Flexible Group Fairness Metrics for Survival Analysis

Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorithmic fairness in regression and classification however there has been little exploration of the field for survival analysis. Survival analysis is the prediction task in which one attempts to predict the probability of an event occurring over time. Survival predictions are particularly important in sensitive settings such as when utilising machine learning for diagnosis and prognosis of patients. In this paper we explore how to utilise existing survival metrics to measure bias with group fairness metrics. We explore this in an empirical experiment with 29 survival datasets and 8 measures. We find that measures of discrimination are able to capture bias well whereas there is less clarity with measures of calibration and scoring rules. We suggest further areas for research including prediction-based fairness metrics for distribution predictions.Comment: Accepted in DSHealth 2022 (Workshop on Applied Data Science for Healthcare

G1 checkpoint protein and p53 abnormalities occur in most invasive transitional cell carcinomas of the urinary bladder

The G1 cell cycle checkpoint regulates entry into S phase for normal cells. Components of the G1 checkpoint, including retinoblastoma (Rb) protein, cyclin D1 and p16INK4a, are commonly altered in human malignancies, abrogating cell cycle control. Using immunohistochemistry, we examined 79 invasive transitional cell carcinomas of the urinary bladder treated by cystectomy, for loss of Rb or p16INK4a protein and for cyclin D1 overexpression. As p53 is also involved in cell cycle control, its expression was studied as well. Rb protein loss occurred in 23/79 cases (29%); it was inversely correlated with loss of p16INK4a, which occurred in 15/79 cases (19%). One biphenotypic case, with Rb+p16– and Rb-p16+ areas, was identified as well. Cyclin D1 was overexpressed in 21/79 carcinomas (27%), all of which retained Rb protein. Fifty of 79 tumours (63%) showed aberrant accumulation of p53 protein; p53 staining did not correlate with Rb, p16INK4a, or cyclin D1 status. Overall, 70% of bladder carcinomas showed abnormalities in one or more of the intrinsic proteins of the G1 checkpoint (Rb, p16INK4a and cyclin D1). Only 15% of all bladder carcinomas (12/79) showed a normal phenotype for all four proteins. In a multivariate survival analysis, cyclin D1 overexpression was linked to less aggressive disease and relatively favourable outcome. In our series, Rb, p16INK4a and p53 status did not reach statistical significance as prognostic factors. In conclusion, G1 restriction point defects can be identified in the majority of bladder carcinomas. Our findings support the hypothesis that cyclin D1 and p16INK4a can cooperate to dysregulate the cell cycle, but that loss of Rb protein abolishes the G1 checkpoint completely, removing any selective advantage for cells that alter additional cell cycle proteins. © 1999 Cancer Research Campaig

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO

Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory

The Auger Engineering Radio Array (AERA) is part of the Pierre Auger Observatory and is used to detect the radio emission of cosmic-ray air showers. These observations are compared to the data of the surface detector stations of the Observatory, which provide well-calibrated information on the cosmic-ray energies and arrival directions. The response of the radio stations in the 30 to 80 MHz regime has been thoroughly calibrated to enable the reconstruction of the incoming electric field. For the latter, the energy deposit per area is determined from the radio pulses at each observer position and is interpolated using a two-dimensional function that takes into account signal asymmetries due to interference between the geomagnetic and charge-excess emission components. The spatial integral over the signal distribution gives a direct measurement of the energy transferred from the primary cosmic ray into radio emission in the AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air shower arriving perpendicularly to the geomagnetic field. This radiation energy -- corrected for geometrical effects -- is used as a cosmic-ray energy estimator. Performing an absolute energy calibration against the surface-detector information, we observe that this radio-energy estimator scales quadratically with the cosmic-ray energy as expected for coherent emission. We find an energy resolution of the radio reconstruction of 22% for the data set and 17% for a high-quality subset containing only events with at least five radio stations with signal.Comment: Replaced with published version. Added journal reference and DO

Measurement of the Radiation Energy in the Radio Signal of Extensive Air Showers as a Universal Estimator of Cosmic-Ray Energy

