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

Distributed Estimation of Graph Spectrum

In this paper, we develop a two-stage distributed algorithm that enables nodes in a graph to cooperatively estimate the spectrum of a matrix $W$ associated with the graph, which includes the adjacency and Laplacian matrices as special cases. In the first stage, the algorithm uses a discrete-time linear iteration and the Cayley-Hamilton theorem to convert the problem into one of solving a set of linear equations, where each equation is known to a node. In the second stage, if the nodes happen to know that $W$ is cyclic, the algorithm uses a Lyapunov approach to asymptotically solve the equations with an exponential rate of convergence. If they do not know whether $W$ is cyclic, the algorithm uses a random perturbation approach and a structural controllability result to approximately solve the equations with an error that can be made small. Finally, we provide simulation results that illustrate the algorithm.Comment: 15 pages, 2 figure

Effects of singular-vector-type initial errors on the short-range prediction of Kuroshio extension transition processes

The effects of optimal initial error on the short-range prediction of transition processes between the Kuroshio Extension (KE) bimodalities are analyzed using a reduced-gravity shallow-water model and the singular vector (SV) approach. Emphasis is placed on the spatial structures, growing processes, and effects of the SVs. The results show that the large values of the SVs are mainly located in the first crest region of the KE (around 35°N, 144°E) and in the Kuroshio large meander (KLM) region south of Japan (around 32°N, 139.5°E). The fast growths of the SVs have important impacts on the prediction of transition of the KE bimodality. The initial error with +SV pattern (with positive anomalies in the first crest region of the KE and negative anomalies in the KLM region) tends to strengthen the KE and shift it toward the high-energy state, while the error with -SV pattern is prone to weaken the KE and shift it toward the low-energy state. In addition, the SV-type initial errors grow more quickly in the transition phase of the KE from the high-energy to the low-energy state than in the opposite transition phase. A perturbation energy analysis illustrates that different physical processes are responsible for the error growth in the KE region for different transition phases of the KE; barotropic instability plays a dominant role in the error growth in the low-to-high (LH) energy phase, while the error evolution in the high-to-low (HL) energy phase is mainly caused by advection processes

Coupled Ensemble Data Assimilation with the Climate Model AWI-CM

The coupled atmosphere-ocean model AWI-CM has been augmented for ensemble data assimilation using the parallel data assimilation framework (PDAF). AWI-CM consists of the atmosphere model ECHAM6 and the unstructured grid finite element ocean model FESOM. PDAF provides the environment for ensemble forecasts and the ensemble filters for the assimilation. The work aims at strongly-coupled data assimilation, hence using cross-covariances between the atmosphere and ocean in the analysis step of the data assimilation process. As a first step oceanic observations are assimilated into the coupled model system in a setup of weakly coupled data assimilation and the effect one the coupled model state is assessed. We discuss the setup of the system, which is generic and hence also applicable for other coupled, but also uncoupled models. Further, challenges of the assimilation into the coupled system and initial results from strongly-coupled assimilation are discussed

Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)

Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g., the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation is ensemble-based methods which utilize an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the ensemble methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the file reading and writing, as well as model restarts during the data assimilation process. This study explains the required modifications to the programs with the example of the coupled atmosphere–sea-ice–ocean model AWI-CM (AWI Climate Model). Using the case of the assimilation of oceanic observations shows that the data assimilation leads only to small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in which the development of data assimilation methods can be separated from the model application

Evidence for line nodes in the energy gap of the overdoped Ba(Fe1−x_{1-x}Cox_{x})2_{2}As2_{2} from low-temperature specific heat measurements

Low-temperature specific heat (SH) is measured on Ba(Fe$_{1-x}$Co$_{x}$)$_2$As$_2$ single crystals in a wide doping region under different magnetic fields. For the overdoped sample, we find the clear evidence for the presence of $T^2$ term in the data, which is absent both for the underdoped and optimal doped samples, suggesting the presence of line nodes in the energy gap of the overdoped samples. Moreover, the field induced electron specific heat coefficient $\Delta\gamma(H)$ increases more quickly with the field for the overdoped sample than the underdoped and optimal doped ones, giving another support to our arguments. Our results suggest that the superconducting gap(s) in the present system may have different structures strongly depending on the doping regions.Comment: 5 pages, 4 figure

Ensemble Data Assimilation for Coupled Models of the Earth System

Data assimilation combines observational information with numerical models taking into account the errors in both the observations and the model. In ensemble data assimilation the errors in the model state are dynamically estimated using an ensemble of model states. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. The coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled atmosphere-ocean models like the AWI Climate Model (AWI-CM), simulate the physics in both compartments and fluxes in between then. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. Ensemble data assimilation methods can be applied with these model systems, but have a high high computing cost. To allow us to efficiently perform the data assimilation, the parallel data assimilation framework (PDAF) has been developed. I will discuss the application and challenges of coupled ensemble data assimilation on the examples of the data assimilative model system of AWI-CM coupled to PDAF and a coupled ocean-biogeochemical model consistent of the ocean circulation model MITgcm and the ecosystem model REcoM2

Involvement of the JNK/FOXO3a/Bim Pathway in Neuronal Apoptosis after Hypoxic-Ischemic Brain Damage in Neonatal Rats.

c-Jun N-terminal kinase (JNK) plays a key role in the regulation of neuronal apoptosis. Previous studies have revealed that forkhead transcription factor (FOXO3a) is a critical effector of JNK-mediated tumor suppression. However, it is not clear whether the JNK/FOXO3a pathway is involved in neuronal apoptosis in the developing rat brain after hypoxia-ischemia (HI). In this study, we generated an HI model using postnatal day 7 rats. Fluorescence immunolabeling and Western blot assays were used to detect the distribution and expression of total and phosphorylated JNK and FOXO3a and the pro-apoptotic proteins Bim and CC3. We found that JNK phosphorylation was accompanied by FOXO3a dephosphorylation, which induced FOXO3a translocation into the nucleus, resulting in the upregulation of levels of Bim and CC3 proteins. Furthermore, we found that JNK inhibition by AS601245, a specific JNK inhibitor, significantly increased FOXO3a phosphorylation, which attenuated FOXO3a translocation into the nucleus after HI. Moreover, JNK inhibition downregulated levels of Bim and CC3 proteins, attenuated neuronal apoptosis and reduced brain infarct volume in the developing rat brain. Our findings suggest that the JNK/FOXO3a/Bim pathway is involved in neuronal apoptosis in the developing rat brain after HI. Agents targeting JNK may offer promise for rescuing neurons from HI-induced damage

Efficient Ensemble Data Assimilation For Earth System Models with the Parallel Data Assimilation Framework (PDAF)

We discuss how to build an ensemble data assimilation system using a direct connection between a coupled Earth system model (ESM) and the ensemble data assimilation software PDAF (Parallel Data Assimilation Framework, http://pdaf.awi.de). The direct connection results in a data assimilation program with high flexibility, efficiency, and parallel scalability. For this we augment the source code of the coupled model by data assimilation routines and hence create an online-coupled assimilative model. This first modifies the coupled model to be able to simulate an ensemble. Using a combination of in-memory access and parallel communication with the Message Passing Interface (MPI) standard we can further add the analysis step of ensemble-based assimilation methods. Thus the assimilation of observations is computed without the need to stop and restart the whole coupled model system. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler that couples the different model compartments. This strategy to build the assimilation system allows us to perform both weakly coupled (in-compartment) and strongly coupled (cross-compartment) assimilation. The assimilation frequency can be kept flexible, so that the assimilation of observations from different compartments of the ESM can be performed at different intervals. Further, the reading and writing of disk files is minimized. The resulting assimilative model can be run in the same way as the regular ESM, but with additional parameters controlling the assimilation and with a higher number of processors to simulate the ensemble. Using the example of the coupled climate model AWI-CM that contains the FESOM model for the ocean and sea ice and ECHAM6 for the atmosphere, both coupled through the OASIS-MCT coupler, we discuss the features of the online assimilation coupling strategy and the performance of the resulting assimilative model