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We study the design of robust and agile controllers for hybrid underactuated systems. Our approach breaks down the task of creating a stabilizing controller into: 1) learning a mapping that is invariant under optimal control, and 2) driving the actuated coordinates to the output of that mapping. This approach, termed Zero Dynamics Policies, exploits the structure of underactuation by restricting the inputs of the target mapping to the subset of degrees of freedom that cannot be directly actuated, thereby achieving significant dimension reduction. Furthermore, we retain the stability and constraint satisfaction of optimal control while reducing the online computational overhead. We prove that controllers of this type stabilize hybrid underactuated systems and experimentally validate our approach on the 3D hopping platform, ARCHER. Over the course of 3000 hops the proposed framework demonstrates robust agility, maintaining stable hopping while rejecting disturbances on rough terrain., Comment: 8 pages, 6 figures, IROS '24

Hexagonal boron nitride encapsulation is the method of choice for protecting graphene from environmental doping and impurity scattering. It was previously demonstrated that metal-organic vapor phase epitaxy (MOVPE) grows epitaxially ordered, uniform BN layers on epigraphene (graphene grown on SiC). Due to graphene non-wetting properties, h-BN growth starts preferentially from the graphene ledges. We use this fact here to selectively promote growth of high-quality flat h-BN on epigraphene by patterning epigraphene microstructures prior to BN growth. Thin h-BN films (down to 6 nm) grown by MOVPE show smooth and pleated surface morphology on epigraphene, while crumpled BN is observed on the SiC. Cross-sectional high-resolution transmission electron microscopy images and fluorescence imaging confirm the higher BN quality grown on the epigraphene. Transport measurements reveal p-doping as expected from hydrogen intercalation of epigraphene and regions of high and low mobility. This method can be used to produce structurally uniform high-quality h-BN/epigraphene micro/nano scale heterostructure.

We show that stable, freely suspended liquid crystal films can be made from the ferroelectric nematic ($\mathrm{N_F}$) phase and from the recently discovered polar, lamellar $\mathrm{SmZ_A}$ and $\mathrm{SmA_F}$ phases. The $\mathrm{N_F}$ films display two-dimensional, smectic-like parabolic focal conic textures comprising director/polarization bend that are a manifestation of the electrostatic suppression of director splay in the film plane. In the $\mathrm{SmZ_A}$ and $\mathrm{SmA_F}$ phases, the smectic layers orient preferentially normal to the film surfaces, a condition never found in typical thermotropic or lyotropic lamellar LC phases, with the $\mathrm{SmZ_A}$ films exhibiting focal-conic fan textures mimicking the appearance of typical smectics in glass cells when the layers are oriented normal to the plates, and the $\mathrm{SmA_F}$ films showing a texture of plaquettes of uniform in-plane orientation where both bend and splay are suppressed, separated by grain boundaries. The $\mathrm{SmA_F}$ phase can also be drawn into thin filaments, in which X-ray scattering reveals that the smectic layer planes are normal to the filament axis. Remarkably, the filaments are mechanically stable even if they break, forming free-standing, fluid filaments supported only at one end. The unique architectures of these films and filaments are stabilized by the electrostatic self-interaction of the liquid crystal polarization field, which enables the formation of confined, fluid structures that are fundamentally different from those of their counterparts made using previously known liquid crystal phases., Comment: Main paper 25 pages (5 figures); Supplement: 7 pages (7 figures)

There is mounting evidence that a second quantum revolution based on the technological capabilities to detect and manipulate single quantum particles (e.g., electrons, photons, ions, etc), a feat not achieved during the first quantum revolution, is progressing fast. It is expected that in less than 10 years, this second quantum revolution shall have a significant impact over numerous industries, including finance, medicine, energy, transportation, etc. Quantum computers threaten the status quo of cybersecurity, due to known quantum algorithms that can break asymmetric encryption, which is what gives us the ability to communicate securely using a public channel. Considering the world's dependence on digital communication through data exchange and processing, retaining the ability to communicate securely even once quantum computers come into play, cannot be stressed enough. Two solutions are available: Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC); which, we emphasise, are not mutually exclusive. The EuroQCI initiative, of which EQUO is a part of, focuses on QKD and aims to build a network whereby EU countries can communicate securely through QKD. To this aim, the DEP (Digital Europe Programme) project aims to bring technological matureness to QKD by deploying a QKD test network and, through this exercise, understand what is lacking from an operator's point of view when the time to integrate QKD in their network comes., Comment: IEEE 2024 International Conference on Quantum Communications, Networking, and Computing (QCNC)

Materials with switchable nanostructured surfaces enable optical and electronic functionalities beyond those of natural materials. Here we report the creation of self-organized, re-writable, laser-induced surface structures in single-crystalline vanadium dioxide. We discover anisotropic features caused by canalized surface plasmon polaritons that can only propagate along one crystal axis. The nanostructures remain single-crystalline and preserve the raw material's metal-to-insulator transition, enabling femtosecond switching by temperature or light., Comment: {\dag}These authors contributed equally to this work

Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics. Decomposing the system into actuated (output) and unactuated (zero) coordinates provides useful insight as to how input enters the system dynamics. In this work, we leverage the structure of this decomposition to formalize the idea of Zero Dynamics Policies (ZDPs) -- a mapping from the unactuated coordinates to desired actuated coordinates. Specifically, we show that a ZDP exists in a neighborhood of the origin, and prove that combining output stabilization with a ZDP results in stability of the full system state. We detail a constructive method of obtaining ZDPs in a neighborhood of the origin, and propose a learning-based approach which leverages optimal control to obtain ZDPs with much larger regions of attraction. We demonstrate that such a paradigm can be used to stabilize the canonical underactuated system of the cartpole, and showcase an improvement over the nominal performance of LQR., Comment: 8 pages, 2 figures, CDC 2024

This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynamic loads acting on the vehicle for ground and flight testing, as well as guidance, navigation, and control applications. Specifically, the approach targets hypersonic flight conditions where direct measurement of the surface pressures is challenging due to the harsh aerothermal environment. For problems employing a linear elastic structural model, we show that the inference problem can be posed as a least-squares problem with a linear constraint arising from a finite element discretization of the governing elasticity partial differential equation. Due to the linearity of the problem, an explicit solution is given by the normal equations. Pre-computation of the resulting inverse map enables rapid evaluation of the surface pressure and corresponding integrated quantities, such as the force and moment coefficients. The inverse approach additionally allows for uncertainty quantification, providing insights for theoretical recoverability and robustness to sensor noise. Numerical studies demonstrate the estimator performance for reconstructing the surface pressure field, as well as the force and moment coefficients, for the Initial Concept 3.X (IC3X) conceptual hypersonic vehicle., Comment: 22 pages, 15 figures

