide reliable measurements on the estimated branches. In addition, we can rapidly and reliably reconstruct a phylogenetic tree with problematic MSA switching NJ+EP and GS+EP methods, because the EP method can be applied to the NJ method. These methods not only improve the accuracy of phylogenetic tree inference, but they also could open the door for revisiting phylogenetics.

Venue: via Zoom

Event Official Language: English

Seminar

The FASER experiment

December 15 at 17:00 - 18:00, 2021

Prof. Hidetoshi Otono (Assistant Professor, Research Center for Advanced Particle Physics, Kyushu University)

FASER, the ForwArd Search ExpeRiment, is an experiment dedicated to searching for light, extremely weakly-interacting particles at the LHC. Such particles may be produced in the LHC’s high-energy collisions and then decay to visible particles in FASER, which is placed 480 m downstream of the ATLAS interaction point. FASER, also includes a sub-detector, FASER$\nu$, designed to detect neutrino’s produced in the LHC collisions and to study their properties. This seminar will describe the physics motivations, detector design, expected performance of FASER, and current status, as well as the physics prospects.

Venue: via Zoom

Event Official Language: English

Seminar

Cosmological particle production as Stokes phenomena

December 15 at 13:30 - 15:00, 2021

Dr. Yusuke Yamada (JSPS Research Fellowship for Young Scientists, Research Center for the Early Universe (RESCEU), The University of Tokyo)

Particle production from “vacuum” takes place in time-dependent backgrounds. In very early universe, particularly just after inflation, expanding metric as well as oscillating scalar fields play the role of such backgrounds. Mathematically, “particle production from vacuum” can be understood as “Stokes phenomena”, and such understanding enables us to estimate the amount of produced particles in a systematic way. In this talk, I will review the relation between Stokes phenomena and particle production. Then, from the Stokes phenomena viewpoint, I will (re)consider particle production associated with expanding universe, an oscillating scalar field, or both of them. I will also discuss the time evolution of particle number, and its relation to the ambiguity of “vacuum states”.

Venue: via Zoom

Event Official Language: English

Seminar

Magnetic field dependence of neutrino-driven core-collapse supernova models

December 10 at 14:00 - 15:00, 2021

Prof. Jin Matsumoto (Assistant Professor, Keio Institute of Pure and Applied Sciences (KiPAS), Graduate School of Science and Technology, Keio University)

Massive stars can explode and release huge energy (typically 10^51 erg) at the end of their life. It is one of the most energetic explosions in the Universe and is called a core-collapse supernova. The impact of the magnetic field on the explosion mechanisms of the core-collapse supernova is a long-standing mystery. Recently, we have updated our neutrino-radiation-hydrodynamics supernova code (3DnSNe, Takiwaki et al. 2016) to include magnetohydrodynamics (MHD). Using this code, we have performed three-dimensional MHD simulations for the evolution of non-rotating stellar cores focusing on the difference in the magnetic field of the progenitors. Initially, 20 and 27 solar mass pre-supernova progenitors are threaded by only the poloidal component of the magnetic field, which strength is 10^10 (weak) or 10^12 (strong) G. We find that the neutrino-driven explosion occurs in both the weak and strong magnetic field models. The neutrino heating is the main driver for the explosion in our models, whereas the strong magnetic field slightly supports the explosion. In my talk, I will introduce the details of this mechanism.

Venue: via Zoom

Event Official Language: English

Seminar

Selective inference for testing trees and edges in hierarchical clustering and phylogeny

December 9 at 10:00 - 11:00, 2021

Prof. Hidetoshi Shimodaira (Professor, Graduate School of Informatics, Kyoto University / Team Leader, Mathematical Statistics Team, RIKEN Center for Advanced Intelligence Project (AIP))

Bootstrap resampling is quite useful for computing “confidence values” or “p-values” of trees and edges. However, they are biased and may lead to false positives (too many wrong discoveries) or false negatives (too few correct discoveries) depending on the “curvature” of the boundary surface of a hypothesis region in the data space. In addition, we face the issue of selection bias because we tend to use the dataset twice for hypothesis selection and its evaluation. I will explain these two types of bias and show methods to adjust the confidence values.

Venue: via Zoom

Event Official Language: English

Seminar

Generalized Bernoulli process and computation of proportional areas for Venn diagram

December 8 at 16:00 - 18:00, 2021

Dr. Ryosuke Iritani (Research Scientist, iTHEMS)

Venue: via Zoom

Event Official Language: English

Seminar

The Conley index of topological dynamical systems

December 3 at 16:00 - 18:00, 2021

Prof. Yosuke Morita (Assistant Professor, Department of Mathematics, Kyoto University)

The study of topological dynamical systems, i.e. continuous self-homeomorphisms (or continuous flows) on topological spaces, is important in both pure mathematics and applications. To each isolated invariant subset of a topological dynamical system, we can assign an invariant called the Conley index, which is (roughly speaking) a based space that describes the dynamics around the isolated invariant subset. It is used not only in the study of topological dynamical systems themselves but also in Manolescu’s construction of the Seiberg-Witten-Floer homotopy type (a spectrum-valued (3+1)-dimensional TQFT). In this talk, I am planning to explain a new construction of Conley indices, which is entirely non-homotopical and uses only basic general topology. *Please contact Keita Mikami or Hiroyasu Miyazaki's mailing address to get access to the Zoom meeting room.

Venue: via Zoom

Event Official Language: English

Seminar

Simulation-based inference for multi-type cortical circuits

November 29 at 13:30 - 15:00, 2021

Dr. Enrico Rinaldi (Research Fellow, Physics Department, University of Michigan, USA)

In many scientific fields, ranging from astrophysics to particle physics and neuroscience, simulators for dynamical systems generate a massive amount of data. One of the crucial tasks scientists are spending their precious time on is comparing observational data to the aforementioned simulations in order to infer physically relevant parameters and their uncertainties, based on the model embedded in the simulator. This poses a problem because the likelihood function for realistic simulations of complex physical systems is intractable. Simulation-based inference techniques attack this problem using machine learning tools and probabilistic programming. I will start with an overview of the problem and explain the general application of simulation-based inference methods. Then I will describe an application of the methods to a model of neurons in the visual cortex of mice."

Venue: via Zoom

Event Official Language: English

Seminar