Publications: Yoshito Hirata

Publications

Google Scholar

Refereed Journals

Mathematical modelling of prostate cancer

Y. Hirata, K. Morino, K. Akakura, C. S. Higano, and K. Aihara, ``Personalizing androgen suppression for prostate cancer using mathematical modeling,'' Scientific Reports 8, 2673 (2018).
Y. Hirata and K. Aihara, ``Ability of intermittent androgen suppression to seletively create a non-trivial periodic orbit for a type of prostate cancer patients,'' Journal of Theoretical Biology 384, 147-152 (2015).
Y. Hirata, K. Morino, K. Akakura, C. S. Higano, N. Bruchovsky, T. Gambol, S. Hall, G. Tanaka, and K. Aihara, ``Intermittent androgen suppression: estimating parameters for individual patients based on initial PSA data in response to androgen deprivation therapy,'' PLoS One 10, e0130372 (2015).
K. Morino, Y. Hirata, R. Tomioka, H. Kashima, K. Yamanishi, N. Hayashi, S. Egawa, and K. Aihara, ``Predicting disease progression from short biomarker series using expert advice algorithm,'' Sci. Rep. 5, 8953 (2015).
T. Hatano, Y. Hirata, H. Suzuki, and K. Aihara, ``Comparison between mathematical models of intermittent androgen suppression for prostate cancer,'' J. Theor. Biol. 366, 33-45 (2015).
Y. Hirata, S. Azuma, and K. Aihara, ``Model predictive control for optimally scheduling intermittent androgen suppression of prostate cancer,'' Methods 67, 278-281 (2014).
Y. Suzuki, D. Sakai, T. Nomura, Y. Hirata, and K. Aihara, ``A new protocol for intermittent androgen suppression therapy for prostate cancer with unstable saddle-point dynamics,'' J. Theor. Biol. 350, 1-16 (2014).
Q. Guo, Z. Lu, Y. Hirata, and K. Aihara, ``Parameter estimation and optimal scheduling algorithm for a mathematical model of intermittent androgen suppression therapy for prostate cancer,'' Chaos 23, 043125 (2013).
H. Kuramae, Y. Hirata, N. Bruchovsky, K. Aihara, and H. Suzuki, ``Nonlinear systems identification by combining regression with bootstrap resampling,'' Chaos 21, 043121 (2011).
Y. Hirata, M. di Bernardo, N. Bruchovsky, and K. Aihara, ``Hybrid optimal scheduling for intermittent androgen suppression of prostate cancer,'' Chaos 20, 045125 (2010).
G. Tanaka, Y. Hirata, S. L. Goldenberg, N. Bruchovsky, and K. Aihara, ``Mathematical modelling of prostate cancer growth and its application to hormone therapy,'' Philos. T. R. Soc. Lond. A 368, 5029-5044 (2010).(Program for analyzing prostate cancer)
Y. Hirata, N. Bruchovsky, and K. Aihara, ``Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer,'' Journal of Theoretical Biology 264, 517-527 (2010).(Program for analyzing prostate cancer)

Nonlinear time series prediction

Y. Hirata, ``Reconstructing latent dynamical noise for better predicting observables,'' Chaos 28, 033112 (2018) (support page).
J. Amigo, Y. Hirata, and K. Aihara, ``On the limits of probabilistic forecasting in nonlinear time series analysis II: Differential entropy,'' Chaos 27, 083125 (2017).
Y. Hirata and K. Aihara, ``Improving time series prediction of solar irradiance after sunrise: Comparison among three methods for time series prediction,'' Solar Energy 149, 294-301 (2017).
J. M. Amigo, Y. Hirata, and K. Aihara, ``On the limits of probabilistic forecasting in nonlinear time series analysis,'' Chaos 26, 123114 (2016).
M. Chayama and Y. Hirata, ``When univariate model-free time series prediction is better than multivairate'' Phys. Lett. A 380, 2359-2365 (2016).
Y. Hirata and K. Aihara, ``Predicting ramps by integrating different sorts of information,'' European Physical Journal Special Topics 225, 513-525 (2016).
Y. Hirata, T. Takeuchi, S. Horai, H. Suzuki, and K. Aihara, ``Parsimonious description for predicting high-dimensional dynamics,'' Sci. Rep. 5, 15736 (2015).
Y. Hirata, M. Shiro, N. Takahashi, K. Aihara, H. Suzuki, and P. Mas, ``Approximating high-dimensional dynamics by barycentric coordinates with linear programming,'' Chaos 25, 013114 (2015).
S. Oya, K. Aihara, and Y. Hirata, ``Forecasting abrupt changes in foreign exchange markets: method using dynamical network marker,'' New J. Phys. 16, 115015 (2014).
Y. Hirata, K. Aihara, and H. Suzuki, ``Predicting multivariate time series in real time with confidence intervals: applications to renewable energy,'' Eur. Phys. J. Spec. Top. 223, 2451-2460 (2014).
Y. Hirata, ``Fast time-series prediction using high-dimensional data: Evaluating confidence interval credibility,'' Phys. Rev. E 89, 052916 (2014).
Y. Hirata, T. Yamada, J. Takahashi, K. Aihara, and H. Suzuki, ``Online multi-step prediction for wind speeds and solar irradiation: evaluation of prediction errors,'' Renew. Energy 67, 35-39 (2014).
E. P. Bravo, K. Aihara, and Y. Hirata, ``Application of joint permutations fro predicting coupled time series,'' Chaos 23, 043104 (2013).
Y. Hirata, T. Yamada, J. Takahashi, and H. Suzuki, ``Real-time multi-step predictors from data streams,'' Phys. Lett. A 376, 3092-3097 (2012).
Y. Hirata and K. Aihara, ``Describing high-dimensional dynamics with low-dimensional piecewise affine models: Applications to renewable energy,'' Chaos 22, 023143 (2012).
Y. Hirata, D. P. Mandic, H. Suzuki, and K. Aihara, ``Wind direction modelling using multiple observation points,'' Philosophical Transactions of the Royal Society A 366, 591-607 (2008).
Y. Hirata, H. Suzuki, and K. Aihara, ``Reconstructing state spaces from multivariate data using variable delays,'' Physical Review E 74, 026202 (2006).

Distances and recurrence plots

Y. Hirata, T. Stemler, D. Eroglu, and N. Marwan, ``Prediction of flowdynamics using point processes,'' Chaos 28, 011101 (2018).
Y. Hirata and K. Aihara, ``Dimensionless embedding for nonlinear time series analysis,'' Phys. Rev. E 96, 032291 (2017).
K. Iwayama, Y. Hirata, and K. Aihara, ``Definition of distance for nonlinear time series analysis of marked point process data,'' Phys. Lett. A 381, 257-262 (2017).
Y. Hirata, K. Iwayama, and K. Aihara, ``Possibility of short-term probabilistic forecasts for large earthquakes making good use of the limitations of existing catalogs,'' Phys. Rev. E 94, 042217 (2016).
Y. Shimada, Y. Hirata, T. Ikeguchi, and K. Aihara, ``Graph distance for complex networks,'' Sci. Rep. 6, 34944 (2016).
Y. Hirata, A. Oda, K. Ohta, and K. Aihara, ``Three-dimensional reconstruction of single-cell chromosome structure using recurrence plots,'' Sci. Rep. 6, 34982 (2016).
M. Fukino, Y. Hirata, and K. Aihara, ``Coarse-graining time series data: recurrence plot of recurrence plots and its application for music,'' Chaos 26, 023116 (2016). (Physics Today, SIAM News)
Y. Hirata and K. Aihara, ``Edit distance for marked point processes revisited: an implementation by binary integer programming,'' Chaos 25, 123117 (2015).
Y. Hirata, M. Komuro, S. Horai, and K. Aihara, ``Faithfulness of recurrence plots: a mathematical proof,'' Int. J. Bifurcat. Chaos 25, 1550168 (2015).
S. Nakano, Y. Hirata, K. Iwayama, and K. Aihara, ``Intra-day response of foregin exchange markets after the Tohoku-Oki earthquake,'' Physica A 419, 203-214 (2015).
S. Oya, K. Aihara, and Y. Hirata, ``An absolute measure for a key currency,'' Physica A 407, 15-23 (2014).
K. Iwayama, Y. Hirata, H. Suzuki, and K. Aihara, ``Change-point detection with recurrence networks'', NOLTA J. 4, 160-171 (2013).
K. Iwayama, Y. Hirata, K. Takahashi, K. Watanabe, K. Aihara, and H. Suzuki, ``Characterizing global evolutions of complex systems via intermediate network representations,'' Sci. Rep. 2, 423 (2012).
Y. Hirata and K. Aihara, ``Timing matters in foreign exchange markets,'' Physica A 391, 760-766 (2012).
Y. Hirata and K. Aihara, ``Statistical tests for serial dependence and laminarity on recurrence plots,'' Int. J. Bifurcat. Chaos 21, 1077-1084 (2011).
Y. Hirata, Y. Shimo, H. L. Tanaka, and K. Aihara, ``Chaotic properties of the Arctic Oscillation Index,'' SOLA 7, 033-036 (2011).
S. Suzuki, Y. Hirata, and K. Aihara, ``Definition of distance for marked point process data and its application to recurrence plot-based analysis of exchange tick data of foreign currencies,'' International Journal of Bifurcation and Chaos 20, 3699-3708 (2010).
Y. Hirata and K. Aihara, ``Devaney's chaos on recurrence plots,'' Physical Review E 82, 036209 (2010).
Y. Hirata and K. Aihara, ``Identifying hidden common causes from bivariate time series: a method using recurrence plots,'' Physical Review E 81, 016203 (2010).
Y. Hirata and K. Aihara, ``Representing spike trains using constant sampling intervals,'' Journal of Neuroscience Methods 183, 277-286 (2009).
M. Tanio, Y. Hirata, and H. Suzuki, ``Reconstruction of driving forces through recurrence plots,'' Physics Letters A 373, 2031-2040 (2009).
Y. Hirata, S. Horai, and K. Aihara, ``Reproduction of distance matrices and original time series from recurrence plots and its applications,'' European Physical Journal Special Topics 164, 13-22 (2008).

Symbolic dynamics

Y. Hirata and K. Aihara, ``Estimating optimal partitions for stochastic complex systems,'' Eur. Phys. J. Spec. Top. 222, 303-315 (2013).
Y. Hirata, K. Judd, and K. Aihara, ``Characterizing chaotic response of a squid axon through generating partitions,'' Physics Letters A 346, 141-147 (2005).
Y. Hirata and K. Judd, ``Constructing dynamical systems with specified symbolic dynamics,'' Chaos 15, 033102 (2005).
Y. Hirata, K. Judd, and D. Kilminster, ``Estimating a generating partition from observed time series: Symbolic shadowing,'' Physical Review E 70, 016215 (2004).
Y. Hirata and A. I. Mees, ``Estimating topological entropy via a symbolic data compression technique,'' Physical Review E 67, 026205 (2003).
Y. Hirata, H. Shimokawa, and K. Aihara, ``Entropy function and source coding of 1-dimensional maps,'' IEICE Transactions J82-A, 1780-1792 (1999) (in Japanese).

Surrogate data analysis

M. Shiro, Y. Hirata, and K. Aihara, ``Failure of pseudo-periodic surrogates,'' Artif. Life Robotics 15, 496-499 (2010).
Y. Hirata, Y. Katori, H. Shimokawa, H. Suzuki, T. A. Blenkinsop, E. J. Lang, and K. Aihara, ``Testing a neural coding hypothesis using surrogate data,'' Journal of Neuroscience Methods 172, 312-322 (2008).
Y. Hirata, S. Horai, H. Suzuki, and K. Aihara, ``Testing serial dependence by Random-shuffle surrogates and the Wayland method,'' Phys. Lett. A 370, 265-274 (2007).
T. Nakamura, Y. Hirata, and M. Small, ``Testing for correlation structures in short term variabilities with long term trends of multivariate time series,'' Physical Review E 74, 041114 (2006).
T. Nakamura, M. Small, and Y. Hirata, ``Testing for nonlinearity in irregular fluctuations with long term trends,'' Physical Review E 74, 026205 (2006).