Charge-spin interconversion processes underpin the generation of spin-orbit torques in magnetic/nonmagnetic bilayers. However, efficient sources of spin currents such as 5d metals are also efficient spin sinks, resulting in a large increase of magnetic damping. Here we show that a partially-oxidized 3d metal can generate a strong orbital torque without a significant increase in damping. Measurements of the torque efficiency {\xi} and Gilbert damping {\alpha} in CoFe/CuOx and CoFe/Pt indicate that {\xi} is comparable. The increase in damping relative to a single CoFe layer is {\Delta}{\alpha}<0.002 in CoFe/CuOx and {\Delta}{\alpha} ~ 0.005 - 0.02 in CoFe/Pt, depending on CoFe thickness. We ascribe the nonreciprocal relationship between {\Delta}{\alpha} and {\xi} in CoFe/CuOx to the small orbital-to-spin current ratio generated by magnetic resonance in CoFe and the lack of an efficient spin sink in CuOx. Our findings provide new perspectives on the efficient excitation of magnetization dynamics via the orbital torque.

In this work, we apply a phase-field modeling framework to elucidate the interplay between nucleation and kinetics in the dynamic evolution of twinning interfaces. The key feature of this phase-field approach is the ability to transparently and explicitly specify nucleation and kinetic behavior in the model, in contrast to other regularized interface models. We use this to study 2 distinct problems where it is essential to explicitly specify the kinetic and nucleation behavior governing twin evolution. First, we study twinning interfaces in 2-d. When these interfaces are driven to move, we find that significant levels of twin nucleation occur ahead of the moving interface. Essentially, the finite interface velocity and the relaxation time of the stresses ahead of the interface allows for nucleation to occur before the interface is able to propagate to that point. Second, we study the growth of needle twins in antiplane elasticity. We show that both nucleation and anisotropic kinetics are essential to obtain predictions of needle twins. While standard regularized interface approaches do not permit the transparent specification of anisotropic kinetics, this is readily possible with the phase-field approach that we have used here., Comment: To appear in Journal of Applied Mechanics

Event Cameras, also known as Neuromorphic sensors, capture changes in local light intensity at the pixel level, producing asynchronously generated data termed ``events''. This distinct data format mitigates common issues observed in conventional cameras, like under-sampling when capturing fast-moving objects, thereby preserving critical information that might otherwise be lost. However, leveraging this data often necessitates the development of specialized, handcrafted event representations that can integrate seamlessly with conventional Convolutional Neural Networks (CNNs), considering the unique attributes of event data. In this study, We evaluate event-based Face and Eye tracking. The core objective of our study is to showcase the viability of integrating conventional algorithms with event-based data, transformed into a frame format while preserving the unique benefits of event cameras. To validate our approach, we constructed a frame-based event dataset by simulating events between RGB frames derived from the publicly accessible Helen Dataset. We assess its utility for face and eye detection tasks through the application of GR-YOLO -- a pioneering technique derived from YOLOv3. This evaluation includes a comparative analysis with results derived from training the dataset with YOLOv8. Subsequently, the trained models were tested on real event streams from various iterations of Prophesee's event cameras and further evaluated on the Faces in Event Stream (FES) benchmark dataset. The models trained on our dataset shows a good prediction performance across all the datasets obtained for validation with the best results of a mean Average precision score of 0.91. Additionally, The models trained demonstrated robust performance on real event camera data under varying light conditions., Comment: This paper has been accepted at The Workshop On Neuromorphic Vision: Advantages and Applications of Event Cameras at the European Conference on Computer Vision (ECCV), 2024

Quantum computers must achieve large-scale, fault-tolerant operation to deliver on their promise of transformational processing power [1-4]. This will require thousands or millions of high-fidelity quantum gates and similar numbers of qubits [5]. Demonstrations using neutral-atom qubits trapped and manipulated by lasers have shown that this modality can provide high two-qubit gate (CZ) fidelities and scalable operation [6-10]. However, the gates in these demonstrations are driven by lasers that do not resolve individual qubits, with universal computation enabled by physical mid-circuit shuttling of the qubits. This relatively slow operation will greatly extend runtimes for useful, large-scale computation. Here we demonstrate a universal neutral-atom quantum computer with gate rates limited by optical switching times, rather than shuttling, by individually addressing tightly focused laser beams at an array of single atoms. We achieve CZ fidelity of 99.35(4)% and local single qubit RZ gate fidelity of 99.902(8)%. Moreover, we demonstrate non-destructive readout of alkali-atom qubits with sub-percent loss, which boosts operational speed. This technique also enables us to measure 99.73(3)% CZ fidelity with atom-loss events excluded, which is a record among long lived neutral-atom qubits and highlights the path to higher fidelity and error correction. Our results represent a critical step towards large-scale, fault-tolerant neutral-atom quantum computers that can execute computations on practical timescales.

A biofilm is a microbial city. It consists of bacteria embedded in an extracellular polymeric substance (EPS) that functions as a protective barrier. Quorum sensing (QS) is a method of bacterial communication, where autoinducers (AIs) propagate via diffusion through the EPS and water channels within the biofilm. This diffusion process is anisotropic due to varying densities between the EPS and water channels. This study introduces a 2D anisotropic diffusion model for molecular communication (MC) within biofilms, analyzing information propagation between a point-to-point transmitter (TX) and receiver (RX) in bounded space. The channel impulse response is derived using Green's function for concentration (GFC) and is validated with particle-based simulation (PBS). The outcomes reveal similar results for both isotropic and anisotropic diffusion when the TX is centrally located due to symmetry. However, anisotropic conditions lead to greater diffusion peaks when the TX is positioned off-center. Additionally, the propagation of AIs is inversely proportional to both overall biofilm size and and diffusion coefficient values. It is hypothesized that anisotropic diffusion supports faster responses to hostile environmental changes because signals can propagate faster from the edge of the biofilm to the center.

