Validating Network Value of Influencers by means of Explanations

Recently, there has been significant interest in social influence analysis. One of the central problems in this area is the problem of identifying influencers, such that by convincing these users to perform a certain action (like buying a new product), a large number of other users get influenced to follow the action. The client of such an application is a marketer who would target these influencers for marketing a given new product, say by providing free samples or discounts. It is natural that before committing resources for targeting an influencer the marketer would be interested in validating the influence (or network value) of influencers returned. This requires digging deeper into such analytical questions as: who are their followers, on what actions (or products) they are influential, etc. However, the current approaches to identifying influencers largely work as a black box in this respect. The goal of this paper is to open up the black box, address these questions and provide informative and crisp explanations for validating the network value of influencers. We formulate the problem of providing explanations (called PROXI) as a discrete optimization problem of feature selection. We show that PROXI is not only NP-hard to solve exactly, it is NP-hard to approximate within any reasonable factor. Nevertheless, we show interesting properties of the objective function and develop an intuitive greedy heuristic. We perform detailed experimental analysis on two real world datasets - Twitter and Flixster, and show that our approach is useful in generating concise and insightful explanations of the influence distribution of users and that our greedy algorithm is effective and efficient with respect to several baselines

W^+W^+ plus dijet production in the POWHEGBOX

We present an implementation of the calculation of the production of W^+W^+ plus two jets at hadron colliders, at next-to-leading order (NLO) in QCD, in the POWHEG framework, which is a method that allows the interfacing of NLO calculations to shower Monte Carlo programs. This is the first 2 -> 4 process to be described to NLO accuracy within a shower Monte Carlo framework. The implementation was built within the POWHEGBOX package. We discuss a few technical improvements that were needed in the POWHEGBOX to deal with the computer intensive nature of the NLO calculation, and argue that further improvements are possible, so that the method can match the complexity that is reached today in NLO calculations. We have interfaced our POWHEG implementation with PYTHIA and HERWIG, and present some phenomenological results, discussing similarities and differences between the pure NLO and the POWHEG+PYTHIA calculation both for inclusive and more exclusive distributions. We have made the relevant code available at the POWHEGBOX web site.Comment: 16 pages, 5 figure

Stability of Deep Neural Networks for Feedback-Optimal Pinpoint Landings

The ability to certify systems driven by neural networks is crucial for future rollouts of machine learning technologies in aerospace applications. In this study, the neural networks are used to represent a fuel-optimal feedback controller for two different 3-degree-of-freedom pinpoint landing problems. It is shown that the standard sum-ofsquares Lyapunov candidate is too restrictive to assess the stability of systems with fuel-optimal control profiles. Instead, a parametric Lyapunov candidate (i.e. a neural network) can be trained to sufficiently evaluate the closed-loop stability of fuel-optimal control profiles. Then, a stability-constrained imitation learning method is applied, which simultaneously trains a neural network policy and neural network Lyapunov function such that feedback-optimal control is achieved, and Lyapunov stability is verified. Phase-space plots of the Lyapunov derivative show the improvement in stability assessment provided by the neural network Lyapunov function, and Monte Carlo simulations demonstrate the stable, feedback-optimal control provided by the policy

Six-degree-of-freedom Optimal Feedback Control of Pinpoint Landing using Deep Neural Networks

Machine learning regression techniques have shown success at feedback control to perform near-optimal pinpoint landings for low fidelity formulations (e.g. 3 degree-of-freedom). Trajectories from these low-fidelity landing formulations have been used in imitation learning techniques to train deep neural network policies to replicate these optimal landings in closed loop. This study details the development of a near-optimal, neural network feedback controller for a 6 degree-of-freedom pinpoint landing system. To model disturbances, the problem is cast as either a multi-phase optimal control problem or a triple single-phase optimal control problem to generate examples of optimal control through the presence of disturbances. By including these disturbed examples and leveraging imitation learning techniques, the loss of optimality is reduced for pinpoint landing scenario

Fall vortex ozone as a predictor of springtime total ozone at high northern latitudes

