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This white paper is the output of a multidisciplinary workshop in Nairobi (Nov 2023). Led by a cross-organisational team including Microsoft Research, NEPAD, Lelapa AI, and University of Oxford. The workshop brought together diverse thought-leaders from various sectors and backgrounds to discuss the implications of Generative AI for the future of work in Africa. Discussions centred around four key themes: Macroeconomic Impacts; Jobs, Skills and Labour Markets; Workers' Perspectives and Africa-Centris AI Platforms. The white paper provides an overview of the current state and trends of generative AI and its applications in different domains, as well as the challenges and risks associated with its adoption and regulation. It represents a diverse set of perspectives to create a set of insights and recommendations which aim to encourage debate and collaborative action towards creating a dignified future of work for everyone across Africa.

This exploratory pilot study investigated the potential of combining a domain-specific model, BERN2, with large language models (LLMs) to enhance automated disease phenotyping from research survey data. Motivated by the need for efficient and accurate methods to harmonize the growing volume of survey data with standardized disease ontologies, we employed BERN2, a biomedical named entity recognition and normalization model, to extract disease information from the ORIGINS birth cohort survey data. After rigorously evaluating BERN2's performance against a manually curated ground truth dataset, we integrated various LLMs using prompt engineering, Retrieval-Augmented Generation (RAG), and Instructional Fine-Tuning (IFT) to refine the model's outputs. BERN2 demonstrated high performance in extracting and normalizing disease mentions, and the integration of LLMs, particularly with Few Shot Inference and RAG orchestration, further improved accuracy. This approach, especially when incorporating structured examples, logical reasoning prompts, and detailed context, offers a promising avenue for developing tools to enable efficient cohort profiling and data harmonization across large, heterogeneous research datasets.

In the paper, we extend the widely used in optimization theory decoupling techniques to infinite collections of functions. Extended concepts of uniform lower semicontinuity and firm uniform lower semicontinuity as well as the new concepts of weak uniform lower semicontinuity and robust infimum are discussed. We study the uniform and robust minimum, and provide conditions under which these minimality concepts reduce to the conventional one. The main theorem gives fuzzy subdifferential necessary conditions (multiplier rules) for a quasiuniform local minimum of the sum of an infinite collection of functions without the traditional Lipschitz continuity assumptions. We consider a general infinite convex optimization problem, and develop a methodology for closing the duality gap and guaranteeing strong duality. In this setting, application of the fuzzy multiplier and sum rules for infinite sums produces general optimality conditions/multiplier rules., Comment: 29 pages

Foundation models, e.g., large language models, possess attributes of intelligence which offer promise to endow a robot with the contextual understanding necessary to navigate complex, unstructured tasks in the wild. In the future of space robotics, we see three core challenges which motivate the use of a foundation model adapted to space-based applications: 1) Scalability of ground-in-the-loop operations; 2) Generalizing prior knowledge to novel environments; and 3) Multi-modality in tasks and sensor data. Therefore, as a first-step towards building a foundation model for space-based applications, we automatically label the AI4Mars dataset to curate a language annotated dataset of visual-question-answer tuples. We fine-tune a pretrained LLaVA checkpoint on this dataset to endow a vision-language model with the ability to perform spatial reasoning and navigation on Mars' surface. In this work, we demonstrate that 1) existing vision-language models are deficient visual reasoners in space-based applications, and 2) fine-tuning a vision-language model on extraterrestrial data significantly improves the quality of responses even with a limited training dataset of only a few thousand samples.

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care., Comment: 5 Figures, 3 Tables, 1 Supplementary Table

The paper explores a new extremality model involving collections of arbitrary families of sets. We demonstrate its applicability to set-valued optimization problems with general preferences, weakening the assumptions of the known results and streamlining their proofs., Comment: 15 pages. Formerly appeared as part of arXiv:2403.16511v2

The integration of artificial intelligence (AI) chatbots into higher education marks a shift towards a new generation of pedagogical tools, mirroring the arrival of milestones like the internet. With the launch of ChatGPT-4 Turbo in November 2023, we developed a ChatGPT-based teaching application (https://chat.openai.com/g/g-1imx1py4K-chatge-medical-imaging) and integrated it into our undergraduate medical imaging course in the Spring 2024 semester. This study investigates the use of ChatGPT throughout a semester-long trial, providing insights into students' engagement, perception, and the overall educational effectiveness of the technology. We systematically collected and analyzed data concerning students' interaction with ChatGPT, focusing on their attitudes, concerns, and usage patterns. The findings indicate that ChatGPT offers significant advantages such as improved information access and increased interactivity, but its adoption is accompanied by concerns about the accuracy of the information provided and the necessity for well-defined guidelines to optimize its use.

The vitrification of aqueous solutions through rapid cooling is a remarkable achievement that launched the field of cryo-electron microscopy (cryo-EM) and has enabled the cryopreservation of biological specimens. For judging the feasibility of a vitrification experiment, the critical cooling rate of pure water is a frequently cited reference quantity. However, an accurate determination has remained elusive, with estimates varying by several orders of magnitude. Here, we employ in situ and time-resolved electron microscopy to obtain a precise measurement. We use shaped microsecond laser pulses to briefly melt an amorphous ice sample before flash freezing it with a variable, well-defined cooling rate. This allows us to directly measure the critical cooling rate of pure water, which we determine to be $6.4\cdot10^{6}$ K/s. Our experimental approach also expands the toolkit of microsecond time-resolved cryo-EM, an emerging technique, in which a cryo sample is similarly flash melted and revitrified with a laser pulse.

