REFORMING PILLAR 2 –TOWARDS SIGNIFICANT AND SUSTAINABLE RURAL DEVELOPMENT?

With the ongoing “Health Check” and the decisions needed for after 2013, the Common Agricultural Policy is likely to see another major reform and an increase in compulsory modulation. By employing a regional model, this paper compares the long-term impact of spending along the Pillar 2 Axes in NUTS3 areas on selected indicators of sustainability in several peripheral areas across Europe. The four case study areas are: Pinzgau-Pongau (a tourism-dominated alpine area in Austria), the Wetterau (an urbanised industrial area in Germany), Gorenjska (a tourism and manufacturing dominated area in Slovenia) and Caithness-Sutherland (a remote area in Scotland). The results suggest although devolution in European rural development policy has taken over the last 10 years, there is further need to restore place-based stewardship of public goods and services as well as private investments across rural areas in the European Union. Increasing the importance of Axis 2 and Axis 3 measures (part of CAP Pillar 2) therefore seems an obvious choice for the future. Furthermore, it is clear that the effects of wider societal trends such as the decreasing importance of agriculture, commuting and migration, can be weakened or amplified by EU funding but can not be reversed or significantly changed.CAP, Pillar 2, rural development, Agricultural and Food Policy, R15, Q18, Q01,

Group equivariant neural posterior estimation

Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science. However, these methods typically treat the underlying forward model as a black box, with no way to exploit geometric properties such as equivariances. Equivariances are common in scientific models, however integrating them directly into expressive inference networks (such as normalizing flows) is not straightforward. We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data. Our method -- called group equivariant neural posterior estimation (GNPE) -- is based on self-consistently standardizing the "pose" of the data while estimating the posterior over parameters. It is architecture-independent, and applies both to exact and approximate equivariances. As a real-world application, we use GNPE for amortized inference of astrophysical binary black hole systems from gravitational-wave observations. We show that GNPE achieves state-of-the-art accuracy while reducing inference times by three orders of magnitude

Water molecules mediate zinc mobility in the bacterial zinc diffusion channel ZIPB.

Regulated ion diffusion across biological membranes is vital for cell function. In a nanoscale ion channel, the active role of discrete water molecules in modulating hydrodynamic behaviors of individual ions is poorly understood because of the technical challenge of tracking water molecules through the channel. Here we report the results of a hydroxyl radical footprinting analysis of the zinc-selective channel ZIPB from the Gram-negative bacterium, Bordetella bronchiseptica Irradiating ZIPB by microsecond X-ray pulses activated water molecules to form covalent hydroxyl radical adducts at nearby residues, which were identified by bottom-up proteomics to detect residues that interact either with zinc or water in response to zinc binding. We found a series of residues exhibiting reciprocal changes in water accessibility attributed to alternating zinc and water binding. Mapping these residues to the previously reported crystal structure of ZIPB, we identified a water-reactive pathway that superimposed on a zinc translocation pathway consisting of two binuclear metal centers and an interim zinc-binding site. The cotranslocation of zinc and water suggested that pore-lining residues undergo a mode switch between zinc coordination and water binding to confer zinc mobility. The unprecedented details of water-mediated zinc transport identified here highlight an essential role of solvated waters in driving zinc coordination dynamics and transmembrane crossing

Synthesis and rearrangements of ortho-selenonium phenoxides

A new class of selenonium zwitterions is prepared from the ortho- substitution of phenols with diphenylselenium bis(trifluoroacetate) 1. The zwitterions undergo a novel thermal rearrangement to produce diaryl ethers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27012/1/0000579.pd

Measurement of the charged pion mass using X-ray spectroscopy of exotic atoms

The $5g-4f$ transitions in pionic nitrogen and muonic oxygen were measured simultaneously by using a gaseous nitrogen-oxygen mixture at 1.4\,bar. Due to the precise knowledge of the muon mass the muonic line provides the energy calibration for the pionic transition. A value of (139.57077\,$\pm$\,0.00018)\,MeV/c$^{2}$ ($\pm$\,1.3ppm) is derived for the mass of the negatively charged pion, which is 4.2ppm larger than the present world average

Chorioamnionitis: Association of Nonreassuring Fetal Heart-rate Patterns and Interval From Diagnosis to Delivery on Neonatal Outcome

Objective: The purpose of this study was to determine whether selected fetal heart-rate (FHR) patterns and the interval from diagnosis to delivery in pregnancies complicated by chorioamnionitis could predict neonatal outcome

Hadronic shift in pionic hydrogen

The hadronic shift in pionic hydrogen has been redetermined to be $\epsilon_{1s}=7.086\,\pm\,0.007(stat)\,\pm\,0.006(sys)$\,eV by X-ray spectroscopy of ground state transitions applying various energy calibration schemes. The experiment was performed at the high-intensity low-energy pion beam of the Paul Scherrer Institut by using the cyclotron trap and an ultimate-resolution Bragg spectrometer with bent crystals.Comment: 10 pages, 6 figure

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights based on the underlying likelihood and prior. This provides (1) a corrected posterior free from network inaccuracies, (2) a performance diagnostic (the sample efficiency) for assessing the proposal and identifying failure cases, and (3) an unbiased estimate of the Bayesian evidence. By establishing this independent verification and correction mechanism we address some of the most frequent criticisms against deep learning for scientific inference. We carry out a large study analyzing 42 binary black hole mergers observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomXPHM waveform models. This shows a median sample efficiency of $\approx 10\%$ (two orders-of-magnitude better than standard samplers) as well as a ten-fold reduction in the statistical uncertainty in the log evidence. Given these advantages, we expect a significant impact on gravitational-wave inference, and for this approach to serve as a paradigm for harnessing deep learning methods in scientific applications