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We present a protocol to implement a spin-1 chain in Rydberg systems using three Rydberg states close to a F\"orster resonance. In addition to dipole-dipole interactions, strong van der Waals interactions naturally appear due to the presence of the F\"orster resonance and give rise to a highly tunable Hamiltonian. The resulting phase diagram is studied using the infinite density-matrix renormalization group and reveals a highly robust Haldane phase -- a prime example of a symmetry protected topological phase. We find experimentally accessible parameters to probe the Haldane phase in current Rydberg systems, and demonstrate an efficient adiabatic preparation scheme. This paves the way to probe the remarkable properties of spin fractionalization in the Haldane phase., Comment: 11 pages, 8 figures

Scientific workflows have become highly heterogenous, leveraging distributed facilities such as High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML), scientific instruments (data-driven pipelines) and edge computing. As a result, Identity and Access Management (IAM) and Cybersecurity challenges across the diverse hardware and software stacks are growing. Nevertheless, scientific productivity relies on lowering access barriers via seamless, single sign-on (SSO) and federated login while ensuring access controls and compliance. We present an implementation of a federated IAM solution, which is coupled with multiple layers of security controls, multi-factor authentication, cloud-native protocols, and time-limited role-based access controls (RBAC) that has been co-designed and deployed for the Isambard-AI and HPC supercomputing Digital Research Infrastructures (DRIs) in the UK. Isambard DRIs as a national research resource are expected to comply with regulatory frameworks. Implementation details for monitoring, alerting and controls are outlined in the paper alongside selected user stories for demonstrating IAM workflows for different roles., Comment: 9 pages, 2 figures

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

The Big Data explosion has necessitated the development of search algorithms that scale sub-linearly in time and memory. While compression algorithms and search algorithms do exist independently, few algorithms offer both, and those which do are domain-specific. We present panCAKES, a novel approach to compressive search, i.e., a way to perform $k$-NN and $\rho$-NN search on compressed data while only decompressing a small, relevant, portion of the data. panCAKES assumes the manifold hypothesis and leverages the low-dimensional structure of the data to compress and search it efficiently. panCAKES is generic over any distance function for which the distance between two points is proportional to the memory cost of storing an encoding of one in terms of the other. This property holds for many widely-used distance functions, e.g. string edit distances (Levenshtein, Needleman-Wunsch, etc.) and set dissimilarity measures (Jaccard, Dice, etc.). We benchmark panCAKES on a variety of datasets, including genomic, proteomic, and set data. We compare compression ratios to gzip, and search performance between the compressed and uncompressed versions of the same dataset. panCAKES achieves compression ratios close to those of gzip, while offering sub-linear time performance for $k$-NN and $\rho$-NN search. We conclude that panCAKES is an efficient, general-purpose algorithm for exact compressive search on large datasets that obey the manifold hypothesis. We provide an open-source implementation of panCAKES in the Rust programming language.

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.

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.

Current research in breast cancer Magnetic Resonance Imaging (MRI), especially with Artificial Intelligence (AI), faces challenges due to the lack of expert segmentations. To address this, we introduce the MAMA-MIA dataset, comprising 1506 multi-center dynamic contrast-enhanced MRI cases with expert segmentations of primary tumors and non-mass enhancement areas. These cases were sourced from four publicly available collections in The Cancer Imaging Archive (TCIA). Initially, we trained a deep learning model to automatically segment the cases, generating preliminary segmentations that significantly reduced expert segmentation time. Sixteen experts, averaging 9 years of experience in breast cancer, then corrected these segmentations, resulting in the final expert segmentations. Additionally, two radiologists conducted a visual inspection of the automatic segmentations to support future quality control studies. Alongside the expert segmentations, we provide 49 harmonized demographic and clinical variables and the pretrained weights of the well-known nnUNet architecture trained using the DCE-MRI full-images and expert segmentations. This dataset aims to accelerate the development and benchmarking of deep learning models and foster innovation in breast cancer diagnostics and treatment planning., Comment: 15 paes, 7 figures, 3 tables

We report the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on germanium, measured at the Spallation Neutron Source at Oak Ridge National Laboratory. The Ge-Mini detector of the COHERENT collaboration employs large-mass, low-noise, high-purity germanium spectrometers, enabling excellent energy resolution, and an analysis threshold of 1.5 keV electron-equivalent ionization energy. We observe a on-beam excess of 20.6$_{+7.1}^{-6.3}$ counts with a total exposure of 10.22 GWhkg and we reject the no-CEvNS hypothesis with 3.9 sigma significance. The result agrees with the predicted standard model of particle physics signal rate within 2 sigma., Comment: 7 pages, 5 figures

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location., Comment: This manuscript (arXiv:2401.13023) is published with a closely related companion entitled, Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which can be found at the following link, arXiv:2401.13022

One of the main difficulties remains the collaboration between the various experts involved in designing the Learning Games (LG). Our literature review focuses on the pitfalls and principles that have been identified by various authors in learning games design. Based on this review, a prototype was designed to support the LG design process and to study more precisely the collaboration between actors (teachers, researchers, game designers, data analyst and computer scientist). Indeed, according to the state of the art, the skills and knowledge involved in design are difficult to integrate. It has been tested in a real-world scenario for designing learning games to teach algorithmic. Through participant observation in thirty-three workshops involving nine experts, we were able to identify recurring pitfalls as we applied the recommendations in the literature. The analysis of these workshops led to propose eight principles aimed at facilitating the collaboration between the learning games design process and re-evaluating research on its.

