Risk-sharing in Rural Pakistan

Risk-sharing is a fundamental form of economic behaviour. It can occur through formal insurance markets, informal family arrangements, community support, legal institutions (such as bankruptcy), or government tax-transfer programmes. Whatever the mechanism used to share risk, the extent of risk mitigation can greatly influence the welfare of all members of society. Understanding the degree of risk-pooling in society is important for policy-makers, since insufficient risk pooling may provide a basis for government intervention. Alternatively, if risks are being pooled adequately without the help of the government, government risk-sharing may be redundant. This study explores the implications of the risk-sharing model, namely, that households which pool risks, either through formal markets or informal personal arrangements, experience correlated changes in their consumption through time. It conducts tests of within-village, across-village, within-district, and across-district risksharing using a new Pakistani panel data set—the Pakistan Food Security Management Survey—collected by the International Food Policy Research Institute (IFPRI), Washington, D. C. Unlike studies for other Less Developed Countries (LDCs), these tests find very little or almost no evidence of risk-sharing among unrelated individuals within- and across-villages in the rural sector of Pakistan.

養鶏業マイクロエンタープライズとバリュチェーン発展のためのMCE法とGISを援用した適地アセスメント : バングラデシュのガジブール地区の研究

筑波大学University of Tsukuba博士（理学）Doctor of Philosophy in Science2013【要旨】thesi

Prevalence and determinants of susceptibility to cigarette smoking among school students in Pakistan: secondary analysis of Global Youth Tobacco Survey

Background: Susceptibility to smoke has been recognized as a strong predictor of smoking experimentation and taking up regular smoking habit. The identification of smoking susceptible individuals and its determinants is important in the efforts to reduce future smoking prevalence. The aims of this study are to estimate prevalence of susceptibility to smoke among adolescents, and identify factors associated with it.<p></p> Methods: Cross sectional data was obtained from Global Youth Tobacco Survey conducted in three cities of Pakistan in year 2004. Study population consisted of students in grades, 8th, 9th, and 10th; aged 13 to 15 years. Secondary analysis using univariate and multivariate logistic regression analyses were performed to estimate the associations between smoking susceptibility and co-variates. Descriptive statistics were reported in proportions, and adjusted odds ratios with 95% confidence interval were used to report logistic regression analyses.<p></p> Results: Approximately 12% of nonsmoking students were found susceptible to smoking. Students, who were females (OR = 1.53, 95% CI [1.24-1.89]); whose parents (OR = 1.64, 95% CI [1.35-1.99]); or close friend smoked (OR = 2.77, 95% CI [2.27- 3.40]) were more susceptible to cigarette smoking. Students who had good knowledge about harmful effects of smoking (OR = 0.54, 95% CI [0.43-0.69]); and had access to anti-smoking media (OR = 0.73, 95% CI [0.59-0.89]) were less likely to be susceptible to smoking.<p></p> Conclusion: Students who were females, had smoking parents, friends or exposure to newspaper/magazines cigarette marketing, were more susceptible to cigarette smoking among Pakistani adolescents. While knowledge of harmful effects of smoking and access to anti-smoking media served as protective factors against susceptibility to smoking.<p></p&gt

Bimodal network architectures for automatic generation of image annotation from text

Medical image analysis practitioners have embraced big data methodologies. This has created a need for large annotated datasets. The source of big data is typically large image collections and clinical reports recorded for these images. In many cases, however, building algorithms aimed at segmentation and detection of disease requires a training dataset with markings of the areas of interest on the image that match with the described anomalies. This process of annotation is expensive and needs the involvement of clinicians. In this work we propose two separate deep neural network architectures for automatic marking of a region of interest (ROI) on the image best representing a finding location, given a textual report or a set of keywords. One architecture consists of LSTM and CNN components and is trained end to end with images, matching text, and markings of ROIs for those images. The output layer estimates the coordinates of the vertices of a polygonal region. The second architecture uses a network pre-trained on a large dataset of the same image types for learning feature representations of the findings of interest. We show that for a variety of findings from chest X-ray images, both proposed architectures learn to estimate the ROI, as validated by clinical annotations. There is a clear advantage obtained from the architecture with pre-trained imaging network. The centroids of the ROIs marked by this network were on average at a distance equivalent to 5.1% of the image width from the centroids of the ground truth ROIs.Comment: Accepted to MICCAI 2018, LNCS 1107

