GRMHD prediction of coronal variability in accreting black holes

On the basis of data from an energy-conserving 3D general relativistic MHD simulation, we predict the statistical character of variability in the coronal luminosity from accreting black holes. When the inner boundary of the corona is defined to be the electron scattering photosphere, its location depends only on the mass accretion rate in Eddington units (\dot{M}). Nearly independent of viewing angle and \dot{M}, the power spectrum over the range of frequencies from approximately the orbital frequency at the innermost stable circular orbit (ISCO) to ~100 times lower is well approximated by a power-law with index -2, crudely consistent with the observed power spectra of hard X-ray fluctuations in AGN and the hard states of Galactic binary black holes. The underlying physical driver for variability in the light curve is variations in the accretion rate caused by the chaotic character of MHD turbulence, but the power spectrum of the coronal light output is significantly steeper. Part of this contrast is due to the fact that the mass accretion rate can be significantly modulated by radial epicyclic motions that do not result in dissipation, and therefore do not drive luminosity fluctuations. The other part of this contrast is due to the inward decrease of the characteristic inflow time, which leads to decreasing radial coherence length with increasing fluctuation frequency.Comment: Accepted for publication in ApJ, 35 pages, 11 figures (8 color and 3 greyscale), AASTEX. High-resolution versions can be found at the following links: [PS] http://www.pha.jhu.edu/~scn/papers/grmhd_var.ps [PDF] http://www.pha.jhu.edu/~scn/papers/grmhd_var.pd

Phase-space characterization of complexity in quantum many-body dynamics

We propose a phase-space Wigner harmonics entropy measure for many-body quantum dynamical complexity. This measure, which reduces to the well known measure of complexity in classical systems and which is valid for both pure and mixed states in single-particle and many-body systems, takes into account the combined role of chaos and entanglement in the realm of quantum mechanics. The effectiveness of the measure is illustrated in the example of the Ising chain in a homogeneous tilted magnetic field. We provide numerical evidence that the multipartite entanglement generation leads to a linear increase of entropy until saturation in both integrable and chaotic regimes, so that in both cases the number of harmonics of the Wigner function grows exponentially with time. The entropy growth rate can be used to detect quantum phase transitions. The proposed entropy measure can also distinguish between integrable and chaotic many-body dynamics by means of the size of long term fluctuations which become smaller when quantum chaos sets in.Comment: 10 pages, 9 figure

An Internal Learning Approach to Video Inpainting

We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in static images. In extending DIP to video we make two important contributions. First, we show that coherent video inpainting is possible without a priori training. We take a generative approach to inpainting based on internal (within-video) learning without reliance upon an external corpus of visual data to train a one-size-fits-all model for the large space of general videos. Second, we show that such a framework can jointly generate both appearance and flow, whilst exploiting these complementary modalities to ensure mutual consistency. We show that leveraging appearance statistics specific to each video achieves visually plausible results whilst handling the challenging problem of long-term consistency.Comment: Accepted by ICCV 2019. Website: https://cs.stanford.edu/~haotianz/publications/video_inpainting

Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity

The clustering of data into physically meaningful subsets often requires assumptions regarding the number, size, or shape of the subgroups. Here, we present a new method, simultaneous coherent structure coloring (sCSC), which accomplishes the task of unsupervised clustering without a priori guidance regarding the underlying structure of the data. sCSC performs a sequence of binary splittings on the dataset such that the most dissimilar data points are required to be in separate clusters. To achieve this, we obtain a set of orthogonal coordinates along which dissimilarity in the dataset is maximized from a generalized eigenvalue problem based on the pairwise dissimilarity between the data points to be clustered. This sequence of bifurcations produces a binary tree representation of the system, from which the number of clusters in the data and their interrelationships naturally emerge. To illustrate the effectiveness of the method in the absence of a priori assumptions, we apply it to three exemplary problems in fluid dynamics. Then, we illustrate its capacity for interpretability using a high-dimensional protein folding simulation dataset. While we restrict our examples to dynamical physical systems in this work, we anticipate straightforward translation to other fields where existing analysis tools require ad hoc assumptions on the data structure, lack the interpretability of the present method, or in which the underlying processes are less accessible, such as genomics and neuroscience

Geometry of the ergodic quotient reveals coherent structures in flows

Dynamical systems that exhibit diverse behaviors can rarely be completely understood using a single approach. However, by identifying coherent structures in their state spaces, i.e., regions of uniform and simpler behavior, we could hope to study each of the structures separately and then form the understanding of the system as a whole. The method we present in this paper uses trajectory averages of scalar functions on the state space to: (a) identify invariant sets in the state space, (b) form coherent structures by aggregating invariant sets that are similar across multiple spatial scales. First, we construct the ergodic quotient, the object obtained by mapping trajectories to the space of trajectory averages of a function basis on the state space. Second, we endow the ergodic quotient with a metric structure that successfully captures how similar the invariant sets are in the state space. Finally, we parametrize the ergodic quotient using intrinsic diffusion modes on it. By segmenting the ergodic quotient based on the diffusion modes, we extract coherent features in the state space of the dynamical system. The algorithm is validated by analyzing the Arnold-Beltrami-Childress flow, which was the test-bed for alternative approaches: the Ulam's approximation of the transfer operator and the computation of Lagrangian Coherent Structures. Furthermore, we explain how the method extends the Poincar\'e map analysis for periodic flows. As a demonstration, we apply the method to a periodically-driven three-dimensional Hill's vortex flow, discovering unknown coherent structures in its state space. In the end, we discuss differences between the ergodic quotient and alternatives, propose a generalization to analysis of (quasi-)periodic structures, and lay out future research directions.Comment: Submitted to Elsevier Physica D: Nonlinear Phenomen

A permutation Information Theory tour through different interest rate maturities: the Libor case

This paper analyzes Libor interest rates for seven different maturities and referred to operations in British Pounds, Euro, Swiss Francs and Japanese Yen, during the period years 2001 to 2015. The analysis is performed by means of two quantifiers derived from Information Theory: the permutation Shannon entropy and the permutation Fisher information measure. An anomalous behavior in the Libor is detected in all currencies except Euro during the years 2006--2012. The stochastic switch is more severe in 1, 2 and 3 months maturities. Given the special mechanism of Libor setting, we conjecture that the behavior could have been produced by the manipulation that was uncovered by financial authorities. We argue that our methodology is pertinent as a market overseeing instrument.Comment: arXiv admin note: text overlap with arXiv:1304.039