Search

This paper investigates the effects of the staggered roll-out of a pollution reduction programme introduced in the UK in the 1950s. The policy allowed local authorities to introduce so-called Smoke Control Areas (SCAs) which banned smoke emissions. We start by digitizing historical pollution data to show that the policy led to an immediate reduction in black smoke concentrations. We then merge data on the exact location, boundary and month of introduction of SCAs to individual-level outcomes in older age using individuals' year-month and location of birth. We show that exposure to the programme increased individuals' birth weights as well as height in adulthood. We find no impact on their years of education or fluid intelligence.

This paper explores the similarity of the streamwise velocity fluctuations in a channel. In the analysis, we employ a one-dimensional scalar variant of the proper orthogonal decomposition (POD). This approach naturally motivates the introduction of two different levels of similarity which we will refer to as strong and weak similarity. Strong similarity requires that the two-point correlation, and thus, all POD modes, show Reynolds number similarity, while weak similarity only requires that the first few POD modes show similarity. As POD concerns information at more than one location, these similarities are more general than various similarities found in the literature concerning single-point flow statistics. We examine flows at $Re_\tau=$180, 540, 1000, and 5200. Strong similarity is observed in the viscous layer and the wake region, and weak similarity is found in both the viscous wall region and the outer part of the logarithmic layer. The presence of weak similarity suggests the existence of an extension to the law of the wall (LoW). We propose such an extension based on the results from the one-dimensional POD analysis. The usefulness of the LoW extension is then assessed by comparing flow reconstructions according to the conventional equilibrium LoW and the extended LoW. We show that the extended LoW provides accurate flow reconstructions in the wall layer, capturing fine-scale motions that are entirely missed by the equilibrium LoW., Comment: 17 pages, 16 figures

We present a formalism for dissipation-optimized decomposition of the strain rate tensor (SRT) of turbulent flow data using Proper Orthogonal Decomposition (POD). The formalism includes a novel inverse spectral SRT operator allowing the mapping of the resulting SRT modes to corresponding velocity fields, which enables a complete dissipation-optimized reconstruction of the velocity field. Flow data snapshots are obtained from a direct numerical simulation of a turbulent channel flow with friction Reynolds number $Re_{\tau}=390$. The lowest dissipation-optimized POD (d-POD) modes are compared to the lowest conventional turbulent kinetic energy (TKE) optimized POD (e-POD) modes. The lowest d-POD modes show a richer small-scale structure, along with traces of the large-scale structure characteristic of e-POD modes, indicating that the former capture structures across a wider range of spatial scales. Profiles of both TKE and dissipation are reconstructed using both decompositions, and reconstruction convergences are compared in all cases. Both TKE and dissipation are reconstructed more efficiently in the dissipation-rich near-wall region using d-POD modes, and in the TKE-rich bulk using e-POD modes. Lower modes of either decomposition tend to contribute more to either reconstructed quantity. Separating each term into eigenvalues and factors relating to the inherent structures in each mode reveals that higher e-POD modes tend to encode more dissipative structures, whereas the structures encoded by d-POD modes have roughly constant inherent TKE content, supporting the hypothesis that structures encoded by d-POD modes tend to span a wide range of spatial scales., Comment: 16 pages, 11 figures, 1 table

Handwritten text recognition for historical documents is an important task but it remains difficult due to a lack of sufficient training data in combination with a large variability of writing styles and degradation of historical documents. While recurrent neural network architectures are commonly used for handwritten text recognition, they are often computationally expensive to train and the benefit of recurrence drastically differs by task. For these reasons, it is important to consider non-recurrent architectures. In the context of handwritten date recognition, we propose an architecture based on the EfficientNetV2 class of models that is fast to train, robust to parameter choices, and accurately transcribes handwritten dates from a number of sources. For training, we introduce a database containing almost 10 million tokens, originating from more than 2.2 million handwritten dates which are segmented from different historical documents. As dates are some of the most common information on historical documents, and with historical archives containing millions of such documents, the efficient and automatic transcription of dates has the potential to lead to significant cost-savings over manual transcription. We show that training on handwritten text with high variability in writing styles result in robust models for general handwritten text recognition and that transfer learning from the DARE system increases transcription accuracy substantially, allowing one to obtain high accuracy even when using a relatively small training sample.

This paper uses a large UK cohort to investigate the impact of early-life pollution exposure on individuals' human capital and health outcomes in older age. We compare individuals who were exposed to the London smog in December 1952 whilst in utero or in infancy to those born after the smog and those born at the same time but in unaffected areas. We find that those exposed to the smog have substantially lower fluid intelligence and worse respiratory health, with some evidence of a reduction in years of schooling.

Data acquisition forms the primary step in all empirical research. The availability of data directly impacts the quality and extent of conclusions and insights. In particular, larger and more detailed datasets provide convincing answers even to complex research questions. The main problem is that 'large and detailed' usually implies 'costly and difficult', especially when the data medium is paper and books. Human operators and manual transcription have been the traditional approach for collecting historical data. We instead advocate the use of modern machine learning techniques to automate the digitisation process. We give an overview of the potential for applying machine digitisation for data collection through two illustrative applications. The first demonstrates that unsupervised layout classification applied to raw scans of nurse journals can be used to construct a treatment indicator. Moreover, it allows an assessment of assignment compliance. The second application uses attention-based neural networks for handwritten text recognition in order to transcribe age and birth and death dates from a large collection of Danish death certificates. We describe each step in the digitisation pipeline and provide implementation insights.

On February 6, 2016 at 21:07:19 UT, a very bright fireball was seen over the eastern part of Denmark. The weather was cloudy over eastern Denmark, but many people saw the sky light up-even in the heavily illuminated Copenhagen. Two hundred and thirty three reports of the associated sound and light phenomena were received by the Danish fireball network. We have formed a consortium to describe the meteorite and the circumstances of the fall and the results are presented in this paper. The first fragment of the meteorite was found the day after the fall, and in the following weeks, a total of 11 fragments with a total weight of 8982 g were found. The meteorite is an unbrecciated, weakly shocked (S2), ordinary H chondrite of petrologic type 5/6 (Bouvier et al. 2017). The concentration of the cosmogenic radionuclides suggests that the preatmospheric radius was rather small ~20 cm. The cosmic ray exposure age of Ejby (83 +/- 11 Ma) is the highest of an H chondrite and the second highest age for an ordinary chondrite. Using the preatmospheric orbit of the Ejby meteoroid (Spurny et al. 2017) locations of the recovered fragments, and wind data from the date of the fall, we have modeled the dark flight (below 18 km) of the fragments. The recovery location of the largest fragment can only be explained if aerodynamic effects during the dark flight phase are included. The recovery location of all other fragments are consistent with the dark flight modeling.

We propose a novel bootstrap procedure for dependent data based on Generative Adversarial networks (GANs). We show that the dynamics of common stationary time series processes can be learned by GANs and demonstrate that GANs trained on a single sample path can be used to generate additional samples from the process. We find that temporal convolutional neural networks provide a suitable design for the generator and discriminator, and that convincing samples can be generated on the basis of a vector of iid normal noise. We demonstrate the finite sample properties of GAN sampling and the suggested bootstrap using simulations where we compare the performance to circular block bootstrapping in the case of resampling an AR(1) time series processes. We find that resampling using the GAN can outperform circular block bootstrapping in terms of empirical coverage.

Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Probably the single most important identifier for linking is personal names. However, personal names are prone to enumeration and transcription errors and although modern linking methods are designed to handle such challenges, these sources of errors are critical and should be minimized. For this purpose, improved transcription methods and large-scale databases are crucial components. This paper describes and provides documentation for HANA, a newly constructed large-scale database which consists of more than 3.3 million names. The database contain more than 105 thousand unique names with a total of more than 1.1 million images of personal names, which proves useful for transfer learning to other settings. We provide three examples hereof, obtaining significantly improved transcription accuracy on both Danish and US census data. In addition, we present benchmark results for deep learning models automatically transcribing the personal names from the scanned documents. Through making more challenging large-scale databases publicly available we hope to foster more sophisticated, accurate, and robust models for handwritten text recognition.

