セミナー
793 イベント
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セミナー
Autoimmune diseases initiated by pathogen infection: mathematical modeling
2020年12月17日(木) 10:00 - 11:00
原 朱音 (九州大学 システム生命科学府 一貫制博士課程)
The pathogen with proteins similar to host’s proteins is likely to cause autoimmunity, which is called “molecular mimicry”. To understand the mechanism of autoimmunity development caused by pathogen infection, we considered the following scenario: the infection activates the immune system, which results in clearance of pathogens, and the enhanced immune responses to the host’s body may remain and attack the host’s cells after the pathogen clearance. We developed a mathematical model describing the dynamics of T helper (Th) cells, viruses, self-antigens, and memory T cells and identified the conditions necessary to realize the scenario. We considered the cross-immunity of three different modes of action: [1] virus elimination by Th cells reactive to the self-antigen, [2] activation of Th cells reactive to viruses by self-antigens and Th cells reactive to self-antigens by viruses, and [3] enhancement of immune responses to self-antigens by Th cells reactive to viruses after the infection. The cross-immunity of type [3] was found to be most important for autoimmunity development. In contrast, [1] and [2] suppressed autoimmunity by effectively decreasing the viral abundance.
会場: via Zoom
イベント公式言語: 英語
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Quantum Wasserstein distance of order 1
2020年12月16日(水) 13:00 - 14:30
濱崎 立資 (理化学研究所 数理創造プログラム (iTHEMS) 上級研究員 / 理化学研究所 開拓研究本部 (CPR) 濱崎非平衡量子統計力学理研白眉研究チーム 理研白眉研究チームリーダー)
The Wasserstein distance is an indicator for the closeness of two probability distributions and is applied to various fields ranging from information theory to neural networks [1]. It is particularly useful to treat the geometry of the underlying space, such as tensor-product structures. In this journal club, I talk about one of the recent proposals on quantum extension of the Wasserstein distance [2]. After reviewing basic properties of classical Wasserstein distance, e.g., its relation to concentration phenomena, I discuss how they might be generalized to quantum realm.
会場: via Zoom
イベント公式言語: 英語
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Statistical model for meaning representation of language
2020年12月16日(水) 10:30 - 12:00
吉野 幸一郎 (理化学研究所 科技ハブ産連本部 (RCSTI) ロボティクスプロジェクト チームリーダー)
One of the final goals of natural language processing is building a model to capture the semantic meaning of language elements. Language modeling is a recent research trend to build a statistical model to express the meaning of language. The language model is based on the distributional hypothesis. The distributional hypothesis indicates that the surrounding elements of the target element describe the meaning of the element. In other words, relative positions between sentence elements (morphologies, words, and sentences) are essential to know the element's meaning. Recent works on distributed representation mainly focus on relations between clear elements: characters, morphologies, words, and sentences. However, it is essential to use structural information of languages such as dependency and semantic roles for building a human-understandable statistical model of languages. In this talk, we describe the statistical language model's basis and then discuss our research direction to introduce the language structure.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Non-perturbative tests of duality cascades in three dimensional supersymmetric gauge theories
2020年12月14日(月) 16:00 - 18:10
久保 尚敬 (京都大学 基礎物理学研究所 特別研究員)
M2-brane is an interesting object in M-theory and string theory. A three-dimensional ?=6 super conformal Chern Simons theory with gauge group U(?1)×?(?2), called ABJ theory, describes the low energy behavior of M2-brane On the one hand, it has been considered that when |?1−?2| is larger than the absolute value of Chern Simons level, the supersymmetry is broken. On the other hand, it was predicted that an interesting phenomenon called duality cascade occurs, and supersymmetry is not broken in some cases. Motivated by this situation, we performed non-perturbative tests by focusing on the partitionfunction on ?3. The result strongly suggests that the duality cascade indeed occurs. We also proposed that the duality cascade occurs in theories with more general gauge groups and we performed non-perturbative tests in the same way. I will review and explain our physical prediction in the first half of my talk. In the second half of my talk , I will explain the non-perturbative tests . This part is mathematical because the partition function reduces to a matrix model by using the supersymmetric localization technique.
会場: via Zoom
イベント公式言語: 英語
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Conserved charges in gravity and entropy
2020年12月10日(木) 13:00 - 14:30
青木 愼也
We propose a manifestly covariant definition of a conserved charge in gravity. We first define a charge density from the energy momentum tensor with a Killing vector, if exists in the system, and calculate the energy (and angular momentum) of the black hole by a volume integral. Our definition of energy leads to a correction of the known mass formula of a compact star, which includes the gravitational interaction energy and is shown to be 68\% of the leading term in some case. Secondly we propose a new method to define a conserved charge in the absence of Killing vectors, and argue that the conserved charge can be regarded as entropy, by showing the 1st law of thermodynamic for a special case. We apply this new definition to the expanding universe, gravitational plane waves and the black hole. We discuss future directions of our research.
