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We present extremely deep Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) observations of the MUSE Ultra Deep Field (MUDF). This unique region of the sky contains two quasars at $z \approx$ 3.22 that are separated by only $\sim$500 kpc, providing a stereoscopic view of gas and galaxies in emission and absorption across $\sim$10 billion years of cosmic time. We have obtained 90 orbits of HST WFC3 G141 near-infrared grism spectroscopy of this field in a single pointing, as well as 142 hours of optical spectroscopy with the Very Large Telescope (VLT) Multi Unit Spectroscopic Explorer (MUSE). The WFC3 (F140W, F125W, and F336W) and archival WFPC2 (F702W and F450W) imaging provides five-filter photometry that we use to detect 3,375 sources between $z \approx$ 0 - 6, including 1,536 objects in a deep central pointing with both spectroscopic and photometric coverage. The F140W and F336W mosaics reach exceptional depths of $m_\mathrm{AB}\approx$ 28 and 29, respectively, providing near-infrared and rest-frame ultraviolet information for 1,580 sources, and we reach 5$\sigma$ continuum detections for objects as faint as $m_\mathrm{AB}\approx$ 27 in the grism spectra. The extensive wavelength coverage of MUSE and WFC3 allows us to measure spectroscopic redshifts for 419 sources, down to galaxy stellar masses of log(M/M$_{\odot}$) $\approx$ 7 at $z \approx$ 1 - 2. In this publication, we provide the calibrated HST data and source catalogs as High Level Science Products for use by the community, which includes photometry, morphology, and redshift measurements that enable a variety of studies aimed at advancing our models of galaxy formation and evolution in different environments., Comment: Accepted for publication in ApJS on February 1, 2023. The paper has 25 pages, 13 figures, and 7 tables. Version two includes minor corrections to match the journal publication. The calibrated data are available through MAST at: https://archive.stsci.edu/hlsp/mudf

We present a simple motorised rotation mount for a half-wave plate that can be used to rapidly change the polarization of light. We use the device to switch a high power laser beam between different optical dipole traps in an ultracold atom experiment. The device uses a stepper motor with a hollow shaft, which allows a beam to propagate along the axis of the motor shaft, minimising inertia and mechanical complexity. A simple machined adapter is used to mount the wave plate. We characterise the performance of the device, focusing on its capability to switch a beam between the output ports of a polarizing beamsplitter cube. We demonstrate a switching time of 15.9(3) ms, limited by the torque of the motor. The mount has a reaction time of 0.52(3) ms, and a rotational resolution of 0.45 degrees. The rotation is highly reproducible, with the stepper motor not missing a step in 2000 repeated tests over 11 hours., Comment: 5 pages, 4 figures, Submitted to Review of Scientific Instruments

We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. Under such an assumption, this work builds upon a recently introduced multi-index Sequential Monte Carlo (SMC) ratio estimator, which provably enjoys the complexity improvements of multi-index Monte Carlo (MIMC) and the efficiency of SMC for inference. The present work leverages a randomization strategy to remove bias entirely, which simplifies estimation substantially, particularly in the MIMC context, where the choice of index set is otherwise important. Under reasonable assumptions, the proposed method provably achieves the same canonical complexity of MSE$^{-1}$ as the original method (where MSE is mean squared error), but without discretization bias. It is illustrated on examples of Bayesian inverse and spatial statistics problems., Comment: 27 pages 8 figures

Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach. Our experiments and analysis validate the effectiveness of the proposed approach with existing architectures, where it enhances the accuracy of the ear-EEG based sleep staging by 3.46% and Cohen's kappa coefficient by a margin of 0.038., Comment: Code available at : https://github.com/Mithunjha/EarEEG_KnowledgeDistillation

Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a surrogate model for the objective function, quantifying the uncertainty in that surrogate through Bayesian inference. Objective evaluations are sequentially determined by maximising an acquisition function at each step. However, this ancilliary optimisation problem can be highly non-trivial to solve, due to the non-convexity of the acquisition function, particularly in the case of batch Bayesian optimisation, where multiple points are selected in every step. In this work we reformulate batch BO as an optimisation problem over the space of probability measures. We construct a new acquisition function based on multipoint expected improvement which is convex over the space of probability measures. Practical schemes for solving this `inner' optimisation problem arise naturally as gradient flows of this objective function. We demonstrate the efficacy of this new method on different benchmark functions and compare with state-of-the-art batch BO methods.

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite the improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which has limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a novel cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. Our method outperforms the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer., Comment: 11 pages, 7 figures, 6 tables

We report the first observation of magnetic Feshbach resonances between ${}^{173}$Yb and $^{133}$Cs. In a mixture of Cs atoms prepared in the $(f=3, m_f=3)$ state and unpolarized fermionic ${}^{173}$Yb we observe resonant atom loss due to two sets of magnetic Feshbach resonances around 622~G and 702~G. Resonances for individual Yb nuclear spin components $m_{i,\mathrm{Yb}}$ are split by its interaction with the Cs electronic spin, which also provides the main coupling mechanism for the observed resonances. The observed splittings and relative resonance strengths are in good agreement with theoretical predictions from coupled-channel calculations., Comment: 7 pages, 2 figures

There are many issues that can cause problems when attempting to infer model parameters from data. Data and models are both imperfect, and as such there are multiple scenarios in which standard methods of inference will lead to misleading conclusions; corrupted data, models which are only representative of subsets of the data, or multiple regions in which the model is best fit using different parameters. Methods exist for the exclusion of some anomalous types of data, but in practice, data cleaning is often undertaken by hand before attempting to fit models to data. In this work, we will employ hierarchical Bayesian data selection; the simultaneous inference of both model parameters, and parameters which represent our belief that each observation within the data should be included in the inference. The aim, within a Bayesian setting, is to find the regions of observation space for which the model can well-represent the data, and to find the corresponding model parameters for those regions. A number of approaches will be explored, and applied to test problems in linear regression, and to the problem of fitting an ODE model, approximated by a finite difference method. The approaches are simple to implement, can aid mixing of Markov chains designed to sample from the arising densities, and are broadly applicable to many inferential problems.

Dispersionless energy bands are a peculiar property gathering increasing attention for the emergence of novel photonic, magnetic and electronic properties. Here we report the first observation of a graphene superstructure n-doped up to the Lifshitz transition and exhibiting a flat band, obtained by ordered Erbium intercalation between a single layer graphene and SiC(0001). STM experiments reveal large graphene areas characterized by a long-range ordered hexagonal superstructure with a lattice parameter of 1.40 nm, rotated by 19 degrees with respect to the original lattice. Angle Resolved Photoelectron Spectroscopy measurements show that this graphene structure exhibits Dirac cones with perfect linear dispersion, and a Dirac point at -1.72 eV +/- 0.02 under the Fermi level, which is one of the highest doping levels ever obtained solely by intercalation. Fermi surface measurements show that the Lifshitz transition has been reached, and that a wide flat band is generated around the M point. We propose that this modification of the band structure is the effect of an induced spin-orbit coupling. This system provides a playground to study the interaction between a novel magnetic order mediated by pi-band states, and a divergent density of states at the Fermi level., Comment: 18 pages, 4 figures

We investigate norms of spectral projectors on thin spherical shells for the Laplacian on generic tori, including generic rectangular tori. We state a conjecture and partially prove it, improving on previous results concerning arbitrary tori., Comment: 27 pages

Device-to-Device (D2D) communication propelled by artificial intelligence (AI) will be an allied technology that will improve system performance and support new services in advanced wireless networks (5G, 6G and beyond). In this paper, AI-based deep learning techniques are applied to D2D links operating at 5.8 GHz with the aim at providing potential answers to the following questions concerning the prediction of the received signal strength variations: i) how effective is the prediction as a function of the coherence time of the channel? and ii) what is the minimum number of input samples required for a target prediction performance? To this end, a variety of measurement environments and scenarios are considered, including an indoor open-office area, an outdoor open-space, line of sight (LOS), non-LOS (NLOS), and mobile scenarios. Four deep learning models are explored, namely long short-term memory networks (LSTMs), gated recurrent units (GRUs), convolutional neural networks (CNNs), and dense or feedforward networks (FFNs). Linear regression is used as a baseline model. It is observed that GRUs and LSTMs present equivalent performance, and both are superior when compared to CNNs, FFNs and linear regression. This indicates that GRUs and LSTMs are able to better account for temporal dependencies in the D2D data sets. We also provide recommendations on the minimum input lengths that yield the required performance given the channel coherence time. For instance, to predict 17 and 23 ms into the future, in indoor and outdoor LOS environments, respectively, an input length of 25 ms is recommended. This indicates that the bulk of the learning is done within the coherence time of the channel, and that large input lengths may not always be beneficial.

