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We experimentally explore the competition between light-mediated and direct matter-matter interactions in waveguide quantum electrodynamics. For this, we couple a superconducting line to a model magnetic material, made of organic free radical DPPH molecules with a spin $S=1/2$ and a $g_{S}$ factor very close to that of a free electron. The microwave transmission has been measured in a wide range of temperatures ($0.013$ K $\leq T \leq 2$ K), magnetic fields ($0\leq B \leq 0.5$ T) and frequencies ($0 \leq \omega/2 \pi \leq 14$ GHz). We find that molecules belonging to the crystal sublattice B form one-dimensional spin chains. Temperature then controls intrinsic spin correlations along the chain in a continuous and monotonic way. In the paramagnetic region ($T > 0.7$ K), the microwave transmission shows evidences for the collective coupling of quasi-identical spins to the propagating photons, with coupling strengths that reach values close to the dissipation rates. As $T$ decreases, the growth of intrinsic spin correlations, combined with the anisotropy in the spin-spin exchange constants, break down the collective spin-photon coupling. In this regime, the temperature dependence of the spin resonance visibility reflects the change in the nature of the dominant spin excitations, from single spin flips to bosonic magnons, which is brought about by the magnetic correlation growth.

Summarisation of research results in plain language is crucial for promoting public understanding of research findings. The use of Natural Language Processing to generate lay summaries has the potential to relieve researchers' workload and bridge the gap between science and society. The aim of this narrative literature review is to describe and compare the different text summarisation approaches used to generate lay summaries. We searched the databases Web of Science, Google Scholar, IEEE Xplore, Association for Computing Machinery Digital Library and arXiv for articles published until 6 May 2022. We included original studies on automatic text summarisation methods to generate lay summaries. We screened 82 articles and included eight relevant papers published between 2020 and 2021, all using the same dataset. The results show that transformer-based methods such as Bidirectional Encoder Representations from Transformers (BERT) and Pre-training with Extracted Gap-sentences for Abstractive Summarization (PEGASUS) dominate the landscape of lay text summarisation, with all but one study using these methods. A combination of extractive and abstractive summarisation methods in a hybrid approach was found to be most effective. Furthermore, pre-processing approaches to input text (e.g. applying extractive summarisation) or determining which sections of a text to include, appear critical. Evaluation metrics such as Recall-Oriented Understudy for Gisting Evaluation (ROUGE) were used, which do not consider readability. To conclude, automatic lay text summarisation is under-explored. Future research should consider long document lay text summarisation, including clinical trial reports, and the development of evaluation metrics that consider readability of the lay summary.

Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided.

Surface cracks are a common sight on public infrastructure nowadays. Recent work has been addressing this problem by supporting structural maintenance measures using machine learning methods. Those methods are used to segment surface cracks from their background, making them easier to localize. However, a common issue is that to create a well-functioning algorithm, the training data needs to have detailed annotations of pixels that belong to cracks. Our work proposes a weakly supervised approach that leverages a CNN classifier in a novel way to create surface crack pseudo labels. First, we use the classifier to create a rough crack localization map by using its class activation maps and a patch based classification approach and fuse this with a thresholding based approach to segment the mostly darker crack pixels. The classifier assists in suppressing noise from the background regions, which commonly are incorrectly highlighted as cracks by standard thresholding methods. Then, the pseudo labels can be used in an end-to-end approach when training a standard CNN for surface crack segmentation. Our method is shown to yield sufficiently accurate pseudo labels. Those labels, incorporated into segmentation CNN training using multiple recent crack segmentation architectures, achieve comparable performance to fully supervised methods on four popular crack segmentation datasets., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Surface crack segmentation poses a challenging computer vision task as background, shape, colour and size of cracks vary. In this work we propose optimized deep encoder-decoder methods consisting of a combination of techniques which yield an increase in crack segmentation performance. Specifically we propose a decoder-part for an encoder-decoder based deep learning architecture for semantic segmentation and study its components to achieve increased performance. We also examine the use of different encoder strategies and introduce a data augmentation policy to increase the amount of available training data. The performance evaluation of our method is carried out on four publicly available crack segmentation datasets. Additionally, we introduce two techniques into the field of surface crack segmentation, previously not used there: Generating results using test-time-augmentation and performing a statistical result analysis over multiple training runs. The former approach generally yields increased performance results, whereas the latter allows for more reproducible and better representability of a methods results. Using those aforementioned strategies with our proposed encoder-decoder architecture we are able to achieve new state of the art results in all datasets., Comment: Accepted Manuscript at Digital Signal Processing (Elsevier)

We perform extensive analysis of graphene Josephson junctions embedded in microwave circuits. By comparing a diffusive junction at 15 mK with a ballistic one at 15 mK and 1 K, we are able to reconstruct the current-phase relation.

