Seminar
737 events
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Seminar
Classical liquids and functional renormalization group
December 17 (Thu) at 13:00 - 14:30, 2020
Takeru Yokota (Postdoctoral Researcher, The Institute for Solid State Physics (ISSP), The University of Tokyo)
Development of methods for classical statistical mechanics is desired for accurate predictions of the structures and thermodynamic properties of liquids. A powerful framework to describe classical liquids is density functional theory (DFT). In the quantum case, there have been recent attempts to develop accurate methods with combining DFT and the functional renormalization group (FRG), which is another framework to deal with many-body systems utilizing evolution equations, and such approaches are expected to work also in the classical case. In this presentation, I will talk about a new approach for classical liquids aided by FRG. The formalism and some ideas to incorporate higher-order correlation functions to systematically improve the accuracy will be shown. I will also present a numerical demonstration in a one-dimensional exactly solvable system and discuss the results by comparing to other conventional methods such as the hypernetted chain.
Venue: via Zoom
Event Official Language: English
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Seminar
Autoimmune diseases initiated by pathogen infection: mathematical modeling
December 17 (Thu) at 10:00 - 11:00, 2020
Akane Hara (Ph.D. Student, Graduate School of Systems Life Sciences, Kyushu University)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Quantum Wasserstein distance of order 1
December 16 (Wed) at 13:00 - 14:30, 2020
Ryusuke Hamazaki (Senior Research Scientist, iTHEMS / RIKEN Hakubi Team Leader, Nonequilibrium Quantum Statistical Mechanics RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research (CPR))
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Statistical model for meaning representation of language
December 16 (Wed) at 10:30 - 12:00, 2020
Koichiro Yoshino (Team Leader, Robotics Project, RIKEN Cluster for Science, Technology and Innovation Hub (RCSTI))
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Non-perturbative tests of duality cascades in three dimensional supersymmetric gauge theories
December 14 (Mon) at 16:00 - 18:10, 2020
Naotaka Kubo (Postdoctoral Researcher, Yukawa Institute for Theoretical Physics, Kyoto University)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Conserved charges in gravity and entropy
December 10 (Thu) at 13:00 - 14:30, 2020
Sinya Aoki (Professor, Yukawa Institute for Theoretical Physics, Kyoto University)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
How to obtain the large amount of sequence data from the eukaryote
December 10 (Thu) at 10:00 - 11:00, 2020
Euki Yazaki (Postdoctoral Researcher, iTHEMS)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Journal Club of Information Theory SG II
December 8 (Tue) at 13:00 - 14:00, 2020
Akinori Tanaka (Senior Research Scientist, iTHEMS)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Scattering theory for half-line Schrödinger operators: analytic and topological results
December 7 (Mon) at 16:00 - 18:10, 2020
Hideki Inoue (Nagoya University)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
KPZ equation, attractive bosons, and the Efimov effect
December 3 (Thu) at 13:00 - 14:30, 2020
Yusuke Nishida (Associate Professor, Department of Physics, Tokyo Institute of Technology)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Directional dark matter search and the technologies
December 3 (Thu) at 10:00 - 11:00, 2020
Tatsuhiro Naka (Lecturer, Department of Physics, Toho University / Specially Appointed Assistant Professor, Kobayashi-Maskawa Institute for the Origin of Particles and the Universe (KMI), Nagoya University)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Journal Club of Information Theory SG
December 1 (Tue) at 13:00 - 14:00, 2020
Akinori Tanaka (Senior Research Scientist, iTHEMS)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Flat and spherical surface approximations
November 30 (Mon) at 16:00 - 17:30, 2020
Martin Skrodzki (Visiting Scientist, iTHEMS / 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.
Venue: via Zoom
Event Official Language: English
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Seminar
Rotifer can be a good model organism for theoretical biology
November 27 (Fri) at 10:00 - 11:00, 2020
Yuri Kominami (Specially Appointed Assistant Professor, Graduate School of Agricultural and Life Sciences, The University of Tokyo)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Symmetry and conservation laws in neural networks
November 20 (Fri) at 10:00 - 11:00, 2020
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Representations of fundamental groups and 3-manifold topology
November 16 (Mon) at 16:00 - 18:10, 2020
Takahiro Kitayama (Associate Professor, Graduate School of Mathematical Sciences, The University of Tokyo)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Efficient probabilistic assessment of building performance: sequential Monte Carlo and decomposition methods
November 13 (Fri) at 16:00 - 18:10, 2020
Tianfeng Hou (Postdoctoral Researcher, iTHEMS / Postdoctoral Researcher, Prediction Science Laboratory, RIKEN Cluster for Pioneering Research (CPR) / Postdoctoral Researcher, Data Assimilation Research Team, RIKEN Center for Computational Science (R-CCS))
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Some idea on quantum tunneling via Lefschetz thimbles
November 12 (Thu) at 10:30 - 12:00, 2020
Yuya Tanizaki (Special Postdoctoral Researcher, Theory Group, RIKEN Nishina Center for Accelerator-Based Science (RNC) / Assistant Professor, Yukawa Institute for Theoretical Physics, Kyoto University)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Mathematics of thermalization in isolated quantum systems
November 10 (Tue) at 16:00 - 18:10, 2020
Naoto Shiraishi (Assistant Professor, Department of Physics, Faculty of Science, Gakushuin University)
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.
Venue: via Zoom
Event Official Language: English
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Seminar
Mathematical aspects of quasi-Monte Carlo integration
November 5 (Thu) at 16:00 - 18:10, 2020
Kosuke Suzuki (Assistant Professor, Graduate School of Advanced Science and Engineering, Hiroshima University)
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.
Venue: via Zoom
Event Official Language: English
737 events