A Deep Reinforcement Learning Approach to Rare Event Estimation

An important step in the design of autonomous systems is to evaluate the probability that a failure will occur. In safety-critical domains, the failure probability is extremely small so that the evaluation of a policy through Monte Carlo sampling is inefficient. Adaptive importance sampling approaches have been developed for rare event estimation but do not scale well to sequential systems with long horizons. In this work, we develop two adaptive importance sampling algorithms that can efficiently estimate the probability of rare events for sequential decision making systems. The basis for these algorithms is the minimization of the Kullback-Leibler divergence between a state-dependent proposal distribution and a target distribution over trajectories, but the resulting algorithms resemble policy gradient and value-based reinforcement learning. We apply multiple importance sampling to reduce the variance of our estimate and to address the issue of multi-modality in the optimal proposal distribution. We demonstrate our approach on a control task with both continuous and discrete actions spaces and show accuracy improvements over several baselines

Credibility perceptions of content contributors and consumers in social media

This panel addresses information credibility issues in the context of social media. During this panel, participants will discuss people's credibility perceptions of online content in social media from the perspectives of both content contributors and consumers. Each panelist will bring her own perspective on credibility issues in various social media, including Twitter (Morris), Wikipedia (Metzger; Francke), blogs (Rieh), and social Q&A (Jeon). This panel aims to flesh out multi‐disciplinary approaches to the investigation of credibility and discuss integrated conceptual frameworks and future research directions focusing on assessing and establishing credibility in social media.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111174/1/meet14505101022.pd

Prostate Surgery for Men with Lower Urinary Tract Symptoms: Do We Need Urodynamics to Find the Right Candidates?:Exploratory Findings from the UPSTREAM Trial

BackgroundIdentifying men whose lower urinary tract symptoms (LUTS) may benefit from surgery is challenging.ObjectiveTo identify routine diagnostic and urodynamic measures associated with treatment decision-making, and outcome, in exploratory analyses of the UPSTREAM trial.Design, setting, and participantsA randomised controlled trial was conducted including 820 men, considering surgery for LUTS, across 26 hospitals in England (ISCTRN56164274).InterventionMen were randomised to a routine care (RC) diagnostic pathway (n = 393) or a pathway that included urodynamics (UDS) in addition to RC (n = 427).Outcome measurements and statistical analysisMen underwent uroflowmetry and completed symptom questionnaires, at baseline and 18 mo after randomisation. Regression models identified baseline clinical and symptom measures that predicted recommendation for surgery and/or surgical outcome (measured by the International Prostate Symptom Score [IPSS]). We explored the association between UDS and surgical outcome in subgroups defined by routine measures.Results and limitationsThe recommendation for surgery could be predicted successfully in the RC and UDS groups (area under the receiver operating characteristic curve 0.78), with maximum flow rate (Qmax) and age predictors in both groups. Surgery was more beneficial in those with higher symptom scores (eg, IPSS >16), age 47.6, and bladder contractility index >123.0. In the UDS group, urodynamic measures were more strongly predictive of surgical outcome for those with Qmax >15, although patient-reported outcomes were also more predictive in this subgroup.ConclusionsTreatment decisions were informed with UDS, when available, but without evidence of change in the decisions reached. Despite the small group sizes, exploratory analyses suggest that selective use of UDS could detect obstructive pathology, missed by routine measures, in certain subgroups.Patient summaryBaseline clinical and symptom measurements were able to predict treatment decisions. The addition of urodynamic test results, while useful, did not generally lead to better surgical decisions and outcomes over routine tests alone

Monitoring Observations of the Jupiter-Family Comet 17P/Holmes during 2014 Perihelion Passage

We performed a monitoring observation of a Jupiter-Family comet, 17P/Holmes, during its 2014 perihelion passage to investigate its secular change in activity. The comet has drawn the attention of astronomers since its historic outburst in 2007, and this occasion was its first perihelion passage since then. We analyzed the obtained data using aperture photometry package and derived the Afrho parameter, a proxy for the dust production rate. We found that Afrho showed asymmetric properties with respect to the perihelion passage: it increased moderately from 100 cm at the heliocentric distance r_h=2.6-3.1 AU to a maximal value of 185 cm at r_h = 2.2 AU (near the perihelion) during the inbound orbit, while dropping rapidly to 35 cm at r_h = 3.2 AU during the outbound orbit. We applied a model for characterizing dust production rates as a function of r_h and found that the fractional active area of the cometary nucleus had dropped from 20%-40% in 2008-2011 (around the aphelion) to 0.1%-0.3% in 2014-2015 (around the perihelion). This result suggests that a dust mantle would have developed rapidly in only one orbital revolution around the sun. Although a minor eruption was observed on UT 2015 January 26 at r_h = 3.0 AU, the areas excavated by the 2007 outburst would be covered with a layer of dust (<~ 10 cm depth) which would be enough to insulate the subsurface ice and to keep the nucleus in a state of low activity.Comment: 25 pages, 6 figures, 2 tables, ApJ accepted on December 29, 201

Two-Qubit Gate Set Tomography with Fewer Circuits

Gate set tomography (GST) is a self-consistent and highly accurate method for the tomographic reconstruction of a quantum information processor's quantum logic operations, including gates, state preparations, and measurements. However, GST's experimental cost grows exponentially with qubit number. For characterizing even just two qubits, a standard GST experiment may have tens of thousands of circuits, making it prohibitively expensive for platforms. We show that, because GST experiments are massively overcomplete, many circuits can be discarded. This dramatically reduces GST's experimental cost while still maintaining GST's Heisenberg-like scaling in accuracy. We show how to exploit the structure of GST circuits to determine which ones are superfluous. We confirm the efficacy of the resulting experiment designs both through numerical simulations and via the Fisher information for said designs. We also explore the impact of these techniques on the prospects of three-qubit GST.Comment: 46 pages, 13 figures. V2: Minor edits to acknowledgment

Spurious Shear in Weak Lensing with LSST

The complete 10-year survey from the Large Synoptic Survey Telescope (LSST) will image $\sim$ 20,000 square degrees of sky in six filter bands every few nights, bringing the final survey depth to $r\sim27.5$, with over 4 billion well measured galaxies. To take full advantage of this unprecedented statistical power, the systematic errors associated with weak lensing measurements need to be controlled to a level similar to the statistical errors. This work is the first attempt to quantitatively estimate the absolute level and statistical properties of the systematic errors on weak lensing shear measurements due to the most important physical effects in the LSST system via high fidelity ray-tracing simulations. We identify and isolate the different sources of algorithm-independent, \textit{additive} systematic errors on shear measurements for LSST and predict their impact on the final cosmic shear measurements using conventional weak lensing analysis techniques. We find that the main source of the errors comes from an inability to adequately characterise the atmospheric point spread function (PSF) due to its high frequency spatial variation on angular scales smaller than $\sim10'$ in the single short exposures, which propagates into a spurious shear correlation function at the $10^{-4}$--$10^{-3}$ level on these scales. With the large multi-epoch dataset that will be acquired by LSST, the stochastic errors average out, bringing the final spurious shear correlation function to a level very close to the statistical errors. Our results imply that the cosmological constraints from LSST will not be severely limited by these algorithm-independent, additive systematic effects.Comment: 22 pages, 12 figures, accepted by MNRA