Country corruption analysis with self organizing maps and support vector machines.

During recent years, the empirical research on corruption has grown considerably. Possible links between government corruption and terrorism have attracted an increasing interest in this research field. Most of the existing literature discusses the topic from a socio-economical perspective and only few studies tackle this research field from a data mining point of view. In this paper, we apply data mining techniques onto a cross-country database linking macro-economical variables to perceived levels of corruption. In the first part, self organizing maps are applied to study the interconnections between these variables. Afterwards, support vector machines are trained on part of the data and used to forecast corruption for other countries. Large deviations for specific countries between these models' predictions and the actual values can prove useful for further research. Finally, projection of the forecasts onto a self organizing map allows a detailed comparison between the different models' behavior.cross-country;

BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections

We introduce an architecture for large-scale image categorization that enables the end-to-end learning of separate visual features for the different classes to distinguish. The proposed model consists of a deep CNN shaped like a tree. The stem of the tree includes a sequence of convolutional layers common to all classes. The stem then splits into multiple branches implementing parallel feature extractors, which are ultimately connected to the final classification layer via learned gated connections. These learned gates determine for each individual class the subset of features to use. Such a scheme naturally encourages the learning of a heterogeneous set of specialized features through the separate branches and it allows each class to use the subset of features that are optimal for its recognition. We show the generality of our proposed method by reshaping several popular CNNs from the literature into our proposed architecture. Our experiments on the CIFAR100, CIFAR10, and Synth datasets show that in each case our resulting model yields a substantial improvement in accuracy over the original CNN. Our empirical analysis also suggests that our scheme acts as a form of beneficial regularization improving generalization performance.Comment: WACV 201

Hierarchical self-organization of non-cooperating individuals

Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network) can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score) of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.Comment: Supplementary videos are to be found at http://hal.elte.hu/~nepusz/research/supplementary/hierarchy

The Microbial Ecology of Bacterial Vaginosis: A Fine Scale Resolution Metagenomic Analysis

The vaginal microbiota play an important protective role in maintaining the health of women. Disruption of the mutualistic relationship that exists between bacterial communities in the vagina and their hosts can lead to bacterial vaginosis (BV), a condition in which lactic acid producing bacteria are supplanted by a diverse array of strictly anaerobic bacteria. BV has been shown to be an independent risk factor for adverse outcomes including preterm delivery and low infant birth weight, acquisition of sexually transmitted infections and HIV, and development of pelvic inflammatory disease. National surveys indicate the prevalence of BV among U.S. women is 29.2%, and yet, despite considerable effort, the etiology of BV remains unknown. Moreover, there are no broadly effective therapies for the treatment of BV, and reoccurrence is common. In the proposed research we will test the overarching hypothesis that vaginal microbial community dynamics and activities are indicators of risk to BV. To do this, we propose to conduct a high resolution prospective study in which samples collected daily from 200 reproductive-age women over two menstrual cycles are used to capture molecular events that take place before, during, and after the spontaneous remission of BV episodes. We will use modern genomic technologies to obtain the data needed to correlate shifts in vaginal microbial community composition and function, metabolomes, and epidemiological and behavioral metadata with the occurrence of BV to better define the syndrome itself and identify patterns that are predictive of BV. The three specific aims of the research are: (1) Evaluate the association between the dynamics of vaginal microbial communities and risk to BV by characterizing the community composition of vaginal specimens archived from a vaginal douching cessation study in which 33 women self-collected vaginal swabs twice-weekly for 16 weeks; (2) Enroll 135 women in a prospective study in which self-collected vaginal swab samples and secretions are collected daily along with data on the occurrence of BV, vaginal pH, and information on time varying habits and practices; (3) Apply model-based statistical clustering and classification approaches to associate the microbial community composition and function, with metadata and clinical diagnoses of BV. The large body of information generated will facilitate understanding vaginal microbial community dynamics, the etiology of BV, and drive the development of better diagnostic tools for BV. Furthermore, the information will enable a more personalized and effective treatment of BV and ultimately help prevent adverse sequelae associated with this highly prevalent disruption of the vaginal microbiome

Latent space models for multidimensional network data

Network data are any relational data recorded among a group of individuals, the nodes. When multiple relations are recorded among the same set of nodes, a more complex object arises, which we refer to as “multidimensional network”, or “multiplex”, where different relations corresponding to different networks. In the past, statistical analysis of networks has mainly focused on single-relation network data, referring to a single relation of interest. Only in recent years statistical models specifically tailored for multiplex data begun to be developed. In this context, only a few works have been introduced in the literature with the aim at extending the latent space modeling framework to multiplex data. Such framework postulates that nodes may be characterized by latent positions in a p-dimensional Euclidean space and that the presence/absence of an edge between any two nodes depends on such positions. When considering multidimensional network data, latent space models can help capture the associations between the nodes and summarize the observed structure in the different networks composing a multiplex. This dissertation discusses some latent space models for multidimensional network data, to account for different features that observed multiplex data may present. A first proposal allows to jointly represent the different networks into a single latent space, so that average similarities between the nodes may be captured as proximities in such space. A second work introduces a class of latent space models with node-specific effects, in order to deal with different degrees of heterogeneity within and between networks in multiplex data, corresponding to different types of node-specific behaviours. A third work addresses the issue of clustering of the nodes in the latent space, a frequently observed feature in many real world network and multidimensional network data. Here, clusters of nodes in the latent space correspond to communities of nodes in the multiplex. The proposed models are illustrated both via simulation studies and real world applications, to study their perfomances and abilities

On-Orbit Validation of a Framework for Spacecraft-Initiated Communication Service Requests with NASA's SCaN Testbed

We design, analyze, and experimentally validate a framework for demand-based allocation of high-performance space communication service in which the user spacecraft itself initiates a request for service. Leveraging machine-to-machine communications, the automated process has potential to improve the responsiveness and efficiency of space network operations. We propose an augmented ground station architecture in which a hemispherical-pattern antenna allows for reception of service requests sent from any user spacecraft within view. A suite of ground-based automation software acts upon these direct-to-Earth requests and allocates access to high-performance service through a ground station or relay satellite in response to immediate user demand. A software-defined radio transceiver, optimized for reception of weak signals from the helical antenna, is presented. Design and testing of signal processing equipment and a software framework to handle service requests is discussed. Preliminary results from on-orbit demonstrations with a testbed onboard the International Space Station are presented to verify feasibility of the concept

Evolutionary Granular Kernel Machines

Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research. For these important problems, powerful and flexible kernel trees called Evolutionary Granular Kernel Trees (EGKTs) are designed to incorporate prior domain knowledge. Granular Kernel Tree Structure Evolving System (GKTSES) is developed to evolve the structures of Granular Kernel Trees (GKTs) without prior knowledge. A voting scheme is also proposed to reduce the prediction deviation of GKTSES. To speed up EGKTs optimization, a master-slave parallel model is implemented. To help SVMs challenge large-scale data mining, a Minimum Enclosing Ball (MEB) based data reduction method is presented, and a new MEB-SVM algorithm is designed. All these kernel methods are designed based on Granular Computing (GrC). In general, Evolutionary Granular Kernel Machines (EGKMs) are investigated to optimize kernels effectively, speed up training greatly and mine huge amounts of data efficiently