コーヒーミーティング過去ログ
2025-08-01
GWAS: unravelling the links between the human genome and measurable traits
リュカ・ソール (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 数理遺伝学理研ECL研究ユニット 特別研究員)
Understanding how the genome defines lifeforms has long been a central question in biology. In an effort to address this, a new class of studies, Genome-Wide Association Studies (GWAS), emerged and proliferated from the mid-2000s, largely driven by advances in DNA sequencing technologies. In this talk, I will introduce how GWAS are conducted to find associations between the genome and specific observable traits, ranging from disease susceptibility to morphological characteristics.
Public2025-07-18
Machine Learning for Holographic Entanglement Inequalities
大栗 博司 (Fred Kavli Professor and Director, Walter Burke Institute for Theoretical Physics, California Institute of Technology, USA)
2025-07-04
Nonperturbative Quantum Gravity in a Closed Universe
野村 泰紀 (理化学研究所 数理創造研究センター (iTHEMS) 国内外連携・人材育成部門 理研バークレーセンター 上級研究員 / Professor/Director, Berkeley Center for Theoretical Physics, University of California, Berkeley, USA)
I explain why the nonperturbative quantum gravitational Hilbert space in a closed universe is one-dimensional and real-valued (for each alpha-sector). This result is derived from an analysis involving spacetime wormholes. I also discuss how meaningful physical predictions can arise in this case as a consequence of partial observability: physical observers can access only a subsystem of the universe.
Public2025-06-27
Describing Quantum Phenomena with Classical Gravity
竹田 大地 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 基礎科学特別研究員)
The holographic principle asserts an equivalence between gravitational theories and quantum theories without gravity. Through the holographic principle, it has become possible to perform non-perturbative analyses of systems such as QCD and condensed matter by using classical field theories in curved spacetime, or to approach unknown quantum gravity theories through the language of quantum field theories. In this talk, we focus on the former and introduce methods to handle linear response and phase transition in quantum theories using classical gravity.
2025-06-20
Maximum Entropy Reinforcement Learning
田中 章詞 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 上級研究員 / 理化学研究所 革新知能統合研究センター (AIP) 上級研究員)
Maximum Entropy Reinforcement Learning (MaxEnt RL) augments standard RL by encouraging policies to remain stochastic through entropy maximization. This leads to better exploration, more robust behavior, and improved performance in complex environments. I'd like to review the idea shortly, and comment some connections to thermodynamics/statistical systems.
2025-06-13
Quantum data and quantum machine learning
松浦 俊司 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 上級研究員)
While classical machine learning has had a great impact on our daily life, the potential impact of quantum machine learning is still unclear. I will discuss this from an information-theoretic perspective. Information-theoretic bounds don’t always provide direct, practical advantages, but they help clarify the fundamental limits and opportunities for quantum machine learning in scientific discovery.
2025-06-06
The Price equation: from breeding to Bayes
トーマス・ヒッチコック (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 基礎科学特別研究員)
In the mid 1960’s an American chemist – George Price - moved to London to understand the origins of altruism. In just the few short years he worked on this problem, he made several key contributions to our understanding of evolution, the most important of which is the equation that now bears his name. Commonly claimed to be the most fundamental in evolution, the Price equation provides an abstractness that allows many other classic results to be derived from it when additional assumptions are made. In this talk will explain how this equation is derived, how other classic expressions emerge as special cases, and finally some of its links beyond evolutionary biology.
2025-05-30
Null hypothesis: Hypotheses in evolutionary biology are not falsifiable
ホセ サイード・グティエレス オルテガ (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 研究員)
Evolutionary biology provides a multidisciplinary framework to understand past events that originated biodiversity. It relies on observational data, and based on observed patterns, researchers can propose theories that clarify the underlying dynamics ruling evolutionary phenomena. However, a common criticism is that hypotheses in evolutionary biology are not falsifiable, since it is impossible to predict an evolutionary phenomenon and set up an experiment to test its certainty (i.e., we don’t have time machines). I would like to mention that falsifiability, through the reasoning of induction, cannot be used to prove historical events, but this is not necessary. Making sense of observational data and proposing plausible hypotheses based on abductive reasoning are the ways of thinking in evolution, which, however, bring some epistemological concerns. For example, phylogenetic analyses based on different genes can produce trees with differing topologies, thereby describing contradictory evolutionary events. Statistical and probability methods are often used to decide which tree topology is most likely or probable to be true. But are not statistics and probability solutions for inductive and deductive (not abductive) problems? Also, how valid is the method of imposing a substitution rate model to a DNA sequence data “after” observing the data? At most, we can tell that evolutionary hypotheses are plausible explanations that can be rejected in the presence of new and stronger data. But in this respect, hypotheses in evolutionary biology are similar to those from other fields: they welcome scrutiny, and their explanatory power increases or decreases as scientific knowledge advances.
2025-05-23
Toy models in data assimilation studies for weather prediction
大塚 成徳 (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 予測科学研究チーム 上級研究員 / 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 上級研究員)
In numerical weather prediction systems, we typically use a huge model based on complex physics of the atmosphere. However, it is inconvenient to use such a model for testing new ideas. Therefore, we have a hierarchy of models, ranging from so-called toy models to high-end, expensive ones. Today I will introduce one of the most used toy models to study data assimilation algorithms, the Lorenz96 40-variable model. I will also introduce the DA-ML study group, in which we use this model to learn about the basics of data assimilation and machine learning.