Applications

Biology and medicine

Y. Hirata, A. Oda, K. Ohta, and K. Aihara, ``Three-dimensional reconstruction of single-cell chromosome structure using recurrence plots,'' Sci. Rep. 6, 34982 (2016).
Y. Hirata, J. M. Amigo, Y. Matsuzaka, R. Yokota, H. Mushiake, and K. Aihara, ``Detecting causality by combined use of multiple methods: climate and brain examples,'' PLoS One 11, e0158572 (2016).
K. Iwayama, L. Zhu, Y. Hirata, M. Aono, M. Hara, and K. Aihara, ``Decision-making ability of Physarum polycephalum enhanced by its coordinated spatiotemporal oscillatory dynamics,'' Bioinspiration & Biomimetics 11, 036001 (2016).
N. Takahashi, Y. Hirata, K. Aihara, and P. Mas, ``A hierarchical multi-oscillator network orchestrates the Arabidopsis circadian system,'' Cell 163, 148-159 (2015).
A. Oda, N. Takemata, Y. Hirata, T. Miyoshi, Y. Suzuki, S. Sugano, and K. Ohta, ``Dynamic transition of transcription and chromatin landscape during ssion yeast adaptation to glucose starvation,'' Genes Cells 20, 392-407 (2015).
M. Aono, Y. Hirata, M. Hara, and K. Aihara, ``Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer,'' Nat. Comput. 10, 1219-1244 (2011).
T. Nakai, Y. Hirata, S. Horai, M. Akagi, and K. Aihara, ``Firm evidence of chaos for heartbeats in dogs under constant flow ventilation,'' Int. J. Bifurcat. Chaos 20, 4151-4158 (2010).
M. Aono, Y. Hirata, M. Hara, and K. Aihara, ``A model of amoeba-based neurocomputer,'' Journal of Computer Chemistry, Japan 9, 143-156 (2010).
M. Aono, Y. Hirata, M. Hara, and K. Aihara, ``Amoeba-based chaotic neurocomputing: combinatorial optimization by coupled biological oscillators,'' New Generation Computing 27, 129-157 (2009).

Geoscience

Y. Hirata, K. Iwayama, and K. Aihara, ``Possibility of short-term probabilistic forecasts for large earthquakes making good use of the limitations of existing catalogs,'' Phys. Rev. E 94, 042217 (2016).
Y. Hirata, J. M. Amigo, Y. Matsuzaka, R. Yokota, H. Mushiake, and K. Aihara, ``Detecting causality by combined use of multiple methods: climate and brain examples,'' PLoS One 11, e0158572 (2016).
T. Omi, Y. Ogata, Y. Hirata, and K. Aihara, ``Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches,'' Journal of Geophysical Research: Solid Earth 120, 2561-2578 (2015).
T. Omi, Y. Ogata, Y. Hirata, and K. Aihara, ``Estimating the ETAS model from early aftershock sequence,'' Geophys. Res. Lett. 41, 850-857 (2014).
T. Omi, Y. Ogata, Y. Hirata, and K. Aihara, ``Forecasting large aftershocks within one day after the main shock,'' Sci. Rep. 3, 2218 (2013).

Social science

T. Omi, Y. Hirata, and K. Aihara, ``Hawkes process model with a timedependent background rate and its application to high-frequency financial data,'' Phys. Rev. E 96, 012303 (2017)
Y. Hirata, ``Mathematically modelling proportions of Japanese populations by industry,'' Physica A 460, 38-43 (2016).
S. Saito, Y. Hirata, K. Sasahara, and H. Suzuki, ``Tracking time evolution of collective attention clusters in Twitter: time evolving nonnegative matrix factorisation,'' PLoS One 10, 0139085 (2015).
K. Sasahara, Y. Hirata, M. Toyoda, M. Kitsuregawa, and K. Aihara, ``Quantifying collective attention from tweet stream'', PLoS ONE 8, e61823 (2013).

