Seminars

Date25, Sep., 2023, (Mon.), 14:00~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo
SpeakerAmit Kumar Chatterjee (Kyoto University)
TitleQuantum Mpemba effect: an anomalous relaxation in quantum systems
AbstractMpemba effect refers to the counter-intuitive phenomenon where a hotter object can cool down faster than a colder copy of the same object. In spite of some theoretical as well as experimental advances in the classical domain, the quantum counterpart of the Mpemba effect, specifically in temperature, has remained unexplored. In this talk, we demonstrate the quantum Mpemba effect by showing that temperatures of two copies of a quantum system, one initially hotter than the other, can cross each other after some time and thereafter reverse their identities, i.e. hotter becomes colder and vice versa, before reaching the same final temperature. In fact, we show such crossing of trajectories characterizing the quantum Mpemba effect, can occur in several other observables including energy, entropy, distance function etc. Our theoretical results on quantum Mpemba effect are primarily based on a quantum dot connected to two reservoirs. In the later part of the talk, we discuss how exceptional points and complex eigenvalues can lead to multiple quantum Mpemba effect (where trajectories cross multiple times) in a two-level driven dissipative system.
Reference:
A. K. Chatterjee, S. Takada, and H. Hayakawa, Phys. Rev. Lett. 131, 080402 (2023).
Date2, Aug., 2023, (Wed.), 14:00~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo / Zoom (If you would like to join, please send an email to sosuke.ito(at)ubi.s.u-tokyo.ac.jp.)
SpeakerMiguel Aguilera, (Basque Center for Applied Mathematics)
TitleNonequilibrium Neural Computation: Stochastic thermodynamics of the asymmetric Sherrington-Kirkpatrick model
AbstractMost systems in nature operate far from equilibrium, exhibiting time-asymmetric, irreversible dynamics; giving rise to entropy production as they exchange energy and matter with their environment. In neuroscience, effective information processing entails flexible architectures integrating multiple sensory streams that vary in time with internal and external events. Physically, neural computation is, in a thermodynamic sense, an out-of-equilibrium, non-stationary process that changes dynamically. Cognitively, nonequilibrium neural activity results in dynamic changes in sensory streams and internal states. In contrast, classical neuroscience theory focuses on stationary, equilibrium information paradigms (e.g., efficient coding theory), which often fail to describe the role of nonequilibrium fluctuations in neural processes.
Inspired by the success of the equilibrium Ising model in investigating disordered systems and related associative-memory neural networks, we study the nonequilibrium thermodynamics of the asymmetric Sherrington-Kirkpatrick system as a prototypical model of large-scale nonequilibrium networks. We employ a path integral method to calculate a generating functional over the trajectories to derive exact solutions of the order parameters, conditional entropy of trajectories, and steady-state entropy production of infinitely large networks. We find that entropy production peaks at a critical order-disorder phase transition but is more prominent in a regime with quasi-deterministic disordered dynamics. While entropy production is becoming popular to characterize various complex systems as well as neural activity, our results reveal that increased entropy production is linked with radically different scenarios, and combining multiple thermodynamic quantities yields a more precise picture of the system. These results contribute to an exact analytical theory for studying the thermodynamic properties of large-scale nonequilibrium systems and their phase transitions. 

Refs: Aguilera, M., Igarashi, M. & Shimazaki, H. Nonequilibrium thermodynamics of the asymmetric Sherrington-Kirkpatrick model. Nature Communications 14, 3685 (2023). https://doi.org/10.1038/s41467-023-39107-y
Date18, Jul., 2023, (Tue.), 14:00~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo
SpeakerJean-Charles Delvenne, (UCLouvain)
Title (Non-)stochastic thermodynamics for computing devices 
AbstractIn this talk I describe several theoretical bounds and illustrate them on how they can place trade-offs on the performance (in terms of dissipation, reliability and speed) of electronic devices and circuits made from them.  These results are consistent with numerical simulations and experimental measurements.  
Date27, Oct., 2022, (Thu.), 15:00~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo / Zoom (If you would like to join, please send an email to sosuke.ito(at)ubi.s.u-tokyo.ac.jp.)
SpeakerNishide Ryosuke (the University of Tokyo) 
TitlePattern propagation driven by surface curvature
AbstractPattern formation occurs on curved surfaces abundantly especially in biological systems. Previous studies have revealed that the surface curvature affects the pattern dynamics and plays biological functions, however, comprehensive understanding is still elusive. Here by employing reaction-diffusion systems showing Turing pattern, we show for the first time that static pattern on a flat surface can be propagating wave on a curved surface. By numerical and theoretical analyses, it is shown that the pattern propagation is conditioned by the symmetry of surface and pattern. We also show the results of weakly nonlinear analysis applied to the problem, which suggests rich dynamics can arise on curved surface.
Date30, May., 2022, (Mon.), 13:30~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo
SpeakerYoshiya Matsubara (National Centre for Biological Sciences) 
TitleError catastrophe can be avoided by proofreading innate to template-directed polymerization
AbstractHow and if information is maintained through polymer replication is among the most fundamental issues in studies of the origins of life, as first noted by Manfred Eigen. The “error catastrophe” problem in replication asserts that maintenance of information is more difficult for longer polymers due to thermodynamically inevitable errors. In this study, we analyzed the population dynamics of replicating templates explicitly incorporated with the kinetics of the fundamental polymerization process. Numerical and theoretical analyses suggest that the template-directed polymerization process entails an inherent error-correction mechanism akin to the kinetic proofreading proposed by J. J. Hopfield. Interestingly, because of such effects, the tolerance to errors increases with the length of the replicating template polymer, which solves the error-catastrophe problem. Therefore, the findings provide novel principles for error correction without sophisticated and specific mechanisms that are potentially applicable to replication under origins of life scenarios.

Y. J. Matsubara, N. Takeuchi, & K. Kaneko arXiv preprint arXiv:2108.09961 (2021) 
Date27, May., 2022, (Fri.), 16:00~17:00
PlaceZoom (If you would like to join, please send an email to sosuke.ito(at)ubi.s.u-tokyo.ac.jp.)
SpeakerMauricio del Razo Sarmina (Freie Universität Berlin) 
TitleChemical diffusion master equation: stochastic formulations of reaction-diffusion processes
AbstractBiological processes occurring at scales of a biological cell can be described in terms of diffusing and interacting molecules. At the level of particle-resolved descriptions, where molecules are represented by particles and chemical reactions are coupled to their spatial diffusion, there exist comprehensive numerical simulation schemes, while the corresponding mathematical formalization is relatively underdeveloped. In this work, we provide different frameworks to systematically formulate the probabilistic evolution equation, termed chemical diffusion master equation (CDME), that governs stochastic reaction-diffusion processes. Such frameworks can be used to unify reaction-diffusion models at different scales, allowing the development of consistent multiscale simulation schemes. Moreover, these frameworks enable theories, such as stochastic thermodynamics, to be applied to reaction-diffusion processes. This can result in fundamental insights of the physical capabilities and limitations of biological systems and beyond.
Date8, Dec., 2021, (Wed.), 10:00~
PlaceZoom (If you would like to join, please send an email to sosuke.ito(at)ubi.s.u-tokyo.ac.jp.)
SpeakerArtemy Kolchinsky (Santa Fe Institute) 
TitleThermodynamic threshold for Darwinian evolution
AbstractUnderstanding the thermodynamics of Darwinian evolution has important implications for biophysics, evolutionary biology, and the study of the origin of life. We show that in a population of nonequilibrium autocatalytic replicators, the critical selection coefficient (i.e., the minimal fitness difference visible to selection) is lower bounded by the free energy dissipated per replication event. This bound represents a fundamental thermodynamic threshold for Darwinian evolution, analogous to selection thresholds that arise from finite population sizes or large mutation rates. Our results apply to a large class of molecular replicators, including many types of multistep autocatalytic reaction mechanisms and autocatalytic sets. We illustrate our approach on a thermodynamically-consistent model of simple replicators in a chemostat.
Date16, Jun., 2020, (Tue.), 15:00~
PlaceZoom (If you would like to join, please send an email to sosuke.ito(at)ubi.s.u-tokyo.ac.jp.)
SpeakerTan Van Vu (The University of Tokyo)
TitleGeometrical bounds of the irreversibility in Markovian systems
AbstractWe derive geometrical bounds on the irreversibility for both classical and open quantum Markovian systems that satisfy the detailed balance conditions. Using the information geometry, we prove that the irreversible entropy production is bounded from below by a modified Wasserstein distance between the initial and final states, thus generalizing the Clausius inequality. The modified Wasserstein metric can be regarded as a discrete-state generalization of the Wasserstein metric, which plays an important role in the optimal transport theory. Notably, the derived bounds can be interpreted as classical and quantum speed limits, implying that the associated entropy production constrains the minimum time required to transform a system state. We illustrate the results on several systems and demonstrate that a tighter bound on the efficiency of quantum heat engines can be obtained. 
Date23, Jan., 2020, (Thu.), 15:00~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo
SpeakerSeth Fraden (Physics, Brandeise University)
TitleProgrammable self-assembly of DNA origami capsids based on the principles of virus structure
AbstractWe provide a general and modular solution for building synthetic icosahedral shells on the scale of 100 nm, motivated by the 1962 Caspar and Klug theory of virus structure. Strategies were explored for controlling the pathways, kinetics, and the yield by which subunits arrange themselves into icosahedral symmetry. The methods of DNA origami were employed to produce accurately-designed and rigid building blocks. We created multiple large virus-like capsids and validated the structures using cryo electron microscopy and studied the capsid assembly process experimentally and with a computational model to elucidate how the kinetics and yield of target structures depends on control parameters. Our capsid building blocks represent a near-ideal manifestation of patchy particles whose geometry and interactions can be designed with sub-nanometer and kBT precision, thus achieving a long sought after goal in soft matter physics. Applications range from drug delivery to a generalized antiviral agent, which is demonstrated for hepatitis B. 

