Search for displaced vertexes arising from decays of new, heavy particles in 7 TeV pp collisions in ATLAS

We present the results of a search for neutralinos decaying at a significant distance from their production point into charged hadrons and a high momentum muon, forming displaced vertexes. The analysis was performed with 33 pb^-1 of pp collision data collected by the ATLAS experiment at the LHC in 2010 at sqrt{s}= 7 TeV. The poster will show some highlights of the analysis.Comment: Presented at the 2011 Hadron Collider Physics symposium (HCP-2011), Paris, France, November 14-18 2011, 3 pages, 9 figure

Considerations on Xi- reconstruction in LHCb

This paper describes an alternative method of charged hyperon reconstruction applicable to the LHCb experiment. It extends the seminal work of the FOCUS collaboration to the specific detector layout of LHCb and addresses the reconstruction ambiguities reported in their earlier work, leading to improvements in the reconstruction efficiency for the specific cases of Xi- and Omega- baryon decays to a charged meson and a Lambda baryon.Comment: 5 pages, 4 figure

Union Membership and Perceived Job Insecurity: 30 Years of Evidence from the American General social Survey

Using the Amercian General Social Survey, we explore the link between union membership and perceived job insecurity. This finding is mainly atributed to the primary and secondary sectors and for recessionary periods. Instrumental-variables estimation and the use of attitudinal proxy variables suggest that the positive correlation union membership and perceived job insecurity is not due to self-selection.Union, Perceived Job Insecurity

Portfolio Allocation for Bayesian Optimization

Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We propose several portfolio strategies, the best of which we call GP-Hedge, and show that this method outperforms the best individual acquisition function. We also provide a theoretical bound on the algorithm's performance.Comment: This revision contains an updated the performance bound and other minor text change

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences