21,216 research outputs found
Labor Board Ruling May Bar Millions of Workers from Forming Unions, 2006
Newspaper article about the National Labor Relations Board\u27s vote to slash federal laws protecting worker\u27s freedom to form unions
Foundation Support for Nonprofit Capital Needs in Southern California
Analyzes trends in foundation funding for nonprofits' capital campaigns, land acquisition, and building and renovation in five counties. Lists foundations that may provide capital support, but suggests securing other primary sources of capital funding
Physics of non-Gaussian fields and the cosmological genus statistic
We report a technique to calculate the impact of distinct physical processes
inducing non-Gaussianity on the cosmological density field. A natural
decomposition of the cosmic genus statistic into an orthogonal polynomial
sequence allows complete expression of the scale-dependent evolution of the
morphology of large-scale structure, in which effects including galaxy bias,
nonlinear gravitational evolution and primordial non-Gaussianity may be
delineated. The relationship of this decomposition to previous methods for
analysing the genus statistic is briefly considered and the following
applications are made: i) the expression of certain systematics affecting
topological measurements; ii) the quantification of broad deformations from
Gaussianity that appear in the genus statistic as measured in the Horizon Run
simulation; iii) the study of the evolution of the genus curve for simulations
with primordial non-Gaussianity. These advances improve the treatment of
flux-limited galaxy catalogues for use with this measurement and further the
use of the genus statistic as a tool for exploring non-Gaussianity.Comment: AASTeX preprint, 24 pages, 8 figures, includes several improvements
suggested by anonymous reviewe
The Hierarchy Solution to the LHC Inverse Problem
Supersymmetric (SUSY) models, even those described by relatively few
parameters, generically allow many possible SUSY particle (sparticle) mass
hierarchies. As the sparticle mass hierarchy determines, to a great extent, the
collider phenomenology of a model, the enumeration of these hierarchies is of
the utmost importance. We therefore provide a readily generalizable procedure
for determining the number of sparticle mass hierarchies in a given SUSY model.
As an application, we analyze the gravity-mediated SUSY breaking scenario with
various combinations of GUT-scale boundary conditions involving different
levels of universality among the gaugino and scalar masses. For each of the
eight considered models, we provide the complete list of forbidden hierarchies
in a compact form. Our main result is that the complete (typically rather
large) set of forbidden hierarchies among the eight sparticles considered in
this analysis can be fully specified by just a few forbidden relations
involving much smaller subsets of sparticles.Comment: 44 pages, 2 figures. Python code providing lists of allowed and
forbidden hierarchy is included in ancillary file
Learning scalable and transferable multi-robot/machine sequential assignment planning via graph embedding
Can the success of reinforcement learning methods for simple combinatorial
optimization problems be extended to multi-robot sequential assignment
planning? In addition to the challenge of achieving near-optimal performance in
large problems, transferability to an unseen number of robots and tasks is
another key challenge for real-world applications. In this paper, we suggest a
method that achieves the first success in both challenges for robot/machine
scheduling problems.
Our method comprises of three components. First, we show a robot scheduling
problem can be expressed as a random probabilistic graphical model (PGM). We
develop a mean-field inference method for random PGM and use it for Q-function
inference. Second, we show that transferability can be achieved by carefully
designing two-step sequential encoding of problem state. Third, we resolve the
computational scalability issue of fitted Q-iteration by suggesting a heuristic
auction-based Q-iteration fitting method enabled by transferability we
achieved.
We apply our method to discrete-time, discrete space problems (Multi-Robot
Reward Collection (MRRC)) and scalably achieve 97% optimality with
transferability. This optimality is maintained under stochastic contexts. By
extending our method to continuous time, continuous space formulation, we claim
to be the first learning-based method with scalable performance among
multi-machine scheduling problems; our method scalability achieves comparable
performance to popular metaheuristics in Identical parallel machine scheduling
(IPMS) problems
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