This perspective outlines several linear optical effects featured by molecular polaritons arising in the collective strong light-matter coupling regime, focusing on the limit when the number of molecules per photon mode is large. We show that, under these circumstances, molecular absorption within a cavity can be understood as the overlap between the polariton transmission and bare molecular absorption spectra, suggesting that polaritons act in part as optical filters. This framework demystifies and provides a straightforward explanation for a large class of theoretical models of polaritonic phenomena, highlighting that similar effects might be achievable outside a cavity with shaped laser pulses. With a few modifications, this simple conceptual picture can also be adapted to understand the incoherent nonlinear response of polaritonic systems. However, we note that there are experimental observations in the collective regime that exhibit phenomena that go beyond this treatment. Our analysis underscores the importance of the notion that the field still needs to establish a clear distinction between polaritonic phenomena that can be fully explained through classical optics and those that require a more advanced theoretical framework. The linear optics approach presented here is exact when the number of molecules tends to infinity and is quite accurate for a large, but finite, number of molecules. We highlight the limitations of this treatment when the rates of the single-molecule processes that facilitate dark-state-to-polariton relaxation cannot be neglected and in systems under strong coupling with few molecules. Further exploration in these areas is needed to uncover novel polaritonic phenomena.

Background. Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software and hardware is an ongoing challenge. Methods. Datasets from 3 medical centers acquired at 3T (n = 150 subjects) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. Results. The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (p = n.s.) whereas it significantly outperformed on the external datasets (p < 0.005 for exD-1 and exD-2). Moreover, the number of image series with "failed" segmentation was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). Conclusions. The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor., Comment: Accepted for publication in JCMR, 2024

Hydrophobic textured surfaces are studied for their low wettability and their capacity to create a 'slippery' fluid on the surface during lubrication. To this end, the flow between two parallel surfaces is numerically addressed by computing two-dimensional numerical simulations. One of the surfaces moves with a uniform rectilinear motion, while the other is fixed, with a cavity in the middle. The steady-state flow is laminar and monophasic with a low Reynolds number. The reduction of the wall shear stress caused by a vortex in the cavity, with respect to a Couette flow, looks like the creation of an equivalent slip of the fluid on the wall at a macroscopic scale.Three methods are used to calculate the slip length: one is based on the wall shear stress and the other two are based on the speed of the fluid flow. When the slip length is calculated according to these three methods, the obtained results differ. The differences show that the slip often used in the literature is a macroscopic representation of local effects that are not necessarily slippery. The speed profiles and the streamlines are then discussed, in order to propose an explanation for this difference.

We report the discovery and spectroscopic confirmation of an ultra-faint Milky Way (MW) satellite in the constellation of Leo. This system was discovered as a spatial overdensity of resolved stars observed with Dark Energy Camera (DECam) data from an early version of the third data release of the DECam Local Volume Exploration survey (DELVE EDR3). The low luminosity ($M_V = -3.56_{-0.37}^{+0.47}$; $L_V = 2300_{-800}^{+1000} L_\odot$), large size ($r_{1/2} = 90_{-30}^{+30}$ pc), and large heliocentric distance ($D = 111_{-4}^{+7}$ kpc) are all consistent with the population of ultra-faint dwarf galaxies (UFDs). Using Keck/DEIMOS observations of the system, we were able to spectroscopically confirm 11 member stars, while measuring a mass to light ratio of $1000_{-700}^{+1900} M_\odot/L_\odot$ and a non-zero metallicity dispersion of $\sigma_{[\rm Fe/H]}=0.33_{-0.14}^{+0.19}$, further confirming Leo VI's identity as an UFD. While the system has an highly elliptical shape, $\epsilon = 0.54_{-0.29}^{+0.19}$, we do not find any evidence that it is tidally disrupting. Moreover, despite its apparent on-sky proximity to members of the proposed Crater-Leo infall group, its relatively lower heliocentric distance and inconsistent position in energy-angular momentum space with the other group members make it unlikely for it to be part of the proposed infall group., Comment: 21 pages, 11 figures, 2 tables; to be submitted to AAS Journals

A static electric field of a few V/cm shifts the energy levels of ultracold Rydberg atoms in a magneto-optical trap. For a given principle quantum number, most of the energy levels are nearly degenerate at zero field and fan out with increasing field to form a manifold. We excite Rydberg atoms to energy levels near the center of the manifold, where the spacing is nearly harmonic, and allow them to exchange energy via resonant dipole-dipole interactions. We measure the time evolution as energy spreads away from the center of the manifold, which reveals that the system fails to thermalize for long interaction times. A computational model that includes only a few essential features of the system qualitatively agrees with this result., Comment: 6 pages, 4 figures

Background: Low vitamin D intake and high prevalence of vitamin D deficiency (serum 25-hydroxyvitamin D concentration < 50 nmol/L) among Aboriginal and Torres Strait Islander peoples highlight a need for public health strategies to improve vitamin D status. As few foods contain naturally occurring vitamin D, fortification strategies may be needed to improve vitamin D intake and status among Aboriginal and Torres Strait Islander peoples. Objective: We aimed to model vitamin D food fortification scenarios among Aboriginal and Torres Strait Islander peoples. Methods: We used nationally representative food consumption data (n=4,109) and vitamin D food composition data to model four food fortification scenarios. The modelling for Scenario 1 included foods and maximum vitamin D concentrations permitted for fortification in Australia: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages, and iv) selected ready-to-eat breakfast cereals. The modelling for Scenarios 2a-c included some vitamin D concentrations higher than permitted in Australia; Scenario 2c included bread, which is not permitted for vitamin D fortification in Australia. Scenario 2a: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages. Scenario 2b: as per Scenario 2a plus selected ready-to-eat breakfast cereals. Scenario 2c: as per Scenario 2b plus bread. Results: Vitamin D fortification of a range of staple foods could potentially increase vitamin D intake among Aboriginal and Torres Strait Islander peoples by ~ 3-6 {\mu}g/day. Scenario 2c showed the highest potential median vitamin D intake increase to ~ 8 {\mu}g/day. Across all modelled scenarios, none of the participants had vitamin D intake above the Australian upper level of intake of 80 {\mu}g/day.