Some New Symmetric Relations and the Prediction of Left and Right Handed Neutrino Masses using Koide's Relation

Masses of the three generations of charged leptons are known to completely satisfy the Koide's mass relation. But the question remains if such a relation exists for neutrinos? In this paper, by considering SeeSaw mechanism as the mechanism generating tiny neutrino masses, we show how neutrinos satisfy the Koide's mass relation, on the basis of which we systematically give exact values of not only left but also right handed neutrino masses

Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network

In many domestic and military applications, aerial vehicle detection and super-resolutionalgorithms are frequently developed and applied independently. However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images. To address this problem, we propose a Joint Super-Resolution and Vehicle DetectionNetwork (Joint-SRVDNet) that tries to generate discriminative, high-resolution images of vehicles fromlow-resolution aerial images. First, aerial images are up-scaled by a factor of 4x using a Multi-scaleGenerative Adversarial Network (MsGAN), which has multiple intermediate outputs with increasingresolutions. Second, a detector is trained on super-resolved images that are upscaled by factor 4x usingMsGAN architecture and finally, the detection loss is minimized jointly with the super-resolution loss toencourage the target detector to be sensitive to the subsequent super-resolution training. The network jointlylearns hierarchical and discriminative features of targets and produces optimal super-resolution results. Weperform both quantitative and qualitative evaluation of our proposed network on VEDAI, xView and DOTAdatasets. The experimental results show that our proposed framework achieves better visual quality than thestate-of-the-art methods for aerial super-resolution with 4x up-scaling factor and improves the accuracy ofaerial vehicle detection

Health professional students’ perceptions regarding their role in tobacco control: findings from the Global Health Professional Students Survey, Pakistan, 2011

Background: An important way of reducing tobacco use is to train the health professional (HP) students to assist in tobacco cessation by educating patients and public. In order to shape their thoughts for the desired role, it is vital to understand their existing perceptions regarding HP’s role in tobacco control. Thus, the aim of our study was to find out the perceptions of Pakistani HP students regarding their future role in tobacco control, and examine factors associated with negative perceptions.<p></p> Methods: Secondary data analysis of the Global Health Professional Students Survey, Pakistan, 2011 was performed. Study population included 3445 health professional students in third year of graduate level programs. The dependent variable (perceptions of HP students), was developed using four questions from the survey. Students who did not regard HP’s role in tobacco control were labeled as having negative perceptions. Logistic regression analyses were conducted to analyze association between HP students’ perceptions and various socio-demographic, attitudinal and knowledge related factors; and were reported as adjusted odds ratios with 95% confidence interval.<p></p> Results: We found that 44.8% (n = 1542) of students do not regard HPs as role model for their patients and public, and perceive that HPs do not play an important part in patient’s quitting tobacco use. These negative perceptions were associated with male sex (OR = 1.25, 95% CI 1.02 – 1.53, p value 0.028), and poor knowledge about tobacco cessation techniques (OR = 1.32, 95% CI 1.12 – 1.55, p value < 0.001). Negative perceptions were also associated with their attitudes towards ban on: tobacco advertisements (OR = 1.67, 95% CI 1.13 – 2.48, p value 0.010); and tobacco use at public places (OR = 1.60, 95% CI 1.26 – 2.03, p value < 0.001).<p></p> Conclusion: The role of HPs for tobacco control is fairly under-perceived by HP students, and the undesired negative perceptions are associated with male sex, poor knowledge about tobacco use cessation techniques and negative attitudes towards legislative control. A comprehensive approach, focusing on these aspects should be adopted to train HPs, in order to utilize them as an effective manpower for tobacco control