Miracle-dz/UniFAR-FTUs huggingface dataset:
Generation of shear flows and vortices in rotating anelastic convection

We consider the effect of stratification on systematic, large-scale flows generated in anelastic convection. We present results from three-dimensional numerical simulations of convection in a rotating plane layer in which the angle between the axis of rotation and gravity is allowed to vary. This model is representative of different latitudes of a spherical body. We consider two distinct parameter regimes: (i) weakly rotating and (ii) rapidly rotating. In each case, we examine the effect of stratification on the flow structure and heat transport properties focussing on the difference between Boussinesq and anelastic convection. Furthermore, we show that regimes (i) and (ii) generate very different large-scale flows and we investigate the role stratification has in modifying these flows. The stratified flows possess a net helicity not present in the Boussinesq cases which we suggest, when combined with the self-generated shear flows, could be important for dynamo action.
Searching for ultra-fast outflows in AGN using variability spectra

We present a qualitative search for ultra-fast outflows (UFOs) in excess variance spectra of radio-quiet active galactic nuclei (AGN). We analyse 42 sources from the Tombesi et al. (2010) spectroscopic UFO detection sample, and an additional 22 different sources from the Kara et al. (2016) variability sample. A total of 58 sources have sufficient observational data from XMM-Newton EPIC-pn and variability for an excess variance spectrum to be calculated. We examine these spectra for peaks corresponding to variable blue-shifted H- and He-like ion absorption lines from UFOs. We find good evidence for such outflows in 28% of the AGN sample and weak evidence in a further 31%, meaning that $\sim$ 30-60% of the AGN sample hosts such UFOs. The mean and median blue-shifted velocity is found to be $\sim$ 0.14c and 0.12c, respectively. Current variability methods allow for a fast, model-independent determination of UFOs, however, further work needs to be undertaken to better characterize the statistical significance of the peaks in these spectra by more rigorous modelling. Detecting good evidence for variable UFO lines in a large number of sources also lays the groundwork for detailed analysis of the variability timescales of the absorbers. This will allow us to probe their densities and hence distances from the central super-massive black hole.
Secure and Robust Machine Learning for Healthcare: A Survey

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.
Pseudospectra of the Damped Wave Equation with Unbounded Damping

We analyze pseudospectra of the generator of the damped wave equation with unbounded damping. We show that the resolvent norm diverges as $\Re z \to - \infty$. The highly non-normal character of the operator is a robust effect preserved even when a strong potential is added. Consequently, spectral instabilities and other related pseudospectral effects are present.
Optimized Generic Feature Learning for Few-shot Classification across Domains

To learn models or features that generalize across tasks and domains is one of the grand goals of machine learning. In this paper, we propose to use cross-domain, cross-task data as validation objective for hyper-parameter optimization (HPO) to improve on this goal. Given a rich enough search space, optimization of hyper-parameters learn features that maximize validation performance and, due to the objective, generalize across tasks and domains. We demonstrate the effectiveness of this strategy on few-shot image classification within and across domains. The learned features outperform all previous few-shot and meta-learning approaches.
Cold gas and dust: Hunting spiral-like structures in early-type galaxies

Context. Observations of neutral hydrogen (H I) and molecular gas show that 50% of all nearby early-type galaxies contain some cold gas. Molecular gas is always found in small gas discs in the central region of the galaxy, while neutral hydrogen is often distributed in a low-column density disc or ring typically extending well beyond the stellar body. Dust is frequently found in early-type galaxies as well. Aims. The goal of our study is to understand the link between dust and cold gas in nearby early-type galaxies as a function of H I content. Methods. We analyse deep optical g − r images obtained with the MegaCam camera at the Canada-France-Hawaii Telescope for a sample of 21 H I-rich and 41 H I-poor early-type galaxies. Results. We find that all H I-rich galaxies contain dust seen as absorption. Moreover, in 57% of these H I-rich galaxies, the dust is distributed in a large-scale spiral pattern. Although the dust detection rate is relatively high in the H I-poor galaxies (∼59%), most of these systems exhibit simpler dust morphologies without any evidence of spiral structures. We find that the H I-rich galaxies possess more complex dust morphology extending to almost two times larger radii than H I-poor objects. We measured the dust content of the galaxies from the optical colour excess and find that H I-rich galaxies contain six times more dust (in mass) than H I-poor ones. In order to maintain the dust structures in the galaxies, continuous gas accretion is needed, and the substantial H I gas reservoirs in the outer regions of early-type galaxies can satisfy this need for a long time. We find that there is a good correspondence between the observed masses of the gas and dust, and it is also clear that dust is present in regions further than 3 Reff. Conclusions. Our findings indicate an essential relation between the presence of cold gas and dust in early-type galaxies and offer a way to study the interstellar medium in more detail than what is possible with H I observations.
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset

