Seminar

Introduction to Boolean modeling and Boolean networks as information processing units

January 21 (Thu) 10:00 - 11:00, 2021

Takashi Okada (Senior Research Scientist, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS))

Boolean networks are widely used in physics, biology, social science, and computer science. In this talk, I will introduce the basics of Boolean networks and give an overview of Biological applications. Then, I will discuss information transfer in Boolean networks and discuss optimal design principles. The latter part of the talk is based on joint work with Fumito Mori (Kyushu Univ).

Venue: via Zoom

Event Official Language: English

Information theory in ecology: Markov chain, Venn diagram, Kronecker (and Cartesian graph) products, and Tsallis entropy

January 20 (Wed) 13:00 - 14:00, 2021

Ryosuke Iritani (Research Scientist, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS))

This is more like an introductory talk on how I was motivated to work with information theory, and include unpublished data. Ecologists have been long interested in understanding diversity (divergence) of natural ecosystems. One possible way of accounting for diversity is to use a species' presence/absence table across spatial locations (species-location table), in which we record 1 if a focal species is present in a given site (otherwise 0). Recent interest lies in assessing how diversity (e.g., the number of species) changes with time: for instance, extinction and colonization of species may result in the modification of such tables with time. However, we are yet to have theoretical toolkits to model the dynamics of spcies-site tables. In this talk, I will introduce my model (in collaboration with R. Hamazaki, S. Tatsumi, and M Cadotte) of the dynamics of species-site tables based on Markovian stochastic processes. Specifically, our apporach allows us to analytically obtain the solution of the full stochastic dynamics by means of localizing the dynamics to a single site and then expanding it towards the global sites with Kronecker's prodcut (in linear algebra) or Cartesian product (in graph theory). Intuition obtains from illustrating the dynamics onto Venn diagram, where we draw several sets (corresponding to locations) and binary numbers (corresponding to presence-absence data) and consider random walks on Venn diagram acorss sets; also this Venn diagram based interpretation is mathematically underpinned by Cartesian product of graphs. Finally I will briefly talk about how we assess diversity of ecosystems using Tsallis entropy (or the generalized Shannon entropy).

Venue: via Zoom

Event Official Language: English

Time-dependent driven quantum critical systems in (1+1) dimension

January 18 (Mon) 10:00 - 11:15, 2021

Xueda Wen (Postdocs, Physics Department, Harvard University, USA)

10:00am ~ 11:15am on Jan. 18th, 2021 (JST) 8:00pm ~ 9:15pm on Jan. 17th, 2021 (EST) I will introduce an analytically solvable setup for time-dependent driven quantum critical systems in (1+1)D, whose low-energy physics are described by conformal field theories. In general, one may observe two different phases (heating and non-heating), where the correlation functions such as the entanglement entropy and energy-momentum density can be analytically solved. The dependence of phase diagrams on (i) the types of driving Hamiltonians and (ii) the types of driving sequences (such as periodic, quasi-periodic and random drivings) will be discussed.

Venue: via Zoom

Event Official Language: English

What are genes and how can we find them?

January 14 (Thu) 10:00 - 11:00, 2021

Jeffrey Fawcett (Senior Research Scientist, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS))

Although 'gene' is a word that is used frequently in the society, most people probably do not know what genes actually are. In fact, its definition is not so straightforward. In this talk, I will first give a historical perspective and our current understanding of what genes are and what they look like. Then, I will talk about 'gene prediction'. Once we obtain the DNA (genome) sequence data of a given species, we must 'find' the genes within the genome. This involves computational prediction utilizing probabilistic models and various sources of external evidence. I will briefly explain how this is done.

Venue: via Zoom

Event Official Language: English

Accelerated equilibration in classical stochastic systems

January 13 (Wed) 13:00 - 14:00, 2021

Kyosuke Adachi (Special Postdoctoral Researcher, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) / Special Postdoctoral Researcher, Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research (BDR))

Shortcuts to adiabaticity (STA) [1] are processes that make a given quantum state evolve into a target state in a fast manner, which can be useful to avoid decoherence in quantum experiments. In this journal club, I will concisely review the concept of STA, and then focus on the recently proposed classical counterparts of STA, sometimes called engineered swift equilibration, in Brownian particle systems [2] and evolutionary systems [3].

