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Ultralight scalar fields can experience runaway `superradiant' amplification near spinning black holes, resulting in a macroscopic `axion cloud' which slowly dissipates via continuous monochromatic gravitational waves. For a particular range of boson masses, $\mathcal{O}(10^{-11}$ -- $10^{-10})$ eV, an axion cloud will radiate in the $10$ -- $100$ kHz band of the Levitated Sensor Detector (LSD). Using fiducial models of the mass, spin, and age distributions of stellar-origin black holes, we simulate the present-day Milky Way population of these hypothetical objects. As a first step towards assessing the LSD's sensitivity to the resultant ensemble of GW signals, we compute the corresponding signal-to-noise ratios which build up over a nominal integration time of $10^{7}$ s, assuming the projected sensitivity of the $1$-m LSD prototype currently under construction, as well as for future $10$-m and $100$-m concepts. For a $100$-m cryogenic instrument, hundreds of resolvable signals could be expected if the boson mass $\mu$ is around $3\times10^{-11}$ eV, and this number diminishes with increasing $\mu$ up to $\approx 5.5\times10^{-11}$ eV. The much larger population of unresolved sources will produce a confusion foreground which could be detectable by a $10$-m instrument if $\mu \in (3-4.5)\times10^{-11}$ eV, or by a $100$-m instrument if $\mu \in (3-6)\times10^{-11}$ eV.

Optically-levitated dielectric objects are promising for precision force, acceleration, torque, and rotation sensing due to their extreme environmental decoupling. While many levitated opto-mechanics experiments employ spherical objects, for some applications non-spherical geometries offer advantages. For example, rod-shaped or dumbbell shaped particles have been demonstrated for torque and rotation sensing and high aspect ratio plate-like particles can exhibit reduced photon recoil heating and may be useful for high-frequency gravitational wave detection or as high bandwidth accelerometers. To achieve optimal sensitivity, cooling, and quantum control in these systems, it is beneficial to achieve optimal displacement detection using scattered light. We describe and numerically implement a method based on Fisher information that is applicable to suspended particles of arbitrary geometry. We demonstrate the agreement between our method and prior methods employed for spherical particles, both in the Rayleigh and Lorentz-Mie regimes. As practical examples we analyze the optical detection limits of an optically-levitated high-aspect-ratio disc-like dielectric object and a rod-shaped object for configurations recently realized in experimental work., Comment: 8 pages, 6 figures

Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic graph convolutional NN (DG-CNN) using different hyperparameters and training regimes to assess their performance in structure identification tasks of atomistic structure data. We show benchmarks on simple crystal structures, where we can compare against established methods. The approach is subsequently extended to structurally more complex SiO$_2$ phases. By making use of this structure recognition tool, we are able to achieve a deeper understanding of the crystallization process in amorphous SiO$_2$ under shock compression. Lastly, we show how the NN based structure identification workflows can be integrated into OVITO using its python interface.

Purpose: This work aims to address the limitations faced by researchers in developing and sharing new MRI sequences by implementing an interpreter for the open-source MRI pulse sequence format, Pulseq, on a Philips MRI scanner. Methods: The implementation involved modifying a few source code files to create a Pulseq interpreter for the Philips MRI system. Validation experiments were conducted using simulations and phantom scans performed on a 7T Achieva MRI system. The observed sequence and waveforms were compared to the intended ones, and the gradient waveforms produced by the scanner were verified using a field camera. Image reconstruction was performed using the raw k-space samples acquired from both the native vendor environment and the Pulseq interpreter. Results: The reconstructed images obtained through the Pulseq implementation were found to be comparable to those obtained through the native implementation. The performance of the Pulseq interpreter was assessed by profiling the CPU utilization of the MRI spectrometer, showing minimal resource utilization for certain sequences. Conclusion: The successful implementation of the Pulseq interpreter on the Philips MRI scanner demonstrates the feasibility of utilizing Pulseq sequences on Philips MRI scanners. This provides an open-source platform for MRI sequence development, facilitating collaboration among researchers and accelerating scientific progress in the field of MRI., Comment: 15 pages, 4 figures and one table. Code for the MRI scanners will be made available for other research sites, through the vendor's code repository and forum, linked at http://openmr.nl/

Nanoporous metals or nanofoams are a promising material class that is considered for sensing, actuation, and catalysis. To date, they mostly based on simple noble metals such as nanoporous gold, which exhibit peculiar stress-strain response different from the bulk material. At the same time bulk alloys such as NiTi feature a reversible martensitic phase transition giving rise to interesting shape memory and superelastic effects. Combining the rich mechanics of NiTi with the geometrical features of a nanofoam is expected to improve the mechanical performance of this material. In this atomistic study we explore the behavior of a NiTi nanofoam at varying temperature and its reaction to (cyclic) compression. Using molecular dynamics simulations we track the microscopic processes enabling reversible deformation as well as the mechanical failure mechanisms of the NiTi nanofoam.

The nature of mechanically induced dislocations in SrTiO3 at low temperatures has been a disputed matter for a long time. Here we provide a systematic overview of the existing knowledge on dislocations in stoichiometric SrTiO3 complemented by computational models of several of the proposed dislocation types. Based on atomistic simulations we reveal their structure and mobility and put our focus on the splitting into partial dislocations, because this mechanism is held responsible for the good dislocation mobility in strontium titanate. Our results reveal that dislocations with a {1-10} glide plane (types A, B, and C) show easy glide behavior due to their glide dissociated configuration. The motion of dislocations on {001} glide planes (type E), however, is prohibited due to high Peierls barriers, as is the case for the {1-1-2} glide plane (type D). For dislocations with (partial) edge character the dissociation into partials is shown to be very sensitive to the charge state of the dislocation core. Since, in turn, dislocation mobility is strongly related to the dissociation of dislocations, the macroscopic ductility may in some cases have a direct relation to the charge at the dislocation core.

