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

Interactions Between Humans and Dogs During the COVID-19 Pandemic: Recent Updates and Future Perspectives

COVID-19 is one of the deadliest epidemics. This pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the role of dogs in spreading the disease in human society is poorly understood. This review sheds light on the limited susceptibility of dogs to COVID-19 infections which is likely attributed to the relatively low levels of angiotensin-converting enzyme 2 (ACE2) in the respiratory tract and the phylogenetic distance of ACE2 in dogs from the human ACE2 receptor. The low levels of ACE2 affect the binding affinity between spike and ACE2 proteins resulting in it being uncommon for dogs to spread the disease. To demonstrate the role of dogs in spreading COVID-19, we reviewed the epidemiological studies and prevalence of SARS-CoV-2 in dogs. Additionally, we discussed the use of detection dogs as a rapid and reliable method for effectively discriminating between SARS-CoV-2 infected and non-infected individuals using different types of samples (secretions, saliva, and sweat). We considered the available information on COVID-19 in the human–dog interfaces involving the possibility of transmission of COVID-19 to dogs by infected individuals and vice versa, the human–dog behavior changes, and the importance of preventive measures because the risk of transmission by domestic dogs remains a concern

Influence of immediate postpartum contraception counseling on the rate of unintended pregnancy in primigravida: a randomized controlled study

Background: The current study aims to assess the influence of immediate postpartum counselling about effective contraceptive methods to be used by primigravida on the rate of unintended pregnancy during first 6 months post-partum.Methods: The study was a prospective randomized controlled trial for assessment the influence of immediate postpartum counseling about effective contraceptive methods to be used by primigravida on the rate of unintended pregnancy during first 6 months post-partum who delivered at the period between the 1st of December 2016 and 31st of December 2017. The study patients were randomly assigned into two groups: Group (A) were received counseling about contraceptive methods using illustrations through postpartum interview with the study researcher. Group (B) were not received any counseling about contraceptive methods. The primary outcome was the difference in the rate of unintended pregnancy in both groups.Results: No significant difference between both groups in preventing unintended pregnancy. In group (A): After 3 months postpartum 140 women (93.3%) were used the contraceptive method correctly. 10 women used method incorrectly and 2 of them get pregnant. After 6 months postpartum 8 women did not use any method but 134 women (95.7%) were correctly used the contraceptive method. In group (B): After 3 months postpartum 127 women (84.7%) were used the contraceptive method correctly. 23 women used method incorrectly and 4 of them get pregnant. After 6 months postpartum 1 woman did not use any method but 30 women (20.7%) were incorrectly used the contraceptive method.Conclusions: Immediate post-partum counseling about contraceptive methods is good tool to educate women who intend to have optimal inter–pregnancy period about the effective methods that suit them and when to initiate

SOLVENT EFFECTS ON THE ELECTRONIC ABSORPTION SPECTRA OF SOME ANALYTICAL INDICATORS

Electronic absorption spectra of selected ligands (Alloxan, Carmine, Naphthol Yellow S, Hematoxylin and Cyanine) were recorded in presence of different solvents (H2O, Ethyl acetate, Ethanol, DMF, Isopropyl alcohol, Amyl alcohol and Butanol) of variable physical properties. The electronic transitions were assigned. The data are analyzed based on the multiple linear regression technique explained from the views of different solvent parameters. Statistical analyses of the effect of solvents on the electronic spectra of the present ligands have been investigated

Spontaneous ovulation and pregnancy in women with polycystic ovarian disease; a cross sectional study

Background: Polycystic ovary disease (PCOD) is the most common endocrine disorder in women of reproductive age, with a prevalence of approximately 5-10%. This study aims to assess the rate of spontaneous ovulation and pregnancy in patients. The present study was a cross sectional study conducted at Woman's Health Hospital, Assiut University, Assiut, Egypt.Methods: The current study was a cross sectional study carried out in Assiut Women's Health Hospital between the 1st October 2016 and 31st July 2017. The patients were selected as infertile patients with PCOD. The patient ages range between 20 and 35 years. The BMI is between 18 and 30 Kg/m2. The main outcome measure was the rate of spontaneous ovulation and spontaneous pregnancy in the 3 cycles.Results: The mean age of the study participants was 26.64±4.59 years and the mean BMI was 24.46±2.62Kg/m2. The sonographic ovarian volume was 12.47±0.69 mm3 for the right ovary and 12.74±0.73 mm3 for the left ovary. No difference in the serum FSH, LH, FSH/LH ratio and prolactin over the 3 consecutive cycles. The rate of spontaneous ovulation in the 3 cycles was 6 women (8.6%) and 2 cases (2.8%) became pregnant spontaneously during the study period. There is no statistical significant difference between ovulating and non-ovulating women according to the BMI and ovarian volume.Conclusions: The present study concluded that the rate of spontaneous ovulation was 8.6% in women with PCOD within 3 cycles with no adverse effects of drugs or surgical interference

Application of Nanometal Oxides In Situ in Nonwoven Polyester Fabric for the Removal of Bacterial Indicators of Pollution from Wastewater

The objective of this study is to investigate and assess the use of in situ deposit nanosilver (nAg2O) or nanocopper oxides (nCuO) into nonwoven polyester fabric (NWPF) as a safe and effective antibacterial filter of pollution from domestic wastewater. The bactericidal effect of both nAg2O and nCuO was examined against Gram-negative bacteria (Escherichia coli, Salmonella typhi) and Gram-positive bacteria (Enterococcus faecalis, Staphylococcus aureus) using agar diffusion disk method. In addition, the capability of nAg2O and nCuO as disinfectants for secondary treated domestic wastewater was investigated as a case study. Transmission electron microscope (TEM) confirmed the formation of nAg2O and nCuO particles with average particle sizes of 15 and 41 nm, respectively. Disk diffusion results showed that nAg2O had a higher bactericidal effect than nCuO. Moreover, the disinfection of secondary treated wastewater using 1.27 mg/cm3 of nAg2O in the nonwoven fabric was capable of hindering 99.6% and 91.7% of total and fecal coliforms within 10 minutes with a residual value of 18 and 15 MPN-index/100 mL, respectively. The residual total and fecal coliform concentrations were far less than that stated in the national and international limits for wastewater reuse in agriculture purpose

The mR scheme to the shallow water equation with horizontal density gradients in one and two dimensions

In this work, we consider the model of shallow water equation with horizontal density gradients. We develop the modified Rusanov (mR) scheme to solve this model in one and two dimensions. Predictor and corrector are the two stages of the suggested scheme. The predictor stage is dependent on a local parameter $(\alpha^n_{i+\frac{1}{2}})$ that allows for diffusion control. The balance conservation equation is recovered in the corrector stage. The proposed approach is well-balanced, conservative, and straightforward. Several 1D and 2D test cases are produced after presenting the shallow water model and the numerical technique. In the 1D case, we compared the proposed scheme with the Rusanov scheme, mR with constant $\alpha$ and analytical solutions. The numerical simulation demonstrates the mR's great resolution and attests to its capacity to produce accurate simulations of the shallow water equation with horizontal density gradients. Our results demonstrate that the mR technique is a highly effective instrument for solving a variety of equations in applied science and developed physics