Elastic neutron scattering in Quantum Critical Antiferromagnet Cr0.963_{0.963}V0.037_{0.037}

We have performed elastic neutron scattering studies of the quantum critical antiferromagnet Cr$_{0.963}$V$_{0.037}$. We have found that unlike pure Cr, which orders at two incommensurate wavevectors, Cr$_{0.963}$V$_{0.037}$ orders at four incommensurate and one commensurate wavevectors. We have found strong temperature dependent scattering at the commensurate and incommensurate wavevectors below 250 K. Results indicate that the primary effect of V doping on Cr is the modification of the nesting conditions of the Fermi surface and not the decreasing of the Neel temperature.Comment: 2 pages, 2 figures, submitted to SCES07 (to be published in Physica B), typos correcte

Using extreme value theory to evaluate the leading pedestrian interval road safety intervention

Improving road safety is hugely important with the number of deaths on the world's roads remaining unacceptably high; an estimated 1.35 million people die each year as a result of road traffic collisions (WHO, 2020). Current practice for treating collision hotspots is almost always reactive: once a threshold level of collisions has been overtopped during some pre-determined observation period, treatment is applied (e.g. road safety cameras). Traffic collisions are rare, so prolonged observation periods are necessary. However, traffic conflicts are more frequent and are a margin of the social cost; hence, traffic conflict before/after studies can be conducted over shorter time periods. We investigate the effect of implementing the leading pedestrian interval (LPI) treatment (Van Houten et al. 2000) at signalised intersections as a safety intervention in a city in north America. Pedestrian-vehicle traffic conflict data were collected from treatment and control sites during the before and after periods. We implement a before/after study on post-encroachment times (PETs) where small PET values denote a near-miss. Hence, extreme value theory is employed to model extremes of our PET processes, with adjustments to the usual modelling framework to account for temporal dependence and treatment effects.Comment: 16 page

Using extreme value theory to evaluate the leading pedestrian interval road safety intervention

Improving road safety is hugely important with the number of deaths on the world's roads remaining unacceptably high; an estimated 1.3 million people die each year as a result of road traffic collisions. Current practice for treating collision hotspots is almost always reactive: once a threshold level of collisions has been overtopped during some pre‐determined observation period, treatment is applied (e.g., road safety cameras). Traffic collisions are rare, so prolonged observation periods are necessary. However, traffic conflicts are more frequent and are a margin of the social cost; hence, traffic conflict before/after studies can be conducted over shorter time periods. We investigate the effect of implementing the leading pedestrian interval treatment at signalised intersections as a safety intervention in a city in north America. Pedestrian‐vehicle traffic conflict data were collected from treatment and control sites during the before and after periods. We implement a before/after study on post‐encroachment times (PETs) where small PET values denote ‘near‐misses’. Hence, extreme value theory is employed to model extremes of our PET processes, with adjustments to the usual modelling framework to account for temporal dependence and treatment effects

Bayesian inference for a spatio-temporal model of road traffic collision data

Improving road safety is hugely important with the number of deaths on the world’s roads remaining unacceptably high; an estimated 1.35 million people die each year (WHO, 2020). Current practice for treating collision hotspots is almost always reactive: once a threshold level of collisions has been exceeded during some predetermined observation period, treatment is applied (e.g. road safety cameras). However, more recently, methodology has been developed to predict collision counts at potential hotspots in future time periods, with a view to a more proactive treatment of road safety hotspots. Dynamic linear models provide a flexible framework for predicting collisions and thus enabling such a proactive treatment. In this paper, we demonstrate how such models can be used to capture both seasonal variability and spatial dependence in time dependent collision rates at several locations. The model allows for within- and out-of-sample forecasting for locations which are fully observed and for locations where some data are missing. We illustrate our approach using collision rate data from 8 Traffic Administration Zones in the US, and find that the model provides a good description of the underlying process and reasonable forecast accuracy

