Experiences in Pattern Recognition for Machine Olfaction

Pattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights. Most importantly, it is clear beyond any doubt that the quantity and selection of samples used to train and test a pattern recognition system are by far the most important factors in ensuring it performs as accurately and reliably as possible. Here we present evidence for this assertion and make suggestions for best practice based on these findings

Neutrino Experiments and the LHC: Friends Across 14 Orders of Magnitude

This proceeding explores some of the questions that connect the LHC and neutrino experiments: What is the origin of mass? What is the meaning of flavor? Is there direct evidence of new forces or particles? The neutrino program investigating these questions is large and diverse. The strategy here, to narrow the discussion, is to focus on relatively new ideas for experiments that may be less known within the LHC community.Comment: Prepared for the proceedings of the LHC Nobel Symposium, held May 13-17, 201

A mechanism for the evolution of the genetic code

Multiple coding mechanism for evolution of genetic cod

Negative volatility spillovers in the unrestricted ECCC-GARCH model

Copyright @ 2010 Cambridge University Press.This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the parameters of the model are nonnegative, which is a sufficient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative feedback, we show that the positive definiteness of the conditional covariance matrix can be guaranteed even if some of the parameters are negative. Thus, we extend the results of Nelson and Cao (1992) and Tsai and Chan (2008) to a multivariate setting. For the bivariate case of order one, we look into the consequences of adopting these less severe restrictions and find that the flexibility of the process is substantially increased. Our results are helpful for the model-builder, who can consider the unrestricted formulation as a tool for testing various economic theories

Limits on Electron Neutrino Disappearance from the KARMEN and LSND electron neutrino - Carbon Cross Section Data

This paper presents a combined analysis of the KARMEN and LSND nu_e-carbon cross section measurements within the context of a search for nu_e disappearance at high Delta m^2. KARMEN and LSND were located at 17.7 m and 29.8 m respectively from the neutrino source, so the consistency of the two measurements, as a function of antineutrino energy, sets strong limits on neutrino oscillations. Most of the allowed region from the nu_e disappearance analysis of the Gallium calibration data is excluded at >95% CL and the best fit point is excluded at 3.6$\sigma$. Assuming CPT conservation, comparisons are also made to the oscillation analyses of reactor antineutrino data.Comment: Published versio

ISSUES IN HAZARDOUS WASTE MANAGEMENT: DISCUSSION

Resource /Energy Economics and Policy,