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

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

Political education in Egypt with reference to England and the Soviet Union

The significance of political education is recognized by most countries in accomplishing the desired values in the society, but differs in form according to their ideologies. This research, is an attempt to study the different approaches to the teaching of political-education in Egypt, in comparison with England and the Soviet Union. The research report is divided into ten chapters. Chapters two and three are devoted to a study of the theoretical framework of political education, and political socialization. The development of political life and ideology in Egypt, is covered in chapters four and five. Chapter six deals with the different approaches to political education, in the light of official statements of the state. Chapters seven and eight focus on the study of the teaching of political education in the school curriculum, and the approaches to political education. Chapter nine is concerned with the work of political parties in political education. Chapters one and ten cover the introduction, conclusion, and recommendations. In this research, political education is mainly seen as the political learning which develops the ability of young people to participate in political life, and to influence the system and its values. However, attempts at political learning which aim to support the system and its values, are regarded as political socialization. It has been noted that whilst Egypt encourages a more open approach to political education than before, the approach to political socialization still exists. Improvements in political education in England are greater than in Egypt. Nevertheless, approaches to political socialization have always been emphasized in the Soviet Union. Some recommendations are suggested to improve political education in Egypt. They are mainly based on developing the political awareness of the young people, and their ability to participate in democratic life and to influence the system

Model checking Branching-Time Properties of Multi-Pushdown Systems is Hard

We address the model checking problem for shared memory concurrent programs modeled as multi-pushdown systems. We consider here boolean programs with a finite number of threads and recursive procedures. It is well-known that the model checking problem is undecidable for this class of programs. In this paper, we investigate the decidability and the complexity of this problem under the assumption of bounded context-switching defined by Qadeer and Rehof, and of phase-boundedness proposed by La Torre et al. On the model checking of such systems against temporal logics and in particular branching time logics such as the modal $\mu$-calculus or CTL has received little attention. It is known that parity games, which are closely related to the modal $\mu$-calculus, are decidable for the class of bounded-phase systems (and hence for bounded-context switching as well), but with non-elementary complexity (Seth). A natural question is whether this high complexity is inevitable and what are the ways to get around it. This paper addresses these questions and unfortunately, and somewhat surprisingly, it shows that branching model checking for MPDSs is inherently an hard problem with no easy solution. We show that parity games on MPDS under phase-bounding restriction is non-elementary. Our main result shows that model checking a $k$ context bounded MPDS against a simple fragment of CTL, consisting of formulas that whose temporal operators come from the set {\EF, \EX}, has a non-elementary lower bound

Modelling the Impact of Credit on Intensification in Mixed Crop-Livestock Systems: A Case Study from Ethiopia

Access to credit is one strategy for promoting the adoption of yield-enhancing technologies. However, advancing credit to smallholder farmers for encouraging technology adoption is a complex policy issue. The objective of this paper is to identify appropriate and sustainable credit repayment policies to encourage intensification in the Ethiopian Highlands. Using a household model, we analyze the impact of advancing in-kind credit in the form of fertilizer and seed to smallholder farmers in the Ethiopian highlands and alternative credit repayment strategies. The results indicate that in kind input credit of fertilizer and seed provided to farmers in the highland of Ethiopia increased the value of household crop output moderately and hence allowed the household to increase its consumption. This scheme requires borrowers to sell their crop immediately at harvest to repay their credit. An alternative repayment scheme of extending the repayment period to allow households to capture seasonal price variation is proposed. The amount repaid is also tied to yields of wheat.Agricultural Finance,

Silver-Doped Cadmium Selenide/Graphene Oxide-Filled Cellulose Acetate Nanocomposites for Photocatalytic Degradation of Malachite Green toward Wastewater Treatment

Silver-doped cadmium selenide/graphene oxide (GO) (Ag-CdSe/GO) nanocomposites have been synthesized, loaded in cellulose acetate (CA) to form Ag-CdSe/GO@CA heterostructure nanofibers, and characterized in terms of structural, morphological, photocatalytic properties, among others. The photocatalytic degradation of malachite green (MG) was estimated using cadmium selenide-filled CA (CdSe@CA), silver-doped cadmium selenide-filled CA (Ag-CdSe@CA), cadmium selenide/GO-filled CA (CdSe/GO@CA), and silver-doped cadmium selenide/GO-filled CA (Ag-CdSe/GO@CA) nanocomposite materials. The Ag-CdSe/GO@CA nanocomposites exhibit and retain an enhanced photocatalytic activity for the degradation of MG dye. This amended performance is associated with the multifunctional supporting impacts of GO, Ag, and CA on the composite structure and properties. The superior photocatalytic activity is related to the fact that both Ag and GO can act as electron acceptors that boost the separation efficiency of photogenerated carriers and the loading of the combined nanocomposite (Ag-CdSe@GO) on CA nanofibers, which can augment the adsorption of electrons and holes and facilitate the movement of carriers. The stability of Ag-CdSe/GO@CA nanocomposite photocatalysts demonstrates suitable results even after five recycles. This study establishes an advanced semiconductor-based hybrid nanocomposite material for efficient photocatalytic degradation of organic dyes.The Academy of Scientific Research and Technology (ASRT), Egypt, Grant No. 6510, supported this project financially

Optimal Sizing of Standalone PV-Wind Hybrid Energy System in Rural Area North Egypt

This paper studies the sizing of stand-alone renewable energy system applied in rural areas in the north of Egypt. The available renewable energy sources in these areas are investigated to be integrated to supply the different types of electrical loads. The quality and quantity of these sources over various weather and climate changes are studied to construct a robust energy system. The load demand in such areas is determined according to all activities require electrical energy. This study considers the different economic levels and technologies which affect the load demand value. The technique and economical indices required to obtain the optimal are investigated and applied in the various estimated cases. The genetic algorithm (GA) technique is applied to determine the size and number of photovoltaic panels and wind turbines. The obtained solution takes into account the loss of power supply probability and the minimization of system cost. This study presents an essential phase in the sustainable development of such rural areas