Relation Liftings on Preorders and Posets

The category Rel(Set) of sets and relations can be described as a category of spans and as the Kleisli category for the powerset monad. A set-functor can be lifted to a functor on Rel(Set) iff it preserves weak pullbacks. We show that these results extend to the enriched setting, if we replace sets by posets or preorders. Preservation of weak pullbacks becomes preservation of exact lax squares. As an application we present Moss's coalgebraic over posets

Algorithm for Adapting Cases Represented in a Tractable Description Logic

Case-based reasoning (CBR) based on description logics (DLs) has gained a lot of attention lately. Adaptation is a basic task in the CBR inference that can be modeled as the knowledge base revision problem and solved in propositional logic. However, in DLs, it is still a challenge problem since existing revision operators only work well for strictly restricted DLs of the \emph{DL-Lite} family, and it is difficult to design a revision algorithm which is syntax-independent and fine-grained. In this paper, we present a new method for adaptation based on the DL $\mathcal{EL_{\bot}}$. Following the idea of adaptation as revision, we firstly extend the logical basis for describing cases from propositional logic to the DL $\mathcal{EL_{\bot}}$, and present a formalism for adaptation based on $\mathcal{EL_{\bot}}$. Then we present an adaptation algorithm for this formalism and demonstrate that our algorithm is syntax-independent and fine-grained. Our work provides a logical basis for adaptation in CBR systems where cases and domain knowledge are described by the tractable DL $\mathcal{EL_{\bot}}$.Comment: 21 pages. ICCBR 201

Demographic determinants of acute gastrointestinal illness in Canada: a population study

<p>Abstract</p> <p>Background</p> <p>Gastrointestinal illness is an important global public health issue, even in developed countries, where the morbidity and economic impact are significant. Our objective was to evaluate the demographic determinants of acute gastrointestinal illness in Canadians.</p> <p>Methods</p> <p>We used data from two population-based studies conducted in select communities between 2001 and 2003. Together, the studies comprised 8,108 randomly selected respondents; proxies were used for all respondents under 12 years and for respondents under 19 years at the discretion of the parent or guardian. Using univariate and multivariate logistic regression, we evaluated the following demographic determinants: age, gender, cultural group, and urban/rural status of the respondent, highest education level of the respondent or proxy, number of people in the household, and total annual household income. Two-way interaction terms were included in the multivariate analyses. The final multivariate model included income, age, gender, and the interaction between income and gender.</p> <p>Results</p> <p>After adjusting for income, gender, and their interaction, children under 10 years had the highest risk of acute gastrointestinal illness, followed by young adults aged 20 to 24 years. For males, the risk of acute gastrointestinal illness was similar across all income levels, but for females the risk was much higher in the lowest income category. Specifically, in those with total annual household incomes of less than $20,000, the odds of acute gastrointestinal illness were 2.46 times higher in females than in males.</p> <p>Conclusion</p> <p>Understanding the demographic determinants of acute gastrointestinal illness is essential in order to identify vulnerable groups to which intervention and prevention efforts can be targeted.</p

Combinatorial Games with a Pass: A dynamical systems approach

By treating combinatorial games as dynamical systems, we are able to address a longstanding open question in combinatorial game theory, namely, how the introduction of a "pass" move into a game affects its behavior. We consider two well known combinatorial games, 3-pile Nim and 3-row Chomp. In the case of Nim, we observe that the introduction of the pass dramatically alters the game's underlying structure, rendering it considerably more complex, while for Chomp, the pass move is found to have relatively minimal impact. We show how these results can be understood by recasting these games as dynamical systems describable by dynamical recursion relations. From these recursion relations we are able to identify underlying structural connections between these "games with passes" and a recently introduced class of "generic (perturbed) games." This connection, together with a (non-rigorous) numerical stability analysis, allows one to understand and predict the effect of a pass on a game.Comment: 39 pages, 13 figures, published versio

ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

Automatically annotating column types with knowledge base(KB) concepts is a critical task to gain a basic understandingof web tables. Current methods rely on either table metadatalike column name or entity correspondences of cells in theKB, and may fail to deal with growing web tables with in-complete meta information. In this paper we propose a neu-ral network based column type annotation framework namedColNetwhich is able to integrate KB reasoning and lookupwith machine learning and can automatically train Convolu-tional Neural Networks for prediction. The prediction modelnot only considers the contextual semantics within a cell us-ing word representation, but also embeds the semantics of acolumn by learning locality features from multiple cells. Themethod is evaluated with DBPedia and two different web ta-ble datasets, T2Dv2 from the general Web and Limaye fromWikipedia pages, and achieves higher performance than thestate-of-the-art approaches

Learning Semantic Annotations for Tabular Data

The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table's contextual semantics, including table locality features learned by a Hybrid Neural Network (HNN), and inter-column semantics features learned by a knowledge base (KB) lookup and query answering algorithm.It exhibits good performance not only on individual table sets, but also when transferring from one table set to another

Collaboration enhances career progression in academic science, especially for female researchers.

Funder: Helsinki Institute of Life ScienceFunder: Leverhulme TrustCollaboration and diversity are increasingly promoted in science. Yet how collaborations influence academic career progression, and whether this differs by gender, remains largely unknown. Here, we use co-authorship ego networks to quantify collaboration behaviour and career progression of a cohort of contributors to biennial International Society of Behavioral Ecology meetings (1992, 1994, 1996). Among this cohort, women were slower and less likely to become a principal investigator (PI; approximated by having at least three last-author publications) and published fewer papers over fewer years (i.e. had shorter academic careers) than men. After adjusting for publication number, women also had fewer collaborators (lower adjusted network size) and published fewer times with each co-author (lower adjusted tie strength), albeit more often with the same group of collaborators (higher adjusted clustering coefficient). Authors with stronger networks were more likely to become a PI, and those with less clustered networks did so more quickly. Women, however, showed a stronger positive relationship with adjusted network size (increased career length) and adjusted tie strength (increased likelihood to become a PI). Finally, early-career network characteristics correlated with career length. Our results suggest that large and varied collaboration networks are positively correlated with career progression, especially for women

Micromechanical finite element analyses of fire retardant woven fabric composites at elevated temperatures using unit cells at multiple length scales

This paper presents a micromechanical Finite Element (FE) model developed to predict the effective mechanical properties of glass fibre-reinforced (woven fabric) polymer composites with/without fire retardant particulate additives at elevated temperatures. The elevated mechanical properties of glass fibre-reinforced epoxy composites with/without fire retardants were predicted using three unit cells of varying length scales in micromechanical FE analysis. Theoretically predictions of flexural behaviour of these fibre-reinforced polymer composites at elevated temperatures were satisfactorily validated against experimentally measured data. The numerical model developed herein was then used for the prediction of other mechanical properties of fibre-reinforced polymer composites that would have been difficult to collect at elevated temperatures. Micromechanical FE models such as the one contained in this paper are useful to architectural engineers as they can be used to guide the design and qualification of new engineering composites that satisfy stringent Building codes in fire prone engineering applications