We consider the problem of landmark matching between two unlabelled point sets, in particular where the number of points in each cloud may differ, and where points in each cloud may not have a corresponding match. We invoke a Bayesian framework to identify the transformation of coordinates that maps one cloud to the other, alongside correspondence of the points. This problem necessitates a novel methodology for Bayesian data selection; simultaneous inference of model parameters, and selection of the data which leads to the best fit of the model to the majority of the data. We apply this to a problem in developmental biology where the landmarks correspond to segmented cell centres, where potential death or division of cells can lead to discrepancies between the point-sets from each image. We validate the efficacy of our approach using in silico tests and a microinjected fluorescent marker experiment. Subsequently we apply our approach to the matching of cells between real time imaging and immunostaining experiments, facilitating the combination of single-cell data between imaging modalities. Furthermore our approach to Bayesian data selection is broadly applicable across data science, and has the potential to change the way we think about fitting models to data., Comment: 40 pages including supplementary material

We report simultaneous Raman sideband cooling of a single $^{87}$Rb atom and a single $^{133}$Cs atom held in separate optical tweezers at 814\,nm and 938\,nm, respectively. Starting from outside the Lamb-Dicke regime, after 45\,ms of cooling we measure probabilities to occupy the three-dimensional motional ground state of 0.86$^{+0.03}_{-0.04}$ for Rb and 0.95$^{+0.03}_{-0.04}$ for Cs. Our setup overlaps the Raman laser beams used to cool Rb and Cs, reducing hardware requirements by sharing equipment along the same beam path. The cooling protocol is scalable, and we demonstrate cooling of single Rb atoms in an array of four tweezers. After motional ground-state cooling, a 938\,nm tweezer is translated to overlap with a 814\,nm tweezer so that a single Rb and a single Cs atom can be transferred into a common 1064\,nm trap. By minimising the heating during the merging and transfer, we prepare the atoms in the relative motional ground state with an efficiency of 0.81$^{+0.08}_{-0.08}$. This is a crucial step towards the formation of single RbCs molecules confined in optical tweezer arrays.

We present a computer program to calculate the quantised rotational and hyperfine energy levels of $^{1}\Sigma $ diatomic molecules in the presence of dc electric, dc magnetic, and off-resonant optical fields. Our program is applicable to the bialkali molecules used in ongoing state-of-the-art experiments with ultracold molecular gases. We include functions for the calculation of space-fixed electric dipole moments, magnetic moments and transition dipole moments.

We prepare pairs of $^{133}$Cs atoms in a single optical tweezer and perform Feshbach spectroscopy for collisions of atoms in the states $(f=3, m_f=\pm3)$. We detect enhancements in pair loss using a detection scheme where the optical tweezers are repeatedly subdivided. For atoms in the state $(3,-3)$, we identify resonant features by performing inelastic loss spectroscopy. We carry out coupled-channel scattering calculations and show that at typical experimental temperatures the loss features are mostly centred on zeroes in the scattering length, rather than resonance centres. We measure the number of atoms remaining after a collision, elucidating how the different loss processes are influenced by the tweezer depth. These measurements probe the energy released during an inelastic collision, and thus give information on the states of the collision products. We also identify resonances with atom pairs prepared in the absolute ground state $(f=3, m_f=3)$, where two-body radiative loss is engineered by an excitation laser blue-detuned from the Cs D$_2$ line. These results demonstrate optical tweezers to be a versatile tool to study two-body collisions with number-resolved detection sensitivity., Comment: 18 pages, 6 figures

We continue our series of papers on intergalactic medium (IGM) tracers using quasi-stellar objects (QSOs), having examined gamma-ray bursts (GRBs) and blazars in earlier studies. We have estimated the IGM properties of hydrogen column density (Nhxigm), temperature and metallicity using XMM-Newton QSO spectra over a large redshift range, with a collisional ionisation equilibrium (CIE) model for the ionised plasma. The Nhxigm parameter results were robust with respect to intrinsic power laws, spectral counts, reflection hump and soft excess features. There is scope for a luminosity bias given both luminosity and Nhxigm scale with redshift, but we find this unlikely given the consistent IGM parameter results across the other tracer types reviewed. The impact of intervening high column density absorbers was found to be minimal. The Nhxigm from the QSO sample scales as (1 + z)^1.5+/-0.2. The mean hydrogen density at z = 0 is n0 = (2.8 +/- 0.3) x 10^-7 cm^-3, the mean IGM temperature over the full redshift range is log(T/K) = 6.5+/-0.1, and the mean metallicity is [X/H] = -1.3+/-0.1(Z 0.05). Aggregating with our previous GRB and blazar tracers, we conclude that we have provided evidence of the IGM contributing substantially and consistently to the total X-ray absorption seen in the spectra. These results are based on the necessarily simplistic slab model used for the IGM, due to the inability of current X-ray data to constrain the IGM redshift distribution, Comment: Accepted for publication in the MNRAS. 14 pages, 10 figures