We present the design, measurement and analysis of a current sensor based on a process of Josephson parametric upconversion in a superconducting microwave cavity. Terminating a coplanar waveguide with a nanobridge constriction Josephson junction, we observe modulation sidebands from the cavity that enable highly sensitive, frequency-multiplexed output of small currents for applications such as transition-edge sensor array readout. We derive an analytical model to reproduce the measurements over a wide range of bias currents, detunings and input powers. Tuning the frequency of the cavity by more than \SI{100}{\mega\hertz} with DC current, our device achieves a minimum current sensitivity of \SI{8.9}{\pico\ampere\per\sqrt{\hertz}}. Extrapolating the results of our analytical model, we predict an improved device based on our platform, capable of achieving sensitivities down to \SI{50}{\femto\ampere\per\sqrt{\hertz}}}, or even lower if one could take advantage of parametric amplification in the Josephson cavity. Taking advantage of the Josephson architecture, our approach can provide higher sensitivity than kinetic inductance designs, and potentially enables detection of currents ultimately limited by quantum noise.

Detecting weak radio-frequency electromagnetic fields plays a crucial role in wide range of fields, from radio astronomy to nuclear magnetic resonance imaging. In quantum mechanics, the ultimate limit of a weak field is a single-photon. Detecting and manipulating single-photons at megahertz frequencies presents a challenge as, even at cryogenic temperatures, thermal fluctuations are significant. Here, we use a gigahertz superconducting qubit to directly observe the quantization of a megahertz radio-frequency electromagnetic field. Using the qubit, we achieve quantum control over thermal photons, cooling to the ground-state and stabilizing photonic Fock states. Releasing the resonator from our control, we directly observe its re-thermalization dynamics with the bath with nanosecond resolution. Extending circuit quantum electrodynamics to a new regime, we enable the exploration of thermodynamics at the quantum scale and allow interfacing quantum circuits with megahertz systems such as spin systems or macroscopic mechanical oscillators., Comment: Changes in v2: condensed main part, error in Table S1 corrected. Raw data and code available in Zenodo with the identifier 10.5281/zenodo.2551258

We explore the superconducting phase diagram of the two-dimensional electron system at the LaAlO3/SrTiO3 interface by monitoring the frequencies of the cavity modes of a coplanar waveguide resonator fabricated in the interface itself. We determine the phase diagram of the superconducting transition as a function of temperature and electrostatic gating, finding that both the superfluid density and the transition temperature follow a dome shape, but that the two are not monotonically related. The ground state of this 2DES is interpreted as a Josephson junction array, where a transition from long- to short-range order occurs as a function of the electronic doping. The synergy between correlated oxides and superconducting circuits is revealed to be a promising route to investigate these exotic compounds, complementary to standard magneto-transport measurements., Comment: 5 pages, 4 figures and 10 pages of supplementary material

Josephson junctions (JJ) are a fundamental component of microwave quantum circuits, such as tunable cavities, qubits and parametric amplifiers. Recently developed encapsulated graphene JJs, with supercurrents extending over micron distance scales, have exciting potential applications as a new building block for quantum circuits. Despite this, the microwave performance of this technology has not been explored. Here, we demonstrate a microwave circuit based on a ballistic graphene JJ embedded in a superconducting cavity. We directly observe a gate-tunable Josephson inductance through the resonance frequency of the device and, using a detailed RF model, we extract this inductance quantitatively. We also observe the microwave losses of the device, and translate this into sub-gap resistances of the junction at {\mu}eV energy scales, not accessible in DC measurements. The microwave performance we observe here suggests that graphene Josephson junctions are a feasible platform for implementing coherent quantum circuits., Comment: 43 pages, 20 figures