Symmetries play a pivotal role in our understanding of the properties of quantum many-body systems. While there are theorems and a well-established toolbox for systems in thermal equilibrium, much less is known about the role of symmetries and their connection to dynamics out of equilibrium. This arises due to the direct link between a system's thermal state and its Hamiltonian, which is generally not the case for nonequilibrium dynamics. Here we present a pathway to identify the effective symmetries and to extract them from data in nonequilibrium quantum many-body systems. Our approach is based on exact relations between correlation functions involving different numbers of spatial points, which can be viewed as nonequilibrium versions of (equal-time) Ward identities encoding the symmetries of the system. We derive symmetry witnesses, which are particularly suitable for the analysis of measured or simulated data at different snapshots in time. To demonstrate the potential of the approach, we apply our method to numerical and experimental data for a spinor Bose gas. We investigate the important question of a dynamical restoration of an explicitly broken symmetry of the Hamiltonian by the initial state. Remarkably, it is found that effective symmetry restoration can occur long before the system equilibrates. We also use the approach to define and identify spontaneous symmetry breaking far from equilibrium, which is of great relevance for applications to nonequilibrium phase transitions. Our work opens new avenues for the classification and analysis of quantum as well as classical many-body dynamics in a large variety of systems, ranging from ultracold quantum gases to cosmology.

This paper proposes a framework for time series generation built to investigate anomaly detection in cloud microservices. In the field of cloud computing, ensuring the reliability of microservices is of paramount concern and yet a remarkably challenging task. Despite the large amount of research in this area, validation of anomaly detection algorithms in realistic environments is difficult to achieve. To address this challenge, we propose a framework to mimic the complex time series patterns representative of both normal and anomalous cloud microservices behaviors. We detail the pipeline implementation that allows deployment and management of microservices as well as the theoretical approach required to generate anomalies. Two datasets generated using the proposed framework have been made publicly available through GitHub., Comment: The paper has been accepted by the 10th International Conference on Machine Learning, Optimization and Data Science

We obtain a quantitative star formation history (SFH) of a shell-like structure ('shell') located in the northeastern part of the Small Magellanic Cloud (SMC). We use the Survey of the MAgellanic Stellar History (SMASH) to derive colour-magnitude diagrams (CMDs), reaching below the oldest main-sequence turnoff, from which we compute the SFHs with CMD fitting techniques. We present, for the first time, a novel technique that uses red clump (RC) stars from the CMDs to assess and account for the SMC's line-of-sight depth effect present during the SFH derivation. We find that accounting for this effect recovers a more accurate SFH. We quantify a 7 kpc line-of-sight depth present in the CMDs, in good agreement with depth estimates from RC stars in the northeastern SMC. By isolating the stellar content of the northeastern shell and incorporating the line-of-sight depth into our calculations, we obtain an unprecedentedly detailed SFH. We find that the northeastern shell is primarily composed of stars younger than 500 Myrs, with significant star formation enhancements around 250 Myr and 450 Myr. These young stars are the main contributors to the shell's structure. We show synchronicity between the northeastern shell's SFH with the Large Magellanic Cloud's (LMC) northern arm, which we attribute to the interaction history of the SMC with the LMC and the Milky Way (MW) over the past 500 Myr. Our results highlight the complex interplay of ram pressure stripping and the influence of the MW's circumgalactic medium in shaping the SMC's northeastern shell., Comment: 17 pages, 13 figures. Accepted to MNRAS for publication

Optical Coherence Tomography (OCT) imaging is pivotal in diagnosing ophthalmic conditions by providing detailed cross-sectional images of the anterior and posterior segments of the eye. Nonetheless, speckle noise and other imaging artifacts inherent to OCT impede the accuracy of diagnosis significantly. In this study, we proposed an enhanced denoising model using a Residual U-Net architecture that effectively diminishes noise and improves image clarity across both Anterior Segment OCT (ASOCT) and polarization-sensitive OCT (PSOCT) images. Our approach demonstrated substantial improvements in image quality metrics: the Peak Signal Noise Ratio (PSNR) was 34.343 $\pm$ 1.113 for PSOCT images, and Structural Similarity Index Measure (SSIM) values were 0.885 $\pm$ 0.030, indicating enhanced preservation of tissue integrity and textural details. For ASOCT images, we observed the PSNR to be 23.525 $\pm$ 0.872 dB and SSIM 0.407 $\pm$ 0.044, reflecting significant enhancements in visual quality and structural accuracy. These metrics substantiate the models efficacy in not only reducing noise but also in maintaining crucial anatomical features, thereby enabling more precise and efficient clinical evaluations. The dual functionality across both ASOCT and PSOCT modalities underscores the versatility and potential for broad application in clinical settings, optimizing diagnostic processes and reducing the necessity for prolonged imaging sessions.

Background/Objective: Vitamin D deficiency (serum 25-hydroxyvitamin D [25(OH)D] concentration <50 nmol/L) is prevalent among Aboriginal and Torres Strait Islander peoples in Australia. Alternative to sun exposure (the primary source of vitamin D), vitamin D can also be obtained from food (e.g., fish, eggs, and meat) and supplements. However, vitamin D intake among Aboriginal and Torres Strait Islander peoples is currently unknown. We aimed to provide the first quantification of vitamin D intake using nationally representative data from Aboriginal and Torres Strait Islander peoples. Methods: We used food consumption data collected in the 2012-2013 National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (n = 4,109) and vitamin D food composition data to quantify mean absolute vitamin D intake by sex, age group, and remoteness of location. Differences in mean vitamin D intake between sexes and between remoteness of location were assessed using the 95% confidence interval (95% CI). Results: The mean (standard deviation (SD)) vitamin D intake among Aboriginal and Torres Strait Islander peoples was 2.9 (3.0) {\mu}g/day. Males had a statistically significantly higher mean (SD) [95% CI] vitamin D intake (3.2 (3.1) [3.0-3.4] {\mu}g/day) than females (2.6 (2.7) [2.4-2.7] {\mu}g/day). There were no statistically significant differences between mean (SD) [95% CI] vitamin D intake in non-remote (2.9 (2.2) [2.7-3.1] {\mu}g/day) and remote areas (2.8 (4.8) [2.6-3.0] {\mu}g/day). Conclusions: Vitamin D intake among Aboriginal and Torres Strait Islander peoples is low. Food-based public health strategies could be developed to promote higher vitamin D intake among this population.

