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People often change their beliefs by succumbing to an opinion of the majority. Such changes are often referred to as majority influence or conformity. While some previous studies have focused on the reinforcement learning mechanisms of conformity or on its internalization, others have reported evidence of changes in sensory processing evoked by majority opinion. In this study, we used magnetoencephalographic (MEG) source imaging to further investigate the remote effects of agreement and disagreement with the majority. During the first session, participants rated the trustworthiness of faces and subsequently learned how the majority of their peers had previously rated each face. To identify the neural correlates of the post-effect of agreeing or disagreeing with the group, we recorded MEG activity while participants rated faces during the next session. We found MEG traces of past disagreement or agreement with the peer group at the parietal cortices as early as approximately 230 ms after the face onset. The neural activity of the superior parietal lobule, intraparietal sulcus, and precuneus was significantly stronger if the participant’s rating had previously differed from the ratings of his or her peers. The early MEG correlates of disagreement with the majority were followed by activity in the orbitofrontal cortex starting at about 320 ms after the face onset. Altogether, the results reveal the temporal dynamics of the neural mechanism of remote effects of disagreement with the peer group: early signatures of modified face processing were followed by later markers of long-term social influence on the valuation process at the ventromedial prefrontal cortex
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering in order to incorporate structural information into predictive model. Nowadays, a family of automated graph feature engineering techniques have been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation.
Our survey aims to describe the core concepts of graph embeddings, and provide several taxonomies for their description. First, we start with methodological approach, and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability to of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different embedding and graph properties are connected to the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization.
As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.
Aiming to understand the data complexity of answering conjunctive queries mediated by an axiom stating that a class is covered by the union of two other classes, we show that deciding their first-order rewritability is PSPACE-hard and obtain a number of sufficient conditions for membership in AC0, L, NL, and P. Our main result is a complete syntactic AC0/NL/P/CONP tetrachotomy of path queries under the assumption that the covering classes are disjoint.
Previously, a mathematical model of primary tumor (PT) growth and secondary distant metastasis (sdMTS) growth in breast cancer (BC) (CoMPaS), considering the TNM classification, was presented. Nowadays, the updated model CoMPaS and the corresponding software tool can help to optimize the process of detecting the different diagnostic periods for sdMTSs in BC patients with different tumor subtypes ER/PR/HER2/Ki-67 and the growth rate of the PT and sdMTSs.