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

Millimetre and submillimetre molecular line observations of the reflection nebula NGC 2023

Observations in the CO J = 2 - 1, CO J = 3 - 2 and HCO+ J = 4 - 3 transitions of the molecular cloud associated with NGC 2023 are presented. The observations reveal the complex structure of the gas in the surrounding cloud, and show the presence of several hot-spots which may represent separate bodies of gas. A search has been made for the source of excitation of two nearby groups of Herbig-Haro objects recently discovered by Malin et al. (1987). No such objects can be clearly identified from the data. CO J = 3 - 2 spectra taken at positions lying on the CO J = 1 - 0 shell observed by Gatley et al. (1987) show marked enhancements in peak line strength relative to coincident CO J = 2 - 1 data. By contrast, no such enhancements are observed away from the shell. Observations of the submillimeter wavelength HCO+ J = 4 - 3 transition show that the line strength is greatest in the vicinity of the shell structure. Simple large velocity gradient modeling of the excitation conditions of the shell material suggests that the gas may be hot (Tkin ~ 140 K), dense, and optically thin

Assessing the Effectiveness of Saving Incentives

In this paper, we argue that there is more to be learned from recent research on the effectiveness of targeted saving incentives than is suggested by the wide variation in empirical estimates. First, we conclude that characterizations of saving appear to stimulate moderate amounts of new saving. Second, we suggest a cost-benefit approach to ask: What is the incremental gain in capital accumulation per dollar of foregone revenue? We find that for quite conservative measures of the saving impacts of IRAs or 401(k)s, the incremental gains in capital accumulation per dollar of lost revenue are large

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust