Reproducing Business Cycle Features: How Important Is Nonlinearity Versus Multivariate Information?

In this paper, we consider the ability of time-series models to generate simulated data that display the same business cycle features found in U.S. real GDP. Our analysis of a range of popular time-series models allows us to investigate the extent to which multivariate information can account for the apparent univariate evidence of nonlinear dynamics in GDP. We find that certain nonlinear specifications yield an improvement over linear models in reproducing business cycle features, even when multivariate information inherent in the unemployment rate, inflation, interest rates, and the components of GDP is taken into account.

On Designing of a Low Leakage Patient-Centric Provider Network

When a patient in a provider network seeks services outside of their community, the community experiences a leakage. Leakage is undesirable as it typically leads to higher out-of-network cost for patient and increases barrier for care coordination, which is particularly problematic for Accountable Care Organization (ACO) as the in-network providers are financially responsible for patient quality and outcome. We aim to design a data-driven method to identify naturally occurring provider networks driven by diabetic patient choices, and understand the relationship among provider composition, patient composition, and service leakage pattern. We construct a healthcare provider network based on patients' historical medical insurance claims. A community detection algorithm is used to identify naturally occurring communities of collaborating providers. Finally, import-export analysis is conducted to benchmark their leakage pattern and identify further leakage reduction opportunity. The design yields six major provider communities with diverse profiles. Some communities are geographically concentrated, while others tend to draw patients with certain diabetic co-morbidities. Providers from the same healthcare institution are likely to be assigned to the same community. While most communities have high within-community utilization and spending, at 85% and 86% respectively, leakage still persists. Hence, we utilize a metric from import-export analysis to detect leakage, gaining insight on how to minimizing leakage. In conclusion, we identify patient-driven provider organization by surfacing providers who share a large number of patients. By analyzing the import-export behavior of each identified community using a novel approach and profiling community patient and provider composition we understand the key features of having a balanced number of PCP and specialists and provider heterogeneity

MOON: MapReduce On Opportunistic eNvironments

Abstract—MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer computing systems, which opportunistically harness idle desktop computers via frameworks like Condor, it results in poor performance due to the volatility of the resources, in particular, the high rate of node unavailability. Specifically, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. The adaptive task and data scheduling algorithms in MOON distinguish between (1) different types of MapReduce data and (2) different types of node outages in order to strategically place tasks and data on both volatile and dedicated nodes. Our tests demonstrate that MOON can deliver a 3-fold performance improvement to Hadoop in volatile, volunteer computing environments

The adaptive nature of the bone-periodontal ligament-cementum complex in a ligature-induced periodontitis rat model.

The novel aspect of this study involves illustrating significant adaptation of a functionally loaded bone-PDL-cementum complex in a ligature-induced periodontitis rat model. Following 4, 8, and 15 days of ligation, proinflammatory cytokines (TNF- α and RANKL), a mineral resorption indicator (TRAP), and a cell migration and adhesion molecule for tissue regeneration (fibronectin) within the complex were localized and correlated with changes in PDL-space (functional space). At 4 days of ligation, the functional space of the distal complex was widened compared to controls and was positively correlated with an increased expression of TNF- α. At 8 and 15 days, the number of RANKL(+) cells decreased near the mesial alveolar bone crest (ABC) but increased at the distal ABC. TRAP(+) cells on both sides of the complex significantly increased at 8 days. A gradual change in fibronectin expression from the distal PDL-secondary cementum interfaces through precementum layers was observed when compared to increased and abrupt changes at the mesial PDL-cementum and PDL-bone interfaces in ligated and control groups. Based on our results, we hypothesize that compromised strain fields can be created in a diseased periodontium, which in response to prolonged function can significantly alter the original bone and apical cementum formations

Generalized Monotonic Functional Mixed Models with Application to Modeling Normal Tissue Complications

Normal tissue complications are a common side effect of radiation therapy. They are the consequence of the dose of radiation received by the normal tissue surrounding the tumor site. It is not known what function of the dose distribution to the normal tissue drives the presence and severity of the complications. Regarding the density of the dose distribution as a curve, a summary measure is obtained by integrating a weighting function of dose (w(d)) over the dose density. For biological reasons the weight function should be monotonic. We propose to study the dose effect on a clinical outcome using a nonparametric method by estimating this weight function smoothly and subject to the monotonicity constraint. In our approach w(d) is written as a integral of a smooth positive function of d. We illustrate our method with data from a head and neck cancer study in which the irradiation of the parotid gland results in loss of saliva flow

Importance Sampling for Evaluation of Video Transcoder Performance

Optimization of a video transcoder is performed, e.g., by fine-tuning their parameters, based on evaluation of the performance of a transcoder over a small, fixed video dataset. The use of a small, fixed video dataset enables reproducibility, fast evaluation, and regression testing. However, transcoders that are fine-tuned based on a small, fixed dataset can often deliver suboptimal transcoding performance when utilized to transcode videos from a much larger dataset, e.g., videos served by a video hosting and sharing service. This is because a small, fixed set of videos is not sufficiently representative of the total corpus of videos hosted by a video sharing service and does not cover the scale and diversity of such videos. This disclosure describes the use of importance sampling in the evaluation of video transcoders using a small dataset of videos. The techniques can deliver a high-performance transcoder even when the transcoder is optimized using a small dataset that is insufficiently representative of a large-scale video corpus

The Taiwan ECDFS Near-Infrared Survey: Ultra-deep J and Ks Imaging in the Extended Chandra Deep Field-South

We present ultra-deep J and Ks imaging observations covering a 30' * 30' area of the Extended Chandra Deep Field-South (ECDFS) carried out by our Taiwan ECDFS Near-Infrared Survey (TENIS). The median 5-sigma limiting magnitudes for all detected objects in the ECDFS reach 24.5 and 23.9 mag (AB) for J and Ks, respectively. In the inner 400 arcmin^2 region where the sensitivity is more uniform, objects as faint as 25.6 and 25.0 mag are detected at 5-sigma. So this is by far the deepest J and Ks datasets available for the ECDFS. To combine the TENIS with the Spitzer IRAC data for obtaining better spectral energy distributions of high-redshift objects, we developed a novel deconvolution technique (IRACLEAN) to accurately estimate the IRAC fluxes. IRACLEAN can minimize the effect of blending in the IRAC images caused by the large point-spread functions and reduce the confusion noise. We applied IRACLEAN to the images from the Spitzer IRAC/MUSYC Public Legacy in the ECDFS survey (SIMPLE) and generated a J+Ks selected multi-wavelength catalog including the photometry of both the TENIS near-infrared and the SIMPLE IRAC data. We publicly release the data products derived from this work, including the J and Ks images and the J+Ks selected multiwavelength catalog.Comment: 25 pages, 25 figures, ApJS in pres

Myelodysplasia-associated mutations in serine/arginine-rich splicing factor SRSF2 lead to alternative splicing of CDC25C

Additional file 2: Figure S1. HA-tagged SRSF2 protein expression in uninduced TF-1 cell lines