会場: via Zoom
イベント公式言語: 英語
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How to obtain the large amount of sequence data from the eukaryote
2020年12月10日(木) 10:00 - 11:00
矢﨑 裕規 (理化学研究所 数理創造プログラム (iTHEMS) 特別研究員)
Most of the modern biology is supported by genetic sequence data. Recent advances in sequencing technology have made it possible to obtain comprehensive and large numbers of sequence data from a small amount of samples, which are deposited in public databases and are easily available. In this talk, I want to give an overview of how these large scale sequence data are obtained from samples and how they become available for us to use in our biological studies, through my eukaryotic sequence studies.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Journal Club of Information Theory SG II
2020年12月8日(火) 13:00 - 14:00
田中 章詞 (理化学研究所 数理創造プログラム (iTHEMS) 上級研究員)
The practical updating process of deep neural networks based on stochastic gradient descent is quite similar to stochastic dynamics described by Langevin equation. Under the Langevin system, we can "derive" 2nd law of thermodynamics, i.e. increasing the total entropy of the system. This fact suggests "2nd law of thermodynamics in deep learning." In this talk, I would like to explain this idea roughly, and there will be no concrete new result, but it may provide us new perspectives to study neural networks, I hope.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Scattering theory for half-line Schrödinger operators: analytic and topological results
2020年12月7日(月) 16:00 - 18:10
井上 秀樹 (名古屋大学)
Levinson’s theorem is a surprising result in quantum scattering theory, which relates the number of bound states and the scattering part of the underlying quantum system. For the last about ten years, it has been proved for several models that once recast in an operator algebraic framework this relation can be understood as an index theorem for the Møller wave operators. Resulting index theorems are called topological version of Levinson’s theorem or shortly topological Levinson’s theorem. In this talk, we first review the background and the framework of our investigation. New analytical and topological results are provided for Schrödinger operators on the half-line. This talk is based on my Ph.D thesis.
会場: via Zoom
イベント公式言語: 英語
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セミナー
KPZ equation, attractive bosons, and the Efimov effect
2020年12月3日(木) 13:00 - 14:30
西田 祐介 (東京工業大学 理学院 物理学系 准教授)
The Kardar-Parisi-Zhang (KPZ) equation for surface growth has been a paradigmatic model in nonequilibrium statistical physics. In particular, it in dimensions higher than two undergoes a roughening transition from smooth to rough phases with increasing the nonlinearity. It is also known that the KPZ equation can be mapped onto quantum mechanics of attractive bosons with a contact interaction, where the roughening transition corresponds to a binding transition of two bosons with increasing the attraction. Such critical bosons in three dimensions actually exhibit the Efimov effect, where a three-boson coupling turns out to be relevant under the renormalization group so as to break the scale invariance down to discrete one. On the basis of these facts linking the two distinct subjects in physics, we predict that the KPZ roughening transition in three dimensions shows either the discrete scale invariance or no intrinsic scale invariance.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Directional dark matter search and the technologies
2020年12月3日(木) 10:00 - 11:00
中 竜大 (東邦大学 理学部物理学科 講師 / 名古屋大学 素粒子宇宙起源研究所 (KMI) 特任助教)
For identification of the dark matter, various methodologies are required. Especially, the direct detection is one of the most important goals to directly understand itself. Now, there are various technologies for direct detection, but almost all detectors have no direction sensitivity. We can obtain essential information such as dependence of motion between the earth and the dark matter, velocity distribution and background from direction information, therefore that becomes a very important methodology to identify the dark matter for future as long as we consider "particle dark matter". In this seminar, I report about the potential of direction sensitive dark matter search and current experimental effort.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Journal Club of Information Theory SG
2020年12月1日(火) 13:00 - 14:00
田中 章詞 (理化学研究所 数理創造プログラム (iTHEMS) 上級研究員)
The practical updating process of deep neural networks based on stochastic gradient descent is quite similar to stochastic dynamics described by Langevin equation. Under the Langevin system, we can "derive" 2nd law of thermodynamics, i.e. increasing the total entropy of the system. This fact suggests "2nd law of thermodynamics in deep learning." In this talk, I would like to explain this idea roughly, and there will be no concrete new result, but it may provide us new perspectives to study neural networks, I hope. *Detailed information about the seminar refer to the email.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Flat and spherical surface approximations
2020年11月30日(月) 16:00 - 17:30
スクロツキ マーティン (理化学研究所 数理創造プログラム (iTHEMS) 客員研究員 / Fellow, German Academic Scholarship Foundation, Germany)
