2,720 research outputs found
Dr. Nathan Nobis, Morehouse College, August 2011
No full textThis video is a conversation with Dr. Nathan Nobis. Dr. Nobis talks about his paper, "The Harmful, Nontherapeutic use of Animals in Research is Morally Wrong." Brad Ost, AUC Woodruff Library, is the interviewer
Place-based attributes predict community membership in a mobile phone communication network.
No full textSocial networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r?=?0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes
Letter from Nathan Bankhead, Bankhead and Henderson, to Carl Hayden
No full textLetter from Nathan Bankhead to Carl Hayden concerning his sheep and the accusations of Horace M. Albright
Nathan Hatch
No full textNathan Hatch, son of Mr. and Mrs. Dennis Hatch, earned his Eagle Scout award
Nathan Newsom diary
No full textNarrative account entitled "A Short summary of a journey, taken by volunteers from Gallia County; for the purpose of destroying Indians and the invasion of Canada," written by Nathan Newsom. Newsom was an orderly sergeant in Captain Calvin Shepard's company from Gallia County, Ohio, during the War of 1812. This volume conveys conditions experienced by soldiers during the war, including low pay, shortages of food and clothing, low morale, and severe weather conditions. Newsom also describes the cooperation of the army with friendly Indians and the disciplinary measures taken for desertion and other offenses
Discovering spatiotemporal mobility profiles of cellphone users
No full textMobility path information of cellphone users play a crucial role in a wide range of cellphone applications, including context-based search and advertising, early warning systems, city-wide sensing applications such as air pollution exposure estimation and traffic planning. However, there is a disconnect between the low level location data logs available from the cellphones and the high level mobility path information required to support these cellphone applications. In this paper, we present formal definitions to capture the cellphone users' mobility patterns and profiles, and provide a complete framework, Mobility Profiler, for discovering mobile user profiles starting from cell based location log data. We use real-world cellphone log data (of over 350 K hours of coverage) to demonstrate our framework and perform experiments for discovering frequent mobility patterns and profiles. Our analysis of mobility profiles of cellphone users expose a significant long tail in a user's location-time distribution: A total of 15% of a user's time is spent on average in locations that each appear with less than 1% of time.National Science Foundation (Career award #0747209
Eigenbehaviors: Identifying Structure in Routine
No full textLongitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research
Guilt, Memory, and the Beta-God: Nathan Englander on kaddish.com
No full textDiane Feigenson Lecture in Jewish Literature… Nathan Englander, Bestselling author, For the Relief of Unbearable Urges, What We Talk About When We Talk, About Anne Frank, and kaddish.com (2019).https://digitalcommons.fairfield.edu/bennettcenter-posters/1360/thumbnail.jp
The use of census migration data to approximate human movement patterns across temporal scales
No full textHuman movement plays a key role in economies and development, the delivery of services, and the spread of infectious diseases. However, it remains poorly quantified partly because reliable data are often lacking, particularly for low-income countries. The most widely available are migration data from human population censuses, which provide valuable information on relatively long timescale relocations across countries, but do not capture the shorter-scale patterns, trips less than a year, that make up the bulk of human movement. Census-derived migration data may provide valuable proxies for shorter-term movements however, as substantial migration between regions can be indicative of well connected places exhibiting high levels of movement at finer time scales, but this has never been examined in detail. Here, an extensive mobile phone usage data set for Kenya was processed to extract movements between counties in 2009 on weekly, monthly, and annual time scales and compared to data on change in residence from the national census conducted during the same time period. We find that the relative ordering across Kenyan counties for incoming, outgoing and between-county movements shows strong correlations. Moreover, the distributions of trip durations from both sources of data are similar, and a spatial interaction model fit to the data reveals the relationships of different parameters over a range of movement time scales. Significant relationships between census migration data and fine temporal scale movement patterns exist, and results suggest that census data can be used to approximate certain features of movement patterns across multiple temporal scales, extending the utility of census-derived migration data
Okechukwu Nzelu, Helen Palmer & Nathan Walker: North Faces
No full textPublic Reading Performance as part of Edinburgh International Book Festival 2024.
Nathan Walker will read from their publication 'Skirting' (Broken Sleep Books)
"Join Barnsley-born poet and Pity author Andrew McMillan as he hosts a showcase of literary talent from the North of England. Tonight McMillan presents a prismatic range of writers – novelists Okechukwu Nzelu and Helen Palmer, poet and performance artist Nathan Walker – as well as Alicia Byrne, the inaugural winner of the Tempest Prize for unpublished LGBTQ+ writers (run in collaboration with New Writing North). Come and hear the groundbreaking work from some of the most exciting literary talent working today.
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