Search Event

Journal Club: Intrinsically Disordered Region (IDR)

May 19 (Wed) at 13:00 - 14:00, 2021

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

A class of protein domain, which is called intrinsically disordered region (IDR), is known to take no rigid three dimensional structure. Recent studies have shown that IDRs can show biological functions through phase separation, and it is important to clarify what kind of amino acid sequence of IDR leads to phase separation and what kind of mutation results in malfunction. In this journal club, I will discuss these topics by reviewing recent papers. *Detailed information about the seminar refer to the email.

Venue: via Zoom

Event Official Language: English

Journal Club: Trace inequalities and their applications

April 14 (Wed) at 14:30 - 15:30, 2021

Yukimi Goto (Special Postdoctoral Researcher, iTHEMS)

In this talk, I will explain trace inequalities and related topics. Mainly, I focus on results concerning quantum entropy. This talk is an elementary introduction to that subjects. *Detailed information about the seminar refer to the email.

Venue: via Zoom

Event Official Language: English

Journal Club: Reinforcement Learning

March 24 (Wed) at 13:00 - 14:00, 2021

Akinori Tanaka (Senior Research Scientist, iTHEMS)

Reinforcement Learning (RL) is a scheme of Machine Learning that is applicable "without training data." Instead, we prepare a "world" that agents (learners) can probe, and try to optimize their behavior. Historically, study of RL has deep connection to studies of psychology and neuroscience. In this journal club, I would like to give a lightning review of RL. *Detailed information about the seminar refer to the email.

Venue: via Zoom

Event Official Language: English

Journal Club: Large deviation statistics of Markovian quantum systems

February 17 (Wed) at 13:00 - 14:30, 2021

Ryusuke Hamazaki (Senior Research Scientist, iTHEMS / RIKEN Hakubi Team Leader, Nonequilibrium Quantum Statistical Mechanics RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research (CPR))

Large deviation is a mathematical framework to treat “rare events” in random processes [1]. In this journal club, I talk about recent development of large deviation analysis in open Markovian quantum systems [2,3]. I first introduce the notion of large deviation statistics using the simple independent and identically distributed random variables. I then review recent development of level 2.5 large deviation statistics for classical Markovian jump processes and its application to thermodynamic uncertainty relation [4]. Finally, I discuss how the classical results are extended to quantum regime. *Detailed information about the seminar refer to the email.

Venue: via Zoom

Event Official Language: English

Journal Club: Sampling the stable structures based on replica-permutation method

January 27 (Wed) at 13:00 - 14:30, 2021

Hiroshi Yokota (Postdoctoral Researcher, iTHEMS)

When we want to search the (meta)stable structures of the macromolecules such as protein, the combination of molecular dynamics simulation and replica exchange method (REM) is useful. In REM, sampling is performed by exchanging replicas (copies) of the system having different temperatures when this process is accepted based on Metropolis algorithm. In this method, the exchange can be rejected, which leads to the decrease in the sampling efficiency. To obtain more efficient sampling than that of REM, Itoh and Okumura proposed replica-permutation method (RPM) in which the replicas are permutated to perform sampling based on Suwa-Toudou algorithm. In this Journal club, I will introduce RPM and some examples of its application.

Event Official Language: English

Accelerated equilibration in classical stochastic systems

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

Kyosuke Adachi (Special Postdoctoral Researcher, 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

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

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

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