Ultra-dense domain walls are increasingly important for many devices but their microscopic properties are so far not fully understood. In this study we combine atomistic and coarse-grained molecular dynamic simulations to study the domain wall stability in the prototypical ferroelectric BaTiO3. We transfer the discussion of the field-driven nucleation and motion of domain walls to thermally induced modifications of the wall without an external driving force. Our simulations show that domain wall dynamics and stability depend crucially on microscopic thermal fluctuations. Enhanced fluctuations at domain walls may result in the formation of critical nuclei for the permanent shift of the domain wall. If two domain walls are close - put in other words, when domains are small - thermal fluctuations can be sufficient to bring domain walls into contact and lead to the annihilation of small domains. This is even true well below the Curie temperature and when domain walls are initially as far apart as 6 unit cells. Such small domains are, thus, not stable and limit the maximum achievable domain wall density in nanoelectronic devices.

We present experimental results on optical trapping of Yb-doped $\beta-$NaYF sub-wavelength-thickness high-aspect-ratio hexagonal prisms with a micron-scale radius. The prisms are trapped in vacuum using an optical standing wave, oriented with the normal vector to their face along the beam propagation direction, and exhibit characteristic modes of three translational and two torsional degrees of freedom. The measured motional spectra are compared with numerical simulations. This plate-like geometry simultaneously enables trapping with low photon-recoil-heating, high mass, and high trap frequency, potentially leading to advances in high frequency gravitational wave searches in the Levitated Sensor Detector (LSD), currently under construction. The material used here has previously been shown to exhibit internal cooling via laser refrigeration when optically trapped and illuminated with light of suitable wavelength. Employing such laser refrigeration methods in the context of our work may enable higher trapping intensity thus and higher trap frequencies for gravitational wave searches approaching the several hundred kHz range., Comment: Title and Fig. 1 updated, minor changes, prepared for journal submission

Since the introduction of the GDPR and CCPA legislation, both public and private facial image datasets are increasingly scrutinized. Several datasets have been taken offline completely and some have been anonymized. However, it is unclear how anonymization impacts face detection performance. To our knowledge, this paper presents the first empirical study on the effect of image anonymization on supervised training of face detectors. We compare conventional face anonymizers with three state-of-the-art Generative Adversarial Network-based (GAN) methods, by training an off-the-shelf face detector on anonymized data. Our experiments investigate the suitability of anonymization methods for maintaining face detector performance, the effect of detectors overtraining on anonymization artefacts, dataset size for training an anonymizer, and the effect of training time of anonymization GANs. A final experiment investigates the correlation between common GAN evaluation metrics and the performance of a trained face detector. Although all tested anonymization methods lower the performance of trained face detectors, faces anonymized using GANs cause far smaller performance degradation than conventional methods. As the most important finding, the best-performing GAN, DeepPrivacy, removes identifiable faces for a face detector trained on anonymized data, resulting in a modest decrease from 91.0 to 88.3 mAP. In the last few years, there have been rapid improvements in realism of GAN-generated faces. We expect that further progression in GAN research will allow the use of Deep Fake technology for privacy-preserving Safe Fakes, without any performance degradation for training face detectors.

We are building an experiment to search for dark matter in the form of dark photons in the nano- to milli-eV mass range. This experiment is the electromagnetic dual of magnetic detector dark radio experiments. It is also a frequency-time dual experiment in two ways: We search for a high-Q signal in wide-band data rather than tuning a high-$Q$ resonator, and we measure electric rather than magnetic fields. In this paper we describe a pilot experiment using room temperature electronics which demonstrates feasibility and sets useful limits to the kinetic coupling $\epsilon \sim 10^{-12}$ over 50--300 MHz. With a factor of 2000 increase in real-time spectral coverage, and lower system noise temperature, it will soon be possible to search a wide range of masses at 100 times this sensitivity. We describe the planned experiment in two phases: Phase-I will implement a wide band, 5-million channel, real-time FFT processor over the 30--300 MHz range with a back-end time-domain optimal filter to search for the predicted $Q\sim 10^6$ line using low-noise amplifiers. We have completed spot frequency calibrations using a biconical dipole antenna in a shielded room that extrapolate to a $5 \sigma$ limit of $\epsilon\sim 10^{-13}$ for the coupling from the dark field, per month of integration. Phase-II will extend the search to 20 GHz using cryogenic preamplifiers and new antennas., Comment: 11 pages, 13 figures. Updated to published version. Corrected minor error in Fig 12 x-axis; results unchanged

In model-based testing (MBT) we may have to deal with a non-deterministic model, e.g. because abstraction was applied, or because the software under test itself is non-deterministic. The same test case may then trigger multiple possible execution paths, depending on some internal decisions made by the software. Consequently, performing precise test analyses, e.g. to calculate the test coverage, are not possible. This can be mitigated if developers can annotate the model with estimated probabilities for taking each transition. A probabilistic model checking algorithm can subsequently be used to do simple probabilistic coverage analysis. However, in practice developers often want to know what the achieved aggregate coverage, which unfortunately cannot be re-expressed as a standard model checking problem. This paper presents an extension to allow efficient calculation of probabilistic aggregate coverage, and moreover also in combination with k-wise coverage.