Simulation Model for Salmonella Typhimurium on a Farrow-to-Finish Herd

A stochastic model which simulates they dynamics of Salmonella Typhimurium in moderate to highly infeted farrow-to-finish farms in Portugal was developed. The model comprises six different stages: three at the reproductive phase (sows) and another three for pig growth

You ≠ Me: Individual differences in the structure of social cognition

This study investigated the structure of social cognition, and how it is influenced by personality; specifically, how various socio-cognitive capabilities, and the pattern of inter-relationships and co-dependencies among them differ between divergent personality styles. To measure social cognition, a large non-clinical sample (n = 290) undertook an extensive battery of self-report and performance-based measures of visual perspective taking, imitative tendencies, affective empathy, interoceptive accuracy, emotion regulation, and state affectivity. These same individuals then completed the Personality Styles and Disorders Inventory. Latent Profile Analysis revealed two dissociable personality profiles that exhibited contrasting cognitive and affective dispositions, and multivariate analyses indicated further that these profiles differed on measures of social cognition; individuals characterised by a flexible and adaptive personality profile expressed higher action orientation (emotion regulation) compared to those showing more inflexible tendencies, along with better visual perspective taking, superior interoceptive accuracy, less imitative tendencies, and lower personal distress and negativity. These characteristics point towards more efficient self-other distinction, and to higher cognitive control more generally. Moreover, low-level cognitive mechanisms served to mediate other higher level socio-emotional capabilities. Together, these findings elucidate the cognitive and affective underpinnings of individual differences in social behaviour, providing a data-driven model that should guide future research in this area

Hyperparameter Importance Across Datasets

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Minin

One-counter Markov decision processes

We study the computational complexity of central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. OC-MDPs are equivalent to a controlled extension of (discrete-time) Quasi-Birth-Death processes (QBDs), a stochastic model studied heavily in queueing theory and applied probability. They can thus be viewed as a natural ``adversarial'' version of a classic stochastic model. Alternatively, they can also be viewed as a natural probabilistic/controlled extension of classic one-counter automata. OC-MDPs also subsume (as a very restricted special case) a recently studied MDP model called ``solvency games'' that model a risk-averse gambling scenario. Basic computational questions about these models include ``termination'' questions and ``limit'' questions, such as the following: does the controller have a ``strategy'' (or ``policy'') to ensure that the counter (which may for example count the number of jobs in the queue) will hit value 0 (the empty queue) almost surely (a.s.)? Or that it will have infinite limsup value, a.s.? Or, that it will hit value 0 in selected terminal states, a.s.? Or, in case these are not satisfied a.s., compute the maximum (supremum) such probability over all strategies. We provide new upper and lower bounds on the complexity of such problems. For some of them we present a polynomial-time algorithm, whereas for others we show PSPACE- or BH-hardness and give an EXPTIME upper bound. Our upper bounds combine techniques from the theory of MDP reward models, the theory of random walks, and a variety of automata-theoretic methods

MetaBags: Bagged Meta-Decision Trees for Regression

Ensembles are popular methods for solving practical supervised learning problems. They reduce the risk of having underperforming models in production-grade software. Although critical, methods for learning heterogeneous regression ensembles have not been proposed at large scale, whereas in classical ML literature, stacking, cascading and voting are mostly restricted to classification problems. Regression poses distinct learning challenges that may result in poor performance, even when using well established homogeneous ensemble schemas such as bagging or boosting. In this paper, we introduce MetaBags, a novel, practically useful stacking framework for regression. MetaBags is a meta-learning algorithm that learns a set of meta-decision trees designed to select one base model (i.e. expert) for each query, and focuses on inductive bias reduction. A set of meta-decision trees are learned using different types of meta-features, specially created for this purpose - to then be bagged at meta-level. This procedure is designed to learn a model with a fair bias-variance trade-off, and its improvement over base model performance is correlated with the prediction diversity of different experts on specific input space subregions. The proposed method and meta-features are designed in such a way that they enable good predictive performance even in subregions of space which are not adequately represented in the available training data. An exhaustive empirical testing of the method was performed, evaluating both generalization error and scalability of the approach on synthetic, open and real-world application datasets. The obtained results show that our method significantly outperforms existing state-of-the-art approaches

A survey of the European Reference Network EpiCARE on clinical practice for selected rare epilepsies

