Use of Equivalent Relative Utility (ERU) to Evaluate Artificial Intelligence-Enabled Rule-Out Devices

We investigated the use of equivalent relative utility (ERU) to evaluate the effectiveness of artificial intelligence (AI)-enabled rule-out devices that use AI to identify and autonomously remove non-cancer patient images from radiologist review in screening mammography.We reviewed two performance metrics that can be used to compare the diagnostic performance between the radiologist-with-rule-out-device and radiologist-without-device workflows: positive/negative predictive values (PPV/NPV) and equivalent relative utility (ERU). To demonstrate the use of the two evaluation metrics, we applied both methods to a recent US-based study that reported an improved performance of the radiologist-with-device workflow compared to the one without the device by retrospectively applying their AI algorithm to a large mammography dataset. We further applied the ERU method to a European study utilizing their reported recall rates and cancer detection rates at different thresholds of their AI algorithm to compare the potential utility among different thresholds. For the study using US data, neither the PPV/NPV nor the ERU method can conclude a significant improvement in diagnostic performance for any of the algorithm thresholds reported. For the study using European data, ERU values at lower AI thresholds are found to be higher than that at a higher threshold because more false-negative cases would be ruled-out at higher threshold, reducing the overall diagnostic performance. Both PPV/NPV and ERU methods can be used to compare the diagnostic performance between the radiologist-with-device workflow and that without. One limitation of the ERU method is the need to measure the baseline, standard-of-care relative utility (RU) value for mammography screening in the US. Once the baseline value is known, the ERU method can be applied to large US datasets without knowing the true prevalence of the dataset

Multi-messenger searches via IceCube’s high-energy neutrinos and gravitational-wave detections of LIGO/Virgo

We summarize initial results for high-energy neutrino counterpart searches coinciding with gravitational-wave events in LIGO/Virgo\u27s GWTC-2 catalog using IceCube\u27s neutrino triggers. We did not find any statistically significant high-energy neutrino counterpart and derived upper limits on the time-integrated neutrino emission on Earth as well as the isotropic equivalent energy emitted in high-energy neutrinos for each event

In-situ estimation of ice crystal properties at the South Pole using LED calibration data from the IceCube Neutrino Observatory

The IceCube Neutrino Observatory instruments about 1 km3 of deep, glacial ice at the geographic South Pole using 5160 photomultipliers to detect Cherenkov light emitted by charged relativistic particles. A unexpected light propagation effect observed by the experiment is an anisotropic attenuation, which is aligned with the local flow direction of the ice. Birefringent light propagation has been examined as a possible explanation for this effect. The predictions of a first-principles birefringence model developed for this purpose, in particular curved light trajectories resulting from asymmetric diffusion, provide a qualitatively good match to the main features of the data. This in turn allows us to deduce ice crystal properties. Since the wavelength of the detected light is short compared to the crystal size, these crystal properties do not only include the crystal orientation fabric, but also the average crystal size and shape, as a function of depth. By adding small empirical corrections to this first-principles model, a quantitatively accurate description of the optical properties of the IceCube glacial ice is obtained. In this paper, we present the experimental signature of ice optical anisotropy observed in IceCube LED calibration data, the theory and parametrization of the birefringence effect, the fitting procedures of these parameterizations to experimental data as well as the inferred crystal properties.</p

Non-standard neutrino interactions in IceCube

Non-standard neutrino interactions (NSI) may arise in various types of new physics. Their existence would change the potential that atmospheric neutrinos encounter when traversing Earth matter and hence alter their oscillation behavior. This imprint on coherent neutrino forward scattering can be probed using high-statistics neutrino experiments such as IceCube and its low-energy extension, DeepCore. Both provide extensive data samples that include all neutrino flavors, with oscillation baselines between tens of kilometers and the diameter of the Earth. DeepCore event energies reach from a few GeV up to the order of 100 GeV - which marks the lower threshold for higher energy IceCube atmospheric samples, ranging up to 10 TeV. In DeepCore data, the large sample size and energy range allow us to consider not only flavor-violating and flavor-nonuniversal NSI in the μ−τ sector, but also those involving electron flavor. The effective parameterization used in our analyses is independent of the underlying model and the new physics mass scale. In this way, competitive limits on several NSI parameters have been set in the past. The 8 years of data available now result in significantly improved sensitivities. This improvement stems not only from the increase in statistics but also from substantial improvement in the treatment of systematic uncertainties, background rejection and event reconstruction