Understanding the impact of atmospheric dynamical variability on observed changes in stratospheric O₃ is a key to understanding how O₃ will change with future climate dynamics and trace gas abundances. In this paper we examine the linkage between interannual variability in total column O₃ at northern high latitudes in March and lower-to-mid stratospheric vortex O₃ in the prior November. We find that these two quantities are significantly correlated in the years available from TOMS, SBUV, and POAM data (1978-2004). Additionally, we find that the increase in March O₃ variability from the 1980s to years post-1990 is also seen in the November vortex O₃, i.e., interannual variability in both quantities is much larger in the later years. The cause of this correlation is not clear, however. Interannual variations in March total O₃ are known to correspond closely with variations in winter stratospheric wave driving consistent with the effects of varying residual circulation, temperature, and chemical loss. Variation in November vortex O₃ may also depend on dynamical wave activity, but the dynamics in fall are less variable than in winter and spring. We do not find significant correlations of dynamic indicators for November such as temperature, heat flux, or polar average total O₃ with the November vortex O₃, nor with dynamical indicators later in winter and spring that might lead to a connection to March. We discuss several potential hypotheses for the observed correlation but do not find strong evidence for any considered mechanism. We present the observations as a phenomenon whose understanding may improve our ability to predict the dependence of O₃ on changing dynamics and chemistry

Phosphoric Acid Invasion in High Temperature PEM Fuel Cell Gas Diffusion Layers

In this work, liquid phosphoric acid was injected into polymer electrolyte membrane fuel cell (PEMFC) gas diffusion layers (GDLs) to visualize the invasion patterns developed at breakthrough. Three-dimensional (3D) images of the GDLs were obtained through X-ray computed tomography, and equivalent pore networks were generated as the basis for pore network simulations using OpenPNM. Strong qualitative agreement was obtained between the simulated and experimentally observed liquid phosphoric acid invasion patterns, which provided validation for the numerical modeling. Different GDL materials were evaluated by examining the effects of a micro porous layer (MPL) and pore size distribution on the saturation and distribution of phosphoric acid. The MPL was shown to restrict liquid phosphoric acid from entering the carbon fiber substrate. The overall phosphoric acid saturation at breakthrough was found to decrease significantly for samples containing an MPL due to the smaller pore sizes. Further, the influence of cracks in an MPL on overall saturation at breakthrough was investigated. It was observed that a crack-free MPL provided a more effective physical barrier to restrict the undesired leaching of liquid phosphoric acid through the GDL

Improvement of Tuberculosis Laboratory Capacity on Pemba Island, Zanzibar: A Health Cooperation Project.

Low-income countries with high Tuberculosis burden have few reference laboratories able to perform TB culture. In 2006, the Zanzibar National TB Control Programme planned to decentralize TB diagnostics. The Italian Cooperation Agency with the scientific support of the "L. Spallanzani" National Institute for Infectious Diseases sustained the project through the implementation of a TB reference laboratory in a low-income country with a high prevalence of TB. The implementation steps were: 1) TB laboratory design according to the WHO standards; 2) laboratory equipment and reagent supplies for microscopy, cultures, and identification; 3) on-the-job training of the local staff; 4) web- and telemedicine-based supervision. From April 2007 to December 2010, 921 sputum samples were received from 40 peripheral laboratories: 120 TB cases were diagnosed. Of all the smear-positive cases, 74.2% were culture-positive. During the year 2010, the smear positive to culture positive rate increased up to 100%. In March 20, 2010 the Ministry of Health and Social Welfare of Zanzibar officially recognized the Public Health Laboratory- Ivo de Carneri as the National TB Reference Laboratory for the Zanzibar Archipelago. An advanced TB laboratory can represent a low cost solution to strengthen the TB diagnosis, to provide capacity building and mid-term sustainability

A Tree-Loop Duality Relation at Two Loops and Beyond

The duality relation between one-loop integrals and phase-space integrals, developed in a previous work, is extended to higher-order loops. The duality relation is realized by a modification of the customary +i0 prescription of the Feynman propagators, which compensates for the absence of the multiple-cut contributions that appear in the Feynman tree theorem. We rederive the duality theorem at one-loop order in a form that is more suitable for its iterative extension to higher-loop orders. We explicitly show its application to two- and three-loop scalar master integrals, and we discuss the structure of the occurring cuts and the ensuing results in detail.Comment: 20 pages. Few typos corrected, some additional comments included, Appendix B and one reference added. Final version as published in JHE