Discriminative Density-ratio Estimation

The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on developing Density-ratio (DR) estimation techniques that apply to both regression and classification problems. Although these methods work well for regression problems, their performance on classification problems is not satisfactory. This is due to a key observation that these methods focus on matching the sample marginal distributions without paying attention to preserving the separation between classes in the reweighted space. In this paper, we propose a novel method for Discriminative Density-ratio (DDR) estimation that addresses the aforementioned problem and aims at estimating the density-ratio of joint distributions in a class-wise manner. The proposed algorithm is an iterative procedure that alternates between estimating the class information for the test data and estimating new density ratio for each class. To incorporate the estimated class information of the test data, a soft matching technique is proposed. In addition, we employ an effective criterion which adopts mutual information as an indicator to stop the iterative procedure while resulting in a decision boundary that lies in a sparse region. Experiments on synthetic and benchmark datasets demonstrate the superiority of the proposed method in terms of both accuracy and robustness

Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce

The kernel $k$-means is an effective method for data clustering which extends the commonly-used $k$-means algorithm to work on a similarity matrix over complex data structures. The kernel $k$-means algorithm is however computationally very complex as it requires the complete data matrix to be calculated and stored. Further, the kernelized nature of the kernel $k$-means algorithm hinders the parallelization of its computations on modern infrastructures for distributed computing. In this paper, we are defining a family of kernel-based low-dimensional embeddings that allows for scaling kernel $k$-means on MapReduce via an efficient and unified parallelization strategy. Afterwards, we propose two methods for low-dimensional embedding that adhere to our definition of the embedding family. Exploiting the proposed parallelization strategy, we present two scalable MapReduce algorithms for kernel $k$-means. We demonstrate the effectiveness and efficiency of the proposed algorithms through an empirical evaluation on benchmark data sets.Comment: Appears in Proceedings of the SIAM International Conference on Data Mining (SDM), 201

Addition of 24‐hour heart rate variability parameters to the Cardiovascular Health Study stroke risk score and prediction of incident stroke: The Cardiovascular Health Study

Background Heart rate variability (HRV) characterizes cardiac autonomic functioning. The association of HRV with stroke is uncertain. We examined whether 24‐hour HRV added predictive value to the Cardiovascular Health Study clinical stroke risk score (CHS‐SCORE), previously developed at the baseline examination. Methods and Results N=884 stroke‐free CHS participants (age 75.3±4.6), with 24‐hour Holters adequate for HRV analysis at the 1994–1995 examination, had 68 strokes over ≤8 year follow‐up (median 7.3 [interquartile range 7.1–7.6] years). The value of adding HRV to the CHS‐SCORE was assessed with stepwise Cox regression analysis. The CHS‐SCORE predicted incident stroke (HR=1.06 per unit increment, P=0.005). Two HRV parameters, decreased coefficient of variance of NN intervals (CV%, P=0.031) and decreased power law slope (SLOPE, P=0.033) also entered the model, but these did not significantly improve the c‐statistic (P=0.47). In a secondary analysis, dichotomization of CV% (LOWCV% ≤12.8%) was found to maximally stratify higher‐risk participants after adjustment for CHS‐SCORE. Similarly, dichotomizing SLOPE (LOWSLOPE <−1.4) maximally stratified higher‐risk participants. When these HRV categories were combined (eg, HIGHCV% with HIGHSLOPE), the c‐statistic for the model with the CHS‐SCORE and combined HRV categories was 0.68, significantly higher than 0.61 for the CHS‐SCORE alone (P=0.02). Conclusions In this sample of older adults, 2 HRV parameters, CV% and power law slope, emerged as significantly associated with incident stroke when added to a validated clinical risk score. After each parameter was dichotomized based on its optimal cut point in this sample, their composite significantly improved prediction of incident stroke during ≤8‐year follow‐up. These findings will require validation in separate, larger cohorts. Keywords: autonomic nervous system, clinical stroke risk model, heart rate variability, prediction, predictors, risk prediction, risk stratification, strok

Pre- and post-processing for Cosmic/NASTRAN on personal computers and mainframes

An interface between Cosmic/NASTRAN and GIFTS has recently been released, combining the powerful pre- and post-processing capabilities of GIFTS with Cosmic/NASTRAN's analysis capabilities. The interface operates on a wide range of computers, even linking Cosmic/NASTRAN and GIFTS when the two are on different computers. GIFTS offers a wide range of elements for use in model construction, each translated by the interface into the nearest Cosmic/NASTRAN equivalent; and the options of automatic or interactive modelling and loading in GIFTS make pre-processing easy and effective. The interface itself includes the programs GFTCOS, which creates the Cosmic/NASTRAN input deck (and, if desired, control deck) from the GIFTS Unified Data Base, COSGFT, which translates the displacements from the Cosmic/NASTRAN analysis back into GIFTS; and HOSTR, which handles stress computations for a few higher-order elements available in the interface, but not supported by the GIFTS processor STRESS. Finally, the versatile display options in GIFTS post-processing allow the user to examine the analysis results through an especially wide range of capabilities, including such possibilities as creating composite loading cases, plotting in color and animating the analysis

Transcriptional responses of Biomphalaria pfeifferi and Schistosoma mansoni following exposure to niclosamide, with evidence for a synergistic effect on snails following exposure to both stressors.

BackgroundSchistosomiasis is one of the world's most common NTDs. Successful control operations often target snail vectors with the molluscicide niclosamide. Little is known about how niclosamide affects snails, including for Biomphalaria pfeifferi, the most important vector for Schistosoma mansoni in Africa. We used Illumina technology to explore how field-derived B. pfeifferi, either uninfected or harboring cercariae-producing S. mansoni sporocysts, respond to a sublethal treatment of niclosamide. This study afforded the opportunity to determine if snails respond differently to biotic or abiotic stressors, and if they reserve unique responses for when presented with both stressors in combination. We also examined how sporocysts respond when their snail host is treated with niclosamide.Principal findingsCercariae-producing sporocysts within snails treated with niclosamide express ~68% of the genes in the S. mansoni genome, as compared to 66% expressed by intramolluscan stages of S. mansoni in snails not treated with niclosamide. Niclosamide does not disable sporocysts nor does it seem to provoke from them distinctive responses associated with detoxifying a xenobiotic. For uninfected B. pfeifferi, niclosamide treatment alone increases expression of several features not up-regulated in infected snails including particular cytochrome p450s and heat shock proteins, glutathione-S-transferases, antimicrobial factors like LBP/BPI and protease inhibitors, and also provokes strong down regulation of proteases. Exposure of infected snails to niclosamide resulted in numerous up-regulated responses associated with apoptosis along with down-regulated ribosomal and defense functions, indicative of a distinctive, compromised state not achieved with either stimulus alone.Conclusions/significanceThis study helps define the transcriptomic responses of an important and under-studied schistosome vector to S. mansoni sporocysts, to niclosamide, and to both in combination. It suggests the response of S. mansoni sporocysts to niclosamide is minimal and not reflective of a distinct repertoire of genes to handle xenobiotics while in the snail host. It also offers new insights for how niclosamide affects snails