We measure the energy emitted by extensive air showers in the form of radio emission in the frequency range from 30 to 80 MHz. Exploiting the accurate energy scale of the Pierre Auger Observatory, we obtain a radiation energy of 15.8 \pm 0.7 (stat) \pm 6.7 (sys) MeV for cosmic rays with an energy of 1 EeV arriving perpendicularly to a geomagnetic field of 0.24 G, scaling quadratically with the cosmic-ray energy. A comparison with predictions from state-of-the-art first-principle calculations shows agreement with our measurement. The radiation energy provides direct access to the calorimetric energy in the electromagnetic cascade of extensive air showers. Comparison with our result thus allows the direct calibration of any cosmic-ray radio detector against the well-established energy scale of the Pierre Auger Observatory.Comment: Replaced with published version. Added journal reference and DOI. Supplemental material in the ancillary file

Multiple Scenario Generation of Subsurface Models:Consistent Integration of Information from Geophysical and Geological Data throuh Combination of Probabilistic Inverse Problem Theory and Geostatistics

Neutrinos with energies above 1017 eV are detectable with the Surface Detector Array of the Pierre Auger Observatory. The identification is efficiently performed for neutrinos of all flavors interacting in the atmosphere at large zenith angles, as well as for Earth-skimming \u3c4 neutrinos with nearly tangential trajectories relative to the Earth. No neutrino candidates were found in 3c 14.7 years of data taken up to 31 August 2018. This leads to restrictive upper bounds on their flux. The 90% C.L. single-flavor limit to the diffuse flux of ultra-high-energy neutrinos with an E\u3bd-2 spectrum in the energy range 1.0 7 1017 eV -2.5 7 1019 eV is E2 dN\u3bd/dE\u3bd < 4.4 7 10-9 GeV cm-2 s-1 sr-1, placing strong constraints on several models of neutrino production at EeV energies and on the properties of the sources of ultra-high-energy cosmic rays

Selbstreinigung durch Sonnenlichtreflexion: Neue Forschungsergebnisse

Fahrzeugoberflächen mit Selbstreinigungseigenschaften auszustatten ist bisher nur begrenzt möglich, da die Einwirkung der typischen Umwelteinflüsse die Selbstreinigungswirkung herabsetzt. Durch den Zusatz von farbneutralen, reflektierenden Pigmenten und Pulvern lässt sich die Selbstreinigungswirkung von Fahrzeugbeschichtungen jedoch dauerhaft erhöhen - wie die im Folgenden dargestellten Forschungsergebnisse zeigen

Novel self-cleaning coatings: Development and testing

Self-cleaning coatings, which follow the concepts of photocatalysis, superhydrophobicity or superhydrophilicity, are commercially available. The self-cleaning properties of such coatings, however, are significantly degraded, when they are exposed to scratching or surface tension affecting substances. Within this work a novel concept to generate surfaces with self-cleaning properties is proved. This concept bases on a usage of UV/IR-reflecting additives in organic or inorganic/organic hybrid coatings. The self-cleaning properties of these coatings are evaluated by the outdoor exposure and with a novel quantitative short-time test. The results obtained show a good correlation. Additional methods for the quantification of the UV-induced degradation of coatings are proposed

Customized Software Environment for Remote Learning: Providing Students a Specialized Learning Experience

The Covid-19 pandemic has challenged educators across the world to move their teaching and mentoring from in-person to remote. During nonpandemic semesters at their institutes (e.g. universities), educators can directly provide students the software environment needed to support their learning - either in specialized computer laboratories (e.g. computational chemistry labs) or shared computer spaces. These labs are often supported by staff that maintains the operating systems (OS) and software. But how does one provide a specialized software environment for remote teaching? One solution is to provide students a customized operating system (e.g., Linux) that includes open-source software for supporting your teaching goals. However, such a solution should not require students to install the OS alongside their existing one (i.e. dual/multi-booting) or be used as a complete replacement. Such approaches are risky because of a) the students' possible lack of software expertise, b) the possible disruption of an existing software workflow that is needed in other classes or by other family members, and c) the importance of maintaining a working computer when isolated (e.g. societal restrictions). To illustrate possible solutions, we discuss our approach that used a customized Linux OS and a Docker container in a course that teaches computational chemistry and Python3