Cellular clocks are almost everywhere. Clues to how they work are coming from the places that they’re not.

Source: www.quantamagazine.org

The availability of historical textual corpora has led to the study of words’ frequency along the historical time line, as representing the public’s focus of attention over time. However, studying of the dynamics of words’ meaning is still in its infancy. In this paper, we propose a methodology for studying the historical trajectory of words’ meaning through Tsallis entropy. First, we present the idea that the meaning of a word may be studied through the entropy of its embedding. Using two historical case studies, we show that this entropy measure is correlated with the intensity in which a word is used. More importantly, we show that using Tsallis entropy with a superadditive entropy index may provide a better estimation of a word’s frequency of use than using Shannon entropy. We explain this finding as resulting from an increasing redundancy between the words that comprise the semantic field of the target word and develop a new measure of redundancy between words. Using this measure, which relies on the Tsallis version of the Kullback–Leibler divergence, we show that the evolving meaning of a word involves the dynamics of increasing redundancy between components of its semantic field. The proposed methodology may enrich the toolkit of researchers who study the dynamics of word senses.

 

Neuman, Y., Cohen, Y., Israeli, N., & Tamir, B. (2017). A proposed methodology for studying the historical trajectory of words’ meaning through Tsallis entropy. Physica A: Statistical Mechanics and its Applications.

Source: www.sciencedirect.com

Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks, etc. Focusing on tag networks (namely, hashtags in twitter), we made a two-layer analysis of tag networks from a massive dataset of Twitter entries. The first layer is constructed by converting the co-occurrences of these tags in a single entry (tweet) into links, while the second layer is constructed converting the semantic relations of the tags into links. We observed that the universal properties of the real networks like small-world property, clustering and power-law distributions in various network parameters are also evident in the multilayer network of hashtags. Moreover, we outlined that co-occurrences of hashtags in tweets are mostly coupled with semantic relations, whereas a small number of semantically unrelated, therefore random links reduce node separation and network diameter in the co-occurrence network layer. Together with the degree distributions, the power-law consistencies of degree difference, edge weight and cosine similarity distributions in both layers are also appealing forms of Zipf’s law evident in nature.

 

İlker Türker and Eyüb Ekmel Sulak, Int. J. Mod. Phys. B
https://doi.org/10.1142/S0217979218500297
A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links

Source: www.worldscientific.com

We are living in the most explosive era of infrastructure expansion in human history (1, 2). In the next 3 years, paved roads are projected to double in length in Asia’s developing nations (3); in the next three decades, the total length of additional paved roads could approach 25 million kilometers worldwide—enough to encircle the planet more than 600 times (1). Nine-tenths of all new infrastructure is being built in developing nations (1), mainly in tropical and subtropical regions that contain Earth’s most diverse ecosystems. In a world that is projected to have 2 billion vehicles by 2030 (4), we need a better understanding of the impacts of roads and other infrastructure on our planet, societies, and economies (1–3, 5)—and more effective planning to ensure that the benefits of infrastructure outstrip its costs.

 

Roads to riches or ruin?
William F. Laurance, Irene Burgués Arrea

Science  27 Oct 2017:
Vol. 358, Issue 6362, pp. 442-444
DOI: 10.1126/science.aao0312

Source: science.sciencemag.org