Indexing Voice: A Morality Tale

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/88054/1/j.1548-1395.2011.01104.x.pd

The Anthropology of Empathy: Experiencing the Lives of Others in Pacific Societies . Douglas W. Hollan and C. Jason Throop , eds. Berghahn Books : New York, NY . 2011 . 1 + 233 pp.

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96414/1/etho12005.pd

Why cognitive anthropology needs to understand social interaction and its mediation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111793/1/soca12113.pd

Caged In on the Outside: Moral Subjectivity, Selfhood, and Islam in Minangkabau, Indonesia. Gregory M. Simon. Honolulu: University of Hawai‘i Press, 2014. 255 pp.

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/120491/1/amet12314_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/120491/2/amet12314.pd

On spirit writing: materialities of language and the religious work of transduction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96381/1/jrai12000.pd

Voice

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72808/1/jlin.1999.9.1-2.271.pd

The eye, the kidney, and cardiovascular disease: old concepts, better tools, and new horizons.

Chronic kidney disease (CKD) is common, with hypertension and diabetes mellitus acting as major risk factors for its development. Cardiovascular disease is the leading cause of death worldwide and the most frequent end point of CKD. There is an urgent need for more precise methods to identify patients at risk of CKD and cardiovascular disease. Alterations in microvascular structure and function contribute to the development of hypertension, diabetes, CKD, and their associated cardiovascular disease. Homology between the eye and the kidney suggests that noninvasive imaging of the retinal vessels can detect these microvascular alterations to improve targeting of at-risk patients. Retinal vessel-derived metrics predict incident hypertension, diabetes, CKD, and cardiovascular disease and add to the current renal and cardiovascular risk stratification tools. The advent of optical coherence tomography (OCT) has transformed retinal imaging by capturing the chorioretinal microcirculation and its dependent tissue with near-histological resolution. In hypertension, diabetes, and CKD, OCT has revealed vessel remodeling and chorioretinal thinning. Clinical and preclinical OCT has linked retinal microvascular pathology to circulating and histological markers of injury in the kidney. The advent of OCT angiography allows contrast-free visualization of intraretinal capillary networks to potentially detect early incipient microvascular disease. Combining OCT's deep imaging with the analytical power of deep learning represents the next frontier in defining what the eye can reveal about the kidney and broader cardiovascular health

HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis

Comprehensive benchmarking of clustering algorithms is rendered difficult by two key factors: (i) the elusiveness of a unique mathematical definition of this unsupervised learning approach and (ii) dependencies between the generating models or clustering criteria adopted by some clustering algorithms and indices for internal cluster validation. Consequently, there is no consensus regarding the best practice for rigorous benchmarking, and whether this is possible at all outside the context of a given application. Here, we argue that synthetic datasets must continue to play an important role in the evaluation of clustering algorithms, but that this necessitates constructing benchmarks that appropriately cover the diverse set of properties that impact clustering algorithm performance. Through our framework, HAWKS, we demonstrate the important role evolutionary algorithms play to support flexible generation of such benchmarks, allowing simple modification and extension. We illustrate two possible uses of our framework: (i) the evolution of benchmark data consistent with a set of hand-derived properties and (ii) the generation of datasets that tease out performance differences between a given pair of algorithms. Our work has implications for the design of clustering benchmarks that sufficiently challenge a broad range of algorithms, and for furthering insight into the strengths and weaknesses of specific approaches