NH International Seminar, Spring 2016: Identity, Marginality & Community

NHIS Spring 2016 - Identity, Marginality & Community Thursday, February 11, 2:10 – 3:30 pm, MUB Theater IIMarla Brettschneider, UNH Department of Women\u27s Studies and Political Science The Jewish Phenomenon in Sub-Saharan Africa Wednesday, March 2, 4:30 - 6:00 pm, MUB Theater IICathy Frierson, UNH Department of HistoryFinding Justice in Post-Soviet Russia for Children of Stalin\u27s \u27Enemies of the People\u27 Wednesday, April 6, 4:10 - 5:30 pm, MUB Theater II Sameer Honwad, UNH Department of EducationBuilding Resilience Among Climate-Underpriviledged Communities in the Middle Himalaya

Sameer Honwad, Assistant Professor of STEM Education, travels to Bhutan

Assistance from the CIE development grant helped me travel to Bhutan, in order to continue my ongoing work on environmental decision-making and formal curriculum design to support sustainable decision-making processes among youth in Bhutan. The trip was also meant to build partnerships with the environmental science faculty in Bhutan and to explore the possibility of designing a cross-cultural collaborative learning environment for undergraduate students at UNH and the Royal Thimpu College in Bhutan

On the local stability of semidefinite relaxations

We consider a parametric family of quadratically constrained quadratic programs (QCQP) and their associated semidefinite programming (SDP) relaxations. Given a nominal value of the parameter at which the SDP relaxation is exact, we study conditions (and quantitative bounds) under which the relaxation will continue to be exact as the parameter moves in a neighborhood around the nominal value. Our framework captures a wide array of statistical estimation problems including tensor principal component analysis, rotation synchronization, orthogonal Procrustes, camera triangulation and resectioning, essential matrix estimation, system identification, and approximate GCD. Our results can also be used to analyze the stability of SOS relaxations of general polynomial optimization problems.Comment: 23 pages, 3 figure

Religious Racial Formation Theory and its Metaphysics

While the intersection between race and religion has been an important site for research for the sociology of religion and religious studies (in its descriptive dimensions) as well s theology (in its religiously normative dimensions), neither of these disciplines has incorporated recent work in the analytic philosophy of race. Analytic philosophy of race, for its part, has largely neglected the race/religion intersection, while analytic theologians by and large ignore the theological significance of race altogether. In this paper I am to draw together these distinct disciplinary contributions—social-historical, philosophical and normative-theological—into a single integrated framework for a research program in analytic theology. I call that framework “religious racial formation theory,” and I claim that the work of specifying a determinate religious racial formation theory is not merely a (normatively driven) sociological and historical task but a necessarily philosophical one. I then detail what sorts of metaphysical determinations are required in order to yield an adequate explanation of the intersection uncovered by the socio-historical data summarized in the first section

Distantly Labeling Data for Large Scale Cross-Document Coreference

Cross-document coreference, the problem of resolving entity mentions across multi-document collections, is crucial to automated knowledge base construction and data mining tasks. However, the scarcity of large labeled data sets has hindered supervised machine learning research for this task. In this paper we develop and demonstrate an approach based on ``distantly-labeling'' a data set from which we can train a discriminative cross-document coreference model. In particular we build a dataset of more than a million people mentions extracted from 3.5 years of New York Times articles, leverage Wikipedia for distant labeling with a generative model (and measure the reliability of such labeling); then we train and evaluate a conditional random field coreference model that has factors on cross-document entities as well as mention-pairs. This coreference model obtains high accuracy in resolving mentions and entities that are not present in the training data, indicating applicability to non-Wikipedia data. Given the large amount of data, our work is also an exercise demonstrating the scalability of our approach.Comment: 16 pages, submitted to ECML 201