Venue: via Zoom

Event Official Language: English

From local resynchronization to global pattern recovery in the zebrafish segmentation clock

January 7 (Thu) 10:00 - 11:00, 2021

Koichiro Uriu (Assistant Professor, College of Science and Engineering, Kanazawa University)

Tissue-scale developmental patterns are often generated by local cellular interactions and global tissue deformation. An example is gene expression rhythms in vertebrate, termed the segmentation clock. The oscillatory spatial pattern of the segmentation clock across a tissue determines the timing of body segment formation. In this seminar, we discuss pattern recovery in the zebrafish segmentation clock after perturbation in oscillator coupling. To predict pattern recovery in embryos, we develop a physical model that describes both cell mechanics and genetic oscillations. We show that the physical model explains experimentally observed intermingled segmental defects, and their axial distributions in different embryonic developmental stages. Our analysis suggests that pattern recovery in developing tissues occurs at two scales; local pattern formation and transport of these patterns through tissue morphogenesis.

Venue: via Zoom

Event Official Language: English

Review on the Lieb-Robinson bound

December 23 (Wed) 13:00 - 14:00, 2020

Yukimi Goto (Special Postdoctoral Researcher, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS))

The Lieb-Robinson bound is inequality on the group velocity of information propagation for quantum many-body systems. In this talk, I review this bound mathematically and explain some consequences of the bound.

Event Official Language: English

Mathematical model-based quantitative data analysis for COVID-19

December 22 (Tue) 10:00 - 11:00, 2020

Shingo Iwami (Associate Professor, Mathematical Biology Laboratory, Department of Biology, Faculty of Science, Kyushu University)

The recent spread of corona threatens the health of people around the world. We urgently need strategies to reduce COVID-19 spread and to enhance antiviral drug development for individual patients. Mathematics could contribute to control of COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, and implementation of social distancing measures and other interventions. My group is conducting interdisciplinary research to elucidate "Quantitative Population Dynamics" with original mathematical theory and computational simulation, which are both our CORE approach. Our mathematical model-based approach has quantitatively improved a current gold-standard approach essentially relying on the statistical analysis of "snapshot data" during dynamic interaction processes in virus infection. In my talk, I would like to discuss how our approach improves our current understanding of COVID-19 research, and help an establishment of a "standard antiviral treatment" for COVID-19 as well.

Venue: via Zoom

Event Official Language: English

Classical liquids and functional renormalization group

December 17 (Thu) 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

Autoimmune diseases initiated by pathogen infection: mathematical modeling

December 17 (Thu) 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

Quantum Wasserstein distance of order 1

December 16 (Wed) 13:00 - 14:30, 2020

Ryusuke Hamazaki (Senior Research Scientist, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (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

Statistical model for meaning representation of language

December 16 (Wed) 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

Non-perturbative tests of duality cascades in three dimensional supersymmetric gauge theories

December 14 (Mon) 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

Conserved charges in gravity and entropy

December 10 (Thu) 13:00 - 14:30, 2020

Sinya Aoki

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

How to obtain the large amount of sequence data from the eukaryote

December 10 (Thu) 10:00 - 11:00, 2020

Euki Yazaki (Postdoctoral Researcher, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (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

Journal Club of Information Theory SG II

December 8 (Tue) 13:00 - 14:00, 2020

Akinori Tanaka (Senior Research Scientist, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (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

Scattering theory for half-line Schrödinger operators: analytic and topological results

December 7 (Mon) 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

KPZ equation, attractive bosons, and the Efimov effect

December 3 (Thu) 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

Directional dark matter search and the technologies

December 3 (Thu) 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

Journal Club of Information Theory SG

December 1 (Tue) 13:00 - 14:00, 2020

Akinori Tanaka (Senior Research Scientist, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (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