The recent advances in single-cell technologies have enabled us to profile genomic features at unprecedented resolution and data sets from multiple domains are available, including data sets that profile different types of genomic features and data sets that profile the same type of genomic features across different species. These data sets typically have different powers in identifying the unknown cell types through clustering, and data integration can potentially lead to a better performance of clustering algorithms. In this work, we formulate the problem in an unsupervised transfer learning framework, which utilizes knowledge learned from auxiliary data set to improve the clustering performance of target data set. The degree of shared information among the target and auxiliary data sets can vary, and their distributions can also be different. To address these challenges, we propose an elastic coupled co-clustering based transfer learning algorithm, by elastically propagating clustering knowledge obtained from the auxiliary data set to the target data set. Implementation on single-cell genomic data sets shows that our algorithm greatly improves clustering performance over the traditional learning algorithms. The source code and data sets are available at https://github.com/cuhklinlab/elasticC3.

Using the already validated Coh-Metrix tool, this study examines whether there are significant linguistic and discourse differences between biomedical abstracts for American and Korean English. Also, the current study accounts for variation among journals’ countries of origin, distinguishing between biomedical journals published in the United States from biomedical journals published in South Korea. The significance of these studies regards the growing number of second language (L2) biomedical researchers attempting to publish their research in national and international English-language journals, but who find themselves locked out of the discussion because of differences in linguistic and discourse conventions. The present study aims to provide a more thorough and quantitative understanding of the prototypical linguistic components in biomedical rhetoric, and to suggest how word-, sentence-, and discourse-level structures can be researched, taught, and developed into materials. This improved understanding is expected to provide a powerful apparatus for the promotion of L2 English writers in the biomedical field.

J.B. Pollack, Implications of Recursive Distributed Representations, Advances in Neural Information Systems 1, D.S. Touretzky (ed), Morgan Kaufmann, San Mateo, Ca, p. 527 (1989).

J.B. Pollack, Implications of Recursive Distributed Representations, Advances in Neural Information Systems 1, D.S. Touretzky (ed), Morgan Kaufmann, San Mateo, Ca, p. 527 (1989).

This listing is intended as an introduction to the literature on Artificial Intelligence, €”i.e., to the literature dealing with the problem of making machines behave intelligently. We have divided this area into categories and cross-indexed the references accordingly. Large bibliographies without some classification facility are next to useless. This particular field is still young, but there are already many instances in which workers have wasted much time in rediscovering (for better or for worse) schemes already reported. In the last year or two this problem has become worse, and in such a situation just about any information is better than none. This bibliography is intended to serve just that purpose-to present some information about this literature. The selection was confined mainly to publications directly concerned with construction of artificial problem-solving systems. Many peripheral areas are omitted completely or represented only by a few citations.IRE Trans. on Human Factors in Electronics, HFE-2, pages 39-55