Semantic Stability in Social Tagging Streams

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language streams alone

The Atacama Cosmology Telescope: Data Characterization and Map Making

We present a description of the data reduction and mapmaking pipeline used for the 2008 observing season of the Atacama Cosmology Telescope (ACT). The data presented here at 148 GHz represent 12% of the 90 TB collected by ACT from 2007 to 2010. In 2008 we observed for 136 days, producing a total of 1423 hours of data (11 TB for the 148 GHz band only), with a daily average of 10.5 hours of observation. From these, 1085 hours were devoted to a 850 deg^2 stripe (11.2 hours by 9.1 deg) centered on a declination of -52.7 deg, while 175 hours were devoted to a 280 deg^2 stripe (4.5 hours by 4.8 deg) centered at the celestial equator. We discuss sources of statistical and systematic noise, calibration, telescope pointing, and data selection. Out of 1260 survey hours and 1024 detectors per array, 816 hours and 593 effective detectors remain after data selection for this frequency band, yielding a 38% survey efficiency. The total sensitivity in 2008, determined from the noise level between 5 Hz and 20 Hz in the time-ordered data stream (TOD), is 32 micro-Kelvin sqrt{s} in CMB units. Atmospheric brightness fluctuations constitute the main contaminant in the data and dominate the detector noise covariance at low frequencies in the TOD. The maps were made by solving the least-squares problem using the Preconditioned Conjugate Gradient method, incorporating the details of the detector and noise correlations. Cross-correlation with WMAP sky maps, as well as analysis from simulations, reveal that our maps are unbiased at multipoles ell > 300. This paper accompanies the public release of the 148 GHz southern stripe maps from 2008. The techniques described here will be applied to future maps and data releases.Comment: 20 pages, 18 figures, 6 tables, an ACT Collaboration pape

Sequential Quantiles via Hermite Series Density Estimation

Sequential quantile estimation refers to incorporating observations into quantile estimates in an incremental fashion thus furnishing an online estimate of one or more quantiles at any given point in time. Sequential quantile estimation is also known as online quantile estimation. This area is relevant to the analysis of data streams and to the one-pass analysis of massive data sets. Applications include network traffic and latency analysis, real time fraud detection and high frequency trading. We introduce new techniques for online quantile estimation based on Hermite series estimators in the settings of static quantile estimation and dynamic quantile estimation. In the static quantile estimation setting we apply the existing Gauss-Hermite expansion in a novel manner. In particular, we exploit the fact that Gauss-Hermite coefficients can be updated in a sequential manner. To treat dynamic quantile estimation we introduce a novel expansion with an exponentially weighted estimator for the Gauss-Hermite coefficients which we term the Exponentially Weighted Gauss-Hermite (EWGH) expansion. These algorithms go beyond existing sequential quantile estimation algorithms in that they allow arbitrary quantiles (as opposed to pre-specified quantiles) to be estimated at any point in time. In doing so we provide a solution to online distribution function and online quantile function estimation on data streams. In particular we derive an analytical expression for the CDF and prove consistency results for the CDF under certain conditions. In addition we analyse the associated quantile estimator. Simulation studies and tests on real data reveal the Gauss-Hermite based algorithms to be competitive with a leading existing algorithm.Comment: 43 pages, 9 figures. Improved version incorporating referee comments, as appears in Electronic Journal of Statistic

Weakly Supervised Action Localization by Sparse Temporal Pooling Network

We propose a weakly supervised temporal action localization algorithm on untrimmed videos using convolutional neural networks. Our algorithm learns from video-level class labels and predicts temporal intervals of human actions with no requirement of temporal localization annotations. We design our network to identify a sparse subset of key segments associated with target actions in a video using an attention module and fuse the key segments through adaptive temporal pooling. Our loss function is comprised of two terms that minimize the video-level action classification error and enforce the sparsity of the segment selection. At inference time, we extract and score temporal proposals using temporal class activations and class-agnostic attentions to estimate the time intervals that correspond to target actions. The proposed algorithm attains state-of-the-art results on the THUMOS14 dataset and outstanding performance on ActivityNet1.3 even with its weak supervision.Comment: Accepted to CVPR 201

Globular Cluster Streams as Galactic High-Precision Scales - The Poster Child Palomar 5

Using the example of the tidal stream of the Milky Way globular cluster Palomar 5 (Pal 5), we demonstrate how observational data on streams can be efficiently reduced in dimensionality and modeled in a Bayesian framework. Our approach combines detection of stream overdensities by a Difference-of-Gaussians process with fast streakline models, a continuous likelihood function built from these models, and inference with MCMC. By generating $\approx10^7$ model streams, we show that the geometry of the Pal 5 debris yields powerful constraints on the solar position and motion, the Milky Way and Pal 5 itself. All 10 model parameters were allowed to vary over large ranges without additional prior information. Using only SDSS data and a few radial velocities from the literature, we find that the distance of the Sun from the Galactic Center is $8.30\pm0.25$ kpc, and the transverse velocity is $253\pm16$ km/s. Both estimates are in excellent agreement with independent measurements of these quantities. Assuming a standard disk and bulge model, we determine the Galactic mass within Pal 5's apogalactic radius of 19 kpc to be $(2.1\pm0.4)\times10^{11}$ M$_\odot$. Moreover, we find the potential of the dark halo with a flattening of $q_z = 0.95^{+0.16}_{-0.12}$ to be essentially spherical within the radial range that is effectively probed by Pal 5. We also determine Pal 5's mass, distance and proper motion independently from other methods, which enables us to perform vital cross-checks. We conclude that with more observational data and by using additional prior information, the precision of this method can be significantly increased.Comment: 28 pages, 14 figures, submitted to ApJ (revised version), comments welcom