Purpose. Photoplethysmography (PPG) is a non-invasive technique that measures changes in blood flow volume through optical means. Previous research has established the feasibility of PPG peak detection based on the crossover of moving averages. This paper proposes the Smoothed Recuarsive Moving Average Crossover, which eliminates the need for post-processing and nonlinear pre-processing of previous crossover-based peak detectors. The proposed model is advantageous regarding memory and computational complexity, making it attractive for implementations on embedded devices. Methods. Along with this paper, we make available a novel dataset comprising 66 minutes of PPG recordings. The optimization and assessment of the proposed peak detection model use this dataset. Its optimization is accomplished with the simple random search heuristic, while the leave-subject-out cross-validation method provides the means to assess its performance. The source code for all experiments reported in this research is also available in an online repository. Results. The experimental study examines the performance of the proposed model considering different arrangements of the PPG data. The experiments show that the proposed model performs better than the previous crossover-based approach from the literature regarding the precision and recall metrics. More specifically, our model has an average precision of 0.9937 and an average recall of 0.9968. Conclusion. The contribution of this research to the scientific community and literature is twofold. The dataset we collected is open for any researcher, and we improve upon the leading edge on crossover-based PPG peak detection. This improvement comes in terms of performance metrics and computational cost., Comment: 11 pages, 7 figures, 4 tables

We investigate the persistent orientation of asymmetric-top molecules induced by time-delayed THz pulses that are either collinearly or cross polarized. Our theoretical and numerical results demonstrate that the orthogonal configuration outperforms the collinear one, and a significant degree of persistent orientation - approximately 10% at 5 K and nearly 3% at room temperature - may be achieved through parameter optimization. The dependence of the persistent orientation factor on temperature and field parameters is studied in detail. The proposed application of two orthogonally polarized THz pulses is both practical and efficient. Its applicability under standard laboratory conditions lays a solid foundation for future experimental realization of THz-induced persistent molecular orientation., Comment: 11 pages, 6 figures

We consider the potential for a 10-kg undoped cryogenic CsI detector operating at the Spallation Neutron Source to measure coherent elastic neutrino-nucleus scattering and its sensitivity to discover new physics beyond the standard model. Through a combination of increased event rate, lower threshold, and good timing resolution, such a detector would significantly improve on past measurements. We considered tests of several beyond-the-standard-model scenarios such as neutrino non-standard interactions and accelerator-produced dark matter. This detector's performance was also studied for relevant questions in nuclear physics and neutrino astronomy, namely the weak charge distribution of CsI nuclei and detection of neutrinos from a core-collapse supernova.

We investigate whether global toroid patterns and the local magnetic field topology of solar active region AR12673 together can hindcast occurrence of the biggest X-flare of solar cycle (SC)-24. Magnetic toroid patterns (narrow latitude-belts warped in longitude, in which active regions are tightly bound) derived from surface distributions of active regions, prior/during AR12673 emergence, reveal that the portions of the South-toroid containing AR12673 was not tipped-away from its north-toroid counterpart at that longitude, unlike the 2003 Halloween storms scenario. During the minimum-phase there were too few emergences to determine multi-mode longitudinal toroid patterns. A new emergence within AR12673 produced a complex/non-potential structure, which led to rapid build-up of helicity/winding that triggered the biggest X-flare of SC-24, suggesting that this minimum-phase storm can be anticipated several hours before its occurrence. However, global patterns and local dynamics for a peak-phase storm, such as that from AR11263, behaved like 2003 Halloween storms, producing the third biggest X-flare of SC-24. AR11263 was present at the longitude where the North/South toroids tipped-away from each other. While global toroid patterns indicate that pre-storm features can be forecast with a lead-time of a few months, its application on observational data can be complicated by complex interactions with turbulent flows. Complex/non-potential field structure development hours before the storm are necessary for short term prediction. We infer that minimum-phase storms cannot be forecast accurately more than a few hours ahead, while flare-prone active regions in peak-phase may be anticipated much earlier, possibly months ahead from global toroid patterns.

The ongoing Big Data explosion has created a demand for efficient and scalable algorithms for similarity search. Most recent work has focused on \textit{approximate} $k$-NN search, and while this may be sufficient for some applications, \textit{exact} $k$-NN search would be ideal for many applications. We present CAKES, a set of three novel, exact algorithms for $k$-NN search. CAKES's algorithms are generic over \textit{any} distance function, and they \textit{do not} scale with the cardinality or embedding dimension of the dataset, but rather with its metric entropy and fractal dimension. We test these claims on datasets from the ANN-Benchmarks suite under commonly-used distance functions, as well as on a genomic dataset with Levenshtein distance and a radio-frequency dataset with Dynamic Time Warping distance. We demonstrate that CAKES exhibits near-constant scaling with cardinality on data conforming to the manifold hypothesis, and has perfect recall on data in \textit{metric} spaces. We also demonstrate that CAKES exhibits significantly higher recall than state-of-the-art $k$-NN search algorithms when the distance function is not a metric. Additionally, we show that indexing and tuning time for CAKES is an order of magnitude, or more, faster than state-of-the-art approaches. We conclude that CAKES is a highly efficient and scalable algorithm for exact $k$-NN search on Big Data. We provide a Rust implementation of CAKES., Comment: As submitted to ACM TALG

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.