Vacuum-ultraviolet frequency-modulation spectroscopy

Frequency-modulation (FM) spectroscopy has been extended to the vacuum-ultraviolet (VUV) range of the electromagnetic spectrum. Coherent VUV laser radiation is produced by resonance-enhanced sum-frequency mixing ($\nu_{\mathrm{VUV}}=2\nu_{\mathrm{UV}}+\nu_2$) in Kr and Xe using two near-Fourier-transform-limited laser pulses of frequencies $\nu_{\mathrm{UV}}$ and $\nu_2$. Sidebands generated in the output of the second laser ($\nu_2$) using an electro-optical modulator operating at the frequency $\nu_{\mathrm{mod}}$ are directly transfered to the VUV and used to record FM spectra. Demodulation is demonstrated both at $\nu_{\mathrm{mod}}$ and $2\nu_{\mathrm{mod}}$. The main advantages of the method are that its sensitivity is not reduced by pulse-to-pulse fluctuations of the VUV laser intensity, compared to VUV absorption spectroscopy is its background-free nature, the fact that its implementation using table-top laser equipment is straightforward and that it can be used to record VUV absorption spectra of cold samples in skimmed supersonic beams simultaneously with laser-induced-fluorescence and photoionization spectra. To illustrate these advantages we present VUV FM spectra of Ar, Kr, and N$_2$ in selected regions between 105000cm$^{-1}$ and 122000cm$^{-1}$.Comment: 23 pages, 10 figure

Differentiable Forward Kinematics for TensorFlow 2

Robotic systems are often complex and depend on the integration of a large number of software components. One important component in robotic systems provides the calculation of forward kinematics, which is required by both motion-planning and perception related components. End-to-end learning systems based on deep learning require passing gradients across component boundaries.Typical software implementations of forward kinematics are not differentiable, and thus prevent the construction of gradient-based, end-to-end learning systems. In this paper we present a library compatible with ROS-URDF that computes forward kinematics while simultaneously giving access to the gradients w.r.t. joint configurations and model parameters, allowing gradient-based learning and model identification. Our Python library is based on Tensorflow~2 and is auto-differentiable. It supports calculating a large number of kinematic configurations on the GPU in parallel, yielding a considerable performance improvement compared to sequential CPU-based calculation. https://github.com/lumoe/dlkinematics.gi

A data driven approach to mapping urban neighbourhoods

Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the “building blocks of public service society”. Despite this, difficulties in data collection combined with the concept’s subjective nature have left most countries lacking official neighbourhood definitions. This issue has implications not only for policy, but for the field of computational social science as a whole (with many studies being forced to use administrative units as proxies despite the fact that these bear little connection to resident perceptions of social boundaries). In this paper we illustrate that the mass linguistic datasets now available on the internet need only be combined with relatively simple linguistic computational models to produce definitions that are not only probabilistic and dynamic, but do not require a priori knowledge of neighbourhood names

Generation of helical magnetic fields from inflation

The generation of helical magnetic fields during single field inflation due to an axial coupling of the electromagnetic field to the inflaton is discussed. We find that such a coupling always leads to a blue spectrum of magnetic fields during slow roll inflation. Though the helical magnetic fields further evolve during the inverse cascade in the radiation era after inflation, we conclude that the magnetic fields generated by such an axial coupling can not lead to observed field strength on cosmologically relevant scales.Comment: 4 pages, 1 figure; Contribution to the proceedings of the International Conference on Gravitation and Cosmology (ICGC), Goa, India, December, 201

Sequence classification with human attention

Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP. Specifically, we use estimated human attention derived from eye-tracking corpora to regularize attention functions in recurrent neural networks. We show substantial improvements across a range of tasks, including sentiment analysis, grammatical error detection, and detection of abusive language

Challenges for creating magnetic fields by cosmic defects

We analyse the possibility that topological defects can act as a source of magnetic fields through the Harrison mechanism in the radiation era. We give a detailed relativistic derivation of the Harrison mechanism at first order in cosmological perturbations, and show that it is only efficient for temperatures above T ~ 0.2 keV. Our main result is that the vector metric perturbations generated by the defects cannot induce vorticity in the matter fluids at linear order, thereby excluding the production of currents and magnetic fields. We show that anisotropic stress in the matter fluids is required to source vorticity and magnetic fields. Our analysis is relevant for any mechanism whereby vorticity is meant to be transferred purely by gravitational interactions, and thus would also apply to dark matter or neutrinos.Comment: 9 pages, 1 figure; minor corrections and additions; accepted for publication in Physical Review