BACKGROUND The purpose of this study was to determine whether third-year medical students can become proficient in open technical skills through simulation laboratory training. METHODS A total of 204 students participated in a structured curriculum including bladder catheterization, breast examination, and knot-tying. Proficiency was documented using global rating scales and validated, object...

This paper provides a new explanation for tying that is not based on any of the standard explanations – efficiency, price discrimination, and exclusion. Our analysis shows how a monopolist sometimes has an incentive to tie a complementary good to its monopolized good in order to transfer profits from a rival producer of the complementary product to the monopolist. This occurs even when consumer...

Abstract We present a novel rationale for bundling in vertical relations. In many markets, upstream firms compete to be the best downstream slots (e.g., shelf retail store or default application on platform). If multi-product firm faces competition subset of its products, we show that tying monopolised product with competitive ones can reduce rivals’ willingness offer slotting fees retailers. T...

This paper presents a system for automatically generating linguistic questions based on a feature table. Such questions are an essential input for tree-based state tying, a technique which is widely used in speech recognition. In general, in order to utilize this technique, linguistic (or more accurately phonetic) questions have to be carefully defined. This may be extremely time consuming and ...

We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This enables an efficient pretraining algorithm and a state initialization scheme for fast inference. The model can be trained just as effi...

We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This enables an efficient pretraining algorithm and a state initialization scheme for fast inference. The model can be trained just as effi...