Sudakov Logarithm Resummation for Vector Boson Production at Hadron Colliders

A complete description of W and Z boson production at high-energy colliders requires the resummation of large Sudakov logarithms which dominate the production at small transverse momentum. Currently there are two techniques for performing this resummation: impact parameter space and transverse momentum space. We argue that the latter can be formulated in a way which retains the advantages of the former, while at the same time allowing a smooth transition to finite order dominance at high transverse momentum.Comment: 5 pages, LaTeX, 2 figures, epsfig, contribution to the proceedings of the UK Phenomenology Workshop on Collider Physics, 19-24 September 1999, Durham, to be published in J. Phys.

A Comparison of Predictions for SM Higgs Boson Production at the LHC

This paper describes a comparison of most of the available predictions for the cross section and transverse momentum distribution for a 125 GeV mass Higgs at the LHC, including those from the PYTHIA and HERWIG parton shower Monte Carlos and from four resummation calculations.Comment: 7 pages, submitted to proceedings of the Workshop on Physics at TeV Colliders, Les Houches 200

Analysis of Contextual Factors Influence on Auditors Informal Learning

In the next decade, more than 1 billion people will need reskilling as professional roles evolve. Even more importantly, the COVID-19 pandemic is forcing organizations to rethink the entire role of organizational learning in both the short- and long-term. As a consequence of this change, professionals need to be motivated to continually further their own skill sets in addition to organizational needs. The purpose of this study was to investigate the hours auditors engage in learning activities integrated into daily work tasks and factors that support engagement in learning. Further, the study provided insight into auditors’ informal learning preferences at different levels of work experience and auditors’ tendency to participate given the level of the perceived organizational learning culture

Parity-Odd Asymmetries in W-Jet Events at the Tevatron

Parity-odd asymmetries in the decay angular distribution of a W boson produced with a hard jet in p\bar{p} collisions arise only from QCD rescattering effects. If observed, these asymmetries will provide a first demonstration that perturbative QCD calculation is valid for the absorptive part of scattering amplitudes. We propose a simple observable to measure these asymmetries and perform realistic Monte Carlo simulations at Tevatron energies. It is shown that the Tevatron Run-II should provide sufficient statistics to test the prediction.Comment: 4pages, 2figures, revtex, references and discussions added, version to appear in PRL, typo correcte

Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach

Intelligent assistants are handling increasingly critical tasks, but until now, end users have had no way to systematically assess where their assistants make mistakes. For some intelligent assistants, this is a serious problem: if the assistant is doing work that is important, such as assisting with qualitative research or monitoring an elderly parent’s safety, the user may pay a high cost for unnoticed mistakes. This paper addresses the problem with WYSIWYT/ML (What You See Is What You Test for Machine Learning), a human/computer partnership that enables end users to systematically test intelligent assistants. Our empirical evaluation shows that WYSIWYT/ML helped end users find assistants’ mistakes significantly more effectively than ad hoc testing. Not only did it allow users to assess an assistant’s work on an average of 117 predictions in only 10 minutes, it also scaled to a much larger data set, assessing an assistant’s work on 623 out of 1,448 predictions using only the users’ original 10 minutes’ testing effort

Same-sign W pair production as a probe of double parton scattering at the LHC

We study the production of same-sign W boson pairs at the LHC in double parton interactions. Compared with simple factorised double parton distributions (dPDFs), we show that the recently developed dPDFs, GS09, lead to non-trivial kinematic correlations between the W bosons. A numerical study of the prospects for observing this process using same-sign dilepton signatures, including same-sign WWjj, di-boson and heavy flavour backgrounds, at 14 TeV centre-of-mass energy is then performed. It is shown that a small excess of same-sign dilepton events from double parton scattering over a background dominated by single scattering WZ(gamma*) production could be observed at the LHC.Comment: 14 pages, 8 figures. Added references, slight changes in the text

Tell me more?: the effects of mental model soundness on personalizing an intelligent agent

What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agent's personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system's reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system's reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user's intentions

Principles of Explanatory Debugging to personalize interactive machine learning

How can end users efficiently influence the predictions that machine learning systems make on their behalf? This paper presents Explanatory Debugging, an approach in which the system explains to users how it made each of its predictions, and the user then explains any necessary corrections back to the learning system. We present the principles underlying this approach and a prototype instantiating it. An empirical evaluation shows that Explanatory Debugging increased participants' understanding of the learning system by 52% and allowed participants to correct its mistakes up to twice as efficiently as participants using a traditional learning system