Identity, Philosophy, and the Problem of Armenian History in the Sixth Century

History and Identity in the Late Antique Near East gathers together the work of distinguished historians and early career scholars with a broad range of expertise to investigate the significance of newly emerged, or recently resurrected, ethnic identities on the borders of the eastern Mediterranean world. It focuses on the "long late antiquity" from the eve of the Arab conquest of the Roman East to the formation of the Abbasid caliphate. The first half of the book offers papers on the Christian Orient on the cusp of the Islamic invasions. These papers discuss how Christians negotiated the end of Roman power, whether in the selective use of the patristic past to create confessional divisions or the emphasis of the shared philosophical legacy of the Greco-Roman world. The second half of the book considers Muslim attempts to negotiate the pasts of the conquered lands of the Near East, where the Christian histories of Hira or Egypt were used to create distinctive regional identities for Arab settlers. Like the first half, this section investigates the redeployment of a shared history, this time the historical imagination of the Qu'ran and the era of the first caliphs. All the papers in the volume bring together studies of the invention of the past across traditional divides between disciplines, placing the re-assessment of the past as a central feature of the long late antiquity. As a whole, History and Identity in the Late Antique Near East represents a distinctive contribution to recent writing on late antiquity, due to its cultural breadth, its interdisciplinary focus, and its novel definition of late antiquity itself.

Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).