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

Magnitude of behavioral deficits varies with job-related chlorpyrifos exposure levels among Egyptian pesticide workers.

Chronic occupational exposure to organophosphorus pesticides (OPs) is consistently associated with deficits on behavioral tests when compared to unexposed comparison groups. However, a dose-response relationship has yet to be established, leading some to doubt an association between occupational OP exposure and behavioral deficits. Pesticide application teams in Egypt who are primarily exposed to one OP, chlorpyrifos (CPF), were recruited into a field assessment. Trail Making A and the more challenging Trail Making B tests were administered to 54 engineers (who supervise the pesticide application process, usually from the side of the field), 59 technicians (who guide the pesticide applicators in the field), 31 applicators (who mix and apply pesticides using knapsack sprayers), and 150 controls (who did not work in the fields) at two different times during the OP application season as well as immediately after applications had ended and 1.5 months later. All participants were males since only males work on pesticide application teams in Egypt. Urinary levels of 3,5,6-trichloro-2-pyridinol (TCPy), a specific metabolite of CPF, confirmed the pattern of lower to higher CPF exposures from engineers to technicians to applicators, and these were all greater than urinary metabolite levels in controls. A consistent relationship between job title and performance speed on the behavioral task was observed: Controls had the best (fastest) performance on Trail Making A and B tests throughout the application season, and applicators had significantly slower performance than engineers on Trail Making A (p = 0.015) and B (p = 0.003). However, individual urinary TCPy, blood acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) levels did not predict individual performance. This study identifies a dose-related effect based on job title, which serves as a surrogate for chronic exposure in that differing job titles exhibit varying group exposure levels. The results establish that chronic occupational exposure to chlorpyrifos is neurotoxic and suggest that the classic biomarkers of recent CPF exposure are not predictive of chronic exposure effects

Effects of food-simulating solutions on the surface properties of two CAD/CAM resin composites

During clinical service, dental materials could experience chemical degradation due to exposure to different diet components which could affect their functions and longevity. So, the objective of this study was to investigate the effect of food simulatin

Morphological and Chemical Properties of Particulate Matter in the Dammam Metropolitan Region: Dhahran, Khobar, and Dammam, Saudi Arabia

Characteristics of airborne particulate matter (PM) as well as its levels in air samples collected from selected sites within cities of Dhahran, Khobar, and Dammam, in the Eastern Province of Saudi Arabia, are investigated. Concentration levels of the 10 microns’ PM (i.e., PM10) are determined using the gravimetric technique. Morphological and chemical characteristics of the PM collected from the sampling cities are studied using Field-Emission Scanning Electron Microscopy (FESEM), energy dispersive X-ray (EDX), and X-Ray Fluorescence (XRF). Moreover, levels and types of hazardous materials related to these samples are assessed using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Results revealed that the average concentration levels of PM10 were approximately 177, 380, and 126 μg/m3 in Dhahran, Khobar, and Dammam, respectively. The structure of PM collected in Dhahran was mainly platy and rod-like shaped with a size between 2 and 6 μm, while PM collected in Khobar was mostly irregular in form, with a size range between 2 and 8 μm, and Dammam’s PM was rounded and between 1 and 3 μm in size. Both EDX and XRF results indicate relatively high weight % of C, O, Si, F, and Ca with lower weight % of Na, Mg, and K at the 3 cities. Finally, the study shows that Ba and Zn were the main trace metals associated with the collected PM in the 3 cities

Physico-mechanical properties and bacterial adhesion of resin composite CAD/CAM blocks : an in-vitro study

The recent introduction of CAD/CAM technology has been strongly impacting the workflow in dental clinics and labs. Among the used CAD/CAM materials, resin composite CAD/CAM blocks offer several advantages. The aim of this study was to evaluate the physic

Functionality of Inorganic Nanostructured Materials onto Wool Fabric

Silver nanoparticles (AgNPs) were prepared through chemical reduction method and characterized by UV-Vis absorption spectra to examine its formation with different AgNO3 and sodium borohydride concentrations and by transmission electron microscope (TEM) to evaluate its particle size and size distribution. The wool fabric was first treated separately with AgNPs and titanium dioxide nanoparticles (TiO2NPs) and then dyed with C.I. Acid Orange 74 (AO74). The dye uptake of pre-treated wool fabric with nanoparticles was compared to conventional dyeing of wool. The existence of AgNPs and TiO2NPs on wool fabric during acid dyeing increases the dye uptake up to 27 and 39%, respectively. The dyeing kinetic of wool fabric was positively affected by treating with AgNPs and TiO2NPs. Also, the activation energy of AO74 diffusion was calculated before and after NPs-treatment that confirms the physicsorption dyeing process. The NPs-treatment leads to produce a wool fabric with excellent antibacterial and photocatalytic properties for TiO2NPs-treated wool fabric and very good antibacterial and good photocatalytic properties for AgNPs-treated wool fabric. In addition, NPs-treatment has no adverse effects on fastness properties of the functionalized dyed wool fabric. Keywords: Silver nanoparticles, Titanium dioxide nanoparticles, Wool, Acid dyeing