scholarly journals Station ALOHA: A Gathering Place for Discovery, Education, and Scientific Collaboration

2018 ◽  
Vol 28 (1) ◽  
pp. 10-12 ◽  
Author(s):  
David M. Karl ◽  
Matthew J. Church
Author(s):  
Melani McAlister

In October 2017, hundreds of faculty, friends, and former students gathered at the National Museum of African American History and Culture (NMAAHC) to remember James Oliver “Jim” Horton. It was a fitting gathering place. As the museum’s director, Lonnie Bunch, commented, Jim’s legacy is everywhere at the museum, from the fact that several of his former doctoral students are now curators to the foundational commitment of the museum itself: that African American history is not a local branch of US history but integral to its core. Jim always insisted in his lectures and classes and on his many TV appearances and public engagements that “American history is African American history.” 


Author(s):  
Khodzinskyi V. ◽  
Cheremnykh N.

The natural collection (n = 280 specimens) of the mole (Talpa europaea L., 1758) from the collections of the State Natural History Museum of the NAS of Ukraine was studied. The main material was collected in May-August by 23 collectors during the period 1868-1998. Morphometry of 86% of mole specimens was carried out, 52% of the individuals were weighed before preparation, the sex was set at 81% of individuals, and the age – 73%. Natural mole from the museum's funds are extracted or found in Bulgaria (1 gathering place) and six regions of West of Ukraine (30 collection sites). The ratio of the sexes of mole individuals, exhibits which are stored in the museum's funds, is 1.0 : 0.7 (♂ : ♀), age groups – 1.0 : 0.3 (ad. : subad.).


2014 ◽  
Author(s):  
Aurrlien Fichet de Clairfontaine ◽  
Rafael Lata ◽  
Manfred F. Paier ◽  
Manfred M. Fischer

Author(s):  
Drew Thomases

This book is based on ethnographic fieldwork in Pushkar, a Hindu pilgrimage site in northwestern India whose population of 20,000 sees an influx of two million visitors each year. Since the 1970s, the town has also received considerable attention from international tourists, a group with distinctly hippie beginnings but that now includes visitors from a wide spectrum of social positions and religious affiliations. To locals, though, Pushkar is more than just a gathering place for pilgrims and tourists: it is where Brahma, the creator god, made his home; it is where pilgrims feel blessed to stay, if only for a short time; and it is where Hindus would feel lucky to be reborn, if only as an insect. In short, it is their paradise. But even paradise needs upkeep. Thus, on a daily basis the town’s locals, and especially those engaged in pilgrimage and tourism, work to make Pushkar paradise. The book explores this massive enterprise to build “heaven on earth,” paying particular attention to how the articulation of sacred space becomes entangled with economic changes brought on by globalization and tourism. As such, the author not only attends to how tourism affects everyday life in Pushkar but also to how Hindu ideas determine the nature of tourism there; the goal, then, is to show how religion and tourism can be mutually constitutive.


2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.


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