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: The first observation of the B-s(0) -> (D) over bar K-0 (+) K- decay is reported, together with the most precise branching fraction measurement of the mode B-0 -> (D) over bar K-0 (+) K- The results are obtained from an analysis of pp collision data corresponding to an integrated luminosity of 3.0 fb(-1). The data were collected with the LHCb detector at center-of-mass energies of 7 and 8 TeV, The branching fraction of the B-0 -> (D) over bar K-0 (+) K- decay is measured relative to that of the decay B-0 -> (D) over bar (0)pi (+) pi(-) to be B(B-0 -> (D) over bar K-0 (+) K-)/B(B-0 -> (D) over bar (0)pi (+) pi(-)) =(69 +/- 0.4 +/- 0.3)%, where the first uncertainty is statistical and the second is systematic. The measured branching fraction of the B-s(0) -> (D) over bar K-0 (+) K- decay mode relative to that of the corresponding B-0 decay is B(B-0 -> (D) over bar K-0 (+) K-)/B(B-0 -> (D) over bar K-0 (+) K-) = (93.0 +/- 809 +/- 6.9)%. Using the known branching fraction of B-0 -> (D) over bar (0)pi (+) pi(-), the values of B-0 -> (D) over bar K-0 (+) K- = (6.1 +/- 0.4 +/- 0.3 +/- 0.3) x 10(-5) and B(B-s(0) -> (D) over bar K-0 (+) K- = (5.7 +/- 0.5 +/- 0.4 +/- 0.5) x 10(-5) are obtained, where the third uncertainties arise from the branching fraction of the decay modes B-0 -> (D) over bar (0)pi (+) pi(-) and B-0 -> (D) over bar K-0 (+) K-, respectively.

Abstract: We present a novel theory of gravity by considering an extension of symmetric teleparallel gravity. This is done by introducing, in the framework of the metric-affine formalism, a new class of theories where the nonmetricity Q is nonminimally coupled to the matter Lagrangian. More specifically, we consider a Lagrangian of the form L similar to f(1)(Q) + f(2)(Q)L-M, where f(1) and f(2) are generic functions of Q, and L-M is the matter Lagrangian. This nonminimal coupling entails the nonconservation of the energy-momentum tensor, and consequently the appearance of an extra force. The formulation of the gravity sector in terms of the Q instead of the curvature may result in subtle improvements of the theory. In the context of nonminimal matter couplings, we are therefore motivated to explore whether the new geometrical formulation in terms of the Q, when implemented also in the matter sector, would allow more universally consistent and viable realizations of the nonminimal coupling. Furthermore, we consider several cosmological applications by presenting the evolution equations and imposing specific functional forms of the functions f(1)(Q) and f(2)(Q), such as power-law and exponential dependencies of the nonminimal couplings. Cosmological solutions are considered in two general classes of models, and found to feature accelerating expansion at late times.

Abstract: A search for resonant and nonresonant pair production of Higgs bosons in the b (b) over bar tau(+)tau(-) final state is presented. The search uses 36.1 fb(-1) of pp collision data with root s = 13 TeV recorded by the ATLAS experiment at the LHC in 2015 and 2016. Decays of the tau-lepton pairs with at least one tau lepton decaying to final states with hadrons and a neutrino are considered. No significant excess above the expected background is observed in the data. The cross-section times branching ratio for nonresonant Higgs boson pair production is constrained to be less than 30.9 fb, 12.7 times the standard model expectation, at 95% confidence level. The data are also analyzed to probe resonant Higgs boson pair production, constraining a model with an extended Higgs sector based on two doublets and a Randall-Sundrum bulk graviton model. Upper limits are placed on the resonant Higgs boson pair production cross-section times branching ratio, excluding resonances X in the mass range 305 GeV < m(X) < 402 GeV in the simplified hMSSM minimal supersymmetric model for tan beta = 2 and excluding bulk Randall-Sundrum gravitons G(KK) in the mass range 325 GeV < m(GKK) < 885 GeV for k/(M) over bar P-1 = 1.