Abstract
Background: Transmission models can aid understanding of disease dynamics and are useful in testing the efficiency of control measures. The aim of this study was to formulate an appropriate stochastic Susceptible-Infectious-Resistant/ Carrier (SIR) model for Salmonella Typhimurium in pigs and thus estimate the transmission parameters between states. Results: The transmission parameters were estimated using data from a longitudinal study of three Danish farrow-to-finish pig herds known to be infected. A Bayesian model framework was proposed, which comprised Binomial components for the transition from susceptible to infectious and from infectious to carrier; and a Poisson component for carrier to infectious. Cohort random effects were incorporated into these models to allow for unobserved cohort-specific variables as well as unobserved sources of transmission, thus enabling a more realistic estimation of the transmission parameters. In the case of the transition from susceptible to infectious, the cohort random effects were also time varying. The number of infectious pigs not detected by the parallel testing was treated as unknown, and the probability of non-detection was estimated using information about the sensitivity and specificity of the bacteriological and serological tests. The estimate of the transmission rate from susceptible to infectious was 0.33 [0.06, 1.52], from infectious to carrier was 0.18 [0.14, 0.23] and from carrier to infectious was 0.01 [0.0001, 0.04]. The estimate for the basic reproduction ration (R-0) was 1.91 [0.78, 5.24]. The probability of non-detection was estimated to be 0.18 [0.12, 0.25]. Conclusions: The proposed framework for stochastic SIR models was successfully implemented to estimate transmission rate parameters for Salmonella Typhimurium in swine field data. R0 was 1.91, implying that there was dissemination of the infection within pigs of the same cohort. There was significant temporal-cohort variability, especially at the susceptible to infectious stage. The model adequately fitted the data, allowing for both observed and unobserved sources of uncertainty (cohort effects, diagnostic test sensitivity), so leading to more reliable estimates of transmission parameters.

Abstract
Algorithm selection methods can be speeded-up substantially by incorporating multi-objective measures that give preference to algorithms that are both promising and fast to evaluate. In this paper, we introduce such a measure, A3R, and incorporate it into two algorithm selection techniques: average ranking and active testing. Average ranking combines algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. The aim of the second method is to iteratively select algorithms to be tested on the new dataset, learning from each new evaluation to intelligently select the next best candidate. We show how both methods can be upgraded to incorporate a multi-objective measure A3R that combines accuracy and runtime. It is necessary to establish the correct balance between accuracy and runtime, as otherwise time will be wasted by conducting less informative tests. The correct balance can be set by an appropriate parameter setting within function A3R that trades off accuracy and runtime. Our results demonstrate that the upgraded versions of Average Ranking and Active Testing lead to much better mean interval loss values than their accuracy-based counterparts.

Abstract
Several algorithms based on PageRank algorithm have been proposed to rank the document sentences in the multi-document summarization field and LexRank and T-LexRank algorithms are well known examples. In literature different concepts such as weighted inter-cluster edge, cluster-sensitive graph model and document-sensitive graph model have been proposed to improve LexRank and T-LexRank algorithms (e.g. DsR-G, DsR-Q) for multi-document summarization. In this paper, a density-based graph model for multi-document summarization is proposed by adding the concept of density to LexRank and T-LexRank algorithms. The resulting generic multi-document summarization systems, DensGS and DensGSD were evaluated on DUC 2004 while the query-based variants, DensQS, DensQSD were evaluated on DUC 2006, DUC 2007 and TAC 2010 task A. ROUGE measure was used in the evaluation. Experimental results show that density concept improves LexRank and T-LexRank algorithms and outperforms previous graph-based models (DsR-G and DsR-Q) in generic and query-based multi-document summarization tasks. Furthermore, the comparison of the number of iterations indicates that the density-based algorithm is faster than the other algorithms based on PageRank.

Abstract
In this work we analyze the evolving dynamics of different strategies of collaborative networks that emerge from the creation and diffusion of knowledge. An evolutionary economic approach is adopted by introducing decision rules that are applied routinely and an agent-based model is developed. Firms (the agents) can collaborate and create networks for research and development purposes. We have compared three collaboration strategies (A-peer-to-peer complementariness, B-concentration process and C-virtual cooperation networks) that were defined on the basis of literature and on empirical evidence. Strategies are introduced exogenously in the simulation. The aims of this paper are twofold: (i) to analyze the importance of the networking effects; and (ii) to test the differences among collaboration strategies. It was possible to conclude that profit is associated with higher stock of knowledge and with smaller network diameter. In addition, concentration strategies are more profitable and more efficient in transmitting knowledge through the network. These processes reinforce the stock of knowledge and the profit of the firms located in the centers of the networks.

Abstract
Most research on multi-document summarization explores methods that generate summaries based on queries regardless of the users' preferences. We note that, different users can generate somewhat different summaries on the basis of the same source data and query. This paper presents our study on how to exploit the information regards how users summarized their texts. Models of different users can be used either separately, or in an ensemble-like fashion. Machine learning methods are explored in the construction of the individual models. However, we explore yet another hypothesis. We believe that the sentences selected into the summary should be coherent and supplement each other in their meaning. One method to model this relationship between sentences is by detecting actor-object relationship (AOR). The sentences that satisfy this relationship have their importance value enhanced. This paper combines ensemble summarizing system and AOR to generate summaries. We have evaluated this method on DUC 2006 and DUC 2007 using ROUGE measure. Experimental results show the supervised method that exploits the ensemble summarizing system combined with AOR outperforms previous models when considering performance in query-based multi-document summarization tasks.

Abstract
This article serves as an introduction to the Special Issue on Metalearning and Algorithm Selection. The introduction is divided into two parts. In the the first section, we give an overview of how the field of metalearning has evolved in the last 1-2 decades and mention how some of the papers in this special issue fit in. In the second section, we discuss the contents of this special issue. We divide the papers into thematic subgroups, provide information about each subgroup, as well as about the individual papers. Our main aim is to highlight how the papers selected for this special issue contribute to the field of metalearning.