Date21, Jan., 2020, (Tue.), 13:00~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo
SpeakerYan Jiawei (Harvard Medical School)
TitleKinetic uncertainty relations for the control of stochastic reaction systems
AbstractNon-equilibrium stochastic reaction networks are commonly found in both biological and non-biological systems, but have remained hard to analyze because small differences in rate functions or topology can change the dynamics drastically. Here we conjecture exact quantitative inequalities that relate the extent of fluctuations in connected components, for various network topologies. Specifically, we find that regardless of how two components affect each other’s production rates, it is impossible to suppress fluctuations below the uncontrolled equivalents for both components: one must increase its fluctuations for the other to be suppressed. For systems in which components control each other in ring-like structures, it appears that fluctuations can only be suppressed in one component if all other components instead increase fluctuations, compared to the case without control. Even the general N-component system, with arbitrary connections and parameters, must have at least one component with increased fluctuations to reduce fluctuations in others. In connected reaction networks it thus appears impossible to reduce the statistical uncertainty in all components, regardless of the control mechanisms or energy dissipation. 

Date9, Dec., 2019, (Thu.), 15:00~
PlaceRoom 1320 in Faculty of Science Building No. 4, The University of Tokyo
SpeakerDavid Wolpert (Santa Fe Institute)
TitleThe stochastic thermodynamics of computation
AbstractThis seminar is mainly organized by Sagawa Lab.. One of the central concerns of computer science is how the resources needed to perform a given computation depend on that computation. Moreover, one of the major resource requirements of computers—ranging from biological cells to human brains to high-performance (engineered) computers—is the energy used to run them, i.e. the thermodynamic costs of running them. Those thermodynamic costs of performing a computation have been a long-standing focus of research in physics, going back (at least) to the early work of Landauer and colleagues. However, one of the most prominent aspects of computers is that they are inherently non-equilibrium systems. Unfortunately, the research by Landauer and co-workers on the thermodynamics of computation was done when non-equilibrium statistical physics was still in its infancy, severely limiting the scope and formal detail of their analyses. The recent breakthroughs in non-equilibrium statistical physics hold the promise of allowing us to go beyond those limitations. Here I present some initial results along these lines, concerning the entropic costs of running (loop-free) digital circuits and Turing machines. These results reveal new, challenging engineering problems for how to design computers to have minimal thermodynamic costs. They also allow us to start to combine computer science theory and stochastic thermodynamics at a foundational level, thereby expanding both. 

Date31, Oct, 2019, (Thu.), 14:00~
PlaceRoom 413 in Faculty of Science Building No. 1, The University of Tokyo
SpeakerSreekanth K. Manikandan (Stockholm University)
TitleInferring entropy production from short experiments
AbstractThis seminar is mainly organized by Sagawa Lab.. We provide a strategy for an exact inference of the average as well as the fluctuations of the entropy production in non-equilibrium systems in the steady state, from the measurements of arbitrary current fluctuations. Our results are built upon the finite time generalization of the thermodynamic uncertainty relation, and require only very short time series data from experiments. We illustrate our results with exact and numerical solutions for two colloidal heat engines.Arxiv link: https://arxiv.org/abs/1910.00476 

Date27-29, May, 2019, (Mon.-Wed.)
PlaceHongo Campus, the University of Tokyo
WorkshopData analysis and machine learning in dynamical systems (website)
AbstractThe goal of this workshop is to bring together researchers from data analysis, machine learning, and dynamical systems to discuss recent progress in data analysis of complex phenomena in dynamical systems with large degrees of freedom, and to fill the gap between theories in these fields.