NOTT (formerly Hi-5) is the L'-band (3.5-4.0~microns) nulling interferometer of Asgard, an instrument suite in preparation for the VLTI visitor focus. The primary scientific objectives of NOTT include characterizing (i) young planetary systems near the snow line, a critical region for giant planet formation, and (ii) nearby main-sequence stars close to the habitable zone, with a focus on detecting exozodiacal dust that could obscure Earth-like planets. In 2023-2024, the final warm optics have been procured and assembled in a new laboratory at KU Leuven. First fringes and null measurements were obtained using a Gallium Lanthanum Sulfide (GLS) photonic chip that was also tested at cryogenic temperatures. In this paper, we present an overall update of the NOTT project with a particular focus on the cold mechanical design, the first results in the laboratory with the final NOTT warm optics, and the ongoing Asgard integration activities. We also report on other ongoing activities such as the characterization of the photonic chip (GLS, LiNbO3, SiO), the development of the exoplanet science case, the design of the dispersion control module, and the progress with the self-calibration data reduction software., Comment: 11 pages (incl. 5 figures); Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama; Japan), Optical and Infrared Interferometry and Imaging VI

A combinatorial object is said to be quasirandom if it exhibits certain properties that are typically seen in a truly random object of the same kind. It is known that a permutation is quasirandom if and only if the pattern density of each of the twenty-four 4-point permutations is close to 1/24, which is its expected value in a random permutation. In other words, the set of all twenty-four 4-point permutations is quasirandom-forcing. Moreover, it is known that there exist sets of eight 4-point permutations that are also quasirandom-forcing. Breaking the barrier of linear dependency of perturbation gradients, we show that every quasirandom-forcing set of 4-point permutations must have cardinality at least five.

Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical observation that applying unsupervised contrastive learning to noisy, web-crawled datasets yields a feature representation under which the in-distribution (ID) and out-of-distribution (OOD) samples are linearly separable. We show that direct estimation of the separating hyperplane can indeed offer an accurate detection of OOD samples, and yet, surprisingly, this detection does not translate into gains in classification accuracy. Digging deeper into this phenomenon, we discover that the near-perfect detection misses a type of clean examples that are valuable for supervised learning. These examples often represent visually simple images, which are relatively easy to identify as clean examples using standard loss- or distance-based methods despite being poorly separated from the OOD distribution using unsupervised learning. Because we further observe a low correlation with SOTA metrics, this urges us to propose a hybrid solution that alternates between noise detection using linear separation and a state-of-the-art (SOTA) small-loss approach. When combined with the SOTA algorithm PLS, we substantially improve SOTA results for real-world image classification in the presence of web noise github.com/PaulAlbert31/LSA, Comment: Accepted in the European Conference on Computer Vision (ECCV) 2024

We developed a state-space age-structured catch-at-length (ACL) assessment model for redfish in NAFO Divisions 3LN. The model was developed to address limitations in the surplus production model that was previously used to assess this stock. The ACL model included temporal variations in recruitment, growth, and mortality rates, which were limitations identified for the surplus production model. Our ACL model revealed some important discrepancies in survey and fishery length compositions. Our model also required large population dynamics process errors to achieve good fits to survey indices and catch estimates, which also demonstrated that additional understanding of these data and other model assumptions is required. As such, we do not propose the ACL model to provide management advice for 3LN redfish, but we do provide research recommendations that should provide a better basis to model the 3LN redfish stock dynamics. Recommendations include implementing sampling programs to determine redfish species/ecotypes in commercial and research survey catches and improving biological sampling for maturity and age., Comment: 27 pages including references, tables, and figures. In addition 12 pages figures in an Appendix

Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks., Comment: 10 + 14 pages, 4 + 7 figures

Autonomous agents driven by Large Language Models (LLMs) offer enormous potential for automation. Early proof of this technology can be found in various demonstrations of agents solving complex tasks, interacting with external systems to augment their knowledge, and triggering actions. In particular, workflows involving multiple agents solving complex tasks in a collaborative fashion exemplify their capacity to operate in less strict and less well-defined environments. Thus, a multi-agent approach has great potential for serving as a backbone in many industrial applications, ranging from complex knowledge retrieval systems to next generation robotic process automation. Given the reasoning abilities within the current generation of LLMs, complex processes require a multi-step approach that includes a plan of well-defined and modular tasks. Depending on the level of complexity, these tasks can be executed either by a single agent or a group of agents. In this work, we focus on designing a flexible agent engineering framework with careful attention to planning and execution, capable of handling complex use case applications across various domains. The proposed framework provides reliability in industrial applications and presents techniques to ensure a scalable, flexible, and collaborative workflow for multiple autonomous agents working together towards solving tasks., Comment: 24 pages. 21 PDF images

We consider a refined version of the string-net model which assigns a different energy cost to each plaquette excitation. Using recent exact calculations of the energy-level degeneracies we compute the partition function of this model and investigate several thermodynamical quantities. In the thermodynamic limit, we show that the partition function is dominated by the contribution of special particles, dubbed pure fluxons, which trivially braid with all other (product of) fluxons. We also analyze the behavior of Wegner-Wilson loops associated to excitations and show that they obey an area law, indicating confinement, for any finite temperature except for pure fluxons that always remain deconfined. Finally, using a recently proposed conjecture, we compute the topological mutual information at finite temperature, which features a nontrivial scaling between system size and temperature., Comment: 22 pages, 21 figures, may be considered as part 2 of arXiv:2309.00343

Multiscale models provide a unique tool for studying complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. They aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms -- coupled with a graphical interface -- is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to easily construct such models, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as a Supplementary Material and all models are provided at: https://physiboss.github.io/tutorial/.

This article explores Multimodal Analytics' use in assessing communication within agile software development, particularly through planning poker, to understand collaborative behavior. Multimodal Analytics examines verbal, paraverbal, and non-verbal communication, crucial for effective collaboration in software engineering, which demands efficient communication, cooperation, and coordination. The study focuses on how planning poker influences speaking time and attention among team members by utilizing advanced audiovisual data analysis technologies. Results indicate that while planning poker doesn't significantly change total speaking or attention time, it leads to a more equitable speaking time distribution, highlighting its benefit in enhancing equitable team participation. These findings emphasize planning poker's role in improving software team collaboration and suggest multimodal analytics' potential to explore new aspects of team communication. This research contributes to better understanding coordination techniques' impact in software development and team education, proposing future investigations into optimizing team collaboration and performance through alternative coordination techniques and multimodal analysis across different collaborative settings.

Many high-state cataclysmic variables (CVs) exhibit blue-shifted absorption features in their ultraviolet (UV) spectra -- a smoking-gun signature of outflows. However, the impact of these outflows on {\em optical} spectra remains much more uncertain. During its recent outburst, the eclipsing dwarf nova V455 And displayed strong optical emission lines whose cores were narrower than expected from a Keplerian disc. Here, we explore whether disc + wind models developed for matching UV observations of CVs can also account for these optical spectra. Importantly, V455~And was extremely bright at outburst maximum: the accretion rate implied by fitting the optical continuum with a standard disc model is $\dot{M}_{\rm acc} \simeq 10^{-7}~{\rm M}_\odot~{\rm yr^{-1}}$. Allowing for continuum reprocessing in the outflow helps to relax this constraint. A disk wind can also broadly reproduce the optical emission lines, but only if the wind is (i) highly mass-loaded, with a mass-loss rate reaching $\dot{M}_{\rm wind} \simeq 0.4 \dot{M}_{\rm acc}$, and/or (ii) clumpy, with a volume filling factor $f_V \simeq 0.1$. The same models can describe the spectral evolution across the outburst, simply by lowering $\dot{M}_{\rm acc}$ and $\dot{M}_{\rm wind}$. Extending these models to lower inclinations and into the UV produces spectra consistent with those observed in face-on high-state CVs. We also find, for the first time in simulations of this type, P-Cygni-like absorption features in the Balmer series, as have been observed in both CVs and X-ray binaries. Overall, dense disc winds provide a promising framework for explaining multiple observational signatures seen in high-state CVs, but theoretical challenges persist., Comment: 13 pages, 9 figures, 3 tables. Accepted for publication in MNRAS

Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includes encyclopedic documents that harbor a vast amount of general knowledge (e.g., Wikipedia) but also potentially overlap with benchmark datasets used for evaluating LLMs. Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions. To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. RepLiQA is a collection of five splits of test sets, four of which have not been released to the internet or exposed to LLM APIs prior to this publication. Each sample in RepLiQA comprises (1) a reference document crafted by a human annotator and depicting an imaginary scenario (e.g., a news article) absent from the internet; (2) a question about the document's topic; (3) a ground-truth answer derived directly from the information in the document; and (4) the paragraph extracted from the reference document containing the answer. As such, accurate answers can only be generated if a model can find relevant content within the provided document. We run a large-scale benchmark comprising several state-of-the-art LLMs to uncover differences in performance across models of various types and sizes in a context-conditional language modeling setting. Released splits of RepLiQA can be found here: https://huggingface.co/datasets/ServiceNow/repliqa.

Perovskite solar cells (PSCs) offer an efficient, inexpensive alternative to current photovoltaic technologies, with the potential for manufacture via high-throughput coating methods. However, challenges for commercial-scale solution-processing of metal-halide perovskites include the use of harmful solvents, the expense of maintaining controlled atmospheric conditions, and the inherent instabilities of PSCs under operation. Here, we address these challenges by introducing a high volatility, low toxicity, biorenewable solvent system to fabricate a range of 2D perovskites, which highly effective precursor phases for subsequent transformation to alpha-formamidinium lead triiodide (FAPbI3), fully processed under ambient conditions. PSCs utilising our FAPbI3 reproducibly show remarkable stability under illumination and elevated temperature (ISOS-L-2) and "damp heat" (ISOS-D-3) stressing, surpassing other state-of-the-art perovskite compositions. We determine that this enhancement is a consequence of the 2D precursor phase crystallisation route, which simultaneously avoids retention of residual low-volatility solvents (such as DMF and DMSO) and reduces the rate of degradation of FA+ in the material. Our findings highlight both the critical role of the initial crystallisation process in determining the operational stability of perovskite materials, and that neat FA+-based perovskites can be competitively stable despite the inherent metastability of the alpha-phase.

Predicting the rate for $\mu\to e$ conversion in nuclei for a given set of effective operators mediating the violation of lepton flavor symmetry crucially depends on hadronic and nuclear matrix elements. In particular, the uncertainties inherent in this non-perturbative input limit the discriminating power that can be achieved among operators by studying different target isotopes. In order to quantify the associated uncertainties, as a first step, we go back to nuclear charge densities and propagate the uncertainties from electron scattering data for a range of isotopes relevant for $\mu\to e$ conversion in nuclei, including $^{40,48}$Ca, $^{48,50}$Ti, and $^{27}$Al. We provide as central results Fourier-Bessel expansions of the corresponding charge distributions with complete covariance matrices, accounting for Coulomb-distortion effects in a self-consistent manner throughout the calculation. As an application, we evaluate the overlap integrals for $\mu\to e$ conversion mediated by dipole operators. In combination with modern ab-initio methods, our results will allow for the evaluation of general $\mu\to e$ conversion rates with quantified uncertainties., Comment: 63 pages, 17 figures, python notebook with charge distributions included as supplementary material; journal version

Dealing with atypical traffic scenarios remains a challenging task in autonomous driving. However, most anomaly detection approaches cannot be trained on raw sensor data but require exposure to outlier data and powerful semantic segmentation models trained in a supervised fashion. This limits the representation of normality to labeled data, which does not scale well. In this work, we revisit unsupervised anomaly detection and present UMAD, leveraging generative world models and unsupervised image segmentation. Our method outperforms state-of-the-art unsupervised anomaly detection., Comment: Daniel Bogdoll and No\"el Ollick contributed equally

Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solution. However, to impact clinical practice, models must achieve a high level of both verifiable performance and utility. We augment the utility of automated report generation by incorporating localisation of individual findings on the image - a task we call grounded report generation - and enhance performance by incorporating realistic reporting context as inputs. We design a novel evaluation framework (RadFact) leveraging the logical inference capabilities of large language models (LLMs) to quantify report correctness and completeness at the level of individual sentences, while supporting the new task of grounded reporting. We develop MAIRA-2, a large radiology-specific multimodal model designed to generate chest X-ray reports with and without grounding. MAIRA-2 achieves state of the art on existing report generation benchmarks and establishes the novel task of grounded report generation., Comment: 72 pages, 21 figures. v2 updates the model and adds results on the PadChest-GR dataset

In the era of Large Language Models (LLMs), predicting judicial outcomes poses significant challenges due to the complexity of legal proceedings and the scarcity of expert-annotated datasets. Addressing this, we introduce \textbf{Pred}iction with \textbf{Ex}planation (\texttt{PredEx}), the largest expert-annotated dataset for legal judgment prediction and explanation in the Indian context, featuring over 15,000 annotations. This groundbreaking corpus significantly enhances the training and evaluation of AI models in legal analysis, with innovations including the application of instruction tuning to LLMs. This method has markedly improved the predictive accuracy and explanatory depth of these models for legal judgments. We employed various transformer-based models, tailored for both general and Indian legal contexts. Through rigorous lexical, semantic, and expert assessments, our models effectively leverage \texttt{PredEx} to provide precise predictions and meaningful explanations, establishing it as a valuable benchmark for both the legal profession and the NLP community.

