1,685 research outputs found

    Permutation Entropy and Bubble Entropy: Possible interactions and synergies between order and sorting relations

    Full text link
    [EN] Despite its widely demonstrated usefulness, there is still room for improvement in the basic Permutation Entropy (PE) algorithm, as several subsequent studies have proposed in the recent years. For example, some improved PE variants try to address possible PE weaknesses, such as its only focus on ordinal information, and not on amplitude, or the possible detrimental impact of equal values in subsequences due to motif ambiguity. Other evolved PE methods try to reduce the influence of input parameters. A good representative of this last point is the Bubble Entropy (BE) method. BE is based on sorting relations instead of ordinal patterns, and its promising capabilities have not been extensively assessed yet. The objective of the present study was to comparatively assess the classification performance of this new method, and study and exploit the possible synergies between PE and BE. The claimed superior performance of BE over PE was first evaluated by conducting a series of time series classification tests over a varied and diverse experimental set. The results of this assessment apparently suggested that there is a complementary relationship between PE and BE, instead of a superior/inferior relationship. A second set of experiments using PE and BE simultaneously as the input features of a clustering algorithm, demonstrated that with a proper algorithm configuration, classification accuracy and robustness can benefit from both measures.Cuesta Frau, D.; Vargas-Rojo, B. (2020). Permutation Entropy and Bubble Entropy: Possible interactions and synergies between order and sorting relations. Mathematical Biosciences and Engineering. 17(2):1637-1658. https://doi.org/10.3934/mbe.2020086S163716581721. C. Bandt and B. Pompe, Permutation entropy: A natural complexity measure for time series, Phys. Rev. Lett., 88 (2002), 174102.2. M. Zanin, L. Zunino, O. A. Rosso and D. Papo, Permutation entropy and its main biomedical and econophysics applications: A review, Entropy, 14 (2012), 1553-1577.14. F. Siokis, Credit market jitters in the course of the financial crisis: A permutation entropy approach in measuring informational efficiency in financial assets, Phys. A Statist. Mechan. Appl., 499 (2018).15. A. F. Bariviera, L. Zunino, M. B. Guercio, L. Martinez and O. Rosso, Efficiency and credit ratings: A permutation-information-theory analysis, J. Statist. Mechan. Theory Exper., 2013 (2013), P08007.16. A. F. Bariviera, M. B. Guercio, L. Martinez and O. Rosso, A permutation information theory tour through different interest rate maturities: the libor case, Philos. Transact. Royal Soc. A Math. Phys. Eng. Sci., 373 (2015).20. B. Fadlallah, B. Chen, A. Keil and J. Príncipe, Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information, Phys. Rev. E, 87 (2013), 022911.Deng, B., Cai, L., Li, S., Wang, R., Yu, H., Chen, Y., & Wang, J. (2016). Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease. Cognitive Neurodynamics, 11(3), 217-231. doi:10.1007/s11571-016-9418-924. D. Cuesta-Frau, Permutation entropy: Influence of amplitude information on time series classification performance, Math. Biosci. Eng., 5 (2019), 1-16.25. F. Traversaro, M. Risk, O. Rosso and F. Redelico, An empirical evaluation of alternative methods of estimation for Permutation Entropy in time series with tied values, arXiv e-prints, arXiv:1707.01517 (2017).26. D. Cuesta-Frau, M. Varela-Entrecanales, A. Molina-Picó and B. Vargas, Patterns with equal values in permutation entropy: Do they really matter for biosignal classification?, Complexity, 2018 (2018), 1-15.29. D. Cuesta-Frau, A. Molina-Picó, B. Vargas and P. González, Permutation entropy: Enhancing discriminating power by using relative frequencies vector of ordinal patterns instead of their shannon entropy, Entropy, 21 (2019).30. H. Azami and J. Escudero, Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation, Comput. Meth. Program. Biomed., 128 (2016), 40-51.32. G. Manis, M. Aktaruzzaman and R. Sassi, Bubble entropy: An entropy almost free of parameters, IEEE Transact. Biomed. Eng., 64 (2017), 2711-2718.34. L. Zunino, F. Olivares, F. Scholkmann and O. A. Rosso, Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions, Phys. Lett. A, 381 (2017), 1883-1892.38. D. E. Lake, J. S. Richman, M. P. Griffin and J. R. Moorman, Sample entropy analysis of neonatal heart rate variability, Am. J. Physiology-Regulatory Integrat. Comparat. Physiol., 283 (2002), R789-R797, PMID: 12185014.41. I. Unal, Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach, Comput. Math. Methods Med., 2017 (2017), 14.47. A. K. Jain, M. N. Murty and P. J. Flynn, Data clustering: A review, ACM Comput. Surv., 31 (1999), 264-323.51. J. Sander, M. Ester, H.-P. Kriegel and X. Xu, Density-based clustering in spatial databases: The algorithm gdbscan and its applications, Data Min. Knowl. Discov., 2 (1998), 169-194.52. J. Wu, Advances in K-means Clustering: A Data Mining Thinking, Springer Publishing Company, Incorporated, 2012.53. S. Panda, S. Sahu, P. Jena and S. Chattopadhyay, Comparing fuzzy-c means and k-means clustering techniques: A comprehensive study, in Advances in Computer Science, Engineering & Applications (eds. D. C. Wyld, J. Zizka and D. Nagamalai), Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, 451-460.54. A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation, 101 (2000), 215-220.58. R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David and C. E. Elger, Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64 (2001), 061907.60. N. Iyengar, C. K. Peng, R. Morin, A. L. Goldberger and L. A. Lipsitz, Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics, Am. J. Physiology-Regulatory Integrat. Comparat. Physiol., 271 (1996), R1078-R1084, PMID: 8898003

