Classifying Network Data with Deep Kernel Machines

Inspired by a growing interest in analyzing network data, we study the problem of node classification on graphs, focusing on approaches based on kernel machines. Conventionally, kernel machines are linear classifiers in the implicit feature space. We argue that linear classification in the feature space of kernels commonly used for graphs is often not enough to produce good results. When this is the case, one naturally considers nonlinear classifiers in the feature space. We show that repeating this process produces something we call "deep kernel machines." We provide some examples where deep kernel machines can make a big difference in classification performance, and point out some connections to various recent literature on deep architectures in artificial intelligence and machine learning

Climatic Controls on Streamflow and Snowpack over the Colorado River Basin

The Colorado River is the main source of surface water for the Southwestern U.S. and Mexico. It is heavily regulated by two large reservoirs, and many smaller ones. Although summer precipitation is about the same amount as winter precipitation averaged over the basin, runoff is mainly generated from the melting of snowpack accumulated during the cold season. Therefore, fresh water availability is challenged by warming temperatures that have occurred over the last few decades. A noticeable downward trend in the basin’s naturalized streamflow appears to be related to the increasing temperatures, as precipitation changes have been small. In this dissertation, I evaluate the effects of climatic controlling factors on two major hydrologic components (runoff and snow water equivalent) of the region’s water cycle. The primary tool I use is offline land surface simulations from macro-scale hydrological models. In particular, this dissertation comprises three studies that have or will be published as journal articles. First, I employed the Variable Infiltration Capacity (VIC) model to investigate the causes of the century-long decreasing trend in Colorado River streamflow. I separated the influences of warming temperatures and unevenly (spatially) distributed (and mostly small) precipitation changes by conducting a parallel set of experiments that isolated the various effects. My experiments suggest that more than half of the downward trend in streamflow is attributable to warming temperatures. Compared with an earlier drought period (1953-1968) caused mainly by insufficient precipitation over the UCRB, about half of runoff losses during the Millennium Drought (2000-2014) is attributed to warm temperature. Second, I explored the factors that control snow ablation processes across the West and differences in their representation in a multi-model suite. I selected ten USDA Snow Telemetry (SNOTEL) stations distributed across the mountainous ranges of the Western U.S. Consistent with earlier studies, I find that during the ablation period net radiation generally has stronger effects on melt rates than does air temperature. However, estimates of melt rates vary greatly across the models, in part because of differences in the way they represent the effects of vegetation on the surface energy balance. The canopy effect of each model on snow melting is also evaluated with parallel experiments. Finally, I reconstructed snowpack in the Upper Colorado River Basin (UCRB) over the last 67 years (1949-2015) using the macroscale VIC model implemented at 1/16� latitude-longitude spatial resolution. I then investigated the storms that were associated with accumulation of snow water equivalent (SWE) using 86 SNOTEL stations distributed across the UCRB. In particular, I classified storms associated with SWE accumulation into Atmospheric River (AR) related and non-AR events. The storms I identified (both AR and non-AR) during the study period account for an average of 78.2% of annual peak SWE. On average, 69% of the storms are AR-related; they contribute 56.3% of the annual snowpack maxima. I find no statistically significant basin-wide trends in the number of storms (of either type) or their contributions to SWE. However, in the middle of the basin, there are a number of grid cells with significant upward trends in the storm contributions to snow, which suggests some movement of snow accumulation towards the UCRB mid-zone over the last few decades

catena-Poly[[[aqua­chlorido­manganese(II)]-bis­[μ-1,1′-(oxydi-p-phenyl­ene)di-1H-imidazole-κ2 N 3:N 3′]] chloride dimethyl­formamide mono­solvate monohydrate]

The title coordination polymer, {[MnCl(C18H14N4O)2(H2O)]Cl·C3H7NO·H2O}n, obtained by the solvothermal reaction of BIDPE and manganese(II) salt in H2O/DMF (DMF is dimethyl­formamide), is composed of a chain of [Mn2(BIDPE)2] [BIDPE is 1,1′-(oxydi-p-phenyl­ene)di-1H-imidazole] metallocyclic rings that exhibit inversion symmetry. The coordination about the Mn(II) ions is distorted octahedral with a MnClN4O coordination set. In the crystal, the polymeric chains are linked by O—H⋯Cl hydrogen bonds, forming a two-dimensional network parallel to (100). A number of C—H⋯Cl and C—H⋯O inter­actions are also present