We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Our flexible encoder-decoder architecture can obtain smooth latent representations of spatial physical fields from a variety of data types, including irregular-grid inputs and point clouds. This versatility eliminates the need for patching and allows efficient processing of diverse geometries. The sequential nature of our latent representation can be interpreted spatially and permits the use of a conditional transformer for modeling the temporal dynamics of PDEs. By employing a diffusion-based formulation, we achieve greater stability and enable longer rollouts compared to conventional MSE training. AROMA's superior performance in simulating 1D and 2D equations underscores the efficacy of our approach in capturing complex dynamical behaviors.

Nuclear Star Clusters (NSCs) are amongst the densest stellar systems in the Universe and are found at the centres of many bright spiral and elliptical galaxies, and up to ${\sim}$40% of dwarf galaxies. However, their formation mechanisms, and possible links to globular clusters (GCs), remain debated. This paper uses the EDGE simulations - a collection of zoom-in, cosmological simulations of isolated dwarf galaxies -- to present a new formation mechanism for NSCs. We find that, at a gas spatial and mass resolution of ${\sim}3\,$pc and ${\sim}161$ M$_\odot$, respectively, NSCs naturally emerge in a subset of our EDGE dwarfs with redshift-zero halo masses of $\rm{M}_{\rm{r}200\rm{c}} \sim 5 \times 10^9$ M$_\odot$. These dwarfs are quenched by reionisation, but retain a significant reservoir of gas that is unable to cool and form stars. Sometime after reionisation, the dwarfs then undergo a major (${\sim}$1:1) merger that excites rapid gas cooling, leading to a significant starburst. An NSC forms in this starburst that then quenches star formation thereafter. The result is a nucleated dwarf that has two stellar populations with distinct age: one pre-reionisation and one post-reionisation. Our mechanism is unique for two key reasons. Firstly, the low mass of the host dwarf means that NSCs, formed in this way, can accrete onto galaxies of almost all masses, potentially seeding the formation of NSCs everywhere. Secondly, our model predicts that NSCs should have at least two stellar populations with a large ($\gtrsim$1 billion year) age separation. This yields a predicted colour magnitude diagram for our nucleated dwarfs that has two distinct main sequence turnoffs. Several GCs orbiting the Milky Way, including Omega Centauri and M54, show exactly this behaviour, suggesting that they may, in fact, be accreted NSCs., Comment: Main text 12 pages, 8 figures. Submitted to MNRAS

Several protocols are proposed to build continuous energy surfaces of many-body quantum systems, regarding both energy and states. The standard variational principle is augmented with constraints on state overlap, ensuring arbitrary precision on continuity. As an illustration, the lowest energy and excited state paths relevant for the $^{240}$Pu asymmetric fission are studied. The scission is clearly signed, with a neutron excess in the neck, the ultimate glue before its rupture. Our approach can potentially connect any couple of Hilbert space states, which opens up new horizons for various applications., Comment: 6 pages, 2 figures

The existence of steady-state multiplicity or multistationarity in the Earth System raises the possibility that the Earth may reach a "tipping point" and rapidly transition to a warmer steady-state from which recovery may be practically impossible. In detailed Earth models that require extensive computation time, it is difficult to make an a priori prediction of the existence of steady-state multiplicity. In this study, we demonstrate Chemical Reaction Network Theory (CRNT) analysis of a simple heuristic box model of the Earth System carbon cycle with the human intervention of direct air capture. The analysis reveals necessary conditions for the combination of system parameters where steady-state multiplicity may exist. With this method, other negative emissions technologies (NET) may be screened in a relatively simple manner to aid in the priority setting by policymakers., Comment: arXiv admin note: text overlap with arXiv:2209.12143

Random walks are widely used for mining networks due to the computational efficiency of computing them. For instance, graph representation learning learns a d-dimensional embedding space, so that the nodes that tend to co-occur on random walks (a proxy of being in the same network neighborhood) are close in the embedding space. Specific local network topology (i.e., structure) influences the co-occurrence of nodes on random walks, so random walks of limited length capture only partial topological information, hence diminishing the performance of downstream methods. We explicitly capture all topological neighborhood information and improve performance by introducing orbit adjacencies that quantify the adjacencies of two nodes as co-occurring on a given pair of graphlet orbits, which are symmetric positions on graphlets (small, connected, non-isomorphic, induced subgraphs of a large network). Importantly, we mathematically prove that random walks on up to k nodes capture only a subset of all the possible orbit adjacencies for up to k-node graphlets. Furthermore, we enable orbit adjacency-based analysis of networks by developing an efficient GRaphlet-orbit ADjacency COunter (GRADCO), which exhaustively computes all 28 orbit adjacency matrices for up to four-node graphlets. Note that four-node graphlets suffice, because real networks are usually small-world. In large networks on around 20,000 nodes, GRADCOcomputesthe28matricesinminutes. Onsixrealnetworksfromvarious domains, we compare the performance of node-label predictors obtained by using the network embeddings based on our orbit adjacencies to those based on random walks. We find that orbit adjacencies, which include those unseen by random walks, outperform random walk-based adjacencies, demonstrating the importance of the inclusion of the topological neighborhood information that is unseen by random walks.

A major prediction of most super-Eddington accretion theories is the presence of anisotropic emission from supercritical disks, but the degree of anisotropy and its dependency with energy remain poorly constrained observationally. A key breakthrough allowing to test such predictions was the discovery of high-excitation photoionized nebulae around Ultraluminous X-ray sources (ULXs). We present efforts to tackle the degree of anisotropy of the UV/EUV emission in super-Eddington accretion flows by studying the emission-line nebula around the archetypical ULX NGC~1313~X--1. We first take advantage of the extensive wealth of optical/near-UV and X-ray data from \textit{Hubble Space Telescope}, \textit{XMM-Newton}, \textit{Swift}-XRT and \textit{NuSTAR} observatories to perform multi-band, state-resolved spectroscopy of the source to constrain the spectral energy distribution (SED) along the line of sight. We then compare spatially-resolved \texttt{Cloudy} predictions using the observed line-of-sight SED with the nebular line ratios to assess whether the nebula `sees' the same SED as observed along the line of sight. We show that to reproduce the line ratios in the surrounding nebula, the photo-ionizing SED must be a factor $\approx 4$ dimmer in ultraviolet emission than along the line-of-sight. Such nearly-iosotropic UV emission may be attributed to the quasi-spherical emission from the wind photosphere. We also discuss the apparent dichotomy in the observational properties of emission-line nebulae around soft and hard ULXs, and suggest only differences in mass-transfer rates can account for the EUV/X-ray spectral differences, as opposed to inclination effects. Finally, our multi-band spectroscopy suggest the optical/near-UV emission is not dominated by the companion star., Comment: Hi there, Accepted for publication to MNRAS, 28 pages, 11 figures PS: Read me please