    Measurement of the branching fractions of psi(2S) -> 3(pi+pi-) and J/psi -> 2(pi+pi-)

    Full text link
    Using data samples collected at sqrt(s) = 3.686GeV and 3.650GeV by the BESII detector at the BEPC, the branching fraction of psi(2S) -> 3(pi+pi-) is measured to be [4.83 +- 0.38(stat) +- 0.69(syst)] x 10^-4, and the relative branching fraction of J/psi -> 2(pi+pi-) to that of J/psi -> mu+mu- is measured to be [5.86 +- 0.19(stat) +- 0.39(syst)]% via psi(2S) -> (pi+pi-)J/psi, J/psi -> 2(pi+pi-). The electromagnetic form factor of 3(pi+pi-) is determined to be 0.21 +- 0.02 and 0.20 +- 0.01 at sqrt(s) = 3.686GeV and 3.650GeV, respectively.Comment: 17pages, 7 figures, submitted to Phys. Rev.

    Measurement of the chi_{c2} Polarization in psi(2S) to gamma chi_{c2}

    Full text link
    The polarization of the chi_{c2} produced in psi(2S) decays into gamma chi_{c2} is measured using a sample of 14*10^6 psi(2S) events collected by BESII at the BEPC. A fit to the chi_{c2} production and decay angular distributions in psi(2S) to gamma chi_{c2}, chi_{c2} to pi pi and KK yields values x=A_1/A_0=2.08+/-0.44 and y=A_2/A_0=3.03 +/-0.66, with a correlation rho=0.92 between them, where A_{0,1,2} are the chi_{c2} helicity amplitudes. The measurement agrees with a pure E1 transition, and M2 and E3 contributions do not differ significantly from zero.Comment: 6 pages, 4 figures, 1 tabl

    A Unified Approach to the Classical Statistical Analysis of Small Signals

    Get PDF
    We give a classical confidence belt construction which unifies the treatment of upper confidence limits for null results and two-sided confidence intervals for non-null results. The unified treatment solves a problem (apparently not previously recognized) that the choice of upper limit or two-sided intervals leads to intervals which are not confidence intervals if the choice is based on the data. We apply the construction to two related problems which have recently been a battle-ground between classical and Bayesian statistics: Poisson processes with background, and Gaussian errors with a bounded physical region. In contrast with the usual classical construction for upper limits, our construction avoids unphysical confidence intervals. In contrast with some popular Bayesian intervals, our intervals eliminate conservatism (frequentist coverage greater than the stated confidence) in the Gaussian case and reduce it to a level dictated by discreteness in the Poisson case. We generalize the method in order to apply it to analysis of experiments searching for neutrino oscillations. We show that this technique both gives correct coverage and is powerful, while other classical techniques that have been used by neutrino oscillation search experiments fail one or both of these criteria.Comment: 40 pages, 15 figures. Changes 15-Dec-99 to agree more closely with published version. A few small changes, plus the two substantive changes we made in proof back in 1998: 1) The definition of "sensitivity" in Sec. V(C). It was inconsistent with our actual definition in Sec. VI. 2) "Note added in proof" at end of the Conclusio