Optimizing fluid-dynamic performance is an important engineering task. Traditionally, experts design shapes based on empirical estimations and verify them through expensive experiments. This costly process, both in terms of time and space, may only explore a limited number of shapes and lead to sub-optimal designs. In this research, a test-proven deep learning architecture is applied to predict the performance under various restrictions and search for better shapes by optimizing the learned prediction function. The major challenge is the vast amount of data points Deep Neural Network (DNN) demands, which is improvident to simulate. To remedy this drawback, a Frequentist active learning is used to explore regions of the output space that DNN predicts promising. This operation reduces the number of data samples demanded from ~8000 to 625. The final stage, a user interface, made the model capable of optimizing with given user input of minimum area and viscosity. Flood fill is used to define a boundary area function so that the optimal shape does not bypass the minimum area. Stochastic Gradient Langevin Dynamics (SGLD) is employed to make sure the ultimate shape is optimized while circumventing the required area. Jointly, shapes with extremely low drags are found explored by a practical user interface with no human domain knowledge and modest computation overhead.
Removing multi-frame Gaussian noise by combining patch-based filters with optical flow

Abstract. Patch-based approaches such as 3D block matching and non-local Bayes are widely accepted filters for removing Gaussian noise from single-frame images. We propose three extensions for these filters when there exist multiple frames of the same scene. The first of them employs reference patches on every frame instead of a commonly used single-reference frame method, thus utilizing the complete available information. The remaining two techniques use a separable spatiotemporal filter to reduce interactions between dissimilar regions, hence mitigating artifacts. In order to deal with non-registered datasets, we combine all our extensions with robust optical flow computation. Two of our proposed multi-frame filters outperform existing extensions on most occasions by a significant margin while also being competitive with a state-of-the-art neural network-based technique. Moreover, one of these two strategies is the fastest among all due to its separable design.
Current-assisted Raman activation of the Higgs mode in superconductors

The Higgs mode in superconductors is a scalar mode without electric or magnetic dipole moment. Thus, it is commonly believed that its excitation is restricted to a nonlinear two-photon Raman process. However, recent efforts have shown that a linear excitation in the presence of a supercurrent is possible, resulting in a new resonant enhancement at $\Omega=2\Delta$ with the driving light frequency $\Omega$ and the energy of the Higgs mode $2\Delta$. This is in contrast to the usual $2\Omega = 2\Delta$ resonance condition found in nonlinear third-harmonic generation experiments. In this communication, we show that such a linear excitation can still be described as an effective Raman two-photon process, with one photon at $\omega=2\Delta$ and one virtual photon at $\omega=0$ which represents the dc supercurrent. At the same time we demonstrate that a straightforward infrared activation with a single photon excitation is negligible. Moreover, we give a general context to our theory, providing an explanation for how the excitation of the Higgs mode in both THz quench and drive experiments can be understood within a conventional difference-frequency generation or sum-frequency generation process, respectively. In such a picture, the observed new resonance condition $\Omega = 2\Delta$ is just a special case. With the same approach, we further discuss another recent experiment, where we find a suppression of odd order higher harmonics in the presence of a dc supercurrent.
Composite Quadrature Methods for Weakly Singular Convolution Integrals

The well-known Caputo fractional derivative and the corresponding Caputo fractional integral occur naturally in many equations that model physical phenomena under inhomogeneous media. The relationship between the two fractional terms can be readily obtained by applying the Laplace transform to a given equation. We seek to numerically approximate Caputo fractional integrals using a Taylor series expansion for convolution integrals. This naturally extends into being able to approximate convolution integrals for a wider class of convolution integral kernels $K(t-s)$. One of the main advantages under this approach is the ability to numerically approximate weakly singular kernels, which fail to converge under traditional quadrature methods. We provide stability and convergence analysis for these composite quadratures, which offer optimal convergence for approximating functions in $C^{\gamma}[0,T]$, where $\alpha \leq \gamma \leq 5$ and $0<\alpha < 1$. For the order $\gamma = 1,2,3,4,5$ scheme, the resulting approximation is $O(\tau^{\gamma})$ accurate, where $\tau$ is the size of the partition of the time domain. By instead utilizing a fractional Taylor series expansion, we are able to obtain for $\gamma \in (0,5)-\{1,2,3,4\}$ order scheme, which yields an approximation of $O(\tau^{\gamma})$ with a constant dependent on the kernel function which improves the order of convergence. This allows for a far wider class of functions to be approximated, and by strengthening the regularity assumption, we are able to obtain more accurate results. General convolution integrals exhibit these results up to $\gamma = 2$ without the assumption of $K$ being decreasing. Finally, some numerical examples are presented, which validate our findings.
Spin transport in a one-dimensional quantum wire

