Adaptive conformal classification with noisy labels

This paper develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, enabling more informative prediction sets with stronger coverage guarantees compared to state-of-the-art approaches. This is made possible by a precise theoretical characterization of the effective coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through new calibration algorithms. Our solution is flexible and can leverage different modeling assumptions about the label contamination process, while requiring no knowledge about the data distribution or the inner workings of the machine-learning classifier. The advantages of the proposed methods are demonstrated through extensive simulations and an application to object classification with the CIFAR-10H image data set.Comment: 35 pages (98 pages including references and appendices

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing effective treatments and reducing mortality rates. One way to gain such understanding is using a multi-class classification framework, in which patients' biological features are used to predict patients' severity classes. In this severity classification problem, it is beneficial to prioritize the identification of more severe classes and control the "under-classification" errors, in which patients are misclassified into less severe categories. The Neyman-Pearson (NP) classification paradigm has been developed to prioritize the designated type of error. However, current NP procedures are either for binary classification or do not provide high probability controls on the prioritized errors in multi-class classification. Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-classification errors with high probability. On an integrated collection of single-cell RNA-seq (scRNA-seq) datasets for 864 patients, we explore ways of featurization and demonstrate the efficacy of the H-NP algorithm in controlling the under-classification errors regardless of featurization. Beyond COVID-19 severity classification, the H-NP algorithm generally applies to multi-class classification problems, where classes have a priority order

Cumulative Exposure to Ideal Cardiovascular Health and Incident Diabetes in a Chinese Population: The Kailuan Study

Background: It is unclear whether ideal cardiovascular health (CVH), and particularly cumulative exposure to ideal CVH (cumCVH), is associated with incident diabetes. We aimed to fill this research gap. Methods and Results: The Kailuan Study is a prospective cohort of 101 510 adults aged 18 to 98 years recruited in 2006-2007 and who were subsequently followed up at 2- (Exam 2), 4- (Exam 3), and 6 (Exam 4)-year intervals after baseline. The main analysis is restricted to those individuals with complete follow-up at all 4 examinations and who had no history of diabetes until Exam 3. Cumulative exposure to ideal CVH (cumCVH) was calculated as the summed CVH score for each examination multiplied by the time between the 2 examinations (score×year). Logistic regression models were used to assess the association between cumCVH and incident diabetes. In fully adjusted models, compared with the lowest quintile of cumCVH, individuals in the highest quintile had ~68% (95% confidence interval [CI] 60-75) lower risk for incident diabetes (compared with 61% [95% CI 52-69] lower risk when using baseline CVH). Every additional year lived with a 1-unit increase in ideal CVH was associated with a 24% (95% CI 21-28) reduction in incident diabetes. Conclusions: Ideal CVH is associated with a reduced incidence of diabetes, but the association is likely to be underestimated if baseline measures of CVH exposure are used. Measures of cumulative exposure to ideal CVH are more likely to reflect lifetime risk of diabetes and possibly other health outcomes. CLINICAL TRIAL REGISTRATION: URL: https://www.chictr.org. Unique identifier: ChiCTRTNC-11001489

Effects of Helium Implantation on the Tensile Properties and Microstructure of Ni₇₃P₂₇ Metallic Glass Nanostructures

We report fabrication and nanomechanical tension experiments on as-fabricated and helium-implanted 130 nm diameter Ni₇₃P₂₇ metallic glass nanocylinders. The nanocylinders were fabricated by a templated electroplating process and implanted with He+ at energies of 50, 100, 150, and 200 keV to create a uniform helium concentration of 3 atom % throughout the nanocylinders. Transmission electron microscopy imaging and through-focus analysis reveal that the specimens contained 2 nm helium bubbles distributed uniformly throughout the nanocylinder volume. In situ tensile experiments indicate that helium-implanted specimens exhibit enhanced ductility as evidenced by a 2-fold increase in plastic strain over as-fabricated specimens with no sacrifice in yield and ultimate tensile strengths. This improvement in mechanical properties suggests that metallic glasses may actually exhibit a favorable response to high levels of helium implantation

Ultrafast, Polarized, Single-Photon Emission from m-Plane InGaN Quantum Dots on GaN Nanowires.

We demonstrate single-photon emission from self-assembled m-plane InGaN quantum dots (QDs) embedded on the side-walls of GaN nanowires. A combination of electron microscopy, cathodoluminescence, time-resolved microphotoluminescence (μPL), and photon autocorrelation experiments give a thorough evaluation of the QD structural and optical properties. The QD exhibits antibunched emission up to 100 K, with a measured autocorrelation function of g(2)(0) = 0.28(0.03) at 5 K. Studies on a statistically significant number of QDs show that these m-plane QDs exhibit very fast radiative lifetimes (260 ± 55 ps) suggesting smaller internal fields than any of the previously reported c-plane and a-plane QDs. Moreover, the observed single photons are almost completely linearly polarized aligned perpendicular to the crystallographic c-axis with a degree of linear polarization of 0.84 ± 0.12. Such InGaN QDs incorporated in a nanowire system meet many of the requirements for implementation into quantum information systems and could potentially open the door to wholly new device concepts