Efficient Correlated Topic Modeling with Topic Embedding

Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.Comment: KDD 2017 oral. The first two authors contributed equall

Storm impact on sea surface temperature and chlorophyll α in the Gulf of Mexico and Sargasso Sea based on daily cloud-free satellite data reconstructions

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 43 (2016): 12,199–12,207, doi:10.1002/2016GL071178.Upper ocean responses to tropical storms/hurricanes have been extensively studied using satellite observations. However, resolving concurrent sea surface temperature (SST) and chlorophyll α (chl α) responses along storm tracks remains a major challenge due to extensive cloud coverage in satellite images. Here we produce daily cloud-free SST and chl α reconstructions based on the Data INterpolating Empirical Orthogonal Function method over a 10 year period (2003–2012) for the Gulf of Mexico and Sargasso Sea regions. Daily reconstructions allow us to characterize and contrast previously obscured subweekly SST and chl α responses to storms in the two main storm-impacted regions of the Atlantic Ocean. Statistical analyses of daily SST and chl α responses revealed regional differences in the response time as well as the response sensitivity to maximum sustained wind speed and translation speed. This study demonstrates that SST and chl α responses clearly depend on regional ocean conditions and are not as universal as might have been previously suggested.Gulf of Mexico Research Initiative/GISR Grant Number: 02-S130202; NOAA Grant Number: NA11NOS0120033; NASA Grant Numbers: NNX12AP84G, NNX13AD80G2017-06-1

Audit quality and properties of analysts’ information environment

© 2018 John Wiley & Sons Ltd We consider how audit quality impacts sell-side analysts’ information environment. Using the method outlined by Barron et al., we examine whether higher audit quality is associated with differences in the weight analysts place on common information relative to private information, as well as the extent to which audit quality separately impacts the precision of analysts’ private and common information. Our results show that, in instances where analysts revise their earnings forecasts for year t+1 shortly after the release of year t earnings, higher audit quality results in analysts placing more weight on public information. The precision of private (as well as public) information is improved. These results extend our understanding of how audit quality impacts on attributes of analysts’ forecasts and provides support for the argument that audit quality has important capital market implications

Preparing students for service-learning and social entrepreneurship experiences

[EN] A critical feature of contemporary models of civic engagement is mutually-beneficial collaboration between campus and community partners, in which all members contribute skills and experience to co-create knowledge. At any given time, multiple relationships require attention – for example, triadic relationships between students, faculty, and staff of community organizations. This model is relevant for both service-learning (SL) and social entrepreneurship (SE), as both seek to work with community partners or in the community to address challenges facing the community. To date, research involving students has focused on the impact of these learning opportunities on student development (e.g., academics, civic participation). For students to be true partners in SL and SE projects, however, we need to understand the reciprocity of these interactions, particularly how to prepare students can become collaborators in developing campus-community partnerships (i.e., participatory readiness). To promote participatory readiness among students, we argue for a competency-based framework that integrates research and recommendations from the fields of service-learning, social entrepreneurship, and educational leadership. Throughout the article, we discuss similarities and differences in SL and SE practices and draw attention to the implications of the work for community engagement and pedagogy in higher education.http://ocs.editorial.upv.es/index.php/HEAD/HEAD18Chung, H.; Taylor, K.; Nehila, C. (2018). Preparing students for service-learning and social entrepreneurship experiences. Editorial Universitat Politècnica de València. 1169-1177. https://doi.org/10.4995/HEAD18.2018.8171OCS1169117

In situ real-time analysis of alloy film composition and segregation dynamics with parallel detection reflection electron energy loss spectroscopy

Real-time measurements of GexSi1 – x/Si(001) composition and segregation dynamics in Sn/Si(001) in molecular beam epitaxy are demonstrated using parallel detection reflection electron energy loss spectroscopy. Parallel detection enables quantitative acquisition of low-loss spectra in a time of < 500 µs and surface composition determination in GexSi1 – x/Si(001) via Ge L2,3 core loss analysis to a precision of approximately 2% in time of order 1 s. Segregation and trapping kinetics of monolayer thickness Sn films during Si epitaxy on Sn-covered Si(100) has also been studied using the Sn M4.5 core loss

Modelling long-memory volatilities with leverage effect: A-LMSV versus FIEGARCH

A new stochastic volatility model, called A-LMSV, is proposed to cope simultaneously with leverage effect and long-memory in volatility. Its statistical properties are derived and compared with the properties of the FIEGARCH model. It is shown that the dependence of the autocorrelations of squares on the parameters measuring the asymmetry and the persistence is different in both models. The kurtosis and autocorrelations of squares do not depend on the asymmetry in the A-LMSV model while they increase with the asymmetry in the FIEGARCH model. Furthermore, the autocorrelations of squares increase with the persistence in the A-LMSV model and decrease in the FIEGARCH model. On the other hand, if the correlation between returns and future volatilities is negative, the autocorrelations of absolute returns increase with the magnitude of the asymmetry in the FIEGARCH model while they decrease in the A-LMSV model. Finally, the cross-correlations between squares and original observations are, in general, larger in absolute value in the FIEGARCH model than in the A-LMSV model. The results are illustrated by fitting both models to represent the dynamic evolution of volatilities of daily returns of the S&P500 and DAX indexes.Publicad