Active automata learning (AAL) is a method to infer state machines by interacting with black-box systems. Adaptive AAL aims to reduce the sample complexity of AAL by incorporating domain specific knowledge in the form of (similar) reference models. Such reference models appear naturally when learning multiple versions or variants of a software system. In this paper, we present state matching, which allows flexible use of the structure of these reference models by the learner. State matching is the main ingredient of adaptive L#, a novel framework for adaptive learning, built on top of L#. Our empirical evaluation shows that adaptive L# improves the state of the art by up to two orders of magnitude., Comment: Extended paper for FM 2024

This paper presents initial flight results for distributed optical angles-only navigation of a swarm of small spacecraft, conducted during the Starling Formation-Flying Optical Experiment (StarFOX). StarFOX is a core payload of the NASA Starling mission, which consists of four CubeSats launched in 2023. Prior angles-only flight demonstrations have only featured one observer and target and have relied upon a-priori target orbit knowledge for initialization, translational maneuvers to resolve target range, and external absolute orbit updates to maintain convergence. StarFOX overcomes these limitations by applying the angles-only Absolute and Relative Trajectory Measurement System (ARTMS), which integrates three novel algorithms. Image Processing detects and tracks multiple targets in images from each satellite's on-board camera. Batch Orbit Determination computes initial swarm orbit estimates from bearing angle batches. Sequential Orbit Determination leverages an unscented Kalman filter to refine swarm state estimates over time. Multi-observer measurements shared over an intersatellite link are seamlessly fused to enable absolute and relative orbit determination. StarFOX flight data presents the first demonstrations of autonomous angles-only navigation for a satellite swarm, including multi-target and multi-observer relative navigation; autonomous initialization of navigation for unknown targets; and simultaneous absolute and relative orbit determination. Relative positioning uncertainties of 1.3% of target range (1$\sigma$) are achieved for a single observer under challenging measurement conditions, reduced to 0.6% (1$\sigma$) with multiple observers. Results demonstrate promising performance with regards to ongoing StarFOX campaigns and the application of angles-only navigation to future distributed missions., Comment: Accepted to the 38th Small Satellite Conference

We investigate the interference of high-order perfect optical vortex (POV) beams with different topological charges. Through numerical simulations, we reveal a remarkable phenomenon: keeping the beam width, and beam radius fixed while changing the topological charge, the splitting of the composite POV beam into two distinct individual perfect vortices occurs exactly at the same inter-axial separation. The observed interference pattern exhibits pronounced sensitivity to factors such as axial separation, phase shift, beam radius, and topological charges of the constituent beams. Notably, our findings are contrasted with the interference of high-order Laguerre-Gauss (LG) beams, highlighting that the splitting of composite vortices into their individual components is more rapid in the case of LG beams. Our research provides significant insights into the distinct interference properties of high-order POV beams, presenting potential applications in the fields of optical manipulation and communication systems.

The paper proposes another extension of the extremal principle. A new extremality model involving collections of arbitrary families of sets is studied. It generalizes the conventional model based on linear translations of given sets as well as its set-valued extensions. This approach leads to a more general and simpler version of fuzzy separation. The new model is capable of treating a wider range of optimization and variational problems., Comment: 12 pages

A bottleneck in modern active automata learning is to test whether a hypothesized Mealy machine correctly describes the system under learning. The search space for possible counterexamples is given by so-called test suites, consisting of input sequences that have to be checked to decide whether a counterexample exists. This paper shows that significantly smaller test suites suffice under reasonable assumptions on the structure of the black box. These smaller test suites help to refute false hypotheses during active automata learning, even when the assumptions do not hold. We combine multiple test suites using a multi-armed bandit setup that adaptively selects a test suite. An extensive empirical evaluation shows the efficacy of our approach. For small to medium-sized models, the performance gain is limited. However, the approach allows learning models from large, industrial case studies that were beyond the reach of known methods., Comment: Extended paper for TACAS 2024

This study explores the intricacies of waiting games, a novel dynamic that emerged with Ethereum's transition to a Proof-of-Stake (PoS)-based block proposer selection protocol. Within this PoS framework, validators acquire a distinct monopoly position during their assigned slots, given that block proposal rights are set deterministically, contrasting with Proof-of-Work (PoW) protocols. Consequently, validators have the power to delay block proposals, stepping outside the honest validator specs, optimizing potential returns through MEV payments. Nonetheless, this strategic behaviour introduces the risk of orphaning if attestors fail to observe and vote on the block timely. Our quantitative analysis of this waiting phenomenon and its associated risks reveals an opportunity for enhanced MEV extraction, exceeding standard protocol rewards, and providing sufficient incentives for validators to play the game. Notably, our findings indicate that delayed proposals do not always result in orphaning and orphaned blocks are not consistently proposed later than non-orphaned ones. To further examine consensus stability under varying network conditions, we adopt an agent-based simulation model tailored for PoS-Ethereum, illustrating that consensus disruption will not be observed unless significant delay strategies are adopted. Ultimately, this research offers valuable insights into the advent of waiting games on Ethereum, providing a comprehensive understanding of trade-offs and potential profits for validators within the blockchain ecosystem., Comment: 23 pages, 13 figures, 3 appendices

