Search

In satellite-based quantum key distribution (QKD), the number of secret bits that can be generated in a single satellite pass over the ground station is severely restricted by the pass duration and the free-space optical channel loss. High channel loss may decrease the signal-to-noise ratio due to background noise, reduce the number of generated raw key bits, and increase the quantum bit error rate (QBER), all of which have detrimental effects on the output secret key length. Under finite-size security analysis, higher QBER increases the minimum raw key length necessary for non-zero secret key length extraction due to less efficient reconciliation and post-processing overheads. We show that recent developments in finite key analysis allow three different small-satellite-based QKD projects CQT-Sat, UK-QUARC-ROKS, and QEYSSat to produce secret keys even under very high loss conditions, improving on estimates based on previous finite key bounds. This suggests that satellites in low Earth orbit can satisfy finite-size security requirements, but remains challenging for satellites further from Earth. We analyse the performance of each mission to provide an informed route toward improving the performance of small-satellite QKD missions. We highlight the short and long-term perspectives on the challenges and potential future developments in small-satellite-based QKD and quantum networks. In particular, we discuss some of the experimental and theoretical bottlenecks, and improvements necessary to achieve QKD and wider quantum networking capabilities in daylight and at different altitudes., Comment: 15 pages, 8 figures, and 5 tables. Improved analysis with detailed discussions and perspectives. Comments welcome

Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function (LV ejection fraction) as a diagnostic and treatment stratification biomarker is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction. However, from time-varying 2-D echocardiography acquisition, a richer description of cardiac function can be obtained by estimating functional biomarkers from the complete cardiac cycle. In this work we propose for the first time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle. These biomarkers will allow clinicians to obtain a much richer picture of the heart in health and disease. The AI model is based on the 'nn-Unet' framework and was trained and tested using four different databases. Results show excellent agreement between manual and automated analysis and showcase the potential of the advanced systolic and diastolic biomarkers for patient stratification. Finally, for a subset of 50 cases, we perform a correlation analysis between clinical biomarkers derived from echocardiography and CMR and we show excellent agreement between the two modalities.

Recently, four remarkably straight very brief flashes of light were captured on video in Perth, Australia within 0.5s of one another. Straight lightning was recently identified as a prediction of macroscopic dark matter (macros) -- a broad class of alternative candidates to particle dark matter. A sufficiently large macro passing through the atmosphere producing a straight column ofplasma would produce fluorescence, or, under atmospheric conditions conducive to lightning, seeda very straight lightning strike visible to the naked eye. Other explanations are considered, but areproblematic., Comment: Withdrawn for consideration of a possible camera effect causing ordinary lightning to appear straight

In 2019, the California Future Health Workforce Commission released "Meeting the Demand for Health," a report that highlights recommendations for addressing California's workforce gaps: the state does not have enough of the right types of health workers in the right places to meet the needs of its growing, aging, and increasingly diverse population. To address these gaps, the Commission made a series of recommendations, including that the State support all students, but especially those who are first-generation, low-income and underrepresented students of color into and through health care professions. This brief identifies the need to increase the number of health care professionals in California, reviews the benefits of diversifying student pipelines into health professions, and makes key recommendations for state policymakers' consideration that would increase the number of first-generation, low-income and underrepresented students of color who enter health care professions. Recommendations include: (1) Diversifying pipeline and advising programs at post-secondary institutions; (2) Broadening the availability and accessibility of academic programs and supports; and (3) Increasing institutional and financial aid and paid internship opportunities. [Vincent Stewart, Adrienne Bell, Maya Kamath, and Nima Rahni of Children Now provided support for this report. This report was produced with Beyond 12.]

