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Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and integrations are difficult to achieve due to the challenges of coordinating and deploying heterogeneous software components on diverse and massive platforms. We present the ExaWorks project, which addresses many of these challenges. We developed a workflow Software Development Toolkit (SDK), a curated collection of workflow technologies that can be composed and interoperated through a common interface, engineered following current best practices, and specifically designed to work on HPC platforms. ExaWorks also developed PSI/J, a job management abstraction API, to simplify the construction of portable software components and applications that can be used over various HPC schedulers. The PSI/J API is a minimal interface for submitting and monitoring jobs and their execution state across multiple and commonly used HPC schedulers. We also describe several leading and innovative workflow examples of ExaWorks tools used on DOE leadership platforms. Furthermore, we discuss how our project is working with the workflow community, large computing facilities, and HPC platform vendors to address the requirements of workflows sustainably at the exascale.

Detecting arousals in sleep is essential for diagnosing sleep disorders. However, using Machine Learning (ML) in clinical practice is impeded by fundamental issues, primarily due to mismatches between clinical protocols and ML methods. Clinicians typically annotate only the onset of arousals, while ML methods rely on annotations for both the beginning and end. Additionally, there is no standardized evaluation methodology tailored to clinical needs for arousal detection models. This work addresses these issues by introducing a novel post-processing and evaluation framework emphasizing approximate localization and precise event count (ALPEC) of arousals. We recommend that ML practitioners focus on detecting arousal onsets, aligning with clinical practice. We examine the impact of this shift on current training and evaluation schemes, addressing simplifications and challenges. We utilize a novel comprehensive polysomnographic dataset (CPS) that reflects the aforementioned clinical annotation constraints and includes modalities not present in existing polysomnographic datasets. We release the dataset alongside this paper, demonstrating the benefits of leveraging multimodal data for arousal onset detection. Our findings significantly contribute to integrating ML-based arousal detection in clinical settings, reducing the gap between technological advancements and clinical needs.

Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP and broader AI-enabled scientific research community., Comment: 12 pages, 2 figures

Free electrons are a widespread and universal source of electromagnetic fields. The past decades witnessed ever-growing control over many aspects of electron-generated radiation, from the incoherent emission produced by X-ray tubes to the exceptional brilliance of free-electron lasers. Reduced to the elementary process of quantized energy exchange between individual electrons and the electromagnetic field, electron beams may facilitate future sources of tunable quantum light. However, the quantum features of such radiation are tied to the correlation of the particles, calling for the joint electronic and photonic state to be explored for further applications. Here, we demonstrate the coherent parametric generation of non-classical states of light by free electrons. We show that the quantized electron energy loss heralds the number of photons generated in a dielectric waveguide. In Hanbury-Brown-Twiss measurements, an electron-heralded single-photon state is revealed via antibunching intensity correlations, while two-quantum energy losses of individual electrons yield pronounced two-photon coincidences. The approach facilitates the tailored preparation of higher-number Fock and other optical quantum states based on controlled interactions with free-electron beams.

In this paper, we present our first proposal of a machine learning system for the classification of freshwater snails of the genus Radomaniola. We elaborate on the specific challenges encountered during system design, and how we tackled them; namely a small, very imbalanced dataset with a high number of classes and high visual similarity between classes. We then show how we employed triplet networks and the multiple input modalities of images, measurements, and genetic information to overcome these challenges and reach a performance comparable to that of a trained domain expert., Comment: Spotlight at ICML 2024 AI for Science workshop

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.

Concentric Tube Robots (CTRs) have garnered significant interest within the surgical robotics community because of their flexibility, dexterity, and ease of miniaturization. However, mastering the unique kinematics and design principles of CTRs can be challenging for newcomers to the field. In this paper, we present an educational kit aimed at lowering the barriers to entry into concentric tube robot research. Our goal is to provide accessible learning resources for CTRs, bridging the knowledge gap between traditional robotic arms and these specialized devices. The proposed kit includes (1) An open-source design and assembly instructions for an economical (cost of materials $\approx$ 700 USD) modular CTR; (2) A set of self-study materials to learn the basics of CTR modeling and control, including automatically-graded assignments. To evaluate the effectiveness of our educational kit, we conducted a human subjects study involving first-year graduate students in engineering. Over a four-week period, participants -- none of whom had any prior knowledge of concentric tube robots -- successfully built their first CTR using the provided materials, implemented the robot's kinematics in MATLAB, and conducted a tip-tracking experiment with an optical tracking device. Our findings suggest that the proposed kit facilitates learning and hands-on experience with CTRs, and furthermore, it has the potential to help early-stage graduate students get rapidly started with CTR research. By disseminating these resources, we hope to broaden participation in concentric tube robot research to a wider a more diverse group of researchers.