We revisit the decoupling approach widely used (often intuitively) in nonlinear analysis and optimization and initially formalized about a quarter of a century ago by Borwein & Zhu, Borwein & Ioffe and Lassonde. It allows one to streamline proofs of necessary optimality conditions and calculus relations, unify and simplify the respective statements, clarify and in many cases weaken the assumptions. In this paper we study weaker concepts of quasiuniform infimum, quasiuniform lower semicontinuity and quasiuniform minimum, putting them into the context of the general theory developed by the aforementioned authors. On the way, we unify the terminology and notation and fill in some gaps in the general theory. We establish rather general primal and dual necessary conditions characterizing quasiuniform $\varepsilon$-minima of the sum of two functions. The obtained fuzzy multiplier rules are formulated in general Banach spaces in terms of Clarke subdifferentials and in Asplund spaces in terms of Fr\'echet subdifferentials. The mentioned fuzzy multiplier rules naturally lead to certain fuzzy subdifferential calculus results. An application from sparse optimal control illustrates applicability of the obtained findings., Comment: 40 pages

Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is focused on generating relatively straightforward levels in simple games, as it is challenging to design an optimisable objective function for complex settings. This limits the applicability of PCG to more complex and modern titles, hindering its adoption in industry. Our work aims to address this limitation by introducing a compositional level generation method that recursively composes simple low-level generators to construct large and complex creations. This approach allows for easily-optimisable objectives and the ability to design a complex structure in an interpretable way by referencing lower-level components. We empirically demonstrate that our method outperforms a non-compositional baseline by more accurately satisfying a designer's functional requirements in several tasks. Finally, we provide a qualitative showcase (in Minecraft) illustrating the large and complex, but still coherent, structures that were generated using simple base generators., Comment: Code is available at https://github.com/Michael-Beukman/MCHAMR. This work has been accepted to IEEE Transactions on Games, with copyright transferred to the IEEE

There is a great deal of scientific interest in characterizing the basaltic asteroids (spectrally classified as V-types), as they are the key to understanding planetesimal formation and evolution in the early Solar System. These have long been recognized as parts of the crusts of fully differentiated planetesimals. Thus, their multiplicity, distribution, and physical characteristics are crucial for providing context for and constraining the theoretical evolution models of the Solar System. In this work, we perform spectral analysis with an extended data set of spectral measurements from the ESA Gaia mission Data Release 3, thus increasing the sample size of the analyzed V-types by more than three times as compared to the literature. Using the data provided by Gaia we identified ~2000 possible V-type asteroids. About 350 of them successfully pass our data validation criteria. This sample includes 31 new V-type asteroids beyond 2.5 au and 6 in the Phocaea region. We confirm that the V-type asteroids in the middle and outer part of the main belt show distinct spectral properties compared to typical vestoids. In the inner main belt, we found a great diversity of spectral parameters among the V-types in all populations. Number of asteroids show band depths even greater than that of (1459) Magnya. Furthermore, some objects present 0.9~\textmu{}m band-centers more than one standard deviation away from the typical value for vestoids. However since the DR3 band centers are often overestimated, those findings are to be confirmed. Overall our results indicate that the inner main belt may contain remnants of multiple differentiated planetesimals, not just (4) Vesta., Comment: Accepted to MNRAS

The paper extends the 2003 radius of metric regularity theorem by Dontchev, Lewis & Rockafellar by providing an exact formula for the radius with respect to Lipschitz continuous perturbations in general Asplund spaces, thus, answering affirmatively an open question raised twenty years ago by Ioffe. In the non-Asplund case, we give natural upper bounds for the radius complementing the conventional lower bound in the theorem by Dontchev, Lewis & Rockafellar., Comment: 14 pages

This work proposes a self-navigated variable density spiral(VDS) based manifold regularization scheme to prospectively improve dynamic speech MRI at 3T. Short readout 1.3ms spirals were used to minimize off-resonance. A custom 16-channel speech coil was used for improved parallel imaging of vocal tract. The manifold model leveraged similarities between frames sharing similar speech postures without explicit motion binning. The self-navigating capability of VDS was leveraged to learn the Laplacian matrix of the manifold. Reconstruction was posed as a SENSE-based non-local soft weighted temporal regularization scheme. Our approach was compared against view-sharing, low-rank, finite difference, extra-dimension-based sparsity reconstruction constraints. Under-sampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges were performed in a retrospectively under-sampled data. For prospective under-sampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring were performed by three voice research experts. Region of interest analysis at articulator boundaries were performed to assess articulatory motion. Our scheme provided improved reconstruction over the others. With prospective under-sampling, a spatial resolution of 2.4mm2/pixel and a temporal resolution of 17.4 ms/frame for single slice imaging, and 52.2 ms/frame for 3-slice imaging were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Our method demonstrated superior image quality scores in reducing spatial and temporal blurring. While it exhibited faint alias artifacts similar to temporal finite-difference, it provided statistically significant improvements over remaining constraints., Comment: 32 pages, 10 figures

Recent efforts have shown machine learning to be useful for the prediction of nonlinear fluid dynamics. Predictive accuracy is often a central motivation for employing neural networks, but the pattern recognition central to the network function is equally valuable for purposes of enhancing our dynamical insight into confounding dynamics. In this paper, convolutional neural networks (CNNs) were trained to recognize several qualitatively different subsonic buffet flows over a high-incidence airfoil, and a near-perfect accuracy was performed with only a small training dataset. The convolutional kernels and corresponding feature maps, developed by the model with no temporal information provided, identified large-scale coherent structures in agreement with those known to be associated with buffet flows. An approach named Gradient-weighted Class Activation Mapping (Grad-CAM) was then applied to the trained model to indicate the importance of these feature maps in classification. Sensitivity to hyperparameters including network architecture and convolutional kernel size was also explored, and results show that smaller kernels are better at coherent structure identification than are larger kernels. A long-short term memory CNN was subsequently used to demonstrate that with the inclusion of temporal information, the coherent structures remained qualitatively comparable to those of the conventional CNN. The coherent structures identified by these models enhance our dynamical understanding of subsonic buffet over high-incidence airfoils over a wide range of Reynolds numbers.

The work done in this paper is an extension of the work done by Mpofu et al in [1]. The motivation for this paper is to show the potential of quantum bio-sensing techniques in applications to complex viruses like the human immunodeficiency virus (HIV) and to show it's potential in binding reactions where the signal is small and can potentially be buried in noise. In this paper we show theoretically that using quantum states of light such as the Fock state improves the precision in the estimation of kinetic parameters measured from the sensorgrams produced by the Kretschmann conguration. Though the Fock state is not the only quantum state in this study we look only at the Fock state because it has been shown to be the state which offers the best enhancement [1]. We show here that the Fock state allows us to measure the parameters more accurately in comparison to the classical (Coherent) state of light. We consider in this paper a binding reaction involving HIV type 1 (HIV-1). Specifically we look at the binding reaction between a variant of HIV-1 protease and nelnavir which is an inhibitor. Such a study is also offers value for drug discovery research as it points to the development of new technologies which can be applied to testing the efficacy of new drugs. Quantum technologies can prove to be useful in the fight against the HIV pandemic and assist in the continual research towards the development of treatments and a cure., Comment: We would like to retract because there was a bit of miscommunication on my part when I submitted the paper. I had assumed that I had the green light to submit but the other authors needed more time to review

The paper continues our previous work [7] on the radius of subregularity that was initiated by Asen Dontchev. We extend the results of [7] to general Banach/Asplund spaces and to other classes of perturbations, and sharpen the coderivative tools used in the analysis of the robustness of well-posedness of mathematical problems and related regularity properties of mappings involved in the statements. We also expand the selection of classes of perturbations, for which the formula for the radius of strong subregularity is valid., Comment: 31 pages

The motivation for PF calorimetry is to experimentally measure the energy of hadron jets with excellent resolution. In particle flow designs, sigma(E)/E < 5% should be possible for a range of jet energies from 50 GeV to 250 GeV, important particularly for experiments at electron-positron colliders (ILC, CLIC, FCCee, CEPC). The high granularity, which is essential for PF calorimetry, can also be very beneficial for removal of background from pile-up on an event-by-event basis making such calorimeters an attractive approach for hadron collider experiments, for example the HGCAL under construction for CMS at the CERN HL-LHC., Comment: Submitted to the Proceedings of Snowmass2021, Instrumentation Frontier, Calorimetry

To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated - none of which is currently achievable. Video-based assessment (VBA) is being deployed in intraoperative and simulation settings to evaluate technical skill execution. However, VBA remains manually- and time-intensive and prone to subjective interpretation and poor inter-rater reliability. Herein, we propose a deep learning (DL) model that can automatically and objectively provide a high-stakes summative assessment of surgical skill execution based on video feeds and low-stakes formative assessment to guide surgical skill acquisition. Formative assessment is generated using heatmaps of visual features that correlate with surgical performance. Hence, the DL model paves the way to the quantitative and reproducible evaluation of surgical tasks from videos with the potential for broad dissemination in surgical training, certification, and credentialing., Comment: 20 pages, 4 figures, 4 extended data figures, 3 tables, 4 extended data tables. Supplementary information is available

A FEM application for the accurate design of composite backing of ultrasonic transducers is presented. The idea is to obtain the dependence between the volume ratio of the tungsten powder in an epoxy matrix used as a backing and the final pulsed RF signal emitted by the backed transducer. An inhomogeneous material model for the backing was adopted instead of the classic complex geometrical FEM. The model was developed in Comsol 5.5 using two of its physics interfaces. The first based on classic FEM or continuous Galerkin for ultrasonic propagation in the backing and the second based on a discontinuous Galerkin option for fluid pulsed propagation. In this way it is possible to avoid the use of an impedance-based model. The calculations for the resulting bandwidth are good enough as predicted for Desilets for the 1D case by using the KLM circuital model. We worked in a 2D space, where the transducer face vibration is far away from the assumed piston-like considered by the unidimensional KLM model., Comment: 14 pages, 9 figures