Markov chain models for extreme wind speeds

SUMMARY Understanding and quantifying the behaviour of extreme wind speeds has important applications for design in civil engineering. As in the extremal analysis of any environmental process, estimates are often required of the probability of events that are rarer than those already recorded. Consequently, research has focused on the development of techniques that make optimal use of the available data. One such approach lies in threshold methods, which, unlike the more traditional annual maxima approach to the modelling of extremes, takes into consideration all extreme events, extreme in the sense that they exceed some high threshold. However, the implications of using all extremes in an analysis include problems of temporal dependence and non-stationarity. Several pragmatic ways of circumventing the problem of temporal dependence have been developed, though these often include the deletion of many extreme observations, for example, filter out a set of independent extremes. This paper looks at another approach to inference-one which explicitly models the temporal dependence of the process and so can use information on all extremes-and investigates the appropriateness of assumptions of short-term temporal dependence for wind speeds. We also examine the success of such methods at estimating some extreme events commonly studied for wind-speed data. Throughout this paper extreme wind speeds are analysed within a Bayesian framework, which can be argued to be particularly advantageous for extreme value analyses. For example, the objective of an extreme value analysis is usually an estimate of the probability of future events reaching extreme levels-something which is handled quite naturally in a Bayesian analysis through predictive distributions

New Magnetic Excitations in the Spin-Density-Wave of Chromium

Low-energy magnetic excitations of chromium have been reinvestigated with a single-Q crystal using neutron scattering technique. In the transverse spin-density-wave phase a new type of well-defined magnetic excitation is found around (0,0,1) with a weak dispersion perpendicular to the wavevector of the incommensurate structure. The magnetic excitation has an energy gap of E ~ 4 meV and at (0,0,1) exactly corresponds to the Fincher mode previously studied only along the incommensurate wavevector.Comment: 4 pages, 4 figure

Charge Stripes and Antiferromagnetism in Insulating Nickelates and Superconducting Cuprates

Neutron and X-ray scattering studies have provided strong evidence for coupled spatial modulations of charge and spin densities in layered nickelates and cuprates. The accumulated results for La(2-x)Sr(x)NiO(4+d) are consistent with the strongly-modulated topological-stripe concept. Clues from Nd-doped La(2-x)Sr(x)CuO(4) suggest similar behavior for the cuprates. The experimental results are summarized, and features that conflict with an interpretation based on a Fermi-surface instability are emphasized. A rationalization for the differences in transport properties between the cuprates and nickelates is given.Comment: 10pp., uses elsart.sty, 3 eps figures embedded with psfig; for proceedings of Spectroscopies in Novel Superconductors '97, J. Phys. Chem. Solid

Transportation Baseline Schedule

The “1999 National Transportation Program - Transportation Baseline Report” presents data that form a baseline to enable analysis and planning for future Department of Energy (DOE) Environmental Management (EM) waste/material transportation. The companion “1999 Transportation ‘Barriers’ Analysis” analyzes the data and identifies existing and potential problems that may prevent or delay transportation activities based on the data presented. The “1999 Transportation Baseline Schedule” (this report) uses the same data to provide an overview of the transportation activities of DOE EM waste/materials. This report can be used to identify areas where stakeholder interface is needed, and to communicate to stakeholders the quantity/schedule of shipments going through their area. Potential bottlenecks in the transportation system can be identified; the number of packages needed, and the capacity needed at receiving facilities can be planned. This report offers a visualization of baseline DOE EM transportation activities for the 11 major sites and the “Geologic Repository Disposal” site (GRD)

Dynamic Bayesian Combination of Multiple Imperfect Classifiers

Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to incorporate any available prior knowledge about their abilities when training data is sparse. In this paper we explore Bayesian classifier combination, using the computationally efficient framework of variational Bayesian inference. We apply the approach to real data from a large citizen science project, Galaxy Zoo Supernovae, and show that our method far outperforms other established approaches to imperfect decision combination. We go on to analyse the putative community structure of the decision makers, based on their inferred decision making strategies, and show that natural groupings are formed. Finally we present a dynamic Bayesian classifier combination approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure