Assessing Correlated Truncation Errors in Modern Nucleon-Nucleon Potentials

We test the BUQEYE model of correlated effective field theory (EFT) truncation errors on Reinert, Krebs, and Epelbaum's semi-local momentum-space implementation of the chiral EFT ($\chi$EFT) expansion of the nucleon-nucleon (NN) potential. This Bayesian model hypothesizes that dimensionless coefficient functions extracted from the order-by-order corrections to NN observables can be treated as draws from a Gaussian process (GP). We combine a variety of graphical and statistical diagnostics to assess when predicted observables have a $\chi$EFT convergence pattern consistent with the hypothesized GP statistical model. Our conclusions are: First, the BUQEYE model is generally applicable to the potential investigated here, which enables statistically principled estimates of the impact of higher EFT orders on observables. Second, parameters defining the extracted coefficients such as the expansion parameter $Q$ must be well chosen for the coefficients to exhibit a regular convergence pattern -- a property we exploit to obtain posterior distributions for such quantities. Third, the assumption of GP stationarity across lab energy and scattering angle is not generally met; this necessitates adjustments in future work. We provide a workflow and interpretive guide for our analysis framework, and show what can be inferred about probability distributions for $Q$, the EFT breakdown scale $\Lambda_b$, the scale associated with soft physics in the $\chi$EFT potential $m_{\rm eff}$, and the GP hyperparameters. All our results can be reproduced using a publicly available Jupyter notebook, which can be straightforwardly modified to analyze other $\chi$EFT NN potentials.Comment: 29 pages, 33 figure

Robust and Deterministic Preparation of Bosonic Logical States in a Trapped Ion

Encoding logical qubits in bosonic modes provides a potentially hardware-efficient implementation of fault-tolerant quantum information processing. Recent advancements in trapped ions and superconducting microwave cavities have led to experimental realizations of high-quality bosonic states and demonstrations of error-corrected logical qubits encoded in bosonic modes. However, current protocols for preparing bosonic code words lack robustness to common noise sources and can be experimentally challenging to implement, limiting the quality and breadth of codes that have been realized to date. Here, we combine concepts of error suppression via robust control with quantum error correction encoding and experimentally demonstrate high-fidelity, deterministic preparation of highly non-classical target bosonic states in the mechanical motion of a trapped ion. Our approach implements numerically optimized dynamical modulation of laser-driven spin-motion interactions to generate the target state in a single step. The optimized control pulses are tailored towards experimental constraints and are designed to be robust against the dominant source of error. Using these protocols, we demonstrate logical fidelities for the Gottesman-Kitaev-Preskill (GKP) state as high as $\bar{\mathcal{F}}=0.940(8)$, achieve the first realization of a distance-3 binomial logical state with an average fidelity of $\mathcal{F}=0.807(7)$, and demonstrate a 12.91(5) dB squeezed vacuum state.Comment: 12 pages, 8 figure

THE STRUCTURE OF 3,5-DI-O-BENZOYL-1,2-DIDEOXY-1-PHENYL-BETA-D-RIBOFURANOSE, C25H22O5

Mr=402.4, orthorhombic, P212~2 l, a= 4-946 (1), b= 15.887 (2), c=26.555 (2)A, V= 2086.7 (5) A 3, Z = 4, D x = 1.28 gcm -a, Cu Ka, 2 = 1.5418/k, B = 6.868 cm -1, F(000) = 848, T= 293 K, final R =0.054 for 648 observed reflections. The molecule is propeller shaped. The benzoyl groups act as protecting groups and the phenyl group is a base substitute. The crystal structure does not involve any intermolecular stacking interactions between the phenyl groups. The molecules pack in typical herring-bone-like arrays. The sugar has a fl-D configuration with C(2')-endo-C(3')-exo pucker (2T3), pseudorotation angle P = 172 (2) °, degree of pucker r m = 39 (2) °

in breast cancer

PSMD9 expression predicts radiotherapy respons

Superconductivity at 36 K in beta-Fe1.01Se with the compression of the interlayer separation under pressure

In this letter, we report that the superconductivity transition temperature in beta-Fe1.01Se increases from 8.5 to 36.7 K under applied pressure of 8.9 GPa. It then decreases at higher pressure. A dramatic change in volume is observed at the same time Tc rises, due to a collapse of the separation between the Fe2Se2 layers. A clear transition to a linear resistivity normal state is seen on cooling at all pressures. No static magnetic ordering is observed for the whole p-T phase diagram. We also report that at higher pressure (starting around 7 GPa and completed at 38 GPa), Fe1.01Se transforms to a hexagonal NiAs-type structure and displays non-magnetic, insulating behavior. The inclusion of electron correlation in band structure caculations is necessary to describe this behavior, signifying that such correlations are important in this chemical system. Our results strongly support unconventional superconductivity in beta-Fe1.01Se.Comment: 17 pages, 4 figure

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

<p>Abstract</p> <p>Background</p> <p>In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.</p> <p>Discussion</p> <p>Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the <it>Roadmap for National Action on Clinical Decision Support </it>commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.</p> <p>Summary</p> <p>A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.</p

Framework for knowledge asset management in community projects in higher education institutions

Innovation in education encourages stakeholders to explore and apply different ways of looking at problems and solving them. Large-scale community projects (LSCPs) in a higher education institution (HEI), provide an ideal environment for combining curriculum outcomes, education innovation, real-world engagement and knowledge assets. However, current research that focuses on knowledge asset management in innovative learning is limited, and this study aims to contribute a holistic approach for managing knowledge assets in this context. In this study, we designed a knowledge asset management framework for LSCPs in higher education taking cognisance of innovative educational model characteristics. We applied the framework by mapping it to a community project module from an HEI using the elements of the framework as a guide. By using the knowledge asset management framework for LSCPs in higher education, an HEI can ensure that their community module enables strong support to the community, that students’ knowledge and skills are enhanced and that all new knowledge assets created during the project delivery, are captured and stored using innovative technology sets.http://link.springer.combookseries/558hj2020Informatic