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

Women\u27s expectations and experiences of childbirth in an Egyptian public hospital

This thesis seeks to understand women\u27s reasons for giving birth at a busy teaching hospital in Greater Cairo, and what their experiences of it have been. It asks whether women\u27s parity and the money paid upon admission to the hospital had an impact on their expectations and experiences. The thesis uses secondary data from interviews conducted in 2001 with mothers who had normal births, before they left the hospital. It is part of a larger project that investigated practices surrounding normal childbirth. The results are analyzed using the perspectives of critical medical anthropology, biomedicine and public health. Fear of the home setting with its lack of qualified personnel and equipment was the leading cause for not giving birth there, and mothers of lower parities were most likely to give this response. Comments concerning the delivering physician did not clearly show that women in the paying section would be more satisfied than those in the free section. Also, more women in the free section reported that they felt discomfort regarding the lack of pain relief. Negative feedback about the availability of pediatricians did not clearly show whether women were more satisfied in the paying section or not. The third hypothesis stated that women who had a higher parity reported a more positive birthing experience than first-time mothers. Comments regarding the nature of communication with the delivering physician support this hypothesis, and the same is true but to a lesser extent concerning the pediatrician. The same hypothesis is not clearly supported when we find that a minority of women across hospital sections and parity groups were given information about their own health or that of their babies and this has a negative impact on their health. Recommendations for improvement were focused on the delivering physician, the pediatrician, the nurses, and the general service level at the hospital. This project could be repeated every five years and include an in-depth ethnographic dimension, to measure improvements in health care provision and women\u27s perceptions throughout their pregnancy and after childbirth. Comparisons could be made between different socioeconomic classes and regions of Egypt

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

Test application of the internet of things for energy efficient outdoor smart lighting

The term Internet of Things (IoT) is immerging lately in many researches and publications with the purpose of standardizing the concept. The IoT tends to connect the physical world virtually through the internet by creating a virtual identity for every physical object. Huge amounts of devices with numerous amounts of data will be connected by the year 2020. Researches are aiming to implement the IoT paradigm in several domains such as smart lighting, traffic control, waste management, and air pollution. Due to the immense necessity for reducing energy consumption and creating adaptive lighting solution, smart lighting earned most of the attention of researchers. This thesis work aims to apply the IoT paradigm for controlling and monitoring of a smart lighting application. The implementation aims to reduce the energy consumption in addition to, achieve adaptability of the lights according to the surrounding environment. The implementation uses Service Oriented Architecture (SOA) to allow heterogeneity and interoperability between components. In this implementation a control algorithm is proposed, which takes into account most of the surrounding environment conditions and conducted tasks. The main purpose of control is to reduce energy consumption and loss by delivering sufficient amount of lighting required without affecting the visibility. On the other hand, energy consumption measuring and monitoring were achieved by automatic subscribe and discovery of energy meters by means of Device Profile Web Service (DPWS). Moreover a set of specific KPIs are designed in order to give a holistic view of the system to the facility managers, in order to evaluate and analyse the performance. In addition, it raises the awareness of personnel with the impact of light usage. Finally a web based dashboard application is developed in order to present system data in real time and display KPIs in a visualized way. The dashboard consumes Web Services (WS) for retrieving data

STR-941: NUMERICAL INVESTIGATION OF REINFORCED CONCRETE BARRIERS SUBJECTED TO BLAST LOADING

People’s lives are threatened by explosions; the tragic terrorist attacks have forced the governments to consider the importance of dealing with these attacks. With the rising threat of terrorism, protecting critical civil infrastructure such as embassies, governmental buildings, and airports against bomb attacks has become a critical issue. In the current research, reinforced concrete barriers subjected to blast loading are numerically investigated using Applied Element Method “AEM”. The blast loads adopts the ASCE guidance for design of blast-resistant buildings in petrochemical facilities. Fully nonlinear dynamic analysis was considered where the barriers thickness and reinforcement, end connections were parametrically investigated. It was found that the thickness and reinforcement of the barriers affect the barriers’ response, where the most significant parameter was the wall thickness