We prove essentially optimal bounds for norms of spectral projectors on thin spherical shells for the Laplacian on the cylinder (R/Z)*R. In contrast to previous investigations into spectral projectors on tori, having one unbounded dimension available permits a compact self-contained proof.

General circulation models (GCMs) provide context for interpreting multi-wavelength, multi-phase data of the atmospheres of tidally locked exoplanets. In the current study, the non-hydrostatic THOR GCM is coupled with the HELIOS radiative transfer solver for the first time, supported by an equilibrium chemistry solver (FastChem), opacity calculator (HELIOS-K) and Mie scattering code (LX-MIE). To accurately treat the scattering of radiation by medium-sized to large aerosols/condensates, improved two-stream radiative transfer is implemented within a GCM for the first time. Multiple scattering is implemented using a Thomas algorithm formulation of the two-stream flux solutions, which decreases the computational time by about 2 orders of magnitude compared to the iterative method used in past versions of HELIOS. As a case study, we present four GCMs of the hot Jupiter WASP-43b, where we compare the temperature, velocity, entropy, and streamfunction, as well as the synthetic spectra and phase curves, of runs using regular versus improved two-stream radiative transfer and isothermal versus non-isothermal layers. While the global climate is qualitatively robust, the synthetic spectra and phase curves are sensitive to these details. A THOR+HELIOS WASP-43b GCM (horizontal resolution of about 4 degrees on the sphere and with 40 radial points) with multi-wavelength radiative transfer (30 k-table bins) running for 3000 Earth days (864,000 time steps) takes about 19-26 days to complete depending on the type of GPU., Comment: 31 pages, 24 figures, accepted for publication at MNRAS

We present recent updates and improvements of the graphical processing unit (GPU) N-body code GENGA. Modern state-of-the-art simulations of planet formation require the use of a very high number of particles to accurately resolve planetary growth and to quantify the effect of dynamical friction. At present the practical upper limit is in the range of 30,000 - 60,000 fully interactive particles; possibly a little more on the latest GPU devices. While the original hybrid symplectic integration method has difficulties to scale up to these numbers, we have improved the integration method by i) introducing higher level changeover functions and ii) code improvements to better use the most recent GPU hardware efficiently for such large simulations. We added treatments of non-Newtonian forces such as general relativity, tidal interaction, rotational deformation, the Yarkovsky effect, and Poynting-Robertson drag, as well as a new model to treat virtual collisions of small bodies in the solar system. We added new tools to GENGA, such as semi-active test particles that feel more massive bodies but not each other, a more accurate collision handling and a real-time openGL visualization. We present example simulations, including a 1.5 billion year terrestrial planet formation simulation that initially started with 65,536 particles, a 3.5 billion year simulation without gas giants starting with 32,768 particles, the evolution of asteroid fragments in the solar system, and the planetesimal accretion of a growing Jupiter simulation. GENGA runs on modern NVIDIA and AMD GPUs., Comment: Accepted for publication in ApJ, 30 pages, 19 figures

Microstructural models of soft tissue deformation are important in applications including artificial tissue design and surgical planning. The basis of these models, and their advantage over their phenomenological counterparts, is that they incorporate parameters that are directly linked to the tissue's microscale structure and constitutive behaviour and can therefore be used to predict the effects of structural changes to the tissue. Although studies have attempted to determine such parameters using diverse, state-of-the-art, experimental techniques, values ranging over several orders of magnitude have been reported, leading to uncertainty in the true parameter values and creating a need for models that can handle such uncertainty. We derive a microstructural, hyperelastic model for transversely isotropic soft tissues and use it to model the mechanical behaviour of tendons. To account for parameter uncertainty, we employ a Bayesian approach and apply an adaptive Markov chain Monte Carlo algorithm to determine posterior probability distributions for the model parameters. The obtained posterior distributions are consistent with parameter measurements previously reported and enable us to quantify the uncertainty in their values for each tendon sample that was modelled. This approach could serve as a prototype for quantifying parameter uncertainty in other soft tissues.