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2025-12-05

Graphs, Linear Algebra, and the "Champagne Glass Problem"

小泉 淳之介 (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)

When people clink glasses in a toast, it is surprisingly hard for four or more glasses to all touch each other at the same time. What is the largest number of people whose glasses can be arranged so that every pair of glasses is in contact simultaneously? If we model the glasses as infinite cylinders, this turns into an open problem in mathematics. In this talk I will present an approach to this problem that uses tools from graph theory and linear algebra.

YouTube: Distances and Durations: Cosmology 101Public

2025-11-28

Distances and Durations: Cosmology 101

アモリ・ミケリ (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)

Cosmology aims to describe the evolution of the Universe and its contents. The light we receive from distant objects serves as our primary observable for understanding this evolution. I will outline how this light enables us to infer the distance of its source and the time of its emission. These considerations swiftly lead to a discussion of the expansion of space, which is the main driver of the Universe's evolution and lies at the heart of many active questions in cosmology, such as the Hubble tension and the nature of dark energy.

2025-11-21

Lower bound on ground state energy in quantum chemistry

小野 清志郎 (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 客員研究員 / 東京大学 物性研究所 物性理論研究部門 助教)

Thanks to the variational principle, we can obtain a meaningful upper bound on the ground-state energy if we have a good variational state. Here, I will review how to obtain a lower bound on the ground-state energy, in contrast to the variational approach. In fact, this problem can be formulated as a well-known optimization problem.

YouTube: Symmetries of gravityPublic

2025-11-14

Symmetries of gravity

プトゥラク・ジャイアクソナ (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)

Symmetry lies at the foundation of modern physics, and gravity offers a unique setting where symmetry and geometry deeply intertwine. This talk will trace how gravitational symmetries evolve: from exact isometries of spacetime to asymptotic symmetries at infinity, and ultimately to the broad class of corner symmetries defined on finite surfaces.

2025-11-07

Hilbert space fragmentation: breakdown of quantum thermalization

関野 裕太 (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 特別研究員 / 理化学研究所 開拓研究本部 (CPR) 濱崎非平衡量子統計力学理研白眉研究チーム 特別研究員)

Thermalization is a ubiquitous phenomenon in nature, ranging from the early universe in cosmology to electron systems in condensed matter physics. However, its understanding based on microscopic dynamics by quantum mechanics is still not so clear. In this talk, I will introduce the microscopic mechanism called Hilbert space fragmentation, which leads to dynamics violating thermalization.

2025-10-31

Graphs and quantum groups

北村 侃 (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)

I will introduce a seemingly classical problem in graph theory that I encountered through a motivation from (relatively modern) quantum group theory. The problem is to find examples of certain strongly regular graphs: collections of nodes and edges having exceptional combinatorial properties, which will be illustrated in the talk.

2025-10-24

Social hierarchy formation in animal groups

イザク・プラナス シッジャ (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)

Dominance hierarchies are common in group-living animals, emerging from repeated social interactions that establish social ranks and often involve escalated aggressive contests that can lead to fatal outcomes. Yet, the mechanisms underlying this process remain challenging to model, as decisions to fight or retreat depend on complex interactions among social information, environmental context, and resource pay-offs. I will discuss key biological and theoretical challenges in understanding the formation of social dominance.

YouTube: Birational classification of algebraic varietiesPublic

2025-10-17

Birational classification of algebraic varieties

張 繼剛 (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)

One of the goal of algebraic geometry is to classify algebraic varieties (that is, zero locus of polynomial function(s)) up to certain equivalence relations. For example, two algebraic varieties are called birationally equivalent if they have open dense subsets that share the same structure as algebraic subvarieties (intutiatively, if they have the same structure after removing "small" closed subsets). In this talk, we will give an introduction about the birational classification theory of algebraic varieties.

2025-10-10

Gravitational redshift and wave packets in QFT

杉下 宗太郎 (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 / 京都大学 大学院理学研究科 助教)

Light emitted near a black hole appears redshifted to a distant observer; this is the gravitational redshift. I will explain how to describe it using wave packets in quantum field theory.

YouTube: Gelfand dualityPublic

2025-10-03

Gelfand duality

ウアジーミャ・ソスニロ (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 a broad sense, geometry studies those mathematical objects that require geometric intuition, while algebra studies abstract structures that are common in all fields of mathematics. In various instances, it turns out that there is a way to describe geometric objects entirely using algebraic objects. As one such example, we discuss Gelfand duality between compact hausdorf topological spaces and commutative C*-algebras.