We present an experimental and theoretical study of symmetric $\textrm{Xe}^{54+}+\textrm{Xe}$ collisions at 50, 30, and 15 MeV/u, corresponding to strong perturbations with $v_K/v_\text{p}$ = 1.20, 1.55, and 2.20, respectively ($v_K$: classical $K$-shell orbital velocity, $v_\text{p}$: projectile velocity), as well as $\textrm{Xe}^{53+}+\textrm{Xe}$ collisions at 15 MeV/u. For each of these systems, x-ray spectra were measured under a forward angle of $35^\circ$ with respect to the projectile beam. Target satellite and hypersatellite radiation, $K\alpha_{2,1}^\mathrm{s}$ and $K\alpha_{2,1}^\mathrm{hs}$, respectively, were analyzed and used to derive cross section ratios for double-to-single target $K$-shell vacancy production. We compare our experimental results to relativistic time-dependent two-center calculations., Comment: 8 pages, 4 figures

We carried out a principal component analysis (PCA) of the fluxes of five polycyclic aromatic hydrocarbon (PAH) bands at 6.2, 7.7, 8.6, 11.0, and 11.2 $\mu$m in the reflection nebula NGC 7023 comprising of the photodissociation region (PDR) and a cavity. We find that only two principal components (PCs) are required to explain the majority of the observed variance in PAH fluxes (98 %). The first PC ($PC_{1}$), which is the primary driver of the variance, represents the total PAH emission. The second PC ($PC_{2}$) is related to the ionization state of PAHs across the nebula. This is consistent with the results of a similar analysis of the PAH emission in NGC 2023. The biplots and the correlations of PCs with the various PAH ratios show that there are two subsets of ionic bands with the 6.2 and 7.7 $\mu$m bands forming one subset and the 8.6 and 11.0 $\mu$m bands the other. However, the distinction between these subsets is only present in the PDR. We have also carried out a separate PCA analysis of the PAH fluxes, this time only considering variations in the cavity. This shows that in the cavity, $PC_{2}$ is not related to the charge state of PAHs, but possibly to structural molecular changes., Comment: 15 pages, 12 figures, accepted for publication in MNRAS

For bare lead ions, decelerated to the low beam energy of 10 MeV/u, the x-ray emission associated with radiative recombination (RR) at "cold collision" conditions has been studied at the electron cooler of CRYRING@ESR at GSI-Darmstadt. Utilizing dedicated x-ray detection chambers installed at 0{\deg} and 180{\deg} observation geometry, we observed for the very first time for stored ions the full x-ray emission spectrum associated with RR under electron cooling conditions. Most remarkably, no line distortion effects due to delayed emission are present in the well resolved spectra, spanning over a wide range of x-ray energies (from about 5 to 100 keV) which enable to identify fine-structure resolved Lyman, Balmer as well as Paschen x-ray lines along with the RR transitions into the K-, L and M-shell of the ions. To compare with theory, an elaborate theoretical model has been applied. By considering the relativistic atomic structure of Pb$^{81+}$, this model is based on a sophisticated computation of the initial population distribution via RR for all atomic levels up to Rydberg states with principal quantum number $n=$ 165 in combination with cascade calculations based on time-dependent rate equations. Within the statistical accuracy, the experimental x-ray line emission is in very good agreement with the results of the theoretical model applied. Most notably, this comparison sheds light on the contribution of prompt and delayed X-ray emission (up to 70 ns) to the observed X-ray spectra, originating in particular from Yrast transitions into inner shells.

Massive stars disrupt their natal molecular cloud material through radiative and mechanical feedback processes. These processes have profound effects on the evolution of interstellar matter in our Galaxy and throughout the Universe, from the era of vigorous star formation at redshifts of 1-3 to the present day. The dominant feedback processes can be probed by observations of the Photo-Dissociation Regions (PDRs) where the far-ultraviolet photons of massive stars create warm regions of gas and dust in the neutral atomic and molecular gas. PDR emission provides a unique tool to study in detail the physical and chemical processes that are relevant for most of the mass in inter- and circumstellar media including diffuse clouds, proto-planetary disks and molecular cloud surfaces, globules, planetary nebulae, and star-forming regions. PDR emission dominates the infrared (IR) spectra of star-forming galaxies. Most of the Galactic and extragalactic observations obtained with the James Webb Space Telescope (JWST) will therefore arise in PDR emission. In this paper we present an Early Release Science program using the MIRI, NIRSpec, and NIRCam instruments dedicated to the observations of an emblematic and nearby PDR: the Orion Bar. These early JWST observations will provide template datasets designed to identify key PDR characteristics in JWST observations. These data will serve to benchmark PDR models and extend them into the JWST era. We also present the Science-Enabling products that we will provide to the community. These template datasets and Science-Enabling products will guide the preparation of future proposals on star-forming regions in our Galaxy and beyond and will facilitate data analysis and interpretation of forthcoming JWST observations., Comment: Submitted to PASP