SONG aims at setting up a network of small 1m telescopes around the globe to observe stars uninterrupted throughout days, weeks and even months. This paper describes the fundamental aspects for putting up such a network and how we will operate each site as part of the full network. The SONG observatories will be working autonomously and automatic and can be fully controlled remotely., Comment: 4 pages, 5 figures, 2 tables, Published 2014

The Stellar Observation Network Group (SONG) is an initiative to build a worldwide network of 1m telescopes with highprecision radial-velocity spectrographs. Here we analyse the first radial-velocity time series of a red-giant star measured by the SONG telescope at Tenerife. The asteroseismic results demonstrate a major increase in the achievable precision of the parameters for redgiant stars obtainable from ground-based observations. Reliable tests of the validity of these results are needed, however, before the accuracy of the parameters can be trusted. We analyse the first SONG time series for the star 46 LMi, which has a precise parallax and an angular diameter measured from interferometry, and therefore a good determination of the stellar radius. We use asteroseismic scaling relations to obtain an accurate mass, and modelling to determine the age. A 55-day time series of high-resolution, high S/N spectra were obtained with the first SONG telescope. We derive the asteroseismic parameters by analysing the power spectrum. To give a best guess on the large separation of modes in the power spectrum, we have applied a new method which uses the scaling of Kepler red-giant stars to 46 LMi. Several methods have been applied: classical estimates, seismic methods using the observed time series, and model calculations to derive the fundamental parameters of 46 LMi. Parameters determined using the different methods are consistent within the uncertainties. We find the following values for the mass M (scaling), radius R (classical), age (modelling), and surface gravity (combining mass and radius): M = 1.09 +- 0.04Msun, R = 7.95 +- 0.11 Rsun, age t = 8.2 +- 1.9Gy, and log g = 2.674 +- 0.013., Comment: 7 pages, 7 figures, 4 tables

We report on the implemented design of a two-colour instrument based on electron multiplying CCD (EMCCD) detectors. This instrument is currently installed at the Danish 1.54m telescope at ESO's La Silla Observatory in Chile, and will be available at the SONG (Stellar Observations Network Group) 1m telescope node at Tenerife and at other SONG nodes as well. We present the software system for controlling the two-colour instrument and calibrating the high frame-rate imaging data delivered by the EMCCD cameras. An analysis of the performance of the Two-Colour Instrument at the Danish telescope shows an improvement in spatial resolution of up to a factor of two when doing shift-and-add compared with conventional imaging, and that it is possible to do high-precision photometry of EMCCD data in crowded fields. The Danish telescope, which was commissioned in 1979, is limited by a triangular coma at spatial resolutions below 0.5" and better results will thus be achieved at the near diffraction limited optical system on the SONG telescopes, where spatial resolutions close to 0.2" have been achieved. Regular EMCCD operations have been running at the Danish telescope for several years and have produced a number of scientific discoveries, including microlensing detected exoplanets, the detection of previously unknown variable stars in dense globular clusters and the discovery of two rings around the small asteroid-like object (10199) Chariklo., Comment: Accepted to A&A, 17 pages, 15 figures

We report the detection of an optical jet in the nearby Seyfert 1 radio galaxy 3C 120. The optical jet coincides with the well-known radio jet and emits continuum radiation (B,V',I) with a radio-to-optical spectral index of 0.65. There are no clear optical counterparts to the radio knots, although the optical condensation A of the galaxy, which includes the bright 4" radio knot, is found to be 12 % polarized with the electric field vectors perpendicular to the jet. These findings indicate that 3C 120 contains the 6th known extragalactic optical synchrotron jet, quite similar in its properties to the jet of PKS 0521-36. The outer parts of the jet is the faintest known optical jet and was discovered as the result of a dedicated effort to detect it. It is therefore possible that more optical jets can be discovered in systematic searches by combining deep imaging in the optical or near-IR with careful galaxy subtraction methods.