We analyze the spin transport through a finite-size one-dimensional interacting wire connected to noninteracting leads. By combining renormalization-group arguments with other analytic considerations such as the memory function technique and instanton tunneling, we find the temperature dependence of the spin conductance in different parameter regimes in terms of interactions and the wire length. The temperature dependence is found to be nonmonotonic. In particular, the system approaches perfect spin conductance at zero temperature for both attractive and repulsive interactions, in contrast with the static spin conductivity. We discuss the connection of our results to recent experiments with ultracold atoms and compare the theoretical prediction to experimental data in the parameter regime where temperature is the largest energy scale.
Fine structure of negatively charged and neutral excitons in monolayer MoS$_{2}$

We present experimental and theoretical results on the high-quality single-layer MoS$_{2}$ which reveal the fine structure of charged excitons, i.e., trions. In the emission spectra we resolve and identify two trion peaks, T$_{1}$ and T$_{2}$, resembling the pair of singlet and triplet trion peaks (T$_S$ and T$_{T}$) in tungsten-based materials. However, in polarization-dependent photoluminescence measurements we identify these peaks as novel intra- and inter-valley singlet trions, constituting the trion fine structure distinct from that already known in bright and dark 2D materials with large conduction-band splitting induced by the spin-orbit coupling. We show that the trion energy splitting in MoS$_{2}$ is a sensitive probe of inter- and intra-valley carrier interaction. With additional support from theory we claim that the existence of these singlet trions combined with an anomalous excitonic g-factor and the characteristic temperature dependence of the emission spectra together suggest that monolayer MoS$_{2}$ has a dark excitonic ground state, despite having "bright" single-particle arrangement of spin-polarized conduction bands.
Joint Wireless and Edge Computing Resource Management With Dynamic Network Slice Selection

Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless devices generate latency sensitive computational tasks. We address the problem of joint dynamic assignment of computational tasks to slices, management of radio resources across slices and management of radio and computing resources within slices. We formulate the Joint Slice Selection and Edge Resource Management (JSS-ERM) problem as a mixed-integer problem with the objective to minimize the completion time of computational tasks. We show that the JSS-ERM problem is NP-hard and develop an approximation algorithm with bounded approximation ratio based on a game theoretic treatment of the problem. We use extensive simulations to provide insight into the performance of the proposed solution from the perspective of the whole system and from the perspective of individual slices. Our results show that the proposed slicing policy can achieve significant gains compared to the equal slicing policy, and that the computational complexity of the proposed task placement algorithm is approximately linear in the number of devices.
The Gaia–Kepler Stellar Properties Catalog. I. Homogeneous Fundamental Properties for 186,301 Kepler Stars

An accurate and precise Kepler Stellar Properties Catalog is essential for the interpretation of the Kepler exoplanet survey results. Previous Kepler Stellar Properties Catalogs have focused on reporting the best-available parameters for each star, but this has required combining data from a variety of heterogeneous sources. We present the Gaia–Kepler Stellar Properties Catalog, a set of stellar properties of 186,301 Kepler stars, homogeneously derived from isochrones and broadband photometry, Gaia Data Release 2 parallaxes, and spectroscopic metallicities, where available. Our photometric effective temperatures, derived from colors, are calibrated on stars with interferometric angular diameters. Median catalog uncertainties are 112 K for , 0.05 dex for , 4% for , 7% for , 13% for , 10% for , and 56% for stellar age. These precise constraints on stellar properties for this sample of stars will allow unprecedented investigations into trends in stellar and exoplanet properties as a function of stellar mass and age. In addition, our homogeneous parameter determinations will permit more accurate calculations of planet occurrence and trends with stellar properties.
The orbital phase space of contracted dark matter haloes

