Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing

We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in intensity functions. We construct a modified Bayesian information criterion (mBIC) to guide model selection, and point-wise approximate Bayesian confidence intervals for assessing the confidence in the segmentation. The model is applied to DNA Copy Number profiling with sequencing data and evaluated on simulated spike-in and real data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS517 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic

Language modeling for an inflected language such as Arabic poses new challenges for speech recognition and machine translation due to its rich morphology. Rich morphology results in large increases in out-of-vocabulary (OOV) rate and poor language model parameter estimation in the absence of large quantities of data. In this study, we present a joint morphological-lexical language model (JMLLM) that takes advantage of Arabic morphology. JMLLM combines morphological segments with the underlying lexical items and additional available information sources with regards to morphological segments and lexical items in a single joint model. Joint representation and modeling of morphological and lexical items reduces the OOV rate and provides smooth probability estimates while keeping the predictive power of whole words. Speech recognition and machine translation experiments in dialectal-Arabic show improvements over word and morpheme based trigram language models. We also show that as the tightness of integration between different information sources increases, both speech recognition and machine translation performances improve

The Heart of the Matter: The Relationship Between Communities, Cardiovascular Services and Racial and Ethnic Gaps in Care

As part of an initiative to address racial/ethnic disparities in the diagnosis and treatment of heart disease, examines factors behind the segmentation of healthcare access and service patterns by income and insurance status and its effect on minorities

Cross-listing Premium in the US and the UK Destination

This paper tests the main hypothesis that firms that cross-list have higher valuations, and provides on the valuation effect of cross-listing on a major non-US market, the UK compared to the US market from source countries in the Asia-Pacific region in 2003-2004. We find evidence that there is a cross-listing premium in both markets. However, the evidence on whethr the premium is significantly different in the two countries is mixed. Using univariate, OLS and random effects methods, we find some evidence that the premium in the US is higher, but using a treatment effect methodology we find that the differecne is not robust. that offers new techniques and data sources.

Automated detection of extended sources in radio maps: progress from the SCORPIO survey

Automated source extraction and parameterization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parameterization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, including also different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU survey. The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure

The Effect of State Community Rating Regulations on Premiums and Coverage in the Individual Health Insurance Market

Some states have implemented community rating regulations to limit the extent to which premiums in the individual health insurance market can vary with a person�s health status. Community rating and guaranteed issues laws were passed with hopes of increasing access to affordable insurance for people with high-risk health conditions, but there are concerns that these laws led to adverse selection. In some sense, the extent to which these regulations ultimately affected the individual market depends in large part on the degree of risk segmentation in unregulated states. In this paper, we examine the relationship between expected medical expenses, individual insurance premiums, and the likelihood of obtaining individual insurance using data from both the National Health Interview Survey and the Community Tracking Study Household Survey. We test for differences in these relationships between states with both community rating and guaranteed issue and states with no such regulations. While we find that people living in unregulated states with higher expected expense due to chronic health conditions pay modestly higher premiums and are somewhat less likely to obtain coverage, the variation between premiums and risk in unregulated individual insurance markets is far from proportional; there is considerable pooling. In regulated states, we find that there is no effect of having higher expected expense due to chronic health conditions on neither premiums nor coverage. Overall, our results suggest that the effect of regulation is to produce a slight increase in the proportion uninsured, as increases in low risk uninsureds more than offset decreases in high risk uninsureds. Community rating and guaranteed issue regulations produce only small changes in risk pooling because the extent of pooling in the absence of regulation is substantial.

Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary

We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i.e., introducing new vocabulary items if they are not included in the initial model). The parameter transfer mechanism is evaluated in two scenarios: i) to adapt a trained single language NMT system to work with a new language pair and ii) to continuously add new language pairs to grow to a multilingual NMT system. In both the scenarios our goal is to improve the translation performance, while minimizing the training convergence time. Preliminary experiments spanning five languages with different training data sizes (i.e., 5k and 50k parallel sentences) show a significant performance gain ranging from +3.85 up to +13.63 BLEU in different language directions. Moreover, when compared with training an NMT model from scratch, our transfer-learning approach allows us to reach higher performance after training up to 4% of the total training steps.Comment: Published at the International Workshop on Spoken Language Translation (IWSLT), 201

Restructuring Health Insurance Markets

Examines six possible structural changes to the health insurance market to expand coverage, including rate compression, high-risk pools, and an insurance exchange. Outlines their benefits and the most effective way to structure and implement them

Does expanding health insurance beyond formal-sector workers encourage informality ? measuring the impact of Mexico's Seguro Popular

Seguro Popular was introduced in 2002 to provide health insurance to the 50 million Mexicans without Social Security. This paper tests whether the program has had unintended consequences, distorting workers'incentives to operate in the informal sector. The analysis examines the impact of Seguro Popular on disaggregated labor market decisions, taking into account that program coverage depends not only on the individual's employment status, but also that of other household members. The identification strategy relies on the variation in Seguro Popular's rollout across municipalities and time, with the difference-in-difference estimation controlling for household fixed effects. The paper finds that Seguro Popular lowers formality by 0.4-0.7 percentage points, with adjustments largely occurring within a few years of the program's introduction. Rather than encouraging exit from the formal sector, Seguro Popular is associated with a 3.1 percentage point reduction (a 20 percent decline) in the inflow of workers into formality. Income effects are also apparent, with significantly decreased flows out of unemployment and lower labor force participation. The impact is larger for those with less education, in larger households, and with someone else in the household guaranteeing Social Security coverage. However, workers pay for part of these benefits with lower wages in the informal sector.Health Monitoring&Evaluation,Labor Markets,Labor Policies,Housing&Human Habitats,Population Policies