Planned Parenthood Federation of America, Inc. v. Gonzales

In one of the most pivotal cases of the Fall 2006 Term, the United States Supreme Court upheld the Partial-Birth Abortion Ban Act of 2003 by a vote of 5-4. The Court found the Act to be facially valid, despite the absence of an exception for cases in which an abortion is necessary to preserve the health of the mother, stating that the Act was not void for vagueness and that it did not impose an undue burden on a woman\u27s right to abortion based on its overbreadth or lack of a health exception. The case signaled a departure from the Court\u27s long-standing abortion jurisprudence, and provided an enormous amount of insight into the Roberts\u27 Court. The decision was the first major indication of how the Court will deal with abortion, how the Court feels about precedent, and how much deference the Court will give congressional findings of fact

Geosocial Graph-Based Community Detection

We apply spectral clustering and multislice modularity optimization to a Los Angeles Police Department field interview card data set. To detect communities (i.e., cohesive groups of vertices), we use both geographic and social information about stops involving street gang members in the LAPD district of Hollenbeck. We then compare the algorithmically detected communities with known gang identifications and argue that discrepancies are due to sparsity of social connections in the data as well as complex underlying sociological factors that blur distinctions between communities.Comment: 5 pages, 4 figures Workshop paper for the IEEE International Conference on Data Mining 2012: Workshop on Social Media Analysis and Minin

Learning Nearest Neighbor Graphs from Noisy Distance Samples

We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people's preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and the triangle inequality. Furthermore, we demonstrate efficiency of our method empirically and theoretically, needing only O(n log(n)Delta^-2) queries in favorable settings, where Delta^-2 accounts for the effect of noise. Using crowd-sourced data collected for a subset of the UT Zappos50K dataset, we apply our algorithm to learn which shoes people believe are most similar and show that it beats both an active baseline and ordinal embedding.Comment: 21 total pages (8 main pages + appendices), 7 figures, submitted to NeurIPS 201

HIEN-LO: An experiment for charge determination of cosmic rays of interplanetary and solar origin

The experiment is designed to measure the heavy ion environment at low altitude (HIEN-LO) in the energy range 0.3 to 100 MeV/nucleon. In order to cover this wide energy range a complement of three sensors is used. A large area ion drift chamber and a time-of-flight telescope are used to determine the mass and energy of the incoming cosmic rays. A third omnidirectional counter serves as a proton monitor. The analysis of mass, energy and incoming direction in combination with the directional geomagnetic cut-off allows the determination of the ionic charge of the cosmic rays. The ionic charge in this energy range is of particular interest because it provides clues to the origin of these particles and to the plasma conditions at the acceleration site. The experiment is expected to be flown in 1988/1989

USA educator perspectives regarding the nature and value of social and emotional learning

This paper discusses the US educator perspectives regarding the nature and value of Social Emotional Learning (SEL) skills. This research is part of a larger study being conducted by 33 career development investigators from 15 countries. SEL skills are becoming increasingly critical to helping youth develop the competencies needed to become employable within the emergent 4th Industrial Revolution. Today’s youth must articulate how their competencies align to multiple career opportunities. They need relationship skills and social awareness to interact with different managers and work environments. Youth need self-management skills to advance in the workplace and engage in lifelong learning. For this study, educators were asked to provide written responses to a series of open-ended questions about their understanding of SEL, their perspective on SEL’s relevance to their own effectiveness as educators, and whether and how they perceive SEL as relevant to teaching in classroom settings. This paper will report on the results of how U.S. educators perceive the value and relevance of SEL. Using a modified grounded theory approach, responses from 40 educators were analyzed and 123 SEL themes emerged. The results will be discussed in relation to existing SEL and career readiness frameworks.First author draf

Learning Nearest Neighbor Graphs from Noisy Distance Samples

We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people\u27s preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and the triangle inequality. Furthermore, we demonstrate efficiency of our method empirically and theoretically, needing only O(n log(n)Δ-2) queries in favorable settings, where Δ-2 accounts for the effect of noise. Using crowd-sourced data collected for a subset of the UT Zappos50K dataset, we apply our algorithm to learn which shoes people believe are most similar and show that it beats both an active baseline and ordinal embedding

Multislice Modularity Optimization in Community Detection and Image Segmentation

Because networks can be used to represent many complex systems, they have attracted considerable attention in physics, computer science, sociology, and many other disciplines. One of the most important areas of network science is the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In this paper, we algorithmically detect communities in social networks and image data by optimizing multislice modularity. A key advantage of modularity optimization is that it does not require prior knowledge of the number or sizes of communities, and it is capable of finding network partitions that are composed of communities of different sizes. By optimizing multislice modularity and subsequently calculating diagnostics on the resulting network partitions, it is thereby possible to obtain information about network structure across multiple system scales. We illustrate this method on data from both social networks and images, and we find that optimization of multislice modularity performs well on these two tasks without the need for extensive problem-specific adaptation. However, improving the computational speed of this method remains a challenging open problem.Comment: 3 pages, 2 figures, to appear in IEEE International Conference on Data Mining PhD forum conference proceeding