Learning the Structure for Structured Sparsity

Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing methods for learning under the assumption of structured sparsity rely on prior knowledge on how to weight (or how to penalize) individual subsets of variables during the subset selection process, which is not available in general. Inferring group weights from data is a key open research problem in structured sparsity.In this paper, we propose a Bayesian approach to the problem of group weight learning. We model the group weights as hyperparameters of heavy-tailed priors on groups of variables and derive an approximate inference scheme to infer these hyperparameters. We empirically show that we are able to recover the model hyperparameters when the data are generated from the model, and we demonstrate the utility of learning weights in synthetic and real denoising problems

Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

In this paper, we develop a novel Aligned-Spatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized aligned grid structures, and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed ASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Moreover, the proposed ASGCN model can adaptively discriminate the importance between specified vertices during the process of spatial graph convolution, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model

Extending local features with contextual information in graph kernels

Graph kernels are usually defined in terms of simpler kernels over local substructures of the original graphs. Different kernels consider different types of substructures. However, in some cases they have similar predictive performances, probably because the substructures can be interpreted as approximations of the subgraphs they induce. In this paper, we propose to associate to each feature a piece of information about the context in which the feature appears in the graph. A substructure appearing in two different graphs will match only if it appears with the same context in both graphs. We propose a kernel based on this idea that considers trees as substructures, and where the contexts are features too. The kernel is inspired from the framework in [6], even if it is not part of it. We give an efficient algorithm for computing the kernel and show promising results on real-world graph classification datasets.Comment: To appear in ICONIP 201

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Игровая поэтика в романе Б. Виана «Пена дней»

The article treats poetic game means of B. Vian, which represents the unique phenomenon of the French literature in 1940s. Author’s laugh and fun, his buffoonery language contain a great destructive impact, revealing alogizms of the world, crushing stereotypes and the power of routine language.В статье рассматривается игровая поэтика Б. Виана, представляющая уникальное явление во французской литературе 40-50-х гг. XX в. Смех писателя, его буффонное слово несет огромный разрушительный заряд, вскрывая алогичность мира, разрушая стереотипы, власть обиходного языка

A flowering-time gene network model for association analysis in Arabidopsis thaliana

In our project we want to determine a set of single nucleotide polymorphisms (SNPs), which have a major effect on the flowering time of Arabidopsis thaliana. Instead of performing a genome-wide association study on all SNPs in the genome of Arabidopsis thaliana, we examine the subset of SNPs from the flowering-time gene network model. We are interested in how the results of the association study vary when using only the ascertained subset of SNPs from the flowering network model, and when additionally using the information encoded by the structure of the network model. The network model is compiled from the literature by manual analysis and contains genes which have been found to affect the flowering time of Arabidopsis thaliana [Far+08; KW07]. The genes in this model are annotated with the SNPs that are located in these genes, or in near proximity to them. In a baseline comparison between the subset of SNPs from the graph and the set of all SNPs, we omit the structural information and calculate the correlation between the individual SNPs and the flowering time phenotype by use of statistical methods. Through this we can determine the subset of SNPs with the highest correlation to the flowering time. In order to further refine this subset, we include the additional information provided by the network structure by conducting a graph-based feature pre-selection. In the further course of this project we want to validate and examine the resulting set of SNPs and their corresponding genes with experimental methods

ИНТЕРТЕКСТУАЛЬНАЯ ИГРА В РОМАНЕ Д. ДЖОЙСА "УЛИСС"

The article is devoted to the description of intertextual game in the novel “Ulysses” by James Joyce. The features of the Joyce’s poetics are explored with the aim of interpretational limits’ determination in the text. The authors carry out analyses of the first episode in which synthesis of characters, applying of details, allusions and oppositions are revealed as the main mechanisms of combination for various plans of narration.В статье описывается интертекстуальная игра в романе Джеймса Джойса «Улисс». Параллельно проводится исследование различных стилей мировой литературы, которые препарируются автором в его произведении. В процессе анализа интертекстуальной игры выявляется замысел Джойса, который заключается в поиске ключа к культурному коду человеческой цивилизации. Особенности поэтики Джойса исследуются с целью изучения границ интерпретации текста. Авторы статьи проводят анализ первого эпизода романа, в котором синтез характеров, включение деталей, аллюзий и оппозиций становятся главными механизмами комбинации различных планов повествования