et al.. Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis.
A B S T R A C TSickness behaviour is characterised by a lethargic state during which the animal reduces its activity, sleeps more and at times when normally awake, reduces its feed and water intake, and interacts less with its environment. Subtle modifications in behaviour can materialise just before clinical signs of a disease. Recent sensor developments enable continuous monitoring of animal behaviour, but the shift to abnormal animal activity remains difficult to detect. We explored the use of Machine Learning (ML) to detect abnormal behaviour from continuous monitoring. We submitted 14 cows (Bos taurus) to Sub-Acute Ruminal Acidosis (SARA), a disease known to induce changes in behaviour. Another 14 control cows were not submitted to SARA. We used a ruminal bolus to monitor pH and detect when a cow experienced SARA. We used a positioning system to infer an animal's activity based on its position in relation to specific elements in the barn (feeder, resting area, and alleys). We tested several ML algorithms: K Nearest Neighbours for Regression (KNNR); Decision Tree for Regression (DTR); MultiLayer Perceptron (MLP); Long Short-Term Memory (LSTM); and an algorithm where activity is assumed to be similar from one day to the next. First, we developed ML models to predict activity on a given day from the previous 24 h, considering all cows together. Then, we calculated the error between observed and predicted values for a given cow. Finally, we compared the error to a threshold chosen to optimise the distinction between normal and abnormal values. KNNR performed best, detecting 83% of SARA cases (true-positives), but it also produced 66% of false-positives, which limits its use in practice. In conclusion, ML can help detect anomalies in behaviour. Further improvements could probably be obtained by applying ML on very large datasets at animal rather than group level.
C onsider the following game. Given a network with a continuum of users at some origins, suppose users wish to reach specific destinations but they are not indifferent to the cost to reach them. They may have multiple possible routes but their choices modify the travel costs on the network. Hence, each user faces the following problem: Given a pattern of travel costs for the different possible routes that reach the destination, find a path of minimal cost. This kind of game belongs to the class of congestion games. In the traditional static approach, travel times are assumed constant during the period of the game.In this paper, we consider the so-called dynamic case where the time-varying nature of traffic conditions is explicitly taken into account. In transportation science, the question of whether there is an equilibrium and how to compute it for such a model is referred to as the dynamic user equilibrium problem.Until now, there was no general model for this problem. Our paper attempts to resolve this issue. We define a new class of games, dynamic congestion games, which capture this time-dependency aspect. Moreover, we prove that under some natural assumptions there is a Nash equilibrium. When we apply this result to the dynamic user equilibrium problem, we get most of the previous known results.
The age of cloud computing has introduced all the mechanisms needed to elastically scale distributed, cloudenabled applications. At roughly the same time, NoSQL databases have been proclaimed as the scalable alternative to relational databases. Since then, NoSQL databases are a core component of many large-scale distributed applications. This paper evaluates the scalability and elasticity features of the three widely used NoSQL database systems Couchbase, Cassandra and MongoDB under various workloads and settings using throughput and latency as metrics. The numbers show that the three database systems have dramatically different baselines with respect to both metrics and also behave unexpected when scaling out. For instance, while Couchbase's throughput increases by 17% when scaled out from 1 to 4 nodes, MongoDB's throughput decreases by more than 50%. These surprising results show that not all tested NoSQL databases do scale as expected and even worse, in some cases scaling harms performances.
The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.
Nicolas Wagner : Morelly : present state of research.
Although little known and superficially read, Morelly has nevertheless been enlisted by critics among the Utopians and precursors of socialism. It is neces¬ sary above all to return to the author himself, as far as possible, and to the manuscript sources, which are listed here for the first time. Their interest is briefly explained (personal documents, Ferme Générale, editors, protectors, police). There is also an important corpus of 18th-century reviews of Morelly's works, which enables us to place him in his political and ideological context (1743-1754). Thus we can evaluate more concretely the significance and literary value of his work, and in particular of the Code.
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