Post-mission Viking data anaysis

Three Mars data analysis projects from the Viking Mars program were identified initially, and three more came into being as the work proceeded. All together, these six pertained to: (1) the veritical distribution of scattering particles in the Martian atmosphere at various locations in various seasons, (2) the physical parameters that define photometric properties of the Martian surface and atmosphere, (3) patterns of dust-cloud and global dust-storm development, (4) a direct comparison of near-simultaneous Viking and ground-based observations, (5) the annual formation and dissipation of polar frost caps, and (6) evidence concerning possible present-day volcanism or venting. A list of publications pertaining to the appropriate projects is included

A new source detection algorithm using FDR

The False Discovery Rate (FDR) method has recently been described by Miller et al (2001), along with several examples of astrophysical applications. FDR is a new statistical procedure due to Benjamini and Hochberg (1995) for controlling the fraction of false positives when performing multiple hypothesis testing. The importance of this method to source detection algorithms is immediately clear. To explore the possibilities offered we have developed a new task for performing source detection in radio-telescope images, Sfind 2.0, which implements FDR. We compare Sfind 2.0 with two other source detection and measurement tasks, Imsad and SExtractor, and comment on several issues arising from the nature of the correlation between nearby pixels and the necessary assumption of the null hypothesis. The strong suggestion is made that implementing FDR as a threshold defining method in other existing source-detection tasks is easy and worthwhile. We show that the constraint on the fraction of false detections as specified by FDR holds true even for highly correlated and realistic images. For the detection of true sources, which are complex combinations of source-pixels, this constraint appears to be somewhat less strict. It is still reliable enough, however, for a priori estimates of the fraction of false source detections to be robust and realistic.Comment: 17 pages, 7 figures, accepted for publication by A

Rydberg transition frequencies from the Local Density Approximation

A method is given that extracts accurate Rydberg excitations from LDA density functional calculations, despite the short-ranged potential. For the case of He and Ne, the asymptotic quantum defects predicted by LDA are in less than 5% error, yielding transition frequency errors of less than 0.1eV.Comment: 4 pages, 6 figures, submitted to Phys. Rev. Let

De-biased Populations of Kuiper Belt Objects from the Deep Ecliptic Survey

The Deep Ecliptic Survey (DES) discovered hundreds of Kuiper Belt objects from 1998-2005. Follow-up observations yielded 304 objects with good dynamical classifications (Classical, Scattered, Centaur, or 16 mean-motion resonances with Neptune). The DES search fields are well documented, enabling us to calculate the probability of detecting objects with particular orbital parameters and absolute magnitudes at a randomized point in each orbit. Grouping objects together by dynamical class leads, we estimate the orbital element distributions (a, e, i) for the largest three classes (Classical, 3:2, and Scattered) using maximum likelihood. Using H-magnitude as a proxy for the object size, we fit a power law to the number of objects for 8 classes with at least 5 detected members (246 objects). The best Classical slope is alpha=1.02+/-0.01 (observed from 5<=H<=7.2). Six dynamical classes (Scattered plus 5 resonances) are consistent in slope with the Classicals, though the absolute number of objects is scaled. The exception to the power law relation are the Centaurs (non-resonant with perihelia closer than Neptune, and thus detectable at smaller sizes), with alpha=0.42+/-0.02 (7.5<H<11). This is consistent with a knee in the H-distribution around H=7.2 as reported elsewhere (Bernstein et al. 2004, Fraser et al. 2014). Based on the Classical-derived magnitude distribution, the total number of objects (H<=7) in each class are: Classical (2100+/-300 objects), Scattered (2800+/-400), 3:2 (570+/-80), 2:1 (400+/-50), 5:2 (270+/-40), 7:4 (69+/-9), 5:3 (60+/-8). The independent estimate for the number of Centaurs in the same H range is 13+/-5. If instead all objects are divided by inclination into "Hot" and "Cold" populations, following Fraser et al. (2014), we find that alphaHot=0.90+/-0.02, while alphaCold=1.32+/-0.02, in good agreement with that work.Comment: 26 pages emulateapj, 6 figures, 5 tables, accepted by A

Managing Risk of Bidding in Display Advertising

In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on a commercial RTB platform over the widely applied bidding strategies

How to project a bipartite network?

The one-mode projecting is extensively used to compress the bipartite networks. Since the one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method in compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do personal recommendation?Comment: 7 pages, 4 figure

Regularization independent of the noise level: an analysis of quasi-optimality

The quasi-optimality criterion chooses the regularization parameter in inverse problems without taking into account the noise level. This rule works remarkably well in practice, although Bakushinskii has shown that there are always counterexamples with very poor performance. We propose an average case analysis of quasi-optimality for spectral cut-off estimators and we prove that the quasi-optimality criterion determines estimators which are rate-optimal {\em on average}. Its practical performance is illustrated with a calibration problem from mathematical finance.Comment: 18 pages, 3 figure