Inverse medium scattering is an ill-posed, nonlinear wave-based imaging problem arising in medical imaging, remote sensing, and non-destructive testing. Machine learning (ML) methods offer increased inference speed and flexibility in capturing prior knowledge of imaging targets relative to classical optimization-based approaches; however, they perform poorly in regimes where the scattering behavior is highly nonlinear. A key limitation is that ML methods struggle to incorporate the physics governing the scattering process, which are typically inferred implicitly from the training data or loosely enforced via architectural design. In this paper, we present a method that endows a machine learning framework with explicit knowledge of problem physics, in the form of a differentiable solver representing the forward model. The proposed method progressively refines reconstructions of the scattering potential using measurements at increasing wave frequencies, following a classical strategy to stabilize recovery. Empirically, we find that our method provides high-quality reconstructions at a fraction of the computational or sampling costs of competing approaches.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum–classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum–classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum–classical HPC ecosystems.
Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep learning model for automated detection of openly dumped dispersed MSW (ODD-MSW) via crowdsourced UAV imagery, trained and evaluated across 29 regions in 10 Sub-Saharan African countries, encompassing diverse environmental contexts. A deep learning model trained on manually annotated image tiles achieved an overall accuracy of 92.87% (F1 = 0.927) in detecting ODD-MSW across all study regions. Applied without any site-specific retraining to 25 independent OpenAerialMap scenes from 21 cities in 19 countries on five continents, the frozen model retained an F1 score of 0.867, providing direct evidence of transferability beyond the training domain. Predicted distributions reveal heterogeneous accumulation patterns, ranging from localized hotspots - often along waterways, where waste can exacerbate flood and public health risks - to more dispersed litter across urban areas. Waste accumulation is most strongly associated with population density (Spearman rho = 0.674, p < 0.001 ) and indicators of lack of local infrastructure access ( rho = 0.700, p < 0.001 ), whereas its relationship with broader measures of regional development is weaker and not statistically significant ( rho = 0.215, p = 0.26 ), highlighting the importance of fine-scale data for understanding localized waste dynamics. By releasing the model, this study provides a ready-to-use tool for UAV imagery collected by municipalities and local mapping communities, enabling ODD-MSW monitoring without extensive technical expertise. This approach empowers local practitioners to convert UAV imagery into actionable insights, supporting targeted interventions and improved municipal solid waste management across Sub-Saharan Africa.
Maternal hypothalamic-pituitary-adrenal axis functioning has important implications for maternal and offspring health, yet findings on cortisol responsivity to acute stress during pregnancy are mixed. Further, little is known about how childhood stress exposure relates to physiological stress regulation during this period, particularly among Black and White women. The present study leveraged multiple waves of prospectively gathered data from a large population-based sample to examine associations between degree of exposure to subsistence-, safety- and caregiving-related stress across childhood and cortisol responsivity during a later pregnancy. Participants (N = 338, 69.7% Black, 26.7% White) were enrolled in the longitudinal Pittsburgh Girls Study in middle childhood. Caregivers reported annually on exposure to a range of stressors when participants were 7–17 years of age. During a prenatal laboratory visit in which participants were between 22 and 31 years of age, participants completed the Trier Social Stress Test (TSST) and provided saliva samples for cortisol assay. Adjusting for covariates, the TSST did not produce a significant increase in cortisol levels over time, though 28.3% of participants were classified as responders. Greater exposure to subsistence-related stress (e.g., resource strain, housing instability) across childhood was associated with blunted cortisol levels during pregnancy. Findings suggest that childhood exposure to subsistence-related stress may have enduring effects on physiological stress regulation and underscore the potential value of targeted preventive interventions to promote intergenerational health. Results also highlight the relevance of public health initiatives aimed at reducing population-level differences in subsistence-related stress exposure.
Picosecond level timing resolution is a prerequisite capability for improved coincidence matching, time-of-flight measurements, and secondary vertex reconstruction. Here, we present the specification, design, and simulation results for a new Application Specific Integrated Circuit (ASIC), called PSEC6, in the TSMC 65nm process. It features 8 channels, a maximum sampling rate of 40 GSa/s, a buffer length of 204.8 nanoseconds, and a 10.24 GHz Phase Locked Loop (PLL), which is the first of its kind in the 65nm CMOS process. The event readout rate is 32 kHz, with the digitization done by an off-chip Analog-to-Digital Converter (ADC). Simulations predict a 4.0 GHz analog input bandwidth and 20 mW per channel during sampling; the 10.24 GHz PLL has a predicted jitter of 550 fs RMS at 15.7 mW. The paper describes the sampling architecture, chip signal paths, PLL design, and presents simulation results.
