Abstract: The absolute mass of neutrinos and their nature are presently unknown. Aggregate matter has a coherent weak charge leading to a repulsive interaction mediated by a neutrino pair. The virtual neutrinos are non-relativistic at micron distances, giving a distinct behavior for Dirac versus Majorana mass terms. This effective potential allows for the disentanglement of the Dirac or Majorana nature of the neutrino via magnitude and distance dependence. We propose an experiment to search for this potential based on the concept that the density-dependent interaction of an atomic probe with a material source in one arm of an atomic clock interferometer generates a differential phase. The appropriate geometry of the device is selected using the saturation of the weak potential as a guide. The proposed experiment has the added benefit of being sensitive to gravity at micron distances. A strategy to suppress the competing Casimir-Polder interaction, depending on the electronic structure of the material source, as well as a way to compensate the gravitational interaction in the two arms of the interferometer is discussed.

Abstract: Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.

Abstract: We show that the space of solutions of a wide class of Ricci-based metric-affine theories of gravity can be put into correspondence with the space of solutions of general relativity (GR). This allows us to use well-established methods and results from GR to explore new gravitational physics beyond it.

Abstract: The occurrence of octupolar shapes in the Ba isotopic chain was recently established experimentally up to N = 90. To further extend the systematics, the evolution of shapes in the most neutron-rich members of the Z = 56 isotopic chain accessible at present, Ba-148,Ba-150, has been studied via beta decay at the ISOLDE Decay Station. This paper reports on the first measurement of the positive-and negative-parity low-spin excited states of 150Ba and presents an extension of the beta-decay scheme of Cs-148. Employing the fast timing technique, half-lives for the 2(1)(+) level in both nuclei have been determined, resulting in T-1/2 = 1.51(1) ns for Ba-148 and T-1/2 = 3.4(2) ns for Ba-150. The systematics of low-spin states, together with the experimental determination of the B(E2 : 2(+) -> 0(+)) transition probabilities, indicate an increasing collectivity in Ba148-150, towards prolate deformed shapes. The experimental data are compared to symmetry conserving configuration mixing (SCCM) calculations, confirming an evolution of increasingly quadrupole deformed shapes with a definite octupolar character.

Abstract: The spent fuel of current nuclear reactors contains fissile plutonium isotopes that can be combined with uranium to make mixed oxide (MOX) fuel. In this way the Pu from spent fuel is used in a new reactor cycle, contributing to the long-term sustainability of nuclear energy. However, an extensive use of MOX fuels, in particular in fast reactors, requires more accurate capture and fission cross sections for some Pu isotopes. In the case of Pu-242 there are sizable discrepancies among the existing capture cross-section measurements included in the evaluations (all from the 1970s) resulting in an uncertainty as high as 35% in the fast energy region. Moreover, postirradiation experiments evaluated with JEFF-3.1 indicate an overestimation of 14% in the capture cross section in the fast neutron energy region. In this context, the Nuclear Energy Agency (NEA) requested an accuracy of 8% in this cross section in the energy region between 500 meV and 500 keV. This paper presents a new time-of-flight capture measurement on Pu-242 carried out at nTOF-EAR1 (CERN), focusing on the analysis and statistical properties of the resonance region, below 4 keV. The Pu-242(n, gamma) reaction on a sample containing 95(4) mg enriched to 99.959% was measured with an array of four C6D6 detectors and applying the total energy detection technique. The high neutron energy resolution of nTOF-EAR1 and the good statistics accumulated have allowed us to extend the resonance analysis up to 4 keV, obtaining new individual and average resonance parameters from a capture cross section featuring a systematic uncertainty of 5%, fulfilling the request of the NEA.