27,842 research outputs found
Formal nursing terminology systems: a means to an end
In response to the need to support diverse and complex information requirements, nursing has developed a number of different terminology systems. The two main kinds of systems that have emerged are enumerative systems and combinatorial systems, although some systems have characteristics of both approaches. Differences in the structure and content of terminology systems, while useful at a local level, prevent effective wider communication, information sharing, integration of record systems, and comparison of nursing elements of healthcare information at a more global level. Formal nursing terminology systems present an alternative approach. This paper describes a number of recent initiatives and explains how these emerging approaches may help to augment existing nursing terminology systems and overcome their limitations through mediation. The development of formal nursing terminology systems is not an end in itself and there remains a great deal of work to be done before success can be claimed. This paper presents an overview of the key issues outstanding and provides recommendations for a way forward
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Bayesian optimization has become a successful tool for hyperparameter
optimization of machine learning algorithms, such as support vector machines or
deep neural networks. Despite its success, for large datasets, training and
validating a single configuration often takes hours, days, or even weeks, which
limits the achievable performance. To accelerate hyperparameter optimization,
we propose a generative model for the validation error as a function of
training set size, which is learned during the optimization process and allows
exploration of preliminary configurations on small subsets, by extrapolating to
the full dataset. We construct a Bayesian optimization procedure, dubbed
Fabolas, which models loss and training time as a function of dataset size and
automatically trades off high information gain about the global optimum against
computational cost. Experiments optimizing support vector machines and deep
neural networks show that Fabolas often finds high-quality solutions 10 to 100
times faster than other state-of-the-art Bayesian optimization methods or the
recently proposed bandit strategy Hyperband
SDL to Fiacre translation
International audienceThe translation of a system model to an intermediate format is an important step towards formal proofs of properties. This paper presents the formulation of rules to enable the translation of SDL standard models to Fiacre language. The translated model is then provided as input to a suitable toolset that performs model checking and other analysis. The formulated translation rules are discussed in view of the correctness of translation and ease of implementation. Implementations of some of the translation rules in an industrial tool (PragmaDev RTDS), and a proof of the concept on a simple SDL model are presented to ascertain the validity of the proposed translation
Making AI Meaningful Again
Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy
Mathematical skills in the workplace: final report to the Science Technology and Mathematics Council
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