We study the orbital phase space of dark matter (DM) haloes in the auriga suite of cosmological hydrodynamics simulations of Milky Way (MW) analogues. We characterize haloes by their spherical action distribution, $F\left(J_{{r}},L\right)$, a function of the specific angular momentum, L, and the radial action, Jr, of the DM particles. By comparing DM-only and hydrodynamical simulations of the same haloes, we investigate the contraction of DM haloes caused by the accumulation of baryons at the centre. We find a small systematic suppression of the radial action in the DM haloes of the hydrodynamical simulations, suggesting that the commonly used adiabatic contraction approximation can result in an underestimate of the density by $\sim 8{{ \rm {per\ cent}}}$. We apply an iterative algorithm to contract the auriga DM haloes given a baryon density profile and halo mass, recovering the true contracted DM profiles with an accuracy of $\sim 15{{ \rm {per\ cent}}}$, that reflects halo-to-halo variation. Using this algorithm, we infer the total mass profile of the MW’s contracted DM halo. We derive updated values for the key astrophysical inputs to DM direct detection experiments: the DM density and velocity distribution in the Solar neighbourhood.
DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning

Multi-level deep-features have been driving state-of-the-art methods for aesthetics and image quality assessment (IQA). However, most IQA benchmarks are comprised of artificially distorted images, for which features derived from ImageNet under-perform. We propose a new IQA dataset and a weakly supervised feature learning approach to train features more suitable for IQA of artificially distorted images. The dataset, KADIS-700k, is far more extensive than similar works, consisting of 140,000 pristine images, 25 distortions types, totaling 700k distorted versions. Our weakly supervised feature learning is designed as a multi-task learning type training, using eleven existing full-reference IQA metrics as proxies for differential mean opinion scores. We also introduce a benchmark database, KADID-10k, of artificially degraded images, each subjectively annotated by 30 crowd workers. We make use of our derived image feature vectors for (no-reference) image quality assessment by training and testing a shallow regression network on this database and five other benchmark IQA databases. Our method, termed DeepFL-IQA, performs better than other feature-based no-reference IQA methods and also better than all tested full-reference IQA methods on KADID-10k. For the other five benchmark IQA databases, DeepFL-IQA matches the performance of the best existing end-to-end deep learning-based methods on average.
Measurement of the B-band galaxy Luminosity Function with Approximate Bayesian Computation

The galaxy Luminosity Function (LF) is a key observable for galaxy formation, evolution studies and for cosmology. In this work, we propose a novel technique to forward model wide-field broad-band galaxy surveys using the fast image simulator UFig and measure the LF of galaxies in the B-band. We use Approximate Bayesian Computation (ABC) to constrain the galaxy population model parameters of the simulations and match data from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). We define a number of distance metrics between the simulated and the survey data. By exploring the parameter space of the galaxy population model through ABC to find the set of parameters that minimize these distance metrics, we obtain constraints on the LFs of blue and red galaxies as a function of redshift. We find that M* fades by Δ M*0.1−1.0,b = 0.68 ± 0.52 and Δ M*0.1−1.0,r = 0.54 ± 0.48 magnitudes between redshift z = 1 and z = 0.1 for blue and red galaxies, respectively. We also find that ϕ* for blue galaxies stays roughly constant between redshift z = 0.1 and z=1, while for red galaxies it decreases of ∼ 35%. We compare our results to other measurements, finding good agreement at all redshifts, for both blue and red galaxies. To further test our results, we compare the redshift distributions for survey and simulated data. We use the spectroscopic redshift distribution from the VIMOS Public Extragalactic Redshift Survey (VIPERS) and we apply the same selection in colours and magnitudes on our simulations. We find a good agreement between the survey and the simulated redshift distributions. We provide best-fit values and uncertainties for the parameters of the LF. This work offers excellent prospects for measuring other galaxy population properties as a function of redshift using ABC.
The Clustering of X-Ray Luminous Quasars

The clustering of active galactic nuclei (AGNs) sheds light on their typical large (Mpc-scale) environments, which can constrain the growth and evolution of supermassive black holes. Here we measure the clustering of luminous X-ray-selected AGNs in the Stripe 82X and XMM-XXL-north surveys around the peak epoch of black hole growth, in order to investigate the dependence of luminosity on large-scale AGN environment. We compute the auto-correlation function of AGNs in two luminosity bins, erg s−1 at z ∼ 0.8 and LX ≥ 1044.5 erg s−1 at z ∼ 1.8, and calculate the AGN bias taking into account the redshift distribution of the sources using three different methods. Our results show that while the less luminous sample has an inferred typical halo mass that is smaller than for the more luminous AGNs, the host halo mass may be less dependent on luminosity than suggested in previous work. Focusing on the luminous sample, we calculate a typical host halo mass of ∼1013 , which is similar to previous measurements of moderate-luminosity X-ray AGNs and significantly larger than the values found for optical quasars of similar luminosities and redshifts. We suggest that the clustering differences between different AGN selection techniques are dominated by selection biases, and not due to a dependence on AGN luminosity. We discuss the limitations of inferring AGN triggering mechanisms from halo masses derived by large-scale bias.