State-of-the-art acquisition devices produce surface representations of increasingly high resolution. While these detailed representations are important for production, they are problematic e.g. when exchanging drafts via the internet or when a quick rendering for comparison is necessary. In the first part of the talk, I will present results and further research questions from a paper I recently co-authored on 'Variational Shape Approximation'. This approach aims at linearizing the input surface and representing it via a set of localized planar segments. In the second part of the talk, I will present some ongoing research on surface representations via balls. This work started with constructions from spherical neodym magnets and provided a set of mathematical questions. These investigations are joint work with FU Berlin and OIST.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Rotifer can be a good model organism for theoretical biology
2020年11月27日(金) 10:00 - 11:00
小南 友里 (東京大学 大学院農学生命科学研究科 特任助教)
Rotifers are cylindrical zooplankton which constitute the phylum Rotifera. They have organs and tissues including ganglia, muscles, digestive organs, ovaries, and sensory organs in their <1mm body. Rotifers are suitable for the study on the population dynamics and longevity due to their short generation time. Furthermore the most attractive characteristic of the rotifers is asexual propagation, makes it easy to obtain clonal cultures. The genomic and transcriptomic database are developed and molecular biological techniques such as RNAi for using rotifers have been established. In this seminar, other attractive characteristics of rotifer as a model organism for theoretical biology and great studies using rotifers will be introduced. Our recent results of investigating the effects of calorie condition on longevity will be discussed.
会場: via Zoom
イベント公式言語: 英語
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Symmetry and conservation laws in neural networks
2020年11月20日(金) 10:00 - 11:00
Hidenori Tanaka (Group Leader & Senior Scientist, Physics & Informatics Laboratories, NTT Research, Inc., USA / Visiting Scholar, Stanford University, USA)
Symmetry is the central guiding principle in the exploration of the physical world but has been underutilized in understanding and engineering neural networks. We first identify simple yet powerful geometrical properties imposed by symmetry. Then, we apply the theory to answer a series of following important questions: (i) What, if anything, can we quantitatively predict about the complex learning dynamics of real-world deep learning models driven by real-world datasets? (ii) How can we make deep learning models more efficient by removing parameters without disconnecting information flow? (iii) How can we distill experimentally testable neuroscientific hypotheses by reducing the complexity of deep learning models mimicking the brain? Overall, our approach demonstrates how we can harness the principles of symmetry and conservation laws to reduce deep learning models' complexity and make advances in the science and engineering of biological and artificial neural networks.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Representations of fundamental groups and 3-manifold topology
2020年11月16日(月) 16:00 - 18:10
北山 貴裕 (東京大学 大学院数理科学研究科 准教授)
In 3-dimensional topology the great progress during the last two decades revealed that various properties of 3-manifolds are well understood from their fundamental groups. I will give an introduction to the study of splittings of 3-manifolds along surfaces, with an emphasis on an application of group representations. A fundamental and difficult problem in general is to find surfaces essentially embedded in a given 3-manifold. I will explain how such surfaces are detected by deformations of representations of the fundamental group, and what information of detected surfaces is described in terms of topological invariants derived from representations.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Efficient probabilistic assessment of building performance: sequential Monte Carlo and decomposition methods
2020年11月13日(金) 16:00 - 18:10
ティエンフォン・ホウ (理化学研究所 数理創造プログラム (iTHEMS) 特別研究員 / 理化学研究所 開拓研究本部 (CPR) 三好予測科学研究室 特別研究員 / 理化学研究所 計算科学研究センター (R-CCS) データ同化研究チーム 特別研究員)
The use of numerical simulations for complex systems is common. However, significant uncertainties may exist for many of the involved variables, and in order to ensure the reliability of our simulation results and the safety of such complex systems, a stochastic approach providing statistics of the probability distribution of the results is of crucial importance. However, when a highly accurate result is required, the conventional Monte Carlo based probabilistic methodology inherently requires many repetitions of the deterministic analysis and in cases where that deterministic simulation is (relatively) time consuming, such probabilistic assessment can easily become computationally intractable. Hence, to reduce the computational expense of such probabilistic assessments as much as possible, the targets of this seminar are twofold: (1), to exploit an efficient sampling strategy to minimize the number of needed simulations of Monte Carlo based probabilistic analysis; (2), to investigate a surrogate model to reduce the computational expense of single deterministic simulation. This seminar contains two parts and will be accompanied by a set of illustrative building physical case studies (analysis of the heat and moisture transfer through building components). The first part of this seminar focusses on the use of quasi-Monte Carlo based probabilistic assessment for building