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Socially contagious itch is ubiquitous in human society, but whether it exists in rodents is unclear. Using a behavioral paradigm that does not entail prior training or reward, we found that mice scratched after observing a conspecific scratching. Molecular mapping showed increased neuronal activity in the suprachiasmatic nucleus (SCN) of the hypothalamus of mice that displayed contagious scratching. Ablation of gastrin-releasing peptide receptor (GRPR) or GRPR neurons in the SCN abolished contagious scratching behavior, which was recapitulated by chemogenetic inhibition of SCN GRP neurons. Activation of SCN GRP/GRPR neurons evoked scratching behavior. These data demonstrate that GRP-GRPR signaling is necessary and sufficient for transmitting contagious itch information in the SCN. The findings may have implications for our understanding of neural circuits that control socially contagious behaviors.

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We study the problem of creating highly compressed fulltext index structures for versioned document collections, that is, collections that contain multiple versions of each document. Important examples of such collections are Wikipedia or the web page archive maintained by the Internet Archive. A straightforward indexing approach would simply treat each document version as a separate document, such that index size scales linearly with the number of versions. However, several authors have recently studied approaches that exploit the significant similarities between different versions of the same document to obtain much smaller index sizes.In this paper, we propose new techniques for organizing and compressing inverted index structures for such collections. We also perform a detailed experimental comparison of new techniques and the existing techniques in the literature. Our results on an archive of the English version of Wikipedia, and on a subset of the Internet Archive collection, show significant benefits over previous approaches.

Abstract-Point-to-point distance estimation in large scale graphs is a fundamental and well studied problem with applications in many areas such as Social Search. Previous work has focused on selecting an appropriate subset of vertices as landmarks, aiming to derive distance upper or lower bounds that are as tight as possible. In order to compute a distance bound between two vertices, the proposed methods apply triangle inequalities on top of the precomputed distances between each of these vertices and the landmarks, and then use the tightest one.In this work we take a fresh look at this setting and approach it as a learning problem. As features, we use structural attributes of the vertices involved as well as the bounds described above, and we learn a function that predicts the distance between a source and a destination vertex. We conduct an extensive experimental evaluation on a variety of real-world graphs and show that the average relative prediction error of our proposed methods significantly outperforms state-of-the-art landmark-based estimates. Our method is particularily efficient when the available space is very limited.