Spheroids are aggregates of cells that can mimic the cellular organization often found in tissues. They are typically formed through the self-assembly of cells in a culture where there is a promotion of interactions and cell-to-cell communication. Spheroids can be created from various cell types, including cancer cells, stem cells, and primary cells, and they serve as valuable tools in biological research. In this letter, molecule propagation from a point source is simulated in the presence of multiple spheroids to observe the impact of the spheroids on the spatial molecule distribution. The spheroids are modeled as porous media with a corresponding effective diffusion coefficient. System variations are considered with a higher spheroid porosity (i.e., with a higher effective diffusion coefficient) and with molecule uptake by the spheroid cells (approximated as a first-order degradation reaction while molecules diffuse within the spheroid). Results provide initial insights about the molecule propagation dynamics and their potential to model transport and drug delivery within crowded spheroid systems., Comment: submitted to TMBMC journal

Sympathetic cooling is a technique often employed to mitigate motional heating in trapped-ion quantum computers. However, choosing system parameters such as number of coolants and cooling duty cycle for optimal gate performance requires evaluating trade-offs between motional errors and other slower errors such as qubit dephasing. The optimal parameters depend on cooling power, heating rate, and ion spacing in a particular system. In this study, we aim to analyze best practices for sympathetic cooling of long chains of trapped ions using analytical and computational methods. We use a case study to show that optimal cooling performance is achieved when coolants are placed at the center of the chain and provide a perturbative upper-bound on the cooling limit of a mode given a particular set of cooling parameters. In addition, using computational tools, we analyze the trade-off between the number of coolant ions in a chain and the center-of-mass mode heating rate. We also show that cooling as often as possible when running a circuit is optimal when the qubit coherence time is otherwise long. These results provide a roadmap for how to choose sympathetic cooling parameters to maximize circuit performance in trapped ion quantum computers using long chains of ions., Comment: 10 pages, 8 figures

While much has been learned in recent decades about the X-ray emission of the extragalactic Ultraluminous X-ray sources (ULXs), their radiative output in the UV band remains poorly constrained. Understanding of the full ULX spectral energy distribution (SED) is imperative to constrain the accretion flow geometry powering them, as well as their radiative power. Here we present constraints on the UV emission of the pulsating ULX (PULX) NGC~1313~X--2 based on the absence of nebular {He{\sc ii} $\lambda$4686} emission in its immediate environment. To this end, we first perform multi-band spectroscopy of the ULX to derive three realistic extrapolations of the SED into the unaccessible UV, each predicting varying levels of UV luminosity. We then perform photo-ionization modelling of the bubble nebula and predict the {He{\sc ii} $\lambda$4686} fluxes that should have been observed based on each of the derived SEDs. We then compare these predictions with the derived upper limit on {\heii} from MUSE data, which allows us to infer a UV luminosity $L_\mathrm{UV} \lesssim 1 \times 10^{39}$ erg/s in the PULX NGC~1313~X--2. Comparing the UV luminosity inferred with other ULXs, our work suggests there may be an intrinsic difference between hard and soft ULXs, either related to different mass-transfer rates and/or the nature of the accretor. However, a statistical sample of ULXs with inferred UV luminosities is needed to fully determine the distinguishing features between hard and soft ULXs. Finally, we discuss ULXs ionising role in the context of the nebular {He{\sc ii} $\lambda$4686} line observed in star-forming, metal-poor galaxies., Comment: Read me, please. Accepted to MNRAS, 17 pages, 11 Figures

High-Level Synthesis enables the rapid prototyping of hardware accelerators, by combining a high-level description of the functional behavior of a kernel with a set of micro-architecture optimizations as inputs. Such optimizations can be described by inserting pragmas e.g. pipelining and replication of units, or even higher level transformations for HLS such as automatic data caching using the AMD/Xilinx Merlin compiler. Selecting the best combination of pragmas, even within a restricted set, remains particularly challenging and the typical state-of-practice uses design-space exploration to navigate this space. But due to the highly irregular performance distribution of pragma configurations, typical DSE approaches are either extremely time consuming, or operating on a severely restricted search space. This work proposes a framework to automatically insert HLS pragmas in regular loop-based programs, supporting pipelining, unit replication, and data caching. We develop an analytical performance and resource model as a function of the input program properties and pragmas inserted, using non-linear constraints and objectives. We prove this model provides a lower bound on the actual performance after HLS. We then encode this model as a Non-Linear Program, by making the pragma configuration unknowns of the system, which is computed optimally by solving this NLP. This approach can also be used during DSE, to quickly prune points with a (possibly partial) pragma configuration, driven by lower bounds on achievable latency. We extensively evaluate our end-to-end, fully implemented system, showing it can effectively manipulate spaces of billions of designs in seconds to minutes for the kernels evaluated.

Toxicity mitigation consists in rephrasing text in order to remove offensive or harmful meaning. Neural natural language processing (NLP) models have been widely used to target and mitigate textual toxicity. However, existing methods fail to detoxify text while preserving the initial non-toxic meaning at the same time. In this work, we propose to apply counterfactual generation methods from the eXplainable AI (XAI) field to target and mitigate textual toxicity. In particular, we perform text detoxification by applying local feature importance and counterfactual generation methods to a toxicity classifier distinguishing between toxic and non-toxic texts. We carry out text detoxification through counterfactual generation on three datasets and compare our approach to three competitors. Automatic and human evaluations show that recently developed NLP counterfactual generators can mitigate toxicity accurately while better preserving the meaning of the initial text as compared to classical detoxification methods. Finally, we take a step back from using automated detoxification tools, and discuss how to manage the polysemous nature of toxicity and the risk of malicious use of detoxification tools. This work is the first to bridge the gap between counterfactual generation and text detoxification and paves the way towards more practical application of XAI methods.