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