The International Linear Collider (ILC) is on the table now as a new global energy-frontier accelerator laboratory taking data in the 2030s. The ILC addresses key questions for our current understanding of particle physics. It is based on a proven accelerator technology. Its experiments will challenge the Standard Model of particle physics and will provide a new window to look beyond it. This document brings the story of the ILC up to date, emphasizing its strong physics motivation, its readiness for construction, and the opportunity it presents to the US and the global particle physics community., Comment: 356 pages, Large pdf file (40 MB) submitted to Snowmass 2021; v2 references to Snowmass contributions added, additional authors; v3 references added, some updates, additional authors

Neutron monitors have been the premier ground-based instruments for monitoring the near-Earth cosmic ray flux for more than 70 years. It is essential to continue with such measurements in order to extend this unique long-term time series. Moreover, with the recent interest of the aviation industry to space weather effects, and especially the radiation risk posed by solar energetic particles and galactic cosmic rays, it is vital to extend the current neutron monitor network in order to provide near-real-time measurements to the space weather community. In this paper we discuss a new electronics system that was retrofitted to the SANAE neutron monitor in Antarctica. We present initial results from this system, featuring very high temporal resolution and discuss the techniques applied to the data analysis. Based on these successful upgrades, we are confident that this system can be used to rejuvenate the aligning neutron monitor network, and even possibly to revive some of the decommissioned instruments., Comment: Accepted to Advances in Space Research

Gaussian Process Regression (GPR) is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. This work investigates the use of a new method, the change-point method, for handling the varying length scales found in different tokamak regimes. The use of the Student's t-distribution for the Bayesian likelihood probability is also investigated and shown to be advantageous in providing good fits in profiles with many outliers. To compare different methods, synthetic data generated from analytic profiles is used to create a database enabling a quantitative statistical comparison of which methods perform the best. Using a full Bayesian approach with the change-point method, Mat\'ern kernel for the prior probability, and Student's t-distribution for the likelihood is shown to give the best results., Comment: submitted to PPCF

Documents containing mathematical content remain largely inaccessible to blind and visually impaired readers because they are predominantly published as untagged PDF which does not include the semantic data necessary for effective accessibility. We present a browsing approach for print-disabled readers specifically aimed at such mathematical content. This approach draws on the navigational mechanisms often used to explore the virtual worlds of text adventure games with audio-visual sensory substitution for graphical content. The relative spatial placement of the elements of an equation are represented as a virtual world, so that the reader can navigate from element to element. Text elements are announced conventionally using synthesised speech while graphical elements, such as roots and fraction lines, are rendered using a modification of the vOICe algorithm. The virtual world allows the reader to interactively discover the spatial structure of the equation, while the rendition of graphical elements as sound allows the shape and identity of elements that cannot be synthesised as speech to be discovered and recognised. The browsing approach was evaluated by eleven blind and fourteen sighted participants in a user trial that included the identification of twelve equations extracted from PDF documents. Overall, equations were identified completely correctly in 78% of cases (74% and 83% respectively for blind and sighted subjects). If partial correctness is considered, the performance is substantially higher. We conclude that the integration of a spatial model represented as a virtual world in conjunction with audio-visual sensory substitution for non-textual elements can be an effective way for blind and visually impaired readers to read currently inaccessible mathematical content in PDF documents.

We experimentally study a gas of quantum degenerate $^{87}$Rb atoms throughout the full dimensional crossover, from a one-dimensional (1D) system exhibiting phase fluctuations consistent with 1D theory to a three-dimensional (3D) phase-coherent system, thereby smoothly interpolating between these distinct, well-understood regimes. Using a hybrid trapping architecture combining an atom chip with a printed circuit board, we continuously adjust the system's dimensionality over a wide range while measuring the phase fluctuations through the power spectrum of density ripples in time-of-flight expansion. Our measurements confirm that the chemical potential $\mu$ controls the departure of the system from 3D and that the fluctuations are dependent on both $\mu$ and the temperature $T$. Through a rigorous study we quantitatively observe how inside the crossover the dependence on $T$ gradually disappears as the system becomes 3D. Throughout the entire crossover the fluctuations are shown to be determined by the relative occupation of 1D axial collective excitations., Comment: 10 pages, 6 figures

The communities who develop and support open source scientific software packages are crucial to the utility and success of such packages. Moreover, these communities form an important part of the human infrastructure that enables scientific progress. This paper discusses aspects of the PETSc (Portable Extensible Toolkit for Scientific Computation) community, its organization, and technical approaches that enable community members to help each other efficiently.

Nonlinear extended MHD simulations demonstrating seeding of neoclassical tearing modes (NTMs) via MHD-transient-induced multimode interactions are presented. Simulations of NTMs are enabled by two recent NIMROD code developments: the implementation of heuristic neoclassical stresses and the application of transient magnetic perturbations (MPs) at the boundary. NTMs are driven unstable by the inherently pressure driven kinetic bootstrap current, which arises due to collisional viscosity between passing and trapped electrons. These simulations use heuristic closures that model the neoclassical electron and ion stresses. NTM growth requires a seed island, which is generated by a transiently applied MP in simulations. The capability is demonstrated using kinetic-based reconstructions with flow of a DIII-D ITER Baseline Scenario discharge [R.J. La Haye, et al., Proceedings IAEA FEC 2020]. The applied MP seeds a 2/1 NTM that grows in two phases: a slow growth phase followed by a faster robust growth phase like that observed experimentally. Additionally, an evolving sequence of higher order core modes are excited at first. Power transfer analysis shows that nonlinear interactions between the core modes and the 2/1 helps drive the initial slow growth. Once the induced 2/1 magnetic island reaches a critical width, the NTM transitions to faster robust growth which is well described by the nonlinear modified Rutherford equation. This work highlights the role of nonlinear mode coupling in seeding NTMs.

