Automated Word Puzzle Generation via Topic Dictionaries

We propose a general method for automated word puzzle generation. Contrary to previous approaches in this novel field, the presented method does not rely on highly structured datasets obtained with serious human annotation effort: it only needs an unstructured and unannotated corpus (i.e., document collection) as input. The method builds upon two additional pillars: (i) a topic model, which induces a topic dictionary from the input corpus (examples include e.g., latent semantic analysis, group-structured dictionaries or latent Dirichlet allocation), and (ii) a semantic similarity measure of word pairs. Our method can (i) generate automatically a large number of proper word puzzles of different types, including the odd one out, choose the related word and separate the topics puzzle. (ii) It can easily create domain-specific puzzles by replacing the corpus component. (iii) It is also capable of automatically generating puzzles with parameterizable levels of difficulty suitable for, e.g., beginners or intermediate learners.Comment: 4 page

Predictive role of neostromal CD10 expression in breast cancer patients treated with neoadjuvant chemotherapy

Background: The therapeutic strategy of invasive breast cancer is based on routine histopathological markers (estrogen-, progesterone receptor, HER2, Ki67) routinely evaluated in tumor cells. However, the assessment of cancer stroma could influence therapeutic strategies. Studies have shown that stromal expression of CD10, a zinc-dependent metalloproteinase, is associated with biological aggressiveness of the tumor. In the present retrospective study, we aimed to evaluate stromal CD10 expression and association between CD10 expression and response to neoadjuvant chemotherapy in invasive breast cancer.Methods: CD10 immunohistochemistry was performed on core biopsies taken before the neoadjuvant therapy. Stromal CD10 expression was determined and compared with well-known predictive and prognostic tissue markers as well as with the following groups defined according to the degree of tumor response: no regression, partial regression, and complete regression.Results: A total of 60 locally advanced invasive breast carcinomas of “no special type” were included. The proportion of CD10 positive tumors was significantly higher in the “no regression” group compared to “complete regression” group (p = 0.000). Stromal CD10 expression was found to be significantly associated with decrease in response to neoadjuvant chemotherapy. According to CD10 expression we did not find any difference in hormone receptor status, Ki67, tumor grade or neostromal area.Conclusion: Our data suggest that CD10 expression can serve as a predictive marker of the effect of neoadjuvant chemotherapy in breast cancer patients. Therefore, its inclusion into the routine assessment of biopsies to tailor tumor-specific therapeutic strategies merits consideration

The Emergence of Multiple Status Systems in Adolescent Communities:a multiplex network theory of group formation

How do informal groups emerge in adolescent communities? What distinguishes a group from just a set of students? Who will end up together in a group and who will be left out? Why are there more groups in some classrooms and fewer in others? What determines whether these groups overlap in their members or they are completely segregated, perhaps antagonistic? While a huge body of research in sociology and social psychology focuses on these questions, an integrated approach that is able to answer all of them is yet to be developed. Without realizing that these five issues are interrelated, we cannot hope to understand how groups influence individuals and how they shape our communities. This thesis proposes an integrative theory of informal group formation in communities. Based on the tradition of Social Network Analysis, it develops a framework in which interpersonal relations and reputations are formed through a process called informal status production. Groups emerge from this micro-process by the alignment of positive relations and agreement in peer-perceptions between actors. The main micro-mechanisms predicted by the theory are tested on a unique longitudinal network dataset from school classrooms. To this end, a new empirical procedure was developed, by which a few aggregated networks can be constructed from tens of relational items. This allows the analysis of rich network data with several relational dimensions. The empirical studies of multiplex network dynamics confirm that there are strong interdependencies between friendships and perceptions. Students who agree about their peers tend to become friends, but more so when they hold a minority opinion in the class. This contributes to group formation. Friends also influence each other's perceptions, but we manage to show that the presence of groups around them interferes with this process by moderating the influence of individual peers

Limits to inferring status from friendship relations

Informal status in communities is a relational phenomenon, it is attributed by others, and can be analysed using social network measures. However, many studies measure a single network relation – friendship – and interpret it related to status hierarchies: the number of friends is assumed to indicate status and asymmetric friendships are assumed to indicate status difference. While this practice is grounded in theoretical models of group structure, its assumptions have not been tested thoroughly and contradict research from various fields. We assess the validity of the approach using multiplex network data with three measures of status attribution from 17,469 high-school students in 5 European countries. We find that (i) asymmetric and symmetric friendships rarely overlap with status attributions, (ii) asymmetric friends are closer in status than expected by chance, and (iii) friendship and status attribution indegrees are only moderately correlated. These findings show that friendship relations cannot substitute for direct measurements of status in the study of informal hierarchies

The emergence and stability of groups in social networks

An important puzzle in social network research is to explain how macro-level structures emerge from micro-level network processes. Explaining the emergence and stability of structural groups in social networks is particularly difficult for two reasons. First, because groups are characterized both by high connectedness within (group cohesion) and lack of connectedness between them (group boundaries). Second, because a large number of theoretical micro-level network processes contribute to their emergence. We argue that traditional social network theories that are concerned with the evolution of positive relations (forces of attraction) are not sufficient to explain the emergence of groups because they lack mechanisms explaining the emergence of group boundaries. Models that additionally account for the evolution of negative ties (forces of repulsion) may be better suited to explain the emergence and stability of groups. We build a theoretical model and illustrate its usefulness by fitting stochastic actor-oriented models (SAOMs) to empirical data of co-evolving networks of friendship and dislike among 479 secondary-school students. The SAOMs include a number of newly developed effects expressing the co-evolution between positive and negative ties. We then simulate networks from the estimated models to explore the micro-macro link. We find that a model that considers forces of attraction and repulsion simultaneously is better at explaining groups in social networks. In the long run, however, the empirically informed simulations generate networks that are too stylized to be realistic, raising further questions about model degeneracy and time heterogeneity of group processes.Funding Agencies|Swiss National Science FoundationSwiss National Science Foundation (SNSF) [10001A_169965]; European Research Council(ERC) under the European Unions Horizon 2020 research and innovation programmeEuropean Research Council (ERC) [648693]; Hungarian Research Fund (OTKA)Orszagos Tudomanyos Kutatasi Alapprogramok (OTKA) [K81336]; Lendulet Grant of the Hungarian Academy of Sciences</p

Short-term and long-term effects of a social network intervention on friendships among university students

Informal social relations, such as friendships, are crucial for the well-being and success of students at all levels of education. Network interventions can aim at providing contact opportunities in school settings to prevent the social isolation of individuals and facilitate integration between otherwise segregated social groups. We investigate the short-term and long-term effects of one specific network intervention in an undergraduate cohort freshly admitted to an engineering department (N=226). In this intervention, we randomly assigned students into small groups at an introduction event two months prior to their first day at university. The groups were designed to increase mixed-gender contact opportunities. Two months after the intervention, we find a higher rate of friendships, common friends, and mixed-gender friendships in pairs of students who were assigned to the same group than in pairs from different groups (short-term effects). These effects gradually diminish over the first academic year (long-term effects). Using stochastic actor-oriented models, we investigate the long-term trajectory of the intervention effects, while considering alternative network processes, such as reciprocity, transitivity, homophily, and popularity. The results suggest that even though the induced friendship ties are less stable than other friendships, they may serve as early seeds for complex social network processes. Our study shows that simple network interventions can have a pronounced short-term effect and indirect long-term effects on the evolution and structure of student communities.ISSN:2045-232