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: A normal-conducting, X-band traveling wave structure operating in the dipole mode has been systematically high-gradient tested to gain insight into the maximum possible gradients in these types of structure. Measured structure conditioning, breakdown behavior, and achieved surface fields are reported as well as a postmortem analysis of the breakdown position and a scanning electron microscope analysis of the high-field surfaces. The results of these measurements are then compared to high-gradient results from monopole-mode cavities. Scaled to a breakdown rate of 10(-6), the cavities were found to operate at a peak electric field of 154 MV/m and a peak modified Poynting vector S-c of 5.48 MW/mm(2). The study provides important input for the further development of dipole-mode cavities for use in the Compact Linear Collider as a crab cavity and dipole-mode cavities for use in x-ray free-electron lasers as well as for studies of the fundamental processes in vacuum arcs. Of particular relevance are the unique field patterns in dipole cavities compared to monopole cavities, where the electric and magnetic fields peak in orthogonal planes, which allow the separation of the role of electric and magnetic fields in breakdown via postmortem damage observation. The azimuthal variation of breakdown crater density is measured and is fitted to sinusoidal functions. The best fit is a power law fit of exponent 6. This is significant, as it shows how breakdown probability varies over a surface area with a varying electric field after conditioning to a given peak field.

Abstract: The gamma-ray emission from the nuclei Fe-62,Fe-64 following Coulomb excitation at bombarding energy of 400-440 AMeV was measured with special focus on E1 transitions in the energy region 4-8 MeV. The unstable neutron-rich nuclei Fe-62,Fe-64 were produced at the FAIR-GSI laboratories and selected with the FRS spectrometer. The gamma decay was detected with AGATA. From the measured gamma-ray spectra the summed E1 strength is extracted and compared to microscopic quasi-particle phonon model calculations. The trend of the E1 strength with increasing neutron number is found to be fairly well reproduced with calculations that assume a rather complex structure of the 1(-) states (three-phonon states) inducing a strong fragmentation of the E1 nuclear response below the neutron binding energy.

Abstract: Inclusive and differential fiducial cross sections of the Higgs boson are measured in the H -> ZZ* -> 4l (l = e, mu) decay channel. The results are based on proton-proton collision data produced at the Large Hadron Collider at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector from 2015 to 2018, equivalent to an integrated luminosity of 139 fb(-1). The inclusive fiducial cross section for the H -> ZZ* -> 4l process is measured to be sigma(fid) = 3.28 +/- 0.32 fb, in agreement with the StandardModel prediction of sigma(fid,SM) = 3.41 +/- 0.18 fb. Differential fiducial cross sections are measured for a variety of observables which are sensitive to the production and decay of the Higgs boson. Allmeasurements are in agreement with the Standard Model predictions. The results are used to constrain anomalous Higgs boson interactions with StandardModel particles.

Abstract: We establish the possibility of Landau damping for gravitational scalar waves which propagate in a non-collisional gas of particles. In particular, under the hypothesis of homogeneity and isotropy, we describe the medium at the equilibrium with a Juttner-Maxwell distribution, and we analytically determine the damping rate from the Vlasov equation. We find that damping occurs only if the phase velocity of the wave is subluminal throughout the propagation within the medium. Finally, we investigate relativistic media in cosmological settings by adopting numerical techniques.