Objective: Clinical care of rare and complex epilepsies is challenging, because evidence-based treatment guidelines are scarce, the experience of many physicians is limited, and interdisciplinary treatment of comorbidities is required. The pathomechanisms of rare epilepsies are, however, increasingly understood, which potentially fosters novel targeted therapies. The objectives of our survey were to obtain an overview of the clinical practice in European tertiary epilepsy centers treating patients with 5 arbitrarily selected rare epilepsies and to get an estimate of potentially available patients for future studies. Methods: Members of the European Reference Network for rare and complex epilepsies (EpiCARE) were invited to participate in a web-based survey on clinical practice of patients with Dravet syndrome, tuberous sclerosis complex (TSC), autoimmune encephalitis, and progressive myoclonic epilepsies including Unverricht Lundborg and Unverricht-like diseases. A consensus-based questionnaire was generated for each disease. Results: Twenty-six of 30 invited epilepsy centers participated. Cohorts were present in most responding centers for TSC (87%), Dravet syndrome (85%), and autoimmune encephalitis (71%). Patients with TSC and Dravet syndrome represented the largest cohorts in these centers. The antiseizure drug treatments were rather consistent across the centers especially with regard to Dravet syndrome, infantile spasms in TSC, and Unverricht Lundborg / Unverricht-like disease. Available, widely used targeted therapies included everolimus in TSC and immunosuppressive therapies in autoimmune encephalitis. Screening for comorbidities was routinely done, but specific treatment protocols were lacking in most centers. Significance: The survey summarizes the current clinical practice for selected rare epilepsies in tertiary European epilepsy centers and demonstrates consistency as well as heterogeneity in the treatment, underscoring the need for controlled trials and recommendations. The survey also provides estimates for potential participants of clinical trials recruited via EpiCARE, emphasizing the great potential of Reference Networks for future studies to evaluate new targeted therapies and to identify novel biomarkers

Impact of cognitive stimulation on ripples within human epileptic and non-epileptic hippocampus

Background: Until now there has been no way of distinguishing between physiological and epileptic hippocampal ripples in intracranial recordings. In the present study we addressed this by investigating the effect of cognitive stimulation on interictal high frequency oscillations in the ripple range (80-250 Hz) within epileptic (EH) and non-epileptic hippocampus (NH). Methods: We analyzed depth EEG recordings in 10 patients with intractable epilepsy, in whom hippocampal activity was recorded initially during quiet wakefulness and subsequently during a simple cognitive task. Using automated detection of ripples based on amplitude of the power envelope, we analyzed ripple rate (RR) in the cognitive and resting period, within EH and NH. Results: Compared to quiet wakefulness we observed a significant reduction of RR during cognitive stimulation in EH, while it remained statistically marginal in NH. Further, we investigated the direct impact of cognitive stimuli on ripples (i.e. immediately post-stimulus), which showed a transient statistically significant suppression of ripples in the first second after stimuli onset in NH only. Conclusion: Our results point to a differential reactivity of ripples within EH and NH to cognitive stimulation

Documentary evidence of past floods in Europe and their utility in flood frequency estimation

International audienceThis review outlines the use of documentary evidence of historical flood events in contemporary flood frequency estimation in European countries. The study shows that despite widespread consensus in the scientific literature on the utility of documentary evidence, the actual migration from academic to practical application has been limited. A detailed review of flood frequency estimation guidelines from different countries showed that the value of historical data is generally recognised, but practical methods for systematic and routine inclusion of this type of data into risk analysis are in most cases not available. Studies of historical events were identified in most countries, and good examples of national databases attempting to collate the available information were identified. The conclusion is that there is considerable potential for improving the reliability of the current flood risk assessments by harvesting the valuable information on past extreme events contained in the historical data sets.Cet article présente une revue de l'utilisation de l'information documentaire sur les crues historiques par les pays européens pour l'analyse fréquentielle des crues. L'étude montre que, malgré l'existence d'un consensus scientifique sur l'intérêt de ce type d'information, son utilisation reste encore limitée d'un point de vue opérationnel. Si les guides pratiques sur l'estimation des crues mentionnent en général bien l'intérêt de l'information historique, il existe encore peu de logiciel disponible utilisant cette information. Des travaux sont en cours dans plusieurs pays pour constituer des bases de données nationales sur les crues historiques. La conclusion est qu'il y aurait un fort bénéfice à exploiter ces informations pour l'estimation du risque de crue

Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

International audienceKnowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research