128 research outputs found
Data-independent neighborhood functions and strict local optima
AbstractThe paper proves that data-independent neighborhood functions with the smooth property (all strict local optima are global optima) for maximum 3-satisfiability (MAX 3-SAT) must contain all possible solutions for large instances. Data-independent neighborhood functions with the smooth property for 0–1 knapsack are shown to have size with the same order of magnitude as the cardinality of the solution space. Data-independent neighborhood functions with the smooth property for traveling salesman problem (TSP) are shown to have exponential size. These results also hold for certain polynomially solvable sub-problems of MAX 3-SAT, 0–1 knapsack and TSP
Votemandering: Strategies and Fairness in Political Redistricting
Gerrymandering, the deliberate manipulation of electoral district boundaries
for political advantage, is a persistent issue in U.S. redistricting cycles.
This paper introduces and analyzes a new phenomenon, 'votemandering'- a
strategic blend of gerrymandering and targeted political campaigning, devised
to gain more seats by circumventing fairness measures. It leverages accurate
demographic and socio-political data to influence voter decisions, bolstered by
advancements in technology and data analytics, and executes better-informed
redistricting. Votemandering is established as a Mixed Integer Program (MIP)
that performs fairness-constrained gerrymandering over multiple election
rounds, via unit-specific variables for campaigns. To combat votemandering, we
present a computationally efficient heuristic for creating and testing district
maps that more robustly preserve voter preferences. We analyze the influence of
various redistricting constraints and parameters on votemandering efficacy. We
explore the interconnectedness of gerrymandering, substantial campaign budgets,
and strategic campaigning, illustrating their collective potential to generate
biased electoral maps. A Wisconsin State Senate redistricting case study
substantiates our findings on real data, demonstrating how major parties can
secure additional seats through votemandering. Our findings underscore the
practical implications of these manipulations, stressing the need for informed
policy and regulation to safeguard democratic processes
Characterization and distribution of an oncofetal antigen (M2A antigen) expressed on testicular germ cell tumours
The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey
The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic
data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data
release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median
z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar
spectra, along with the data presented in previous data releases. These spectra
were obtained with the new BOSS spectrograph and were taken between 2009
December and 2011 July. In addition, the stellar parameters pipeline, which
determines radial velocities, surface temperatures, surface gravities, and
metallicities of stars, has been updated and refined with improvements in
temperature estimates for stars with T_eff<5000 K and in metallicity estimates
for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars
presented in DR8, including stars from SDSS-I and II, as well as those observed
as part of the SDSS-III Sloan Extension for Galactic Understanding and
Exploration-2 (SEGUE-2).
The astrometry error introduced in the DR8 imaging catalogs has been
corrected in the DR9 data products. The next data release for SDSS-III will be
in Summer 2013, which will present the first data from the Apache Point
Observatory Galactic Evolution Experiment (APOGEE) along with another year of
data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at
http://www.sdss3.org/dr
SEEDING IN THE NCAA MEN’S BASKETBALL TOURNAMENT: WHEN IS A HIGHER SEED BETTER?
A number of methods have been proposed for predicting game winners in the National Collegiate Athletic Association’s (NCAA) annual men’s college basketball championship tournament. Since 1985, more than 70% of the teams in the fourth, fifth, and sixth rounds of the tournament have been high-seeded teams (i.e., teams assigned seeds of one, two, or three); a method that can accurately compare two such teams is often necessary to predict games in these rounds. This paper statistically analyzes tournaments from 1985 to 2009. A key finding is that there is an insignificant difference between the historical win percentages of high-seeded teams in each of the fourth, fifth, and sixth tournament rounds, which implies that choosing the higher seed to win games between these seeds does not provide accurate predictions in these rounds, and alternate predictors or methods should be sought. Implications on gambling point spreads are discussed.
Kanban assignment problem in serial just-in-time production systems
IIE Transactions (Institute of Industrial Engineers)26276-85IIET
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