In autonomous driving, there has been an explosion in the use of deep neural networks for perception, prediction and planning tasks. As autonomous vehicles (AVs) move closer to production, multi-modal sensor inputs and heterogeneous vehicle fleets with different sets of sensor platforms are becoming increasingly common in the industry. However, neural network architectures typically target specific sensor platforms and are not robust to changes in input, making the problem of scaling and model deployment particularly difficult. Furthermore, most players still treat the problem of optimizing software and hardware as entirely independent problems. We propose a new end to end architecture, Generalized Sensor Fusion (GSF), which is designed in such a way that both sensor inputs and target tasks are modular and modifiable. This enables AV system designers to easily experiment with different sensor configurations and methods and opens up the ability to deploy on heterogeneous fleets using the same models that are shared across a large engineering organization. Using this system, we report experimental results where we demonstrate near-parity of an expensive high-density (HD) LiDAR sensor with a cheap low-density (LD) LiDAR plus camera setup in the 3D object detection task. This paves the way for the industry to jointly design hardware and software architectures as well as large fleets with heterogeneous configurations., Comment: Presented in Machine Learning for Autonomous Driving Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia. 11 pages, 8 figures

It has recently been theoretically shown that Quantum Memories (QM) could enable truly global quantum networking when deployed in space thereby surpassing the limited range of land-based quantum repeaters. Furthermore, QM in space could enable novel protocols and long-range entanglement and teleportation applications suitable for Deep-Space links and extended scenarios for fundamental physics tests. In this white paper we will make the case for the importance of deploying QMs to space, and also discuss the major technical milestones and development stages that will need to be considered., Comment: Toptical white paper submitted to National Academies of Sciences, Engineering and Medicine's Decadal Survey on Biological and Physical Sciences Research in Space 2023-2032

A global quantum repeater network involving satellite-based links is likely to have advantages over fiber-based networks in terms of long-distance communication, since the photon losses in free space scale only polynomially with the distance -- compared to the exponential losses in optical fibers. To simulate the performance of such networks, we have introduced a scheme of large-scale event-based Monte Carlo simulation of quantum repeaters with multiple memories that can faithfully represent loss and imperfections in these memories. In this work, we identify the quantum key distribution rates achievable in various satellite and ground station geometries for feasible experimental parameters. The power and flexibility of the simulation toolbox allows us to explore various strategies and parameters, some of which only arise in these more complex, multi-satellite repeater scenarios. As a primary result, we conclude that key rates in the kHz range are reasonably attainable for intercontinental quantum communication with three satellites, only one of which carries a quantum memory., Comment: 10+7 pages, 6+6 figures, 2+1 tables; v2: replaced with accepted version plus tiny changes. extended model and additional analysis along satellite orbit

Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare. Such models might have the capability to encode and analyse large sets of data but they often lack comprehensive interpretability methods, preventing clinical trust in predictive outcomes. Quantifying uncertainty of a prediction is one way to provide such interpretability and promote trust. However, relatively little attention has been paid to how to include such requirements into the training of the model. In this paper we: (i) quantify the data (aleatoric) and model (epistemic) uncertainty of a DL model for Cardiac Resynchronisation Therapy response prediction from cardiac magnetic resonance images, and (ii) propose and perform a preliminary investigation of an uncertainty-aware loss function that can be used to retrain an existing DL image-based classification model to encourage confidence in correct predictions and reduce confidence in incorrect predictions. Our initial results are promising, showing a significant increase in the (epistemic) confidence of true positive predictions, with some evidence of a reduction in false negative confidence., Comment: STACOM 2021 Workshop

Documentation for version 1.1 of the open-source software SatQuMA: Satellite Quantum Modelling & Analysis., Comment: https://github.com/cnqo-qcomms/SatQuMA

Coherent states of the quantum electromagnetic field, the quantum description of ideal laser light, are prime candidates as information carriers for optical communications. A large body of literature exists on their quantum-limited estimation and discrimination. However, very little is known about the practical realizations of receivers for unambiguous state discrimination (USD) of coherent states. Here we fill this gap and outline a theory of USD with receivers that are allowed to employ: passive multimode linear optics, phase-space displacements, auxiliary vacuum modes, and on-off photon detection. Our results indicate that, in some regimes, these currently-available optical components are typically sufficient to achieve near-optimal unambiguous discrimination of multiple, multimode coherent states., Comment: 20 pages, 13 figures, and 2 tables, including appendices. Accepted version with reference styling for Quantum

Advances in quantum technologies are giving rise to a revolution in the way fundamental physics questions are explored at the empirical level. At the same time, they are the seeds for future disruptive technological applications of quantum physics. Remarkably, a space-based environment may open many new avenues for exploring and employing quantum physics and technologies. Recently, space missions employing quantum technologies for fundamental or applied studies have been proposed and implemented with stunning results. The combination of quantum physics and its space application is the focus of this review: we cover both the fundamental scientific questions that can be tackled with quantum technologies in space and the possible implementation of these technologies for a variety of academic and commercial purposes.

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the `nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At inference time, this framework can be used with 2D echocardiography data only, whilst taking advantage of the implicit relationship between CMR and echocardiography features learnt from the model. We evaluate our pipeline on a cohort of 50 CRT patients for whom paired echocardiography/CMR data were available, and results show that the proposed multimodal classifier results in a statistically significant improvement in accuracy compared to the baseline approach that uses only 2D echocardiography data. The combination of multimodal data enables CRT response to be predicted with 77.38% accuracy (83.33% sensitivity and 71.43% specificity), which is comparable with the current state-of-the-art in machine learning-based CRT response prediction. Our work represents the first multimodal deep learning approach for CRT response prediction.

EIP-1559 is a new proposed pricing mechanism for the Ethereum protocol developed to bring stability to fluctuating gas prices. To properly understand this as a stochastic process, it is necessary to develop the mathematical foundations to understand under what conditions the base fee gas price outcomes behave as a stationary process, and when it does not. Understanding these mathematical fundamentals is critical to properly engineering a stable system.

Concerted efforts are underway to establish an infrastructure for a global quantum internet to realise a spectrum of quantum technologies. This will enable more precise sensors, secure communications, and faster data processing. Quantum communications are a front-runner with quantum networks already implemented in several metropolitan areas. A number of recent proposals have modelled the use of space segments to overcome range limitations of purely terrestrial networks. Rapid progress in the design of quantum devices have enabled their deployment in space for in-orbit demonstrations. We review developments in this emerging area of space-based quantum technologies and provide a roadmap of key milestones towards a complete, global quantum networked landscape. Small satellites hold increasing promise to provide a cost effective coverage required to realised the quantum internet. We review the state of art in small satellite missions and collate the most current in-field demonstrations of quantum cryptography. We summarise important challenges in space quantum technologies that must be overcome and recent efforts to mitigate their effects. A perspective on future developments that would improve the performance of space quantum communications is included. We conclude with a discussion on fundamental physics experiments that could take advantage of a global, space-based quantum network., Comment: 26 pages, 9 figures. Comments welcome. This paper is a preprint of a paper submitted to IET Quantum Communication. If accepted, the copy of record will be available at the IET Digital Library

The use of non-differentiable priors in Bayesian statistics has become increasingly popular, in particular in Bayesian imaging analysis. Current state of the art methods are approximate in the sense that they replace the posterior with a smooth approximation via Moreau-Yosida envelopes, and apply gradient-based discretized diffusions to sample from the resulting distribution. We characterize the error of the Moreau-Yosida approximation and propose a novel implementation using underdamped Langevin dynamics. In misson-critical cases, however, replacing the posterior with an approximation may not be a viable option. Instead, we show that Piecewise-Deterministic Markov Processes (PDMP) can be utilized for exact posterior inference from distributions satisfying almost everywhere differentiability. Furthermore, in contrast with diffusion-based methods, the suggested PDMP-based samplers place no assumptions on the prior shape, nor require access to a computationally cheap proximal operator, and consequently have a much broader scope of application. Through detailed numerical examples, including a non-differentiable circular distribution and a non-convex genomics model, we elucidate the relative strengths of these sampling methods on problems of moderate to high dimensions, underlining the benefits of PDMP-based methods when accurate sampling is decisive., Comment: Accepted for publication by the Journal of the American Statistical Association