no code implementations • 12 Jul 2021 • Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara
In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.
Conventional evaluation methods based on predictions of scores are considered unreliable and predict rare events throughout the entire game, and it is difficult to evaluate various plays leading up to a score.
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.
This survey focuses on data-driven analysis for quantitative understanding of invasion team sports behaviors such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways.
1 code implementation • 16 Dec 2020 • M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, Patrick J. Coles
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost.
Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence.
Fault-tolerant quantum computing (FTQC) is a form of universal quantum computing that suppresses physical errors via quantum error correction (QEC).
Extracting the rules of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields.
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution.
To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches.
A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties.
no code implementations • 5 Mar 2019 • Philip Bambade, Tim Barklow, Ties Behnke, Mikael Berggren, James Brau, Philip Burrows, Dmitri Denisov, Angeles Faus-Golfe, Brian Foster, Keisuke Fujii, Juan Fuster, Frank Gaede, Paul Grannis, Christophe Grojean, Andrew Hutton, Benno List, Jenny List, Shinichiro Michizono, Akiya Miyamoto, Olivier Napoly, Michael Peskin, Roman Poeschl, Frank Simon, Jan Strube, Junping Tian, Maksym Titov, Marcel Vos, Andrew White, Graham Wilson, Akira Yamamoto, Hitoshi Yamamoto, Kaoru Yokoya
In this report, we review of all aspects of the ILC program: the physics motivation, the accelerator design, the run plan, the proposed detectors, the experimental measurements on the Higgs boson, the top quark, the couplings of the W and Z bosons, and searches for new particles.
High Energy Physics - Experiment High Energy Physics - Phenomenology Accelerator Physics
However, in certain cases, the indirect measurement can be reduced to the direct measurement, where a quantum state is destructively measured.
In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem.
The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely.
The development of a metric for structural data is a long-term problem in pattern recognition and machine learning.
Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.
To reduce this requirement, we propose a high-threshold fault-tolerant quantum computation with GKP qubits using topologically protected measurement-based quantum computation with the surface code.
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end.