Convolutional Neural Networks Via Node-Varying Graph Filters

Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular structures such as images. Motivated by the recent interest in processing signals defined in irregular domains, we advocate a CNN architecture that operates on signals supported on graphs. The proposed design replaces the classical convolution not with a node-invariant graph filter (GF), which is the natural generalization of convolution to graph domains, but with a node-varying GF. This filter extracts different local features without increasing the output dimension of each layer and, as a result, bypasses the need for a pooling stage while involving only local operations. A second contribution is to replace the node-varying GF with a hybrid node-varying GF, which is a new type of GF introduced in this paper. While the alternative architecture can still be run locally without requiring a pooling stage, the number of trainable parameters is smaller and can be rendered independent of the data dimension. Tests are run on a synthetic source localization problem and on the 20NEWS dataset.Comment: Submitted to DSW 2018 (IEEE Data Science Workshop

Meeting the Editors at the 9th Iberoamerican Academy of Management Conference

The following Editorial Comment presents a summary of the main ideas and suggestions presented at the “Meeting with the Editors” at the 9th Iberoamerican Academy of Management (IAM) Conference, in Santiago (Chile). The meeting was conducted by three editors: Jonathan Doh from the Journal of World Business, Martin Larraza from Management Research and Herman Aguinis, President of the Iberoamerican Academy of Management and former editor of Organizational Research Methods. The conference took place from 3rd to 5th December 2015, and was hosted by Universidad Del Desarrollo. This editorial does not change the focus of the previous recent editorial comments of the Iberoamerican Journal of Strategic Management (IJSM). Its purpose of helping researchers and students in their quest to conduct quality research and publish it remains unchanged. These specific editorial comments are grouped in the menu section of the IJSM website under the title How to publish (or perish)? (available at http://www.revistaiberoamericana.org/ojs/index.php/ibero/pages/view/publish%20or%20perish)

Strategy as Practice in the Structurationist Perspective: What it is and why it is? : Toward an Ontology of Practice of Strategy in Organizations

As a developing approach, Strategy as Practice appropriates other theories with converging ontological and epistemological assumptions to build its analytical body. Therefore, in this discipline the designs of Structurationism and the Historical-Cultural Activity Theory generally serve as the analytical basis, even though other theories such as Critical Realism, Sensemaking and Bourdieu’s concept of Habitus are alternative and/or complementary theories for the basic frameworks. This theoretical study offers a discussion on the appropriation of Structurationism that serves as one of the analytical theoretical structures of Strategy as Practice. The analytical procedure is guided by the central goal of discussing the ontological assumptions of Structurationism that support this perspective under the aegis of Strategy as Practice in the field of Organizational Strategy. For this purpose, the specific objectives are: a) to conduct a theoretical (albeit not exhaustive) review of Strategy as Practice; and b) to conduct a review of Giddens’ Theory of Structuration, followed by c) to offer a discussion on the theoretical/analytical specifics that Structurationism shown in studies of Strategy as Practice. The conclusion of the discussion shows adequate ontological agreement with the Structurationist assumptions adopted by the Strategy as Practice discipline, i.e., there is here a parallel intention to reveal an Ontology of Practice of Strategy in Organizations

Polymorphisms in the glutathione pathway modulate cystic fibrosis severity: a cross-sectional study

BACKGROUND: Cystic fibrosis (CF) clinically manifests with various levels of severity, which are thought to be modulated by mutations in the cystic fibrosis transmembrane conductance regulator gene (CFTR), modifier genes, and the environment. This study verified whether polymorphisms in modifier genes associated with glutathione (GSH) metabolism influence CF severity. METHODS: A cross-sectional study of 180 CF patients was carried out from 2011 to 2012. We analyzed CFTR mutations, polymorphisms (GSTM1 and GSTT1 deletions, GSTP1 + 313A > G, GCLC-129C > T, and GCLC-3506A > G) in modifier genes and CF clinical severity as assessed by 28 clinical and laboratory variables. RESULTS: Significant associations were found between modifier gene polymorphisms and particular phenotypes or genotype changes. These included GCLC-129C > T with a higher frequency of the Pseudomonas aeruginosa mucoid to CC genotype (p = 0.044), and GCLC-3506A > G with a higher frequency of the no-mucoid P. aeruginosa (NMPA) to AA genotype (p = 0.012). The GSTT1 deletion was associated with a higher frequency of the NMPA to homozygous deletion (p = 0.008), GSTP1 + 313A > G with a minor risk of osteoporosis (p = 0.036), and patient age ≤ 154 months (p = 0.044) with the AA genotype. The Bhalla score was associated with GCLC-3506A > G (p = 0.044) and GSTM1/GSTT1 deletion polymorphisms (p = 0.02), while transcutaneous hemoglobin oxygen saturation levels were associated with GSTT1 deletions (p = 0.048). CONCLUSION: CF severity is associated with polymorphisms in GSH pathways and CFTR mutations

A importância da pesquisa baseada em fenômenos em estratégia para os pesquisadores iberoamericanos

In the end, careful observation, rigorous pattern-finding and targeted problem solving can be valuable forms of research. We must take care not to crowd out the Flemings, Bantings and Semmelweises of our discipline due to our obsession with theory-building or theory-testing

Dez recomendações para aumentar a possibilidade de publicação do seu artigo

Neste comentário editorial quero reforçar o que um bom periódico espera de um artigo. De certa forma, embora ainda tenhamos um considerável número de comentários a preparar sobre a publicação e divulgação da pesquisa em estratégia, este comentário sintetiza os que já foram publicados na RIAE, e reforça o primeiro comentário editorial desta série (http://dx.doi.org/10.5585/riae.v12i2.2034)

Comentário editorial: a construção da revisão de literatura

With a view to examining the entire proposed structure for an empirical article, this editorial focuses on the Literature Review, also known as the Theoretical Framework. The literature review may be defined as “a documented review of published or unpublished works (articles, books, etc.) in specific fields of interest to the work of the researcher” (Ferreira, 2015: 36). It is to be found in conceptual articles such as empirical articles, whether qualitative or quantitative. It has a clear link to the article as a whole and provides support for the section on the development of the concept and the hypotheses/propositions that follow it in the structure of an empirical article

Convolutional Neural Network Architectures for Signals Supported on Graphs

Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a possibly nonlinear subsampling stage where nearby nodes pool their information in a set of preselected sample nodes. A key component of the architecture is to remember the position of sampled nodes to permit computation of convolutional features at deeper layers. The second architecture, dubbed aggregation GNN, diffuses the signal through the graph and stores the sequence of diffused components observed by a designated node. This procedure effectively aggregates all components into a stream of information having temporal structure to which the convolution and pooling stages of regular CNNs can be applied. A multinode version of aggregation GNNs is further introduced for operation in large scale graphs. An important property of selection and aggregation GNNs is that they reduce to conventional CNNs when particularized to time signals reinterpreted as graph signals in a circulant graph. Comparative numerical analyses are performed in a source localization application over synthetic and real-world networks. Performance is also evaluated for an authorship attribution problem and text category classification. Multinode aggregation GNNs are consistently the best performing GNN architecture.Comment: Submitted to IEEE Transactions on Signal Processin