Modeling sequences and temporal networks with dynamic community structures

In evolving complex systems such as air traffic and social organizations, collective effects emerge from their many components' dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks' large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.Comment: 15 Pages, 6 figures, 2 table

Shear wave structure of a transect of the Los Angeles basin from multimode surface waves and H/V spectral ratio analysis

We use broad-band stations of the ‘Los Angeles Syncline Seismic Interferometry Experiment’ (LASSIE) to perform a joint inversion of the Horizontal to Vertical spectral ratios (H/V) and multimode dispersion curves (phase and group velocity) for both Rayleigh and Love waves at each station of a dense line of sensors. The H/V of the autocorrelated signal at a seismic station is proportional to the ratio of the imaginary parts of the Green’s function. The presence of low-frequency peaks (∼0.2 Hz) in H/V allows us to constrain the structure of the basin with high confidence to a depth of 6 km. The velocity models we obtain are broadly consistent with the SCEC CVM-H community model and agree well with known geological features. Because our approach differs substantially from previous modelling of crustal velocities in southern California, this research validates both the utility of the diffuse field H/V measurements for deep structural characterization and the predictive value of the CVM-H community velocity model in the Los Angeles region. We also analyse a lower frequency peak (∼0.03 Hz) in H/V and suggest it could be the signature of the Moho. Finally, we show that the independent comparison of the H and V components with their corresponding theoretical counterparts gives information about the degree of diffusivity of the ambient seismic field

Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships

Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the molecular representation becomes a critical ingredient in ML model predictive accuracy. We introduce a series of revised autocorrelation functions (RACs) that encode relationships between the heuristic atomic properties (e.g., size, connectivity, and electronegativity) on a molecular graph. We alter the starting point, scope, and nature of the quantities evaluated in standard ACs to make these RACs amenable to inorganic chemistry. On an organic molecule set, we first demonstrate superior standard AC performance to other presently-available topological descriptors for ML model training, with mean unsigned errors (MUEs) for atomization energies on set-aside test molecules as low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs on set-aside test molecules in spin-state splitting in comparison to 15-20x higher errors from feature sets that encode whole-molecule structural information. Systematic feature selection methods including univariate filtering, recursive feature elimination, and direct optimization (e.g., random forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5x smaller than RAC-155 produce sub- to 1-kcal/mol spin-splitting MUEs, with good transferability to metal-ligand bond length prediction (0.004-5 {\AA} MUE) and redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature selection results across property sets reveals the relative importance of local, electronic descriptors (e.g., electronegativity, atomic number) in spin-splitting and distal, steric effects in redox potential and bond lengths.Comment: 43 double spaced pages, 11 figures, 4 table