Cloud computing is a ubiquitous solution to handle today's complex computing demands. However, it comes with data privacy concerns, as the cloud service provider has complete access to code and data running on their infrastructure. VM-based Trusted Execution Environments (TEEs) are a promising solution to solve this issue. They provide strong isolation guarantees to lock out the cloud service provider, as well as an attestation mechanism to enable the end user to verify their trustworthiness. Attesting the whole boot chain of a VM is a challenging task that requires modifications to several software components. While there are open source solutions for the individual components, the tooling and documentation for properly integrating them remains scarce. In this paper, we try to fill this gap by elaborating on two common boot workflows and providing open source tooling to perform them with low manual effort. The first workflow assumes that the VM image does only require integrity but not confidentiality, allowing for an uninterrupted boot process. The second workflow covers booting a VM with an encrypted root filesystem, requiring secure provisioning of the decryption key during early boot. While our tooling targets AMD Secure Encrypted Virtualization (SEV) VMs, the concepts also apply to other VM-based TEEs such as Intel Trusted Domain Extensions (TDX)., Comment: to appear at SysTEX'24

The System for Measuring Overlap with Gas (SMOG2) at the LHCb detector enables the study of fixed-target ion-ion collisions at relativistic energies ($\sqrt{s_{\rm NN}}\sim100$ GeV in the centre-of-mass). With input from \textit{ab initio} calculations of the structure of $^{16}$O and $^{20}$Ne, we compute 3+1D hydrodynamic predictions for the anisotropic flow of Pb+Ne and Pb+O collisions, to be tested with upcoming LHCb data. This will allow the detailed study of quark-gluon plasma (QGP) formation as well as experimental tests of the predicted nuclear shapes. Elliptic flow ($v_2$) in Pb+Ne collisions is greatly enhanced compared to the Pb+O baseline due to the shape of $^{20}$Ne, which is deformed in a bowling-pin geometry. Owing to the large $^{208}$Pb radius, this effect is seen in a broad centrality range, a unique feature of this collision configuration. Larger elliptic flow further enhances the quadrangular flow ($v_4$) of Pb+Ne collisions via non-linear coupling, and impacts the sign of the kurtosis of the elliptic flow vector distribution ($c_2\{4\}$). Exploiting the shape of $^{20}$Ne proves thus an ideal method to investigate the formation of QGP in fixed-target experiments at LHCb, and demonstrates the power of SMOG2 as a tool to image nuclear ground states.

We consider two finite population Markov chain models, the two-island Wright-Fisher model with mutation, and the seed-bank model with mutation. Despite the relatively simple descriptions of the two processes, the the exact form of their stationary distributions is in general intractable. For each of the two models we provide two approximation theorems with explicit upper bounds on the distance between the stationary distributions of the finite population Markov chains, and either the stationary distribution of a two-island diffusion model, or the beta distribution. We show that the order of the bounds, and correspondingly the appropriate choice of approximation, depends upon the relative sizes of mutation and migration. In the case where migration and mutation are of the same order, the suitable approximation is the two-island diffusion model, and if migration dominates mutation, then the weighted average of both islands is well approximated by a beta random variable. Our results are derived from a new development of Stein's method for the stationary distribution of the two-island diffusion model for the weak migration results, and utilising the existing framework for Stein's method for the Dirichlet distribution., Comment: 37 pages

Breaking the error-threshold would mark a milestone in establishing quantum advantage for a wide range of relevant problems. One possible route is to encode information redundantly in a logical qubit by combining several noisy qubits, providing an increased robustness against external perturbations. We propose a setup for a logical qubit built from superconducting qubits (SCQs) coupled to a microwave cavity-mode. Our design is based on a recently discovered geometric stabilizing mechanism in the Bose-Hubbard wheel (BHW), which manifests as energetically well-separated clusters of many-body eigenstates. We investigate the impact of experimentally relevant perturbations between SCQs and the cavity on the spectral properties of the BHW. We show that even in the presence of typical fabrication uncertainties, the occurrence and separation of clustered many-body eigenstates is extremely robust. Introducing an additional, frequency-detuned SCQ coupled to the cavity yields duplicates of these clusters, that can be split up by an on-site potential. We show that this allows to (i) redundantly encode two logical qubit states that can be switched and read out efficiently and (ii) can be separated from the remaining many-body spectrum via geometric stabilization. We demonstrate at the example of an X-gate that the proposed logical qubit reaches single qubit-gate fidelities $>0.999$ in experimentally feasible temperature regimes $\sim10-20\,\mathrm{mK}$., Comment: 20 pages, 11 figures

