Article
Computer Science, Information Systems
Padraig Corcoran, Christopher B. Jones
Summary: Topological data analysis (TDA) is a new and emerging research field that applies topology to data analysis. It has been successfully used in geographic information science (GIS) research and has potential for future applications.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
(2023)
Article
Materials Science, Multidisciplinary
Jiangzhou Mei, Gang Ma, Jiaying Liu, Francois Nicot, Wei Zhou
Summary: This paper investigates the transition between the solid and liquid phases of sheared granular materials from the perspective of the contact network. Persistent homology tools are used to quantify the dynamics of the contact network, and two important topological invariants, components and loops, are analyzed through numerical simulations. The study reveals the heterogeneous composition of the contact network and suggests a partition threshold for distinguishing strong and weak contact subnetworks. Mechanical precursors of the solid-liquid transition are identified during the shearing process. The study demonstrates the capability of the persistent homology method in bridging microscopic dynamics with macroscopic responses through the contact network.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
(2023)
Review
Optics
Jiawei Zhang, Zhijun Wang, Xiaoxue Huo, Xue Meng, Yu Wang, Hao Suo, Panlai Li
Summary: Persistent luminescent materials have significant application value in the field of anti-counterfeiting, enabling information protection and anti-counterfeiting. By combining with other anti-counterfeiting technologies, multi-modal and multi-process anti-counterfeiting designs can be achieved. This paper reviews the research background and important breakthroughs of persistent luminescent materials in the field of anti-counterfeiting in recent years, and discusses current issues and future development directions.
LASER & PHOTONICS REVIEWS
(2023)
Article
Automation & Control Systems
Bernadette J. Stolz
Summary: This article proposes a novel approach to select landmarks specifically for persistent homology (PH) that preserves coarse topological information of the original dataset. The method is tested on artificial datasets with different levels of noise and outperforms standard methods and a subsampling technique based on an outlier-robust version of the k-means algorithm in terms of robustness to outliers under low sampling densities.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Multidisciplinary Sciences
Kouji Kashiwa, Takehiro Hirakida, Hiroaki Kouno
Summary: This study introduces the isospin chemical potential to a three-dimensional three-state Potts model to mimic dense QCD. Through persistent homology analysis, the dense spatial structure of QCD is explored.
Article
Computer Science, Interdisciplinary Applications
Dmitriy Prokhorov, Vadim Lisitsa, Yaroslav Bazaikin
Summary: The paper introduces an original algorithm for reducing three-dimensional digital images to enhance the computing performance of persistence diagrams, which can be used for topological optimization of porous materials. The algorithm has linear complexity and is efficient in calculating one-dimensional persistence Betti numbers for models of up to 5003 voxels.
COMPUTERS AND GEOTECHNICS
(2021)
Review
Biochemical Research Methods
Ciara F. Loughrey, Padraig Fitzpatrick, Nick Orr, Anna Jurek-Loughrey
Summary: Topological Data Analysis (TDA) has emerged as a reliable and interpretable framework for extracting information from high-dimensional data in biomedical research. It has the potential to support future developments in healthcare as biomedical datasets rise in complexity and dimensionality. Previous applications of TDA in fields like neuroscience, oncology, immunology, and medical image analysis have shown its broad utility in revealing hidden patterns and features.
Article
Chemistry, Physical
Dhananjay Bhaskar, William Y. Zhang, Ian Y. Wong
Summary: Topological data analysis (TDA) is an unsupervised machine learning method used to analyze active systems. The study demonstrates the robustness of topological loops to variations in particle number and density. This approach can be applied to analyze systems ranging from cytoskeletal motors to motile cells to flocking or swarming animals.
Article
Mathematics
Robert MacPherson, Amit Patel
Summary: This paper presents lower bounds for the homology of the fibers of a map to a manifold using new sheaf theoretic methods. The lower bounds persist over whole open sets of the manifold and are stable under perturbations of the map, generalizing certain ideas of persistent homology to higher dimensions.
ADVANCES IN MATHEMATICS
(2021)
Article
Optics
Daniel Leykam, Dimitris G. Angelakis
Summary: Persistent homology is a machine learning technique that classifies complex systems or datasets by computing their topological features, showing promise for characterizing and optimizing band structures of periodic photonic media. It can reliably classify a variety of band structures falling outside the usual paradigms of topological band theory, including moat band and multi-valley dispersion relations, thereby controlling the properties of quantum emitters embedded in the lattice.
