Accountability for developing, deploying, and using any emerging weapons system is affirmed as a guiding principle by the Group of Governmental Experts on Emerging Technologies in the Area of Lethal Autonomous Weapons Systems. Yet advances in emerging technologies present accountability challenges throughout the life cycle of a weapons system. Mindful of a lack of progress at the Convention on Certain Conventional Weapons since 2019, this essay argues for a mechanism capable of imputing accountability when individual agent accountability is exceeded, forensic accountability unreliable, and aspects of political accountability fail.

Streamflow predictions are vital for detecting flood and drought events. Such predictions are even more critical to Sub-Saharan African regions that are vulnerable to the increasing frequency and intensity of such events. These regions are sparsely gaged, with few available gaging stations that are often plagued with missing data due to various causes, such as harsh environmental conditions and constrained operational resources. This work presents a novel workflow for predicting streamflow in the presence of missing gage observations. We leverage bias correction of the Group on Earth Observations Global Water and Sustainability Initiative ECMWF streamflow service (GESS) forecasts for missing data imputation and predict future streamflow using the state-of-the-art temporal fusion transformers (TFTs) at 10 river gaging stations in the Benin Republic. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in poor imputation performance over the 10 Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior performance relative to traditional imputation by established methods. We also show that the TFT yields high predictive skill and further provides explanations for predictions through the weights of its attention mechanism. The findings of this work provide a basis for integrating Global streamflow prediction model data and the state-of-the-art machine learning models into operational early-warning decision-making systems in resource-constrained countries vulnerable to drought and flooding due to extreme weather events.

The exploitation of hydrocarbon reservoirs may potentially lead to contamination of soils, shallow water resources, and greenhouse gas emissions. Fluids such as methane or CO2 may in some cases migrate toward the groundwater zