1,145 research outputs found
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
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