HERA Inclusive Diffraction and Factorisation Tests

HERA measurements of diffractive ep scattering - the quasi-elastic scattering of the photon in the proton colour field - are summarised. Emphasis is placed on the most recent data.Comment: 9 pages, proceedings of PHOTON'0

The Hadronic Final State at HERA

The hadronic final state in electron-proton collisions at HERA has provided a rich testing ground for development of the theory of the strong force, QCD. In this review, over 200 publications from the H1 and ZEUS Collaborations are summarised. Short distance physics, the measurement of processes at high energy scales, has provided rigorous tests of perturbative QCD and constrained the structure of the proton as well as allowing precise measurements of the strong coupling constant to be made. Non-perturbative or low energy processes have also been investigated and results on hadronisation interpreted together with those from other experiments. Searches for exotic QCD objects, such as pentaquarks, glueballs and instantons have been performed. The subject of diffraction has been re-invigorated through its precise measurement, such that it can now be described by perturbative QCD. After discussion of HERA, the H1 and ZEUS detectors and the techniques used to reconstruct differing hadronic final states, the above subject areas are elaborated. The major achievements are then condensed further in a final section summarising what has been learned.Comment: 60 pages, 65 figures, submitted to Reviews of Modern Physics. Updated version includes comments to the text from journal referee

HERA Diffractive Structure Function Data and Parton Distributions

Recent diffractive structure function measurements by the H1 and ZEUS experiments at HERA are reviewed. Various data sets, obtained using systematically different selection and reconstruction methods, are compared. NLO DGLAP QCD fits are performed to the most precise H1 and ZEUS data and diffractive parton densities are obtained in each case. Differences between the Q^2 dependences of the H1 and ZEUS data are reflected as differences between the diffractive gluon densities.Comment: Contributed to the Proceedings of the Workshop on HERA and the LHC, DESY and CERN, 2004-200

Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes

This paper is about alerting acoustic event detection and sound source localisation in an urban scenario. Specifically, we are interested in spotting the presence of horns, and sirens of emergency vehicles. In order to obtain a reliable system able to operate robustly despite the presence of traffic noise, which can be copious, unstructured and unpredictable, we propose to treat the spectrograms of incoming stereo signals as images, and apply semantic segmentation, based on a Unet architecture, to extract the target sound from the background noise. In a multi-task learning scheme, together with signal denoising, we perform acoustic event classification to identify the nature of the alerting sound. Lastly, we use the denoised signals to localise the acoustic source on the horizon plane, by regressing the direction of arrival of the sound through a CNN architecture. Our experimental evaluation shows an average classification rate of 94%, and a median absolute error on the localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of 2.5{\deg} when operating on frames of 2.5s. The system offers excellent performance in particularly challenging scenarios, where the noise level is remarkably high.Comment: 6 pages, 9 figure

The breakup of the Southern Hemisphere spring polar ozone and temperature minimums from 1979 to 1987

The purpose of this study is to quantify the observations of the polar vortex breakup. The data used in this study consist of Total Ozone Mapping Spectrometer (TOMS) data, and National Meteorological Center (NMC) analyses. The final warming is diagnosed using the difference between zonal means at 80 degrees and 50 degrees S for temperature, ozone, and layer mean temperature. The polar vortex breakup can also be diagnosed by the onset of weak zonal mean zonal winds (i.e., u, overbar denotes a zonal average) at 60 degrees S. Computations of the polar vortex breakdown date using NMC meteorological data and TOMS total ozone data indicate that the breakdown is occurring later in the spring in the lowest portion of the stratosphere. At altitudes above 100 mb, the large interannual variance of the breakdown date renders any trend determination of the breakdown date difficult. Individual plots of TOMS total ozone indicate that the total ozone minimum remains intact for a longer period of time than is observed in earlier years

Observations of stratospheric temperature changes coincident with the recent Antarctic ozone depletions

A high degree of correlation between the recent decline in Antarctic total ozone and cooling of the stratosphere during Austral spring has been noted in several recent studies (e.g., Sekiguchi, 1986; Angel, 1986). This study analyzes the observed temperature trends in detail, focusing on the spatial and temporal aspects of the observed cooling. Ozone losses and stratospheric cooling can be correlated for several reasons: (1) ozone losses (from an unspecified cause) will directly reduce temperatures due to decreased solar ultraviolet absorption (Shine, 1986), and/or (2) changes in both ozone and temperature structure due to modification of stratospheric circulation patterns (Mahlman and Fels, 1986). In order to scrutinize various ozone depletion scenarios, detailed information on the observed temperature changes is necessary; the goal is to provide such data. The data used are National Meteorological Center (NMC) Climate Analysis Center (CAC) derived temperatures, covering 1000 to 1 mb (0 to 48 km), for the period 1979 to 1987. Discussions on data origin and quality (assessed by extensive comparisons with radiosonde observations), along with other details of these observations, can be found in Newman and Randel (1988)

Meshed Up: Learnt Error Correction in 3D Reconstructions

Dense reconstructions often contain errors that prior work has so far minimised using high quality sensors and regularising the output. Nevertheless, errors still persist. This paper proposes a machine learning technique to identify errors in three dimensional (3D) meshes. Beyond simply identifying errors, our method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene. This enables us to improve the reconstruction accuracy. We train a suitably deep network architecture with two 3D meshes: a high-quality laser reconstruction, and a lower quality stereo image reconstruction. The network predicts the amount of error in the lower quality reconstruction with respect to the high-quality one, having only view the former through its input. We evaluate our approach by correcting two-dimensional (2D) inverse-depth images extracted from the 3D model, and show that our method improves the quality of these depth reconstructions by up to a relative 10% RMSE.Comment: Accepted for the International Conference on Robotics and Automation (ICRA) 201