Distributed Holistic Clustering on Linked Data

Link discovery is an active field of research to support data integration in the Web of Data. Due to the huge size and number of available data sources, efficient and effective link discovery is a very challenging task. Common pairwise link discovery approaches do not scale to many sources with very large entity sets. We here propose a distributed holistic approach to link many data sources based on a clustering of entities that represent the same real-world object. Our clustering approach provides a compact and fused representation of entities, and can identify errors in existing links as well as many new links. We support a distributed execution of the clustering approach to achieve faster execution times and scalability for large real-world data sets. We provide a novel gold standard for multi-source clustering, and evaluate our methods with respect to effectiveness and efficiency for large data sets from the geographic and music domains

Transformation Optics for Plasmonics

A new strategy to control the flow of surface plasmon polaritons at metallic surfaces is presented. It is based on the application of the concept of Transformation Optics to devise the optical parameters of the dielectric medium placed on top of the metal surface. We describe the general methodology for the design of Transformation-Optical devices for surface plasmons and analyze, for proof-of-principle purposes, three representative examples with different functionalities: a beam shifter, a cylindrical cloak and a ground-plane cloak.Comment: 15 pages, 3 figure

Zeta Determinant for Laplace Operators on Riemann Caps

The goal of this paper is to compute the zeta function determinant for the massive Laplacian on Riemann caps (or spherical suspensions). These manifolds are defined as compact and boundaryless $D-$dimensional manifolds deformed by a singular Riemannian structure. The deformed spheres, considered previously in the literature, belong to this class. After presenting the geometry and discussing the spectrum of the Laplacian, we illustrate a method to compute its zeta regularized determinant. The special case of the deformed sphere is recovered as a limit of our general formulas.Comment: 19 pages, 1 figur

Theorem on the Distribution of Short-Time Particle Displacements with Physical Applications

The distribution of the initial short-time displacements of particles is considered for a class of classical systems under rather general conditions on the dynamics and with Gaussian initial velocity distributions, while the positions could have an arbitrary distribution. This class of systems contains canonical equilibrium of a Hamiltonian system as a special case. We prove that for this class of systems the nth order cumulants of the initial short-time displacements behave as the 2n-th power of time for all n>2, rather than exhibiting an nth power scaling. This has direct applications to the initial short-time behavior of the Van Hove self-correlation function, to its non-equilibrium generalizations the Green's functions for mass transport, and to the non-Gaussian parameters used in supercooled liquids and glasses.Comment: A less ambiguous mathematical notation for cumulants was adopted and several passages were reformulated and clarified. 40 pages, 1 figure. Accepted by J. Stat. Phy

Charge-Dependence of the Nucleon-Nucleon Interaction

Based upon the Bonn meson-exchange-model for the nucleon-nucleon ($NN$) interaction, we calculate the charge-independence breaking (CIB) of the $NN$ interaction due to pion-mass splitting. Besides the one-pion-exchange (OPE), we take into account the $2\pi$-exchange model and contributions from three and four irreducible pion exchanges. We calculate the CIB differences in the $^1S_0$ effective range parameters as well as phase shift differences for partial waves up to total angular momentum J=4 and laboratory energies below 300 MeV. We find that the CIB effect from OPE dominates in all partial waves. However, the CIB effects from the $2\pi$ model are noticable up to D-waves and amount to about 40% of the OPE CIB-contribution in some partial waves, at 300 MeV. The effects from 3$\pi$ and 4$\pi$ contributions are negligible except in $^1S_0$ and $^3P_2$.Comment: 12 pages, RevTex, 14 figure

MALT1 Phosphorylation Controls Activation of T Lymphocytes and Survival of ABC-DLBCL Tumor Cells

The CARMA1/CARD11-BCL10-MALT1 (CBM) complex bridges T and B cell antigen receptor (TCR/BCR) ligation to MALT1 protease activation and canonical nuclear factor kappa B (NF-kappa B) signaling. Using unbiased mass spectrometry, we discover multiple serine phosphorylation sites in the MALT1 C terminus after T cell activation. Phospho-specific antibodies reveal that CBM-associated MALT1 is transiently hyper-phosphorylated upon TCR/CD28 co-stimulation. We identify a dual role for CK1 alpha as a kinase that is essential for CBM signalosome assembly as well as MALT1 phosphorylation. Although MALT1 phosphorylation is largely dispensable for protease activity, it fosters canonical NF-kappa B signaling in Jurkat and murine CD4 T cells. Moreover, constitutive MALT1 phosphorylation promotes survival of activated B cell-type diffuse large B cell lymphoma (ABC-DLBCL) cells addicted to chronic BCR signaling. Thus, MALT1 phosphorylation triggers optimal NF-kappa B activation in lymphocytes and survival of lymphoma cells

Charge-Asymmetry of the Nucleon-Nucleon Interaction

Based upon the Bonn meson-exchange model for the nucleon-nucleon ($NN$) interaction, we study systematically the charge-symmetry-breaking (CSB) of the $NN$ interaction due to nucleon mass splitting. Particular attention is payed to CSB generated by the $2\pi$-exchange contribution to the $NN$ interaction, $\pi\rho$ diagrams, and other multi-meson-exchanges. We calculate the CSB differences in the $^1S_0$ effective range parameters as well as phase shift differences in $S$, $P$ and higher partial waves up to 300 MeV lab. energy. We find a total CSB difference in the singlet scattering length of 1.6 fm which explains the empirical value accurately. The corresponding CSB phase-shift differences are appreciable at low energy in the $^1S_0$ state. In the other partial waves, the CSB splitting of the phase shifts is small and increases with energy, with typical values in the order of 0.1 deg at 300 MeV in $P$ and $D$ waves.Comment: 11 pages, RevTex, 14 figure

Magnetic vortex oscillator driven by dc spin-polarized current

Transfer of angular momentum from a spin-polarized current to a ferromagnet provides an efficient means to control the dynamics of nanomagnets. A peculiar consequence of this spin-torque, the ability to induce persistent oscillations of a nanomagnet by applying a dc current, has previously been reported only for spatially uniform nanomagnets. Here we demonstrate that a quintessentially nonuniform magnetic structure, a magnetic vortex, isolated within a nanoscale spin valve structure, can be excited into persistent microwave-frequency oscillations by a spin-polarized dc current. Comparison to micromagnetic simulations leads to identification of the oscillations with a precession of the vortex core. The oscillations, which can be obtained in essentially zero magnetic field, exhibit linewidths that can be narrower than 300 kHz, making these highly compact spin-torque vortex oscillator devices potential candidates for microwave signal-processing applications, and a powerful new tool for fundamental studies of vortex dynamics in magnetic nanostructures.Comment: 14 pages, 4 figure

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur