Geodetic Observations of Weak Determinism in Rupture Evolution of Large Earthquakes.

The moment evolution of large earthquakes is a subject of fundamental interest to both basic and applied seismology. Specifically, an open problem is when in the rupture process a large earthquake exhibits features dissimilar from those of a lesser magnitude event. The answer to this question is of importance for rapid, reliable estimation of earthquake magnitude, a major priority of earthquake and tsunami early warning systems. Much effort has been made to test whether earthquakes are deterministic, meaning that observations in the first few seconds of rupture can be used to predict the final rupture extent. However, results have been inconclusive, especially for large earthquakes greater than M w 7. Traditional seismic methods struggle to rapidly distinguish the size of large-magnitude events, in particular near the source, even after rupture completion, making them insufficient to resolve the question of predictive rupture behavior. Displacements derived from Global Navigation Satellite System data can accurately estimate magnitude in real time, even for the largest earthquakes. We employ a combination of seismic and geodetic (Global Navigation Satellite System) data to investigate early rupture metrics, to determine whether observational data support deterministic rupture behavior. We find that while the earliest metrics (~5 s of data) are not enough to infer final earthquake magnitude, accurate estimates are possible within the first tens of seconds, prior to rupture completion, suggesting a weak determinism. We discuss the implications for earthquake source physics and rupture evolution and address recommendations for earthquake and tsunami early warning

An \emph{ab initio} method for locating characteristic potential energy minima of liquids

It is possible in principle to probe the many--atom potential surface using density functional theory (DFT). This will allow us to apply DFT to the Hamiltonian formulation of atomic motion in monatomic liquids [\textit{Phys. Rev. E} {\bf 56}, 4179 (1997)]. For a monatomic system, analysis of the potential surface is facilitated by the random and symmetric classification of potential energy valleys. Since the random valleys are numerically dominant and uniform in their macroscopic potential properties, only a few quenches are necessary to establish these properties. Here we describe an efficient technique for doing this. Quenches are done from easily generated "stochastic" configurations, in which the nuclei are distributed uniformly within a constraint limiting the closeness of approach. For metallic Na with atomic pair potential interactions, it is shown that quenches from stochastic configurations and quenches from equilibrium liquid Molecular Dynamics (MD) configurations produce statistically identical distributions of the structural potential energy. Again for metallic Na, it is shown that DFT quenches from stochastic configurations provide the parameters which calibrate the Hamiltonian. A statistical mechanical analysis shows how the underlying potential properties can be extracted from the distributions found in quenches from stochastic configurations

Computing Inferences for Large-Scale Continuous-Time Markov Chains by Combining Lumping with Imprecision

If the state space of a homogeneous continuous-time Markov chain is too large, making inferences - here limited to determining marginal or limit expectations - becomes computationally infeasible. Fortunately, the state space of such a chain is usually too detailed for the inferences we are interested in, in the sense that a less detailed - smaller - state space suffices to unambiguously formalise the inference. However, in general this so-called lumped state space inhibits computing exact inferences because the corresponding dynamics are unknown and/or intractable to obtain. We address this issue by considering an imprecise continuous-time Markov chain. In this way, we are able to provide guaranteed lower and upper bounds for the inferences of interest, without suffering from the curse of dimensionality.Comment: 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018

Connie Myers v. Albertsons, Inc. : Brief of Appellee

Appeal of the Judgment of Michael Glasmann Based upon a Jury Verdict Second Judicial District Court Weber County, State of Uta

The SPUR adherence profiling tool: preliminary results of algorithm development

Objective: The SPUR (Social, Psychological, Usage, and Rational) Adherence Profiling Tool is a recently developed adaptive instrument for measuring key patient-level risk factors for adherence problems. This study describes the SPUR questionnaire’s psychometric refinement and evaluation. Methods: Data were collected through an online survey among individuals with type 2 diabetes in the United States. 501 participants completed multiple questionnaires, including SPUR and several validated adherence measures. A Partial Credit Model (PCM) analysis was performed to evaluate the structure of the SPUR tool and verify the assumption of a single underlying latent variable reflecting adherence. Partial least-squares discriminant analyses (PLS-DA) were conducted to identify which hierarchically-defined items within each dimension needed to be answered by a given patient. Lastly, correlations were calculated between the latent trait of SPUR adherence and other patient-reported adherence measures. Results: Of the 45 candidate SPUR items, 39 proved to fit well to the PCM confirming that SPUR responses reflected one underlying latent trait hypothesized as non-adherence. Correlations between the latent trait of the SPUR tool and other adherence measures were positive, statistically significant, and ranged from 0.32 to 0.48 (p-values \u3c.0001). The person-item map showed that the items reflected well the range of adherence behaviors from perfect adherence to high levels of non-adherence. The PLS-DA results confirmed the relevance of using four meta-items as filters to open or close subsequent items from their corresponding SPUR dimensions. Conclusions: The SPUR tool represents a promising new adaptive instrument for measuring adherence accurately and efficiently using the digital behavioral diagnostic tool

Liquid state properties from first principles DFT calculations: Static properties

In order to test the Vibration-Transit (V-T) theory of liquid dynamics, ab initio density functional theory (DFT) calculations of thermodynamic properties of Na and Cu are performed and compared with experimental data. The calculations are done for the crystal at T = 0 and T_m, and for the liquid at T_m. The key theoretical quantities for crystal and liquid are the structural potential and the dynamical matrix, both as function of volume. The theoretical equations are presented, as well as details of the DFT computations. The properties compared with experiment are the equilibrium volume, the isothermal bulk modulus, the internal energy and the entropy. The agreement of theory with experiment is uniformly good. Our primary conclusion is that the application of DFT to V-T theory is feasible, and the resulting liquid calculations achieve the same level of accuracy as does ab initio lattice dynamics for crystals. Moreover, given the well established reliability of DFT, the present results provide a significant confirmation of V-T theory itself.Comment: 9 pages, 3 figures, 5 tables, edited to more closely match published versio