Soft Gluon Approach for Diffractive Photoproduction of J/psi

We study diffractive photoproduction of $J/\psi$ by taking the charm quark as a heavy quark. A description of nonperturbative effect related to $J/\psi$ can be made by using NRQCD. In the forward region of the kinematics, the interaction between the $c\bar c$-pair and the initial hadron is due to exchange of soft gluons. The effect of the exchange can be studied by using the expansion in the inverse of the quark mass $m_c$. At the leading order we find that the nonperturbative effect related to the initial hadron is represented by a matrix element of field strength operators, which are separated in the moving direction of $J/\psi$ in the space-time. The S-matrix element is then obtained without using perturbative QCD and the results are not based on any model. Corrections to the results can be systematically added. Keeping the dominant contribution of the S-matrix element in the large energy limit we find that the imaginary part of the S-matrix element is related to the gluon distribution for $x\to 0$ with a reasonable assumption, the real part can be obtained with another approximation or with dispersion relation. Our approach is different than previous approaches and also our results are different than those in these approaches. The differences are discussed in detail. A comparison with experiment is also made and a qualitative agreement is found.Comment: 25 pages, 6 figures. Tiny changes in two figures, conclusion and text unchanged, accpeted by Nucl. Phys.

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets

Combined automotive safety and security pattern engineering approach

Automotive systems will exhibit increased levels of automation as well as ever tighter integration with other vehicles, traffic infrastructure, and cloud services. From safety perspective, this can be perceived as boon or bane - it greatly increases complexity and uncertainty, but at the same time opens up new opportunities for realizing innovative safety functions. Moreover, cybersecurity becomes important as additional concern because attacks are now much more likely and severe. However, there is a lack of experience with security concerns in context of safety engineering in general and in automotive safety departments in particular. To address this problem, we propose a systematic pattern-based approach that interlinks safety and security patterns and provides guidance with respect to selection and combination of both types of patterns in context of system engineering. A combined safety and security pattern engineering workflow is proposed to provide systematic guidance to support non-expert engineers based on best practices. The application of the approach is shown and demonstrated by an automotive case study and different use case scenarios.EC/H2020/692474/EU/Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems/AMASSEC/H2020/737422/EU/Secure COnnected Trustable Things/SCOTTEC/H2020/732242/EU/Dependability Engineering Innovation for CPS - DEIS/DEISBMBF, 01IS16043, Collaborative Embedded Systems (CrESt