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Microcontroller-based embedded systems are vulnerable to memory safety errors and must be robust and responsive because they are often used in unmanned and mission-critical scenarios. The Rust programming language offers an appealing compile-time solution for memory safety but leaves stack overflows unresolved and foils zero-latency interrupt handling. We present Hopter, a Rust-based embedded operating system (OS) that provides memory safety, sys- tem robustness, and interrupt responsiveness to embedded systems while requiring minimal application cooperation. Hopter executes Rust code under a novel finite-stack semantics that converts stack overflows into Rust panics, enabling recovery from fatal errors through stack unwinding and restart. Hopter also employs a novel mechanism called soft-locks so that the OS never disables interrupts. We compare Hopter with other well-known embedded OSes using controlled workloads and report our experience using Hopter to develop a flight control system for a miniature drone and a gateway system for Internet of Things (IoT). We demonstrate that Hopter is well-suited for resource-constrained microcontrollers and supports error recovery for real-time workloads. 

ABSTRACT In starvingBacillus subtilisbacteria,the initiation of two survival programs—biofilm formation and sporulation—is controlled by the same phosphorylated master regulator, Spo0A~P. Its gene,spo0A,is transcribed from two promoters, Pvand Ps,that are, respectively, regulated by RNA polymerase (RNAP) holoenzymes bearing σ^Aand σ^H. Notably, transcription is directly autoregulated by Spo0A~P binding sites known as 0A1, 0A2, and 0A3 box, located in between the two promoters. It remains unclear whether, at the onset of starvation, these boxes activate or repressspo0Aexpression, and whether the Spo0A~P transcriptional feedback plays a role in the increase inspo0Aexpression. Based on the experimental data of the promoter activities under systematic perturbation of the promoter architecture, we developed a biophysical model of transcriptional regulation ofspo0Aby Spo0A~P binding to each of the 0A boxes. The model predicts that Spo0A~P binding to its boxes does not affect the RNAP recruitment to the promoters but instead affects the transcriptional initiation rate. Moreover, the effects of Spo0A~P binding to 0A boxes are mainly repressive and saturated early at the onset of starvation. Therefore, the increase inspo0Aexpression is mainly driven by the increase in RNAP holoenzyme levels. Additionally, we reveal that Spo0A~P affinity to 0A boxes is strongest at 0A3 and weakest at 0A2 and that there are attractive forces between the occupied 0A boxes. Our findings, in addition to clarifying how the sporulation master regulator is controlled, offer a framework to predict regulatory outcomes of complex gene-regulatory mechanisms. IMPORTANCECell differentiation is often critical for survival. In bacteria, differentiation decisions are controlled by transcriptional master regulators under transcriptional feedback control. Therefore, understanding how master regulators are transcriptionally regulated is required to understand differentiation. However, in many cases, the underlying regulation is complex, with multiple transcription factor binding sites and multiple promoters, making it challenging to dissect the exact mechanisms. Here, we address this problem for theBacillus subtilismaster regulator Spo0A. Using a biophysical model, we quantitatively characterize the effect of individual transcription factor binding sites on eachspo0Apromoter. Furthermore, the model allows us to identify the specific transcription step that is affected by transcription factor binding. Such a model is promising for the quantitative study of a wide range of master regulators involved in transcriptional feedback. 

Positronium lifetime imaging (PLI) is a newly demonstrated technique possible with time-of-flight (TOF) positron emission tomography (PET), capable of producing an image reflecting the lifetime of the positron, more precisely ortho-positronium (o-Ps), before annihilation, in addition to the traditional uptake image of the PET tracer. Due to the limited time resolution of TOF-PET systems and the added complexities in physics and statistics, lifetime image reconstruction presents a challenge. Recently, we described a maximum-likelihood approach for PLI by considering only o-Ps. In real-world scenarios, other populations of positrons that exhibit different lifetimes also exist. This paper introduces a novel two-component model aimed at enhancing the accuracy of o-Ps lifetime images. Through simulation studies, we compare this new model with the existing single-component model and demonstrate its superior performance in accurately capturing complex lifetime distributions. 

The positronium lifetime imaging (PLI) reconstruction is a technique used in time-of-flight (TOF) positron emission tomography (PET) imaging that involves measuring the lifespan of positronium, which is a metastable electron-positron pair that arises when a PET molecule releases a positron, prior to its annihilation. We have previously developed a maximum likelihood (ML) algorithm for PLI reconstruction and demonstrated that it can generate quantitatively accurate lifetime images for a 570 ps (pico-seconds) TOF PET system. In this study, we conducted further investigations into the statistical properties of the algorithm, including the variability of the reconstruction results, the sensitivity of the algorithm to the number of acquired PLI events and its robustness to hyperparameter choices. Our findings indicate that the proposed ML method produces sufficiently stable lifetime images to enable reliable distinction of regions of interest. Moreover, the number of PLI events required to produce quantitatively accurate lifetime images is computationally plausible. These results demonstrate the potential of our ML algorithm for advancing the capabilities of TOF PET imaging. 

Abstract Quantum systems have entered a competitive regime in which classical computers must make approximations to represent highly entangled quantum states^1,2. However, in this beyond-classically-exact regime, fidelity comparisons between quantum and classical systems have so far been limited to digital quantum devices^2–5, and it remains unsolved how to estimate the actual entanglement content of experiments⁶. Here, we perform fidelity benchmarking and mixed-state entanglement estimation with a 60-atom analogue Rydberg quantum simulator, reaching a high-entanglement entropy regime in which exact classical simulation becomes impractical. Our benchmarking protocol involves extrapolation from comparisons against an approximate classical algorithm, introduced here, with varying entanglement limits. We then develop and demonstrate an estimator of the experimental mixed-state entanglement⁶, finding our experiment is competitive with state-of-the-art digital quantum devices performing random circuit evolution^2–5. Finally, we compare the experimental fidelity against that achieved by various approximate classical algorithms, and find that only the algorithm we introduce is able to keep pace with the experiment on the classical hardware we use. Our results enable a new model for evaluating the ability of both analogue and digital quantum devices to generate entanglement in the beyond-classically-exact regime, and highlight the evolving divide between quantum and classical systems. 

Quantum systems have entered a competitive regime in which classical computers must make approximations to represent highly entangled quantum states1,2. However, in this beyond-classically-exact regime, fidelity comparisons between quantum and classical systems have so far been limited to digital quantum devices2,3,4,5, and it remains unsolved how to estimate the actual entanglement content of experiments6. Here, we perform fidelity benchmarking and mixed-state entanglement estimation with a 60-atom analogue Rydberg quantum simulator, reaching a high-entanglement entropy regime in which exact classical simulation becomes impractical. Our benchmarking protocol involves extrapolation from comparisons against an approximate classical algorithm, introduced here, with varying entanglement limits. We then develop and demonstrate an estimator of the experimental mixed-state entanglement6, finding our experiment is competitive with state-of-the-art digital quantum devices performing random circuit evolution2,3,4,5. Finally, we compare the experimental fidelity against that achieved by various approximate classical algorithms, and find that only the algorithm we introduce is able to keep pace with the experiment on the classical hardware we use. Our results enable a new model for evaluating the ability of both analogue and digital quantum devices to generate entanglement in the beyond-classically-exact regime, and highlight the evolving divide between quantum and classical systems.