Approximate necessary optimality conditions in terms of Fr\'echet subgradients and normals for a rather general optimization problem with a potentially non-Lipschitzian objective function are established with the aid of Ekeland's variational principle, the fuzzy Fr\'echet subdifferential sum rule, and a novel notion of lower semicontinuity relative to a set-valued mapping or set. Feasible points satisfying these optimality conditions are referred to as approximately stationary. As applications, we derive a new general version of the extremal principle. Furthermore, we study approximate stationarity conditions for an optimization problem with a composite objective function and geometric constraints, a qualification condition guaranteeing that approximately stationary points of such a problem are M-stationary, and a multiplier-penalty-method which naturally computes approximately stationary points of the underlying problem. Finally, necessary optimality conditions for an optimal control problem with a non-Lipschitzian sparsity-promoting term in the objective function are established., Comment: 47 pages, 1 figure

Online portfolio selection is an integral componentof wealth management. The fundamental undertaking is tomaximise returns while minimising risk given investor con-straints. We aim to examine and improve modern strategiesto generate higher returns in a variety of market conditions.By integrating simple data mining, optimisation techniques andmachine learning procedures, we aim to generate aggressive andconsistent high yield portfolios. This leads to a new methodologyof Pattern-Matching that may yield further advances in dynamicand competitive portfolio construction. The resulting strategiesoutperform a variety of benchmarks, when compared using Max-imum Drawdown, Annualised Percentage Yield and AnnualisedSharpe Ratio, that make use of similar approaches. The proposedstrategy returns showcase acceptable risk with high reward thatperforms well in a variety of market conditions. We concludethat our algorithm provides an improvement in searching foroptimal portfolios compared to existing methods., Comment: 6 pages, 4 figures, 5 tables, 8 equations

Kinetic models are essential for describing how molecules interact in a variety of biochemical processes. The estimation of a model's kinetic parameters by experiment enables researchers to understand how pathogens, such as viruses, interact with other entities like antibodies and trial drugs. In this work, we report a simple proof-of-principle experiment that uses quantum sensing techniques to give a more precise estimation of kinetic parameters than is possible with a classical approach. The interaction we study is that of bovine serum albumin (BSA) binding to gold via an electrostatic mechanism. BSA is an important protein in biochemical research as it can be conjugated with other proteins and peptides to create sensors with a wide range of specificity. We use single photons generated via parametric down-conversion to probe the BSA-gold interaction in a plasmonic resonance sensor. We find that sub-shot-noise level fluctuations in the sensor signal allow us to achieve an improvement in the precision of up to 31.8% for the values of the kinetic parameters. This enhancement can in principle be further increased in the setup. Our work highlights the potential use of quantum states of light for sensing in biochemical research., Comment: 10 pages, 5 figures

The measurement of parameters that describe kinetic processes is important in the study of molecular interactions. It enables a deeper understanding of the physical mechanisms underlying how different biological entities interact with each other, such as viruses with cells, vaccines with antibodies, or new drugs with specific diseases. In this work, we study theoretically the use of quantum sensing techniques for measuring the kinetic parameters of molecular interactions. The sensor we consider is a plasmonic resonance sensor -- a label-free photonic sensor that is one of the most widely used in research and industry. The first type of interaction we study is the antigen BSA interacting with antibody IgG1, which provides a large sensor response. The second type is the enzyme carbonic anhydrase interacting with the tumor growth inhibitor benzenesulfonamide, which produces a small sensor response. For both types of interaction we consider the use of two-mode Fock states, squeezed vacuum states and squeezed displaced states. We find that these quantum states offer an enhancement in the measurement precision of kinetic parameters when compared to that obtained with classical light. The results may help in the design of more precise quantum-based sensors for studying kinetics in the life sciences., Comment: 18 pages, 14 figures, appendix

The paper utilizes H\"older graphical derivatives for characterizing H\"older strong subregularity, isolated calmness and sharp minimum. As applications, we characterize H\"older isolated calmness in linear semi-infinite optimization and H\"older sharp minimizers of some penalty functions for constrained optimization., Comment: 19 pages

To address the demands in healthcare and industrial settings for spatially resolved magnetic imaging, we present a modular optically pumped magnetometer (OPM) system comprising a multi-sensor array of highly sensitive quantum magnetometers. This system is designed and built to facilitate fast prototyping and testing of new measurement schemes by enabling quick reconfiguration of the self-contained laser and sensor modules as well as allowing for the construction of various array layouts with a shared light source. The modularity of this system facilitates the development of methods for managing high-density arrays for magnetic imaging. The magnetometer sensitivity and bandwidth are first characterised in both individual channel and differential gradiometer configurations before testing in a real-world magnetoencephalography environment by measuring alpha rhythms from the brain of a human participant. We demonstrate the OPM system in a first-order axial gradiometer configuration with a magnetic field gradient sensitivity of 10 $\mathrm{fT/cm/\sqrt{Hz}}$. Bandwidths exceeding 200 Hz were achieved for two independent modules. The system's increased temporal resolution allows for the measurement of spinal cord signals, which we demonstrate by using phantom signal trials and comparing with an existing commercial sensor.