performance, since it has the potential to outperform the standard Monte Carlo method. More specifically, the quasi-Monte Carlo sampling strategies and related error estimation techniques will be introduced in detail. In addition, questions on under which conditions the quasi-Monte Carlo can outperform the standard Monte Carlo method will be answered by a set of analyses. The second part of this seminar targets the investigation of using model order reduction methods for optimizing the deterministic simulation, given that it generally allows a (large) reduction of the simulation time without losing the dynamic behavior of the conventional models (such as the transient finite element analysis). Particularly, the fundamental concepts of one common model order reduction method – proper orthogonal decomposition (POD) will be provided, and its potential use for simulating (building physical) problems with different levels of non-linearity and complexity will be illustrated.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Some idea on quantum tunneling via Lefschetz thimbles
2020年11月12日(木) 10:30 - 12:00
谷崎 佑弥 (理化学研究所 仁科加速器科学研究センター (RNC) 理論研究グループ 基礎科学特別研究員 / 京都大学 基礎物理学研究所 助教)
In this talk, I will explain my previous study with Takayuki Koike on a possible approach to quantum tunneling via Lefschetz thimbles. We classified all the complex saddle points for the real-time path integral for the symmetric double-well quantum mechanics. We looked at various properties of those complex solutions, which motivated us to conclude that the computation of tunneling amplitudes for the symmetric double well requires the interference of infinitely many Lefschetz thimbles. I would also like to talk about some speculations, admittingly being very optimistic.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Mathematics of thermalization in isolated quantum systems
2020年11月10日(火) 16:00 - 18:10
白石 直人 (学習院大学 理学部 物理学科 助教)
If an isolated macroscopic quantum system is left at a nonequilibrium state, then this system will relax to the unique equilibrium state, which is called thermalization. Most of quantum many-body systems thermalize, while some many-body systems including integrable systems do not thermalize. What determines the presence/absence of thermalization and how to understand thermalization from microscopic quantum mechanics are profound long-standing problems. In the first part of my talk, I briefly review some established results of quantum thermalization. I first clarify the problem of thermalization in a mathematical manner, and then introduce several important results and insights: typicality of equilibrium states [1], relaxation caused by large effective dimension [2], and eigenstate thermalization hypothesis (ETH) [3,4] and weak-ETH [5]. In the second part of my talk, I explain some of my results. First, I introduce a model which is non-integrable and thermalizes but does not satisfy the ETH [6,7]. This finding disproves the conjectures that all nonintegrable systems satisfy the ETH and that the ETH is a necessary condition for thermalization. I also discuss the hardness of the problem of thermalization from the viewpoint of computational science [8]. Then, I move to an analytical approach to a concrete model, and prove that S=1/2 XYZ chain with a magnetic field is nonintegrable [9]. This is the first example of proof of nonintegrability in a concrete quantum many-body system, which will help a mathematical approach to thermalization.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Mathematical aspects of quasi-Monte Carlo integration
2020年11月5日(木) 16:00 - 18:10
鈴木 航介 (広島大学 大学院先進理工系科学研究科 助教)
In this talk, I will introduce mathematical aspects of quasi-Monte Carlo (QMC) integration. We aim to approximate the integral of a function on the d-dimensional hypercube [0,1]^d. A useful approach is Monte-Carlo (MC) integration, which uses randomly chosen samples. A drawback of MC is the rate of convergence; the standard deviation of the estimator converges as 1/sqrt(n) asymptotically in n. To have a better rate of convergence as O(log^d N/N) or more, QMC uses deterministic, uniformly distributed points. In the first part, I will give an overview of QMC, such as star-discrepancy, Koksma-Hlawka inequality, and some explicit constructions as lattices and digital nets. In the second part, I will show that QMC using lattices and digital nets can achieve a higher rate of convergence for smooth integrands.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Evolution of a peak of genetic divergence driven by local adaptation
2020年11月5日(木) 10:00 - 11:00
坂本 貴洋 (総合研究大学院大学 先導科学研究科 特別研究員)
In species that are distributed in various environments, each subpopulation adapts to the local environment. In general, when there is migration between subpopulations, genetic divergence does not proceed because the genomes are exchanged between subpopulations. However, around the loci involved in local adaptation, genetic divergence proceeds. This is because different genotypes are favored between subpopulations, so that the alleles of migrants are purged by natural selection and the exchange of genomes is suppressed. It has not been theoretically known how the degree of genetic differentiation evolves over time, making the interpretation of population genomic data difficult. In this study, we constructed and analyzed a model of population genetics to clarify the dynamics of genetic divergence.
会場: via Zoom
イベント公式言語: 英語
793 イベント