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Accurate scoring of syntactic structures such as head-modifier arcs in dependency parsing typically requires rich, high-dimensional feature representations. A small subset of such features is often selected manually. This is problematic when features lack clear linguistic meaning as in embeddings or when the information is blended across features. In this(More)
Epilepsy is commonly associated with cognitive impairment. Astrocyte activation and oxidative stress occur following seizures, and play a role in the pathological injury of epilepsy with cognitive impairment. The peroxisome proliferator-activated receptor gamma (PPARγ) has been shown to exhibit neuroprotective and antioxidative effects in CNS diseases.(More)
OBJECTIVES To comprehensively evaluate the association of ERCC1 C8092A and ERCC2 Lys751Gln polymorphisms with the risk of glioma. METHODS Potential studies were searched and selected through the Pubmed/MEDLINE, EMBASE, the China National Knowledge Infrastructure (CNKI) platforms, WanFang and VIP database up to June 2013. Two investigators independently(More)
Matrix factorization (MF) has evolved as one of the most accurate approaches to collaborative filtering. In this paper, we extend the probabilistic MF framework as to account for multiple observations for each matrix element. This significantly improves the accuracy of recommender systems in several areas: (1) aggregation of ratings concerning items(More)
— We study the distinguishability of multipartite quantum states by separable operations. We first present a necessary and sufficient condition for a finite set of orthogonal quantum states to be distinguishable by separable operations. An analytical version of this condition is derived for the case of (D − 1) pure states, where D is the total dimension of(More)
Accounting for missing ratings in available training data was recently shown [3, 17] to lead to large improvements in the top-k hit rate of recommender systems, compared to state-of-the-art approaches optimizing the popular root-mean-square-error (RMSE) on the observed ratings. In this paper, we take a Bayesian approach, which lends itself naturally to(More)
OBJECTIVE Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors(More)
Intercellular adhesion molecule-1 (ICAM-1), an important immune adhesion molecule, is related to the atherosclerosis. We explored the association between the polymorphisms of the ICAM-1 gene and coronary atherosclerotic stenosis to determine whether any risk factors correlate with genetic polymorphisms in Chinese patients with coronary atherosclerosis.(More)