2025-09-26

Representation learning for astronomy

平島 敬也 (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)

Stellar mass is a fundamental driver of stellar evolution, yet estimating it in star-forming regions is difficult due to heavy obscuration and strong inhomogeneity, which undermines simple dynamical models. Supervised ML is promising but limited by the cost of generating large, high-quality labeled datasets from high-resolution MHD simulations. In this talk, I will present a data-efficient alternative: pretraining a Vision Transformer with DINOv2 on one million synthetic fractal images, then transferring the frozen encoder to limited MHD maps. Synthetic pretraining improves frozen-feature stellar-mass predictions, slightly outperforming a supervised model trained on the same limited simulations. Principal component analysis of the embeddings reveals semantically meaningful structure (e.g., dense cores and inflows), enabling unsupervised segmentation without labels or lightweight fine-tuning.

YouTube: Scale transformations in physics and beyondPublic

2025-09-19

Scale transformations in physics and beyond

アルバロ・パストール グティエレス (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 evolution of systems across different scales is a common theme in many areas of research, from fundamental physics to biology and machine learning. Understanding how macroscopic behaviour arises from microscopic dynamics is essential both for explaining emergent phenomena and for reconstructing a fundamental picture of nature. In this talk, I will introduce the renormalisation group in a non-technical way, highlighting its interpretation as a framework for performing scale transformations applicable to a broad range of contexts beyond physics. Beginning with the block-spinning idea, I will show how scale transformations lead to phase transitions and universal phenomena, and then sketch the modern Wilsonian perspective. Finally, I will show how this framework underlies our most fundamental picture of nature and offers a toolkit for addressing the open problems and puzzles in the Standard Model of particle physics.

2025-09-12

The coalescent history of humans

シュパイデル 玲雄 (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 理研ECL研究ユニットリーダー / 理化学研究所 開拓研究本部 (CPR) シュパイデル数理遺伝学理研ECL研究ユニット 理研ECL研究ユニットリーダー)

Our DNA differs from person to person, but how have these differences come about? I will introduce the fundamental statistical framework for understanding genetic variation, the standard coalescent model. I will demonstrate how the coalescent model captures key properties of genetic variation. The coalescent is central to statistical inference techniques used to reconstruct human evolution, and remarkably, allow us to learn about the earliest days of our species and up to events in a nation’s recent past.

2025-09-05

What is the definition of black holes in quantum theory?

横倉 祐貴 (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)

What is a black hole? No one knows the answer yet. Current observation data has not confirmed the existence of horizons yet, and there is no theoretical description consistent with quantum theory. Therefore, one natural approach would be to reconsider the definition of black holes in the context of quantum theory. In this talk, I will share my idea and provide one possibility for quantum black holes.

2025-08-29

Balancing Research and Parenthood: From Pregnancy to Parenting a School-Aged Child

辰馬 未沙子 (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)

The decision of when to have children is never simple for researchers, with each career stage posing different challenges. In this talk, I will share personal insights on balancing pregnancy, childbirth, and parenting with an academic career, even though I have not always been able to balance them successfully. Topics include the physical and mental problems of pregnancy, the pressure in a publish-or-perish environment, the difficulty of traveling with young children, and systemic issues such as daycare availability and school schedules. By sharing these experiences, I hope to foster a more open conversation about how academia can better support researchers with caregiving responsibilities.

2025-08-22

Predicting and Explaining Animal Behavior with a Neural Network

藤原 輝史 (理化学研究所 開拓研究本部 (CPR) 藤原適応運動制御理研白眉研究チーム 理研白眉研究チームリーダー)

Explaining behavior with brain activity is a central goal of systems neuroscience. An insect, such as a fruit fly, exhibits sophisticated movements despite its compact brain, suggesting that low-dimensional neural computation underlies its high-dimensional behavior. Nevertheless, the technical challenge in measuring the entire neural activity, even for a small insect, limits our understanding of how the brain coordinates behavior. Therefore, we adopt a neural network simulating animal behavior and utilize its encoding feature to gain insight into low-dimensional computation. We train a neural network to predict a walking fly’s future step location (output) based on its previous step history (input). We then analyze the compressed representation at the computational bottleneck, paired with its input, to explain how the fly decides on the next step location. We have recently been particularly interested in a Mixture Density Network. Here, the fly’s “mind”, represented as a mixture of Gaussian distributions, can select a particular step location not only from a single Gaussian lobe but also from well-separated, multiple Gaussian lobes, providing greater flexibility, as observed in the actual animal. We hypothesize that analyzing the structure of the Gaussian mixture and corresponding input-output step patterns elucidates how a small animal can take complex action with limited neural resources.

2025-08-08

Mapping the Universe with DESI

Andrei Cuceu (NASA Einstein Fellow, Lawrence Berkeley National Laboratory (LBNL), USA)

The Dark Energy Spectroscopic Instrument (DESI) is in the process of creating the largest three-dimensional map of the Universe. I will explain how we are creating this map by measuring precise distances to more than 40 million galaxies, and also how this map is being used to study the evolution of our Universe.

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.

YouTube: Machine Learning for Holographic Entanglement InequalitiesPublic

2025-07-18

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.