Anthropogenic alluvium: An evidence-based meta-analysis for the UK Holocene

An exploratory meta-analysis of 14C-dated Holocene anthropogenic alluvium (AA) in the UK is presented. AA units were categorized by grain size, catchment area and location, depositional environment, and according to diagnostic criteria linked to recorded types of anthropogenic activity. The oldest AA units date to the Early Bronze Age (c. 4400 cal. BP) and there is an apparent 1500 year lag between the adoption of agriculture (c. 6000 cal. BP) in the UK and any impact on floodplain sedimentation. The earliest influence of farming on UK rivers appears to have been hydrological rather than sedimentological. The mediaeval period was characterized by accelerated sedimentation of fine-grained AA, notably in the smallest catchments. There are some apparent regional differences in the timing of AA formation with earlier prehistoric dates in central and southern parts of the UK

Hypervelocity impact survivability experiments for carbonaceous impactors

We performed a series of hypervelocity impact experiments using carbon-bearing impactors (diamond, graphite, fullerenes, phthalic acid crystals, and Murchison meteorite) into Al plate at velocities between 4.2 and 6.1 km/s. These tests were made to do the following: (1) determine the survivability of carbon forms and organize molecules in low hypervelocity impact; (2) characterize carbonaceous impactor residues; and (3) determine whether or not fullerenes could form from carbonaceous impactors, under our experimental conditions, or survive as impactors. An analytical protocol of field emission SEM imagery, SEM-EDX, laser Raman spectroscopy, single and 2-stage laser mass spectrometry, and laser induced fluorescence (LIF) found the following: (1) diamonds did not survive impact at 4.8 km/s, but were transformed into various forms of disordered graphite; (2) intact, well-ordered graphite impactors did survive impact at 5.9 km/sec, but were only found in the crater bottom centers; the degree of impact-induced disorder in the graphite increases outward (walls, rims, ejecta); (3) phthalic acid crystals were destroyed on impact (at 4.2 km/s, although a large proportion of phthalic acid molecules did survive impact); (4) fullerenes did not form as products of carbonaceous impactors (5.9 - 6.1 km/s, fullerene impactor molecules mostly survived impact at 5.9 km/s; and (5) two Murchison meteorite samples (launched at 4.8 and 5.9 km/s) show preservation of some higher mass polycyclic aromatic hydrocarbons (PAHs) compared with the non-impacted sample. Each impactor type shows unique impactor residue morphologies produced at a given impact velocity. An expanded methodology is presented to announce relatively new analytical techniques together with innovative modifications to other methods that can be used to characterize small impact residues in LDEF craters, in addition to other acquired extraterrestrial samples

Heralded state preparation in a superconducting qubit

We demonstrate high-fidelity, quantum nondemolition, single-shot readout of a superconducting flux qubit in which the pointer state distributions can be resolved to below one part in 1000. In the weak excitation regime, continuous measurement permits the use of heralding to ensure initialization to a fiducial state, such as the ground state. This procedure boosts readout fidelity to 93.9% by suppressing errors due to spurious thermal population. Furthermore, heralding potentially enables a simple, fast qubit reset protocol without changing the system parameters to induce Purcell relaxation.Comment: 5 pages, 5 figure

Decoupling Interrupts From Virtual Machines in Smalltalk

Architecture must work. Given the trends in client-server epistemologies, statisticians dubiously note the evaluation of e-commerce. WIN, our new solution for introspective communication, is the solution to all of these problems

A review of mechanistic learning in mathematical oncology

Mechanistic learning, the synergistic combination of knowledge-driven and data-driven modeling, is an emerging field. In particular, in mathematical oncology, the application of mathematical modeling to cancer biology and oncology, the use of mechanistic learning is growing. This review aims to capture the current state of the field and provide a perspective on how mechanistic learning may further progress in mathematical oncology. We highlight the synergistic potential of knowledge-driven mechanistic mathematical modeling and data-driven modeling, such as machine and deep learning. We point out similarities and differences regarding model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Then, organizing combinations of knowledge- and data-driven modeling into four categories (sequential, parallel, intrinsic, and extrinsic mechanistic learning), we summarize a variety of approaches at the interface between purely data- and knowledge-driven models. Using examples predominantly from oncology, we discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. We see that mechanistic learning, with its intentional leveraging of the strengths of both knowledge and data-driven modeling, can greatly impact the complex problems of oncology. Given the increasing ubiquity and impact of machine learning, it is critical to incorporate it into the study of mathematical oncology with mechanistic learning providing a path to that end. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input dat