We show that several qubits can be integrated in a single magnetic ion, using its internal electronic spin states with energies tuned by a suitably chosen molecular environment. This approach is illustrated with a nearly-isotropic Gd(III) ion entrapped in a polyoxometalate molecule. Experiments with microwave technologies, either three dimensional cavities or quantum superconducting circuits, show that this magnetic molecule possesses the number of spin states and the set of coherently addressable transitions connecting these states that are needed to perform a universal three-qubit processor or, equivalently, a d=8-level 'qudit'. Our findings open prospects for developing more sophisticated magnetic molecules which can result in more powerful and noise resilient quantum computation schemes., Comment: 5 pages, 4 figures

This thesis explores the coupling of magnetic systems to quantum circuits in the context of quantum computing applications. In particular we study the coupling of superconducting coplanar waveguide resonators to Single Molecule Magnets (SMMs) . The combination of approaches from the fields of Cavity Quantum electrodynamics (QED) and Circuit QED with those from the field of molecular magnetism con provide unique opportunities for quantum computing. We investigate the necessary conditions for coupling single spins and spin ensembles to resonators and what characteristics SMMs should have in order to provide interesting alternatives as quantum bits. We present test measurements of several magnetic samples using both broadband spectroscopy with open waveguides and EPR spectroscopy using coplanar waveguide resonators. We also design, fabricate, and test nanometric constrictions in superconducting resonators with the objective of improving their coupling to single spins. We evaluate the performance of these constricted resonators in comparison to unmodified resonators., Comment: 237 pages, 159 figures, Ph.D Thesis (Universidad de Zaragoza, Spain, July 2015), English (includes Spanish version of the introduction and conclusions)

We report on the design, fabrication and characterization of superconducting coplanar waveguide resonators with nanoscopic constrictions. By reducing the size of the center line down to 50 nm, the radio frequency currents are concentrated and the magnetic field in its vicinity is increased. The device characteristics are only slightly modified by the constrictions, with changes in resonance frequency lower than 1% and internal quality factors of the same order of magnitude as the original ones. These devices could enable the achievement of higher couplings to small magnetic samples or even to single molecular spins and have applications in circuit quantum electrodynamics, quantum computing and electron paramagnetic resonance., Comment: 4 pages, 4 figures

An excellent molecule-based cryogenic magnetic refrigerant, gadolinium acetate tetrahydrate, is here used to decorate selected portions of silicon substrate. By quantitative magnetic force microscopy for variable applied magnetic field near liquid-helium temperature, we demonstrate that the molecules hold intact their magnetic properties, and therefore their cooling functionality, after their deposition. Our result represents a step forward towards the realization of a molecule-based microrefrigerating device for very low temperatures., Comment: 16 pages, 4+1 figures, S.I. available from authors

The Little Higgs (LH) idea attempts to cure the little amount of fine-tuning necessary to bridge the gap between the Higgs mass (electroweak scale) and the new physics scale suggested by electroweak precision tests (~10 TeV). However, we show that LH models do not survive the confrontation with experimental limits on lepton flavor mixing, assuming the same naturalness arguments that motivate their introduction. Two different LH models are analyzed and several aspects of their one-loop predictions for lepton flavor-changing processes are discussed., Comment: 6 pages, 1 figure; contribution to Loops and Legs in Quantum Field Theory, Woerlitz, Germany, April 25-30, 2010

We report on our study of the LFV processes \mu \to e\gamma, \mu\to eee and \mu \to e conversion in the context of Little Higgs models. Specifically we examine the Littlest Higgs with T-parity (LHT) and the Simplest Little Higgs (SLH) as examples of a Product group and Simple group Little Higgs models respectively. The necessary Feynman rules for both models are obtained in the 't Hooft Feynman Gauge up to order v^2/f^2 and predictions for the branching ratios and conversion rates of the LFV processes are calculated to leading order (one-loop level). Comparison with current experimental constraints show that there is some tension and, in order to be within the limits, one requires a higher breaking scale f, alignment of the heavy and light lepton sectors or almost degenerate heavy lepton masses. These constraints are more demanding in the SLH than in the LHT case., Comment: 6 pages, 3 figures, to appear in Proceedings of the XXXIII Intl. Conf. of Theoretical Physics, "Matter to the Deepest", Ustron, Poland, September 11-16, 2009; v2: comments and references added