These data demonstrate that the sirolimus-eluting and the bare SMART stent are effective, safe, and free from restenosis in a majority of patients for up to 24 months. Because the restenosis rate in the bare stent group is unexpectedly low, no significant difference could be found between the sirolimus-eluting and the bare SMART stents.

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In this volume, the author presents a theory of human cognitive functioning and a set of experiments that test the theory, as well as a review of the literature relevant to the theory. A computer simulation model called ACT embodies the theory. The ACT model consists of two major components: an associative network model of longterm memory that encodes the propositional knowledge of the world, and a production system that operates on the propositional network to perform various cognitive tasks.Here is a complete and up-to-date model of higher mental processes. It expands on the HAM model of Anderson and Bower by improving deficits in the memory system of HAM to offer a broadly predictive model with formal analysis and a highly developed contact with the experimental literature and with recent research in memory, language, and computer simulation.

The 5-year results of the COBEST demonstrated that the CS has an enduring patency advantage over the BMS in both the short and long terms. Furthermore, the CS showed acceptable patency rates for the treatment of more severe TASC C and D lesions, and patients who received a CS required fewer revascularization procedures. However, the choice of stent did not affect the rate of major limb amputations.

Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to combine two or more embeddings, e.g., most notably in matrix factorization. In recent years, it was suggested to replace the dot product with a learned similarity e.g. using a multilayer perceptron (MLP). This approach is often referred to as neural collaborative filtering (NCF). In this work, we revisit the experiments of the NCF paper that popularized learned similarities using MLPs. First, we show that with a proper hyperparameter selection, a simple dot product substantially outperforms the proposed learned similarities. Second, while a MLP can in theory approximate any function, we show that it is non-trivial to learn a dot product with an MLP. Finally, we discuss practical issues that arise when applying MLP based similarities and show that MLPs are too costly to use for item recommendation in production environments while dot products allow to apply very efficient retrieval algorithms. We conclude that MLPs should be used with care as embedding combiner and that dot products might be a better default choice. CCS CONCEPTS • Information systems → Recommender systems; Collaborative filtering; • Computing methodologies → Learning from implicit feedback; Neural networks; Factorization methods.