    Observation of p pbar pi^0 and p pbar eta in psi' decays

    Full text link
    The processes psi'-->p pbar pi^0 and psi'-->p pbar eta are studied using a sample of 14 million psi' decays collected with the Beijing Spectrometer at the Beijing Electron-Positron Collider. The branching fraction of psi'-->p pbar pi^0 is measured with improved precision as (13.2\pm 1.0\pm 1.5)\times 10^{-5}, and psi'-->p pbar eta is observed for the first time with a branching fraction of (5.8\pm 1.1\pm 0.7)\times 10^{-5}, where the first errors are statistical and the second ones are systematic.Comment: 15 pages, 8 figures and 3 table

    Experimental study of ψ(2S)\psi(2S) decays to \K^+ K^- \pi^+ \pi^- \pi^0 final states

    Full text link
    K+Kπ+ππ0K^+K^-\pi^+\pi^-\pi^0 final states are studied using a sample of 14×10614\times10^6 ψ(2S)\psi(2S) decays collected with the Beijing Spectrometer (BESII) at the Beijing Electron-Position Collider. The branching fractions of ψ(2S)\psi(2S) decays to K+Kπ+ππ0 K^+K^-\pi^+\pi^-\pi^0, ωK+K\omega K^+ K^-, ωf0(1710)\omega f_0(1710), K(892)0Kπ+π0+c.c. K^{\ast}(892)^0 K^- \pi^+\pi^0+c.c., K(892)+Kπ+π+c.c.K^{\ast}(892)^{+} K^- \pi^+\pi^- +c.c., K(892)+Kρ0+c.c.K^{\ast}(892)^{+} K^- \rho^0+c.c. and K(892)0Kρ++c.c.K^{\ast}(892)^0 K^-\rho^+ + c.c. are determined. The first two agree with previous measurements, and the last five are first measurements.Comment: 19 pages, 9 figure

    First observation of ψ(2S)pnˉπ+c.c.\psi(2S) \to p \bar{n} \pi^- +c.c.

    Full text link
    Using 14 million ψ(2S)\psi(2S) events collected with the Beijing Spectrometer (BESII) at the Beijing Electron-Positron Collider, the branching fractions of ψ(2S)\psi(2S) decays to pnˉπp \bar{n} \pi^- and pˉnπ+\bar{p} n \pi^+ and the branching fractions of the main background channels ψ(2S)pnˉππ0\psi(2S) \to p \bar{n} \pi^-\pi^0, ψ(2S)γχc0γpnˉπ\psi(2S) \to \gamma\chi_{c0} \to \gamma p \bar{n} \pi^-, ψ(2S)γχc2γpnˉπ\psi(2S) \to \gamma\chi_{c2} \to \gamma p \bar{n} \pi^-, and ψ(2S)γχcJγpnˉππ0\psi(2S) \to \gamma \chi_{cJ} \to \gamma p \bar{n} \pi^- \pi^0 are determined. The contributions of the NN^{\ast} resonances in ψ(2S)pnˉπ+c.c.\psi(2S) \to p \bar{n} \pi^- +c.c. are also discussed.Comment: 19 pages, 8 figures, add vertex requirement systematic erro

    Measurements of J/psi decays into phi pi^0, phi eta, and phi eta^prime

    Full text link
    Based on 5.8x10^7 J/psi events detected in BESII, the branching fractions of J/psi--> phi eta and phi eta^prime are measured for different eta and eta^prime decay modes. The results are significantly higher than previous measurements. An upper limit on B(J/psi--> phi pi^0) is also obtained.Comment: 9 pages, 10 figure

    Measurements of the continuum RudsR_{\rm uds} and RR values in e+ee^+e^- annihilation in the energy region between 3.650 and 3.872 GeV

    Full text link
    We report measurents of the continuum RudsR_{\rm uds} near the center-of-mass energy of 3.70 GeV, the Ruds(c)+ψ(3770)(s)R_{{\rm uds(c)}+\psi(3770)}(s) and the Rhad(s)R_{\rm had}(s) values in e+ee^+e^- annihilation at 68 energy points in the energy region between 3.650 and 3.872 GeV with the BES-II detector at the BEPC Collodier. We obtain the RudsR_{\rm uds} for the continuum light hadron (containing u, d and s quarks) production near the DDˉD\bar D threshold to be Ruds=2.141±0.025±0.085R_{\rm uds}=2.141 \pm 0.025 \pm 0.085.Comment: 5 pages, 3 figure
    corecore