2025-05-16
Shocks: from fluid discontinuities to cosmic particle accelerators
カミリア・デミデム (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 研究員)
In my previous coffee meeting talk, I attempted to offer a gentle introduction to turbulence, a key ingredient of particle acceleration in cosmic environments. This presentation will borrow the same general approach to discuss another important phenomenon of high-energy astrophysics: shock waves. My goal is to provide an intuitive grasp of shocks as understood in fluid dynamics, while also highlighting the distinct complexities inherent to astrophysical collisionless plasmas.
Public2025-05-09
Statistical Signatures of Quantum Chaos in Non-Hermitian Systems
プラティック・ナンディ (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 特別研究員)
Recent advances have sparked growing interest in the chaotic behavior of open quantum systems, particularly those governed by non-Hermitian Hamiltonians. In this talk, I will present a framework for analyzing the statistical correlations between the complex eigenvalues and the singular values of such Hamiltonians. The methods I will discuss draw from and extend tools in random matrix theory and spectral analysis, with potential applications that reach beyond physics, such as in statistical data analysis, machine learning, and complex network theory.
Public2025-04-25
Computers as a Playground for Mathematics
佐野 岳人 (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 数学応用研究チーム 研究員)
Computers are invaluable tools not only in the natural sciences but also in pure mathematics, enabling simulations and computational experiments that would be infeasible by hand. In this talk, I take this idea one step further and propose that computers are not just for performing computations—they can also be used to implement mathematical structures as computer programs. There are nice correspondences between concepts in mathematics and programming, rooted in abstraction and inheritance. This approach also allows us to play and interact with mathematical ideas that might otherwise feel vague or untouchable. I will present a demo showing how we can implement the gcd function for Euclidean Rings, which will be automatically inherited to integers and polynomials.
2025-04-18
Bitcoin: A Peer-to-Peer Electronic Cash System
邱 靖凱 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 上級研究員)
I am not Satoshi Nakamoto, but I’ll talk about his white paper—the one that laid the foundation for a trillion-dollar economy. I’ll introduce the main idea behind blockchains: a decentralized and trustless system for securely recording transactions without the need for a central authority.
2025-04-11
Where is the limit of the periodic table of elements?
内藤 智也 (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 量子数理科学チーム 学振特別研究員PD)
Superheavy elements have been synthesised. Then, a question arises: where is the limit of the elements? In this talk, I will explain the current status on the discussion in terms of atomic physics.
2025-03-28
Creativity in convolutional diffusion models
シュテッフェン・バッケス (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 創発物性科学研究センター (CEMS) 計算物質科学研究チーム 上級研究員)
Public2025-03-21
The Free Will Theorem in Quantum Mechanics
クリスティ・コウジ・ケリー (理化学研究所 数理創造プログラム (iTHEMS) 基礎科学特別研究員)
The free will theorem, discovered by Conway and Kochen, is a technical variation of the Bell-Kochen-Specker theorem with a conclusion directed towards a basic problem in philosophy rather than physics. It has, however, often been seriously misunderstood, including by Conway and Kochen themselves. We discuss the reformulation by Landsman that presents the free will theorem as a no-go theorem for compatibilist forms of deterministic free will.
Public2025-03-14
Renormalization revisited
金 東昱 (理化学研究所 数理創造プログラム (iTHEMS) 特別研究員)
As a supplement to Masazumi’s coffee meeting talk last February, I will talk more about the key concept in modern quantum field theory, renormalization. I will follow the statistical mechanics approach by Kadanoff and Wilson. First, I will briefly sketch Kadanoff’s block decimation idea as an illustrating example of renormalization group flow in real space. Then, I will move on to Wilson’s momentum space renormalization group and show a simple toy model involving ordinary Gaussian integral.
2025-03-07
Steady-State Size Distribution for a Collision Cascade
辰馬 未沙子 (理化学研究所 数理創造プログラム (iTHEMS) 研究員)
A collision cascade describes a self-similar process where large bodies fragment into smaller ones through successive collisions, maintaining a constant mass flux over time. This phenomenon is observed in various contexts, from space debris evolution to asteroid collisions. In this talk, I will derive the steady-state mass distribution for a collision cascade and introduce its application to the observed grain size distributions in planet-forming disks.
2025-02-28
Space-time as syntax of Particle Physics
森脇 湧登 (理化学研究所 数理創造プログラム (iTHEMS) 基礎科学特別研究員)
In natural language, syntax is the vocabulary and grammar, and semantics is the meaning (model) of a sentence constructed according to syntax. Lawever duality in mathematics states that from all semantics (possible models), the category of syntax (grammar) can be recovered. In particle physics, we interpret syntax as the conditions that a physical theory must satisfy (grammar of the theory), and semantics as specific models (gauge theory, Standard Model, etc.). Then, from all the models of quantum field theories (semantics of QFT), we argue that the category of spacetime is expected to be recovered. In other words, spacetime seems to be the grammar of quantum field theory.
Public2025-02-21
Renormalization: Awareness of Ignorance
本多 正純 (理化学研究所 数理創造プログラム (iTHEMS) 上級研究員 / 埼玉大学 大学院理工学研究科 連携教授)
Renormalization is one of the most complicated concepts in learning quantum field theory despite realistic field theories cannot be predictable without renormalization. In this talk, I am going to introduce the concept of renormalization into broad audience by using a simple example in quantum mechanics which is technically an eigenvalue problem of ordinary differential equation. While I will mainly explain a "physicist" way of doing renormalization, I will also briefly mention more mathematical understanding from perspective of self adjoint extension of Hamiltonian