The zodiacal cloud is one of the largest structures in the solar system and strongly governed by meteoroid collisions near the Sun. Collisional erosion occurs throughout the zodiacal cloud, yet it is historically difficult to directly measure and has never been observed for discrete meteoroid streams. After six orbits with Parker Solar Probe (PSP), its dust impact rates are consistent with at least three distinct populations: bound zodiacal dust grains on elliptic orbits ($\alpha$-meteoroids), unbound $\beta$-meteoroids on hyperbolic orbits, and a third population of impactors that may either be direct observations of discrete meteoroid streams, or their collisional byproducts ("$\beta$-streams"). $\beta$-streams of varying intensities are expected to be produced by all meteoroid streams, particularly in the inner solar system, and are a universal phenomenon in all exozodiacal disks. We find the majority of collisional erosion of the zodiacal cloud occurs in the range of $10-20$ solar radii and expect this region to also produce the majority of pick-up ions due to dust in the inner solar system. A zodiacal erosion rate of at least $\sim$100 kg s$^{-1}$ and flux of $\beta$-meteoroids at 1 au of $0.4-0.8 \times 10^{-4}$ m$^{-2}$ s$^{-1}$ is found to be consistent with the observed impact rates. The $\beta$-meteoroids investigated here are not found to be primarily responsible for the inner source of pick-up ions, suggesting nanograins susceptible to electromagnetic forces with radii below $\sim$50 nm are the inner source of pick-up ions. We expect the peak deposited energy flux to PSP due to dust to increase in subsequent orbits, up to 7 times that experienced during its sixth orbit.

We report measurements related to hadronic $B$ decays to final states that contain charm mesons. The analyses are performed on a $62.8~\mathrm{fb}^{-1}$ data set collected by the Belle II experiment at a center-of-mass energy corresponding to the mass of the $\Upsilon(4S)$ resonance. The measurements reported are for the decay modes $B^-\to D^0 h^-$, $B^{-}\to D^{*0}h^-$, $\bar{B}^{0}\to D^{+} h^{-}$ and $\bar{B}^{0}\to D^{*+} h^{-}$, where $h=\pi$ or $K$. These modes are either signal or control channels for measurements related to the unitarity triangle angle $\gamma$ in direct or time-dependent $CP$-violation measurements. The reported observables are the ratios between the $B\to D^{(*)}K$ and $B\to D^{(*)}\pi$ decay rates, which are found to be in agreement with previous measurements., Comment: 19 pages, 5 figures, Moriond QCD

Surgical training in medical school residency programs has followed the apprenticeship model. The learning and assessment process is inherently subjective and time-consuming. Thus, there is a need for objective methods to assess surgical skills. Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency. There is considerable recent interest in deep neural networks (DNN) due to the availability of powerful algorithms, multiple datasets, some of which are publicly available, as well as efficient computational hardware to train and host them. We have reviewed 530 papers, of which we selected 25 for this systematic review. Based on this review, we concluded that DNNs are powerful tools for automated, objective surgical skill assessment using both kinematic and video data. The field would benefit from large, publicly available, annotated datasets that are representative of the surgical trainee and expert demographics and multimodal data beyond kinematics and videos., Comment: 23 pages, 3 figures, 3 tables, Journal Paper

The rapid pace of replacing fossil fuel propelled transport by electric vehicles is critically dependent on high-performing, high energy density and efficient batteries. Optimal and safe use of existing battery cells and development of much-needed novel battery chemistries and geometries require a large range of diagnostic and monitoring tools. While structural and chemical information is readily extracted through a host of imaging techniques, non-invasive functional detection of interior battery processes remains limited. Here we introduce sensitive magnetometry performed outside the battery that reveals a battery cell's internal current distribution. As a key application, we use an array of sensors to image the magnetic field present under cycling of a pouch cell between charge states. We find good agreement between measured and modelled fields with sufficient resolution to detect percent-level deviations around areas of high current density. This opens the path towards rapid and reliable assessment throughout the battery life cycle, from battery development and manufacturing quality assurance to operational safety and optimised use., Comment: 7 pages, 4 figures

Quantum technology is approaching a level of maturity, recently demonstrated in space-borne experiments and in-field measurements, which would allow for adoption by non-specialist users. Parallel advancements made in microprocessor-based electronics and database software can be combined to create robust, versatile and modular experimental monitoring systems. Here, we describe a monitoring network used across a number of cold atom laboratories with a shared laser system. The ability to diagnose malfunction, unexpected or unintended behaviour and passively collect data for key experimental parameters, such as vacuum chamber pressure, laser beam power, or resistances of important conductors, significantly reduces debugging time. This allows for efficient control over a number of experiments and remote control when access is limited., Comment: 15 pages, 5 figures

We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional latent vectors. We use the deep convolutional neural network (CNN) to represent the non-linear transformation. The parameters of the generator as well as the low-dimensional latent vectors are jointly estimated only from the undersampled measurements. This approach is different from traditional CNN approaches that require extensive fully sampled training data. We penalize the norm of the gradients of the non-linear mapping to constrain the manifold to be smooth, while temporal gradients of the latent vectors are penalized to obtain a smoothly varying time-series. The proposed scheme brings in the spatial regularization provided by the convolutional network. The main benefit of the proposed scheme is the improvement in image quality and the orders-of-magnitude reduction in memory demand compared to traditional manifold models. To minimize the computational complexity of the algorithm, we introduce an efficient progressive training-in-time approach and an approximate cost function. These approaches speed up the image reconstructions and offers better reconstruction performance.