Others

M. Nagata, Y. Hirata, N. Fujiwara, G. Tanaka, H. Suzuki, K. Aihara, ``Smoothing effect for spatially distributed renewable resources and its impact on power grid robustness'' Chaos 27, 033104 (2017).
T. Sase, J. Ramirez, K. Kitajo, K. Aihara, and Y. Hirata, ``Estimating the level of dynamical noise in time series by using fractal dimensions,'' Physics Letters A 380, 1151-1163 (2016).
S. Uenohara, T. Mitsui, Y. Hirata, T. Morie, Y. Horio, and K. Aihara, ``Experimental distinction between chaotic and strange nonchaotic attractors on the basis on consistency'', Chaos 23, 023110 (2013).
Y. Hirata, M. Aono, M. Hara, and K. Aihara, ``Spontaneous mode switching in coupled oscillators competing for constant amounts of resources,'' Chaos 20, 013117 (2010).
T. Nakamura, Y. Hirata, K. Judd, D. Kilminster, and M. Small, ``Improved parameter estimation from noisy time series for nonlinear dynamical systems,'' International Journal of Bifurcation and Chaos 17, 1741-1752 (2007).

Review articles

M. Fukino, Y. Hirata, and K. Aihara, ``Music visualized by nonlinear time series analysis,'' SIAM News (2016).
合原一幸, 平田祥人, 奥牧人, 「複雑系と生命ビッグデータ」実験医学 34, 84-88 (2016).
Y. Hirata, K. Morino, T. Suzuki, Q. Guo, H. Fukuhara, and K. Aihara, ``System identification and parameter estimation in mathematical medicine: examples demonstrated for prostate cancer,'' Quantitative Biology 4, 13-19 (2016).
K. Aihara, and Y. Hirata, "Special section on complex systems modelling and its transdisciplinary applications," NOLTA J. 6, 1-1 (2015).
平田祥人, 合原一幸, 外力が加わっているシステムの状態の再構成, 生体の科学 65, 442-443 (2014).
M. A. Dahlem, S. Rode, A. May, N. Fujiwara, Y. Hirata, K. Aihara, and J. Kurths, ``Towards dynamical network biomarkers in neuromodulation of episodic migraine,'' Transl. Neurosci. 4, 282-294 (2013).
Y. Hirata, M. Oku, and K. Aihara, ``Chaos in neurons and its application: Perspective of chaos engineering,'' Chaos 22, 047511 (2012).
平田祥人, 点過程時系列データの非線形時系列解析，システム/制御/情報 56, 355-360 (2012).
平田祥人, 前立腺がん治療の数理モデル，応用数理 22, 27-36 (2012).
Y. Hirata, K. Akakura, C. S. Higano, N. Brhchovsky, and K. Aihara, ``Quantitative mathematical modeling of PSA dynamics of prostate cancer patients treated with intermittent androgen suppression,'' J. Mol. Cell Biol. 4, 127-132 (2012).
Y. Hirata, G. Tanaka, N. Brhchovsky, and K. Aihara, ``Mathematically modelling and controlling prostate cancer under intermittent hormone therapy,'' Asian J. Androl. 14, 270-277 (2012).
平田祥人, リカレンスプロット：時系列の視覚化を超えて，数理解析研究所講究録 1768, 150-162 (2011).
田中剛平, 平田祥人, 山田泰司, 高橋純, 合原一幸, 数理モデルに基づくテーラーメード前立腺癌間歇的内分泌療法, 泌尿器外科 24, 31-38 (2011).

Technical reports

S. Okuno, T. Takeuchi, S. Horai, K. Aihara, and Y. Hirata, ``Avoiding underestimates for time series prediction by state-dependent local integration,'' Mathematical engineering technical reports METR 2017-22, November 2017.
Y. Hirata, N. Bruchovsky, and K. Aihara, ``A mathematical model identifies outcome of hormone therapy for prostate cancer,'' Mathematical engineering technical reports METR2008-34, August 2008.
K. Fukuda, Y. Hirata, and K. Aihara, ``Estimating good local cross sections of expansive flows,'' Mathematical engineering technical reports METR2006-45, July 2006.
K. Fukuda, Y. Hirata, and K. Aihara, ``Non-generating partitions for surjective tent map,'' Mathematical engineering technical reports METR2006-44, July 2006.
Y. Hirata, Y. Katori, H. Suzuki, and K. Aihara, ``Testing a neural coding hypothesis using surrogate data,'' Mathematical engineering technical reports METR2005-35, November 2005.
Y. Hirata, S. Horai, H. Suzuki, and K. Aihara, ``Wayland method can rate some datasets as less deterministic than their random shuffle surrogates,'' Mathematical engineering technical reports METR2005-05, February 2005.