Unsupervised Learning of Style-sensitive Word Vectors

This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) model (Mikolov et al., 2013) to learn style-sensitive word vectors using a wider context window under the assumption that the style of all the words in an utterance is consistent. In addition, we introduce a novel task to predict lexical stylistic similarity and to create a benchmark dataset for this task. Our experiment with this dataset supports our assumption and demonstrates that the proposed extensions contribute to the acquisition of style-sensitive word embeddings.Comment: 7 pages, Accepted at The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018

Scanning Tunneling Thermometry

Temperature imaging of nanoscale systems is a fundamental problem which has myriad potential technological applications. For example, nanoscopic cold spots can be used for spot cooling electronic components while hot spots could be used for precise activation of chemical or biological reactions. More fundamentally, imaging the temperature fields in quantum coherent conductors can provide a wealth of information on heat flow and dissipation at the smallest scales. However, despite significant technological advances, the spatial resolution of temperature imaging remains in the few nanometers range. Here we propose a method to map electronic temperature variations in operating nanoscale conductors by relying solely upon electrical tunneling current measurements. The scanning tunneling thermometer, owing to its operation in the tunneling regime, would be capable of mapping sub-angstrom temperature variations, thereby enhancing the resolution of scanning thermometry by some two orders of magnitude.Comment: Main article has 11 pages and 3 figures. Supplementary Information has 9 pages and 4 figure