We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent. We penalize the norm of the gradient of the generator to encourage the learning of a smooth surface/manifold, while temporal gradients of the latent vectors are penalized to encourage the time series to be smooth. The main benefits of the proposed scheme are (a) the quite significant reduction in memory demand compared to the analysis based SToRM model, and (b) the spatial regularization brought in by the CNN model. We also introduce efficient progressive approaches to minimize the computational complexity of the algorithm.

The radiance of thermal emission, as described by Planck law, depends only on the emissivity and temperature of a body, and increases monotonically with the temperature rise at any emitted wavelength. Nonthermal radiation, such as photoluminescence, is a fundamental light matter interaction that conventionally involves the absorption of an energetic photon, thermalization, and the emission of a redshifted photon. Such a quantum process is governed by rate conservation, which is contingent on the quantum efficiency. In the past, the role of rate conservation for significant thermal excitation had not been studied. Recently, we presented the theory and an experimental demonstration that showed, in contrast to thermal emission, that the PL rate is conserved when the temperature increases while each photon is blueshifted. A further rise in temperature leads to an abrupt transition to thermal emission where the photon rate increases sharply. We also demonstrated how such thermally enhanced PL, TEPL, generates orders of magnitude more energetic photons than thermal emission at similar temperatures. These findings show that TEPL is an ideal optical heat pump that can harvest thermal losses in photovoltaics with a maximal theoretical efficiency of 70%, and practical concepts potentially reaching 45% efficiency. Here we move the TEPL concept onto the engineering level and present Cr:Nd:YAG as device grade PL material, absorbing solar radiation up to 1 micrometer wavelength and heated by thermalization of energetic photons. Its blueshifted emission, which can match GaAs cells, is 20% of the absorbed power. Based on a detailed balance simulation, such a material coupled with proper photonic management can reach 34% power conversion efficiency. These results raise confidence in the potential of TEPL becoming a disruptive technology in photovoltaics.

The challenge in solar energy today is not the cost of photovoltaic (PV) electricity generation, already competing with fossil fuel prices, but rather utility-scale energy storage and flexibility in supply. Low-cost thermal energy storage (TES) exists but relies on expensive heat engines. Here, we introduce the concept of luminescent solar power (LSP), where sunlight is absorbed in a photoluminescent (PL) absorber, followed by red-shifted PL emission matched to an adjacent PV cell's band-edge. This way the PV cell operates nearly as efficiently as under direct illumination, but with minimal excessive heat. The PL-absorber temperature rises due to thermalization, allowing it to store the excessive heat, which can later be converted into electricity. Tailored luminescent materials that support an additional 1.5kWh PV-electricity for every 1 kWh of (virtual) heat engine-electricity, with a dynamic shift between the two sources are experimentally demonstrated. Such an ideal hybrid system may lead to a potential reduction in the cost of electricity for a base-load solution.

We propose a unifying general framework of quantitative primal and dual sufficient and necessary error bound conditions covering linear and nonlinear, local and global settings. The function is not assumed to possess any particular structure apart from the standard assumptions of lower semicontinuity in the case of sufficient conditions and (in some cases) convexity in the case of necessary conditions. We expose the roles of the assumptions involved in the error bound assertions, in particular, on the underlying space: general metric, normed, Banach or Asplund. Employing special collections of slope operators, we introduce a succinct form of sufficient error bound conditions, which allows one to combine in a single statement several different assertions: nonlocal and local primal space conditions in complete metric spaces, and subdifferential conditions in Banach and Asplund spaces., Comment: 27 pages

The Portable Extensible Toolkit for Scientific computation (PETSc) library delivers scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization.The PETSc design for performance portability addresses fundamental GPU accelerator challenges and stresses flexibility and extensibility by separating the programming model used by the application from that used by the library, and it enables application developers to use their preferred programming model, such as Kokkos, RAJA, SYCL, HIP, CUDA, or OpenCL, on upcoming exascale systems. A blueprint for using GPUs from PETSc-based codes is provided, and case studies emphasize the flexibility and high performance achieved on current GPU-based systems., Comment: 15 pages, 10 figures, 2 tables

For mixed-initiative control between cyber-physical systems (CPS) and its users, it is still an open question how machines can safely hand over control to humans. In this work, we propose a concept to provide technological support that uses formal methods from AI -- description logic (DL) and automated planning -- to predict more reliably when a hand-over is necessary, and to increase the advance notice for handovers by planning ahead of runtime. We combine this with methods from human-computer interaction (HCI) and natural language generation (NLG) to develop solutions for safe and smooth handovers and provide an example autonomous driving scenario. A study design is proposed with the assessment of qualitative feedback, cognitive load and trust in automation., Comment: In Proceedings of Workshop at CHI