A method to measure vacuum birefringence at FCC-ee

It is well-known that the Heisenberg-Euler-Schwinger effective Lagrangian predicts that a vacuum with a strong static electromagnetic field turns birefringent. We propose a scheme that can be implemented at the planned FCC-ee, to measure the nonlinear effect of vacuum birefringence in electrodynamics arising from QED corrections. Our scheme employs a pulsed laser to create Compton backscattered photons off a high energy electron beam, with the FCC-ee as a particularly interesting example. These photons will pass through a strong static magnetic field, which changes the state of polarization of the radiation - an effect proportional to the photon energy. This change will be measured by the use of an aligned single-crystal, where a large difference in the pair production cross-sections can be achieved. In the proposed experimental setup the birefringence effect gives rise to a difference in the number of pairs created in the analyzing crystal, stemming from the fact that the initial laser light has a varying state of polarization, achieved with a rotating quarter wave plate. Evidence for the vacuum birefringent effect will be seen as a distinct peak in the Fourier transform spectrum of the pair-production rate signal. This tell-tale signal can be significantly above background with only few hours of measurement, in particular at high energies.Comment: Presented by UIU at the International Symposium on "New Horizons in Fundamental Physics: From Neutrons Nuclei via Superheavy Elements and Supercritical Fields to Neutron Stars and Cosmic Rays," held to honor Walter Greiner on his 80th birthday at Makutsi Safari Farm, South Africa, November 23-29, 201

ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery

On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms. We propose a computationally inexpensive detection network for vehicle detection in UAV imagery which we call ShuffleDet. In order to enhance the speed-wise performance, we construct our method primarily using channel shuffling and grouped convolutions. We apply inception modules and deformable modules to consider the size and geometric shape of the vehicles. ShuffleDet is evaluated on CARPK and PUCPR+ datasets and compared against the state-of-the-art real-time object detection networks. ShuffleDet achieves 3.8 GFLOPs while it provides competitive performance on test sets of both datasets. We show that our algorithm achieves real-time performance by running at the speed of 14 frames per second on NVIDIA Jetson TX2 showing high potential for this method for real-time processing in UAVs.Comment: Accepted in ECCV 2018, UAVision 201

Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures

Reconfigurable architectures are becoming mainstream: Amazon, Microsoft and IBM are supporting such architectures in their data centres. The computationally intensive nature of atmospheric modelling is an attractive target for hardware acceleration using reconfigurable computing. Performance of hardware designs can be improved through the use of reduced-precision arithmetic, but maintaining appropriate accuracy is essential. We explore reduced-precision optimisation for simulating chaotic systems, targeting atmospheric modelling, in which even minor changes in arithmetic behaviour will cause simulations to diverge quickly. The possibility of equally valid simulations having differing outcomes means that standard techniques for comparing numerical accuracy are inappropriate. We use the Hellinger distance to compare statistical behaviour between reduced-precision CPU implementations to guide reconfigurable designs of a chaotic system, then analyse accuracy, performance and power efficiency of the resulting implementations. Our results show that with only a limited loss in accuracy corresponding to less than 10% uncertainty in input parameters, the throughput and energy efficiency of a single-precision chaotic system implemented on a Xilinx Virtex-6 SX475T Field Programmable Gate Array (FPGA) can be more than doubled

PRESENT STATUS OF THE POHANG LIGHT-SOURCE

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A multi-model superensemble algorithm for seasonal climate prediction using DEMETER forecasts

In this paper, a multi-model ensemble approach with statistical correction for seasonal precipitation forecasts using a coupled DEMETER model data set is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability; consequently, the predictive skill of extended forecasts remains quite low. One of the approaches to the improvement of seasonal prediction is the empirical weighted multi-model ensemble, or superensemble, combination. In the superensemble approach, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The skill of a superensemble relies strongly on the past performance of the individual member models used in its construction. The algorithm proposed here involves empirical orthogonal function (EOF) filtering of the actual data set prior to the construction of a multi-model ensemble or superensemble as an alternative solution for seasonal prediction. This algorithm generates a new data set from the input multi-model data set by finding a consistent spatial pattern between the observed analysis and the individual model forecast. This procedure is a multiple linear regression problem in the EOF space. The newly generated EOF-filtered data set is then used as an input data set for the construction of a multi-model ensemble and superensemble. The skill of forecast anomalies is assessed using statistics of categorical forecast, spatial anomaly correlation and root mean square (RMS) errors. The various verifications show that the unbiased multi-model ensemble of DEMETER forecasts improves the prediction of spatial patterns (i.e. the anomaly correlation), but it shows poor skill in categorical forecast. Due to the removal of seasonal mean biases of the different models, the forecast errors of the bias-corrected multi-model ensemble and superensemble are already quite small. Based on the anomaly correlation and RMS measures, the forecasts produced by the proposed method slightly outperform the other conventional forecasts

ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating three components of ER: (a) Classifiers for duplicate/non-duplicate record pairs built using machine learning (ML) techniques, (b) MDs for supporting both the blocking phase of ML and the merge itself; and (c) The use of the declarative language LogiQL -an extended form of Datalog supported by the LogicBlox platform- for data processing, and the specification and enforcement of MDs.Comment: To appear in Proc. SUM, 201

Incidence and prevalence of epilepsy and associated factors in a health district in North-West Cameroon: A population survey

This population-based cross-sectional survey with a follow-up case-control study assessed the prevalence, incidence, and risk factors for epilepsy in a rural health district in the North-West Region of Cameroon. Community-based epilepsy screening targeted all inhabitants, six years and older, in all 16 health areas in the Batibo Health District. During door-to-door visits, trained fieldworkers used a validated questionnaire to interview consenting household heads to screen for epilepsy in eligible residents. Trained physicians subsequently assessed people with suspected seizures. After clinical assessment, they confirmed or refuted the diagnosis and estimated the date of epilepsy onset. A trained nurse interviewed people with epilepsy and randomly selected healthy individuals, obtaining relevant demographic details and information on exposure to risk factors for epilepsy. Out of 36,282 residents screened, 524 had active epilepsy. The age-standardized prevalence of active epilepsy was 33.9/1,000 (95% CI: 31.0-37.1/1,000). We estimated the one-year age-standardized epilepsy incidence at 171/100,000 (95%CI: 114.0-254.6). Active epilepsy prevalence varied widely between health areas, ranging between 12 and 75 per 1,000. The peak age-specific prevalence was in the 25-34 age group. In adults, multivariate analysis showed that having a relative with epilepsy was positively associated with epilepsy. Epilepsy characteristics in this population, geographical heterogeneity, and the age-specific prevalence pattern suggest that endemic neurocysticercosis and onchocerciasis may be implicated. Further investigations are warranted to establish the full range of risk factors for epilepsy in this population

Finite temperature phase diagram of a polarised Fermi condensate

The two-component Fermi gas is the simplest fermion system displaying superfluidity, and as such finds applications ranging from the theory of superconductivity to QCD. Ultracold atomic gases provide an exceptionally clean realization of this system, where the interatomic interaction and the atom species population are both independent, tuneable parameters. This allows one to investigate the Fermi gas with imbalanced spin populations, which had previously been experimentally elusive, and this prospect has stimulated much theoretical activity. Here we show that the finite temperature phase diagram contains a region of phase separation between the superfluid and normal states that touches the boundary of second-order superfluid transitions at a tricritical point, reminiscent of the phase diagram of $^3$He-$^4$He mixtures. A variation of interaction strength then results in a line of tricritical points that terminates at zero temperature on the molecular Bose-Einstein condensate (BEC) side. On this basis, we argue that tricritical points will play an important role in the recent experiments on polarised atomic Fermi gases.Comment: 6 pages, 4 figures. Manuscript extended and figures modified. For final version, see Nature Physic

Propionate and butyrate dependent bacterial sulfate reduction at extremely haloalkaline conditions and description of Desulfobotulus alkaliphilus sp. nov.

Evidence on the utilization of simple fatty acids by sulfate-reducing bacteria (SRB) at extremely haloalkaline conditions are practically absent, except for a single case of syntrophy by Desulfonatronum on acetate. Our experiments with sediments from soda lakes of Kulunda Steppe (Altai, Russia) showed sulfide production with sulfate as electron acceptor and propionate and butyrate (but not acetate) as an electron donor at a pH 10–10.5 and a salinity 70–180 g l−1. With propionate as substrate, a highly enriched sulfidogenic culture was obtained in which the main component was identified as a novel representative of the family Syntrophobacteraceae. With butyrate as substrate, a pure SRB culture was isolated which oxidized butyrate and some higher fatty acids incompletely to acetate. The strain represents the first haloalkaliphilic representative of the family Desulfobacteraceae and is described as Desulfobotulus alkaliphilus sp. nov

High rate nitrogen removal by ANAMMOX internal circulation reactor (IC) for old landfill leachate treatment

© 2017 Elsevier Ltd This study aimed to evaluate the performance of a high rate nitrogen removal lab-scale ANAMMOX reactor, namely Internal Circulation (IC) reactor, for old landfill leachate treatment. The reactor was operated with pre-treated leachate from a pilot Partial Nitritation Reactor (PNR) using a high nitrogen loading rate ranging from 2 to 10 kg N m−3 d−1. High rate removal of nitrogen (9.52 ± 1.11 kg N m−3 d−1) was observed at an influent nitrogen concentration of 1500 mg N L−1. The specific ANAMMOX activity was found to be 0.598 ± 0.026 gN2-N gVSS−1 d−1. Analysis of ANAMMOX granules suggested that 0.5–1.0 mm size granular sludge was the dominant group. The results of DNA analysis revealed that Candidatus Kueneniastuttgartiensis was the dominant species (37.45%) in the IC reactor, whereas other species like uncultured Bacteroidetes bacterium only constituted 5.37% in the system, but they were still responsible for removing recalcitrant organic matter