Book Review: \u3ci\u3eAnthony deMello: The Happy Wanderer.\u3c/i\u3e

Book review of, Anthony deMello: The Happy Wanderer. by Bill deMello

Book Review: \u3cem\u3eThe Language of Disenchantment: Protestant Literalism and Colonial Discourse in British India\u3c/em\u3e

A review of The Language of Disenchantment: Protestant Literalism and Colonial Discourse in British India by Robert A. Yelle

Higher-order QCD corrections for the W-boson transverse momentum distribution

We present results for W-boson production at large transverse momentum at LHC and Tevatron energies. We calculate complete next-to-leading-order (NLO) QCD corrections and higher-order soft-gluon corrections to the differential cross section. The soft-gluon contributions are resummed at next-to-next-to-leading-logarithm (NNLL) accuracy via the two-loop soft anomalous dimensions. Both NLO and approximate next-to-next-to-leading-order (NNLO) p_T distributions are presented. Our numerical results are in good agreement with recent data from the LHC.Comment: 15 pages; 11 figures; v2 has new results at 8 TeV LHC energy, more details on scale variation, and calculations of PDF uncertainties; v3 has a new presentation of the figures and a comparison to LHC dat

NNLL resummation for W-boson production at large pT

We present new results for W-boson production at large transverse momentum at the LHC and the Tevatron. The contribution of soft-gluon corrections is derived from NNLL resummation and added to the exact NLO result. Numerical results and their uncertainties for the approximate NNLO W-boson transverse momentum distributions are derived and compared to recent data from the LHC.Comment: 6 pages, 6 figures; presented at ICHEP 2012, Melbourne, Australia, July 4-11, 201

Maximum likelihood receiver for digital data transmission

Maximum likelihood receiver for digital data transmission via pulse amplitude modulatio

NNLO soft-gluon corrections for the Z-boson and W-boson transverse momentum distributions

We present results for the $Z$-boson and $W$-boson transverse momentum ($p_T$) distributions for large $p_T$ at LHC and Tevatron energies. We calculate complete next-to-leading-order (NLO) QCD corrections as well as soft-gluon corrections at next-to-next-to-leading-order (NNLO) to the differential cross section. The NNLO soft-gluon contributions are derived from next-to-next-to-leading-logarithm (NNLL) resummation at two loops. We find enhancements of the $p_T$ distributions and reductions of the scale dependence when the NNLO corrections are included.Comment: 17 pages, 14 figure

Reconnaissance Surveying using Satellite-derived Bathymetry

False Pass, AK, USA, is the eastern-most passage through the Aleutian Islands between the Bering Sea and the Pacific Ocean and provides a passage for small to mid-size vessels. The passage is considered an alternative route to Unimak Pass, AK for vessels from mainland Alaska and is estimated to be shorter by 160 to 240km. False Pass is closed every winter due to sea-ice cover that freezes the inlet system around OctoberNovember and melts only towards the spring (around March). As a result, the soft sediment of the seafloor contains mud and sand that may change the path of the channel after the sea ice has melted. Preparation of False Pass for the Summer/Fall vessel traffic requires many resources in a narrow springtime window to identify the main channel and to delineate it with Aids to Navigation. The surveys are typically conducted by the US Coast Guard (USCG) buoy tenders using small boats and reconnaissance-style single-beam lines. This paper demonstrates the potential of using a turbidity map generated from a single-image Satellite-derived Bathymetry (SDB) to play a key role in the future of the survey planning and determination of survey prioritie

Eliminating Redundant Training Data Using Unsupervised Clustering Techniques

Training data for supervised learning neural networks can be clustered such that the input/output pairs in each cluster are redundant. Redundant training data can adversely affect training time. In this paper we apply two clustering algorithms, ART2 -A and the Generalized Equality Classifier, to identify training data clusters and thus reduce the training data and training time. The approach is demonstrated for a high dimensional nonlinear continuous time mapping. The demonstration shows six-fold decrease in training time at little or no loss of accuracy in the handling of evaluation data