Article
Computer Science, Artificial Intelligence
Dongsheng Ye, Hao Jiang, Ying Jiang, Hao Li
Summary: Persistent homology theory is widely used for analyzing topological features, but existing methods are not suitable for the time-varying structure in dynamic graphs. This paper proposes Stable Distance of Persistent Homology (SDPH) to compare and quantify the differences in the topological properties of dynamic graphs. The proposed approach, utilizing Dynamic Dowker Filtration and Time-interlevel Kernel, effectively measures the topological difference of dynamic graphs.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Psychology
Kristina Wiebels, David Moreau
Summary: Containers have gained popularity in computing, software engineering, and scientific research. Despite being crucial for reproducible science, they have not yet become mainstream in psychology. This tutorial describes the logic behind containers, how to use them, practical problems they can solve, and provides examples of implementing containerization in research workflows using Docker and R.
ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE
(2021)
Article
Mathematics
Muyi Chen, Daling Wang, Shi Feng, Yifei Zhang
Summary: This paper presents a novel method for controlling the internal representation of deep neural networks from a topological perspective, providing a theoretical framework for better generalization. By studying the push-forward probability measure induced by the feature extractor and quantifying the notion of separation using persistent homology, the authors propose a new weight function and a topology-aware regularizer to enforce this property. Experimental results demonstrate the effectiveness and superiority of the proposed method in point cloud optimization and image classification tasks.
Article
Multidisciplinary Sciences
Helena B. Cooper, Kurt L. Krause, Paul P. Gardner
Summary: Ribosome-targeting antibiotics are widely used in medicine, but our knowledge of their binding sites mainly comes from non-pathogenic species. Recent advancements in electron cryomicroscopy have revealed species-specific differences in ribosome structures from pathogenic bacteria. More novel ribosome structures, especially from pathogens, are needed for a better understanding of the entire bacterial ribosome diversity and for innovative advancements in antibiotic research. This study used advanced models to analyze ribosomal sequences and found that current non-pathogenic structures do not represent certain pathogenic bacteria or whole phyla.
Article
Computer Science, Artificial Intelligence
Prachi Loliencar, Giseon Heo
Summary: This article addresses the diagnosis issue of sleep apnea and investigates it using clustering analysis and topological methods.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Multidisciplinary Sciences
Asuka Oyama, Yasuaki Hiraoka, Ippei Obayashi, Yusuke Saikawa, Shigeru Furui, Kenshiro Shiraishi, Shinobu Kumagai, Tatsuya Hayashi, Jun'ichi Kotoku
SCIENTIFIC REPORTS
(2019)
Article
Engineering, Multidisciplinary
Kota Okamoto, Shinya Aoi, Ippei Obayashi, Hiroshi Kokubu, Kei Senda, Kazuo Tsuchiya
BIOINSPIRATION & BIOMIMETICS
(2020)
Article
Biophysics
Takashi Ichinomiya, Ippei Obayashi, Yasuaki Hiraoka
BIOPHYSICAL JOURNAL
(2020)
Article
Materials Science, Multidisciplinary
Chihiro Koyama, Shuta Tahara, Shinji Kohara, Yohei Onodera, Didrik R. Smabraten, Sverre M. Selbach, Jaakko Akola, Takehiko Ishikawa, Atsunobu Masuno, Akitoshi Mizuno, Junpei T. Okada, Yuki Watanabe, Yui Nakata, Koji Ohara, Haruka Tamaru, Hirohisa Oda, Ippei Obayashi, Yasuyuki Hiraoka, Osami Sakata
NPG ASIA MATERIALS
(2020)
Article
Computer Science, Interdisciplinary Applications
A. Suzuki, M. Miyazawa, A. Okamoto, H. Shimizu, I Obayashi, Y. Hiraoka, T. Tsuji, P. K. Kang, T. Ito
COMPUTERS & GEOSCIENCES
(2020)
Article
Materials Science, Multidisciplinary
Yohei Onodera, Shinji Kohara, Philip S. Salmon, Akihiko Hirata, Norimasa Nishiyama, Suguru Kitani, Anita Zeidler, Motoki Shiga, Atsunobu Masuno, Hiroyuki Inoue, Shuta Tahara, Annalisa Polidori, Henry E. Fischer, Tatsuya Mori, Seiji Kojima, Hitoshi Kawaji, Alexander I. Kolesnikov, Matthew B. Stone, Matthew G. Tucker, Marshall T. McDonnell, Alex C. Hannon, Yasuaki Hiraoka, Ippei Obayashi, Takenobu Nakamura, Jaakko Akola, Yasuhiro Fujii, Koji Ohara, Takashi Taniguchi, Osami Sakata
NPG ASIA MATERIALS
(2020)
Article
Multidisciplinary Sciences
Anna Suzuki, Miyuki Miyazawa, James M. Minto, Takeshi Tsuji, Ippei Obayashi, Yasuaki Hiraoka, Takatoshi Ito
Summary: Topological data analysis is an emerging concept that utilizes persistent homology as a state-of-the-art tool to summarize topological and geometric features. This study focuses on the connectivity and apertures of flow channels detected from persistent homology analysis, proposing a method to estimate permeability in fracture networks based on these parameters. Results show that persistent homology can estimate fluid flow in fracture networks based on image data, providing a method to derive flow phenomena from structural information.
SCIENTIFIC REPORTS
(2021)
Article
Materials Science, Coatings & Films
Emi Minamitani, Takuma Shiga, Makoto Kashiwagi, Ippei Obayashi
Summary: The correlation between local structure and thermal conductivity of amorphous carbon was investigated. It was found that there is a significant correlation between thermal conductivity and density. Geometrical and topological analyses showed that bond ratios and topological characteristics are correlated with density. Persistent homology analysis can be used to quantify these structural characteristics and provide a predictive model of thermal conductivity.
JOURNAL OF VACUUM SCIENCE & TECHNOLOGY A
(2022)
Article
Chemistry, Physical
Emi Minamitani, Takuma Shiga, Makoto Kashiwagi, Ippei Obayashi
Summary: This study addresses the long-standing problem of quantifying the correlation between complex structures of amorphous materials and their physical properties. By combining topological data analysis, machine learning, and molecular dynamics simulations, the researchers were able to determine the relationship between the medium-range order (MRO) in amorphous Si and its thermal conductivity. The results provide a potential avenue for controlling material characteristics through the topology of nanostructures.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Computer Science, Theory & Methods
Ippei Obayashi, Michio Yoshiwaki
Summary: This paper addresses the issue of coefficient field choice in persistent homology. It is important to understand the dependence of the persistence diagram on the coefficient field in order to effectively compute and interpret the diagram. The paper examines the relationship between the dependence and the torsion part of Z relative homology in the filtration and presents sufficient and necessary conditions for the independence of coefficient field choice. An efficient algorithm is proposed to verify the independence, and experimental results show that persistence diagrams rarely change when the coefficient field changes in the case of a filtration in R-3.
DISCRETE & COMPUTATIONAL GEOMETRY
(2023)
Article
Chemistry, Physical
Emi Minamitani, Ippei Obayashi, Koji Shimizu, Satoshi Watanabe
Summary: High-accuracy prediction of physical properties of amorphous materials is challenging in condensed-matter physics. Machine-learning potentials, using descriptors invariant to symmetry operations, have shown promise in achieving this. In this study, a novel descriptor based on persistent homology was proposed, which demonstrated the ability to predict energy per atom of amorphous carbon and had similar characteristics to a latent space in a graph neural network.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Mathematics, Applied
Ippei Obayashi, Masao Kimura
Summary: This paper proposes a data analysis method using persistent homology and nonnegative matrix factorization to extract coexisting structures from the persistence diagrams of different dimensions hidden behind the data. The method is successfully applied to 3D voxel data of iron ore sinters obtained by X-ray computed tomography, capturing the coexistence structures in these samples.
Proceedings Paper
Automation & Control Systems
Kota Okamoto, Shinya Aoi, Ippei Obayashi, Hiroshi Kokubu, Kei Senda, Kazuo Tsuchiya
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2020)
Article
Materials Science, Multidisciplinary
Akihiko Hirata, Tomohide Wada, Ippei Obayashi, Yasuaki Hiraoka
COMMUNICATIONS MATERIALS
(2020)
