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Judges should be impartial and independent, judging based solely on the law. Current constitutional literature suggests an important factor in securing this may be the length of tenure. The assumption is that judges with non-renewable terms are more independent than judges with renewable terms since they do not have to worry about reappointment, but proving this assumption empirically is not straightforward. Obstacles include difficulties in comparing different courts and the fact that there is often no obvious case outcome that proves independence. This article aims to overcome these obstacles with a mixed-methods study on the European Court of Human Rights during a time when the tenure rules changed. The study goes beyond the counting of votes and analyses the arguments used in separate opinions as indicators of independence. Our main findings are that, after the introduction of non-renewable terms, judges write more opinions overall, and more of them criticize the judges' appointing states, while fewer defend it. We also find that judges on non-renewable terms are on average more likely to write opinions addressing violations as systemic problems and to use their opinions to provide guidance for their appointing states on implementing judgments and improving human rights protection. [ABSTRACT FROM AUTHOR]

Low transfer rates are a major bottleneck for brain–computer interfaces based on electroencephalography (EEG). This problem has led to the development of more robust and accurate classifiers. In this study, we investigated the performance of variational quantum, quantum-enhanced support vector, and hypergraph case-based reasoning classifiers in the binary classification of EEG data from a P300 experiment. On the one hand, quantum classification is a promising technology to reduce computational time and improve learning outcomes. On the other hand, case-based reasoning has an excellent potential to simplify the preprocessing steps of EEG analysis. We found that the balanced training (prediction) accuracy of each of these three classifiers was 56.95 (51.83), 83.17 (50.25), and 71.10% (52.04%), respectively. In addition, case-based reasoning performed significantly lower with a simplified (49.78%) preprocessing pipeline. These results demonstrated that all classifiers were able to learn from the data and that quantum classification of EEG data was implementable; however, more research is required to enable a greater prediction accuracy because none of the classifiers were able to generalize from the data. This could be achieved by improving the configuration of the quantum classifiers (e.g., increasing the number of shots) and increasing the number of trials for hypergraph case-based reasoning classifiers through transfer learning. [ABSTRACT FROM AUTHOR]

ydata-profiling is an open-source Python package for advanced exploratory data analysis that enables users to generate data profiling reports in a simple, fast, and efficient manner, fostering a standardized and visual understanding of the data. Beyond traditional descriptive properties and statistics, ydata-profiling follows a Data-Centric AI approach to exploratory analysis, as it focuses on the automatic detection and highlighting of complex data characteristics often associated with potential data quality issues, such as high ratios of missing or imbalanced data, infinite, unique, or constant values, skewness, high correlation, high cardinality, non-stationarity, seasonality, duplicate records, and other inconsistencies. The source code, documentation, and examples are available in the GitHub repository: https://github.com/ydataai/ydata-profiling. [ABSTRACT FROM AUTHOR]