In vibration-based condition monitoring, optimal filter design improves fault detection by enhancing weak fault signatures within vibration signals. This process involves optimising a derived objective function from a defined objective. The objectives are often based on proxy health indicators to determine the filter's parameters. However, these indicators can be compromised by irrelevant extraneous signal components and fluctuating operational conditions, affecting the filter's efficacy. Fault detection primarily uses the fault component's prominence in the squared envelope spectrum, quantified by a squared envelope spectrum-based signal-to-noise ratio. New optimal filter objective functions are derived from the proposed generalised envelope spectrum-based signal-to-noise objective for machines operating under variable speed conditions. Instead of optimising proxy health indicators, the optimal filter coefficients of the formulation directly maximise the squared envelope spectrum-based signal-to-noise ratio over targeted frequency bands using standard gradient-based optimisers. Four derived objective functions from the proposed objective effectively outperform five prominent methods in tests on three experimental datasets., Comment: 27 pages, 15 figures, tables 1, submitted MSSP review

Multifidelity surrogate modelling combines data of varying accuracy and cost from different sources. It strategically uses low-fidelity models for rapid evaluations, saving computational resources, and high-fidelity models for detailed refinement. It improves decision-making by addressing uncertainties and surpassing the limits of single-fidelity models, which either oversimplify or are computationally intensive. Blending high-fidelity data for detailed responses with frequent low-fidelity data for quick approximations facilitates design optimisation in various domains. Despite progress in interpolation, regression, enhanced sampling, error estimation, variable fidelity, and data fusion techniques, challenges persist in selecting fidelity levels and developing efficient data fusion methods. This study proposes a new fusion approach to construct multi-fidelity surrogate models by constructing gradient-only surrogates that use only gradients to construct regression surfaces. Results are demonstrated on foundational example problems that isolate and illustrate the fusion approach's efficacy, avoiding the need for complex examples that obfuscate the main concept., Comment: 8 pages, 4 figures, SACAM2024 Conference, 22-23 January 2024

We propose a tangible experimental scheme for demonstrating quantum entanglement between swift electrons and light, relying on coherent cathodoluminescence for photon generation in a transmission electron microscope, and a quantum eraser setup for formation and verification of entanglement. The entanglement of free electrons with light is key to developing free-electron quantum optics and its potential applications such as quantum sensing, novel photonic and electron state generation, and entanglement between free electrons.

Whether or not femto-scale droplets of quark-gluon plasma (QGP) are formed in so-called small systems at high-energy colliders is a pressing question in the phenomenology of the strong interaction. For proton-proton or proton-nucleus collisions the answer is inconclusive due to the large theoretical uncertainties plaguing the description of these processes. While upcoming data on collisions of $^{16}$O nuclei may mitigate these uncertainties in the near future, here we demonstrate the unique possibilities offered by complementing $^{16}$O$^{16}$O data with collisions of $^{20}$Ne ions. We couple both NLEFT and PGCM ab initio descriptions of the structure of $^{20}$Ne and $^{16}$O to hydrodynamic simulations of $^{16}$O$^{16}$O and $^{20}$Ne$^{20}$Ne collisions at high energy. We isolate the imprints of the bowling-pin shape of $^{20}$Ne on the collective flow of hadrons, which can be used to perform quantitative tests of the hydrodynamic QGP paradigm. In particular, we predict that the elliptic flow of $^{20}$Ne$^{20}$Ne collisions is enhanced by as much as 1.170(8)$_{\rm stat.}$(30)$_{\rm syst.}$ for NLEFT and 1.139(6)$_{\rm stat.}$(39)$_{\rm syst.}$ for PGCM relative to $^{16}$O$^{16}$O collisions for the 1% most central events. At the same time, theoretical uncertainties largely cancel when studying relative variations of observables between two systems. This demonstrates a method based on experiments with two light-ion species for precision characterizations of the collective dynamics and its emergence in a small system., Comment: 17 pages, 14 figures. The Trajectum code can be found at https://sites.google.com/view/govertnijs/trajectum and plotting routines can be found at http://wilkevanderschee.nl/trajectum

Ultracentral heavy ion collisions due to their exceptionally large multiplicity probe an interesting regime of quark-gluon plasma where the size is (mostly) fixed and fluctuations in the initial condition dominate. Spurred by the recent measurement of the CMS collaboration we investigate the driving factors of the increase of transverse momentum, including a complete analysis of the influence of the QCD equation of state. Particularly interesting is the influence of the centrality selection as well as the initial energy deposition., Comment: 4 pages, 5 figures and an appendix. The Trajectum code can be found at https://sites.google.com/view/govertnijs/trajectum and plotting routines can be found at http://wilkevanderschee.nl/trajectum

Existing fluidic soft logic gates for the control of soft robots either rely on extensive manual fabrication processes or expensive printing techniques. In our work, we explore Fused Deposition Modeling for creating fully 3D printed fluidic logic gates. We print a soft bistable valve from thermoplastic polyurethane using a desktop FDM printer. We introduce a new printing nozzle for extruding tubing. Our fabrication strategy reduces the production time of soft bistable valves from 27 hours with replica molding to 3 hours with a FDM printer. Our rapid and cost-effective fabrication process for fluidic logic gates seeks to democratize fluidic circuitry for the control of soft robots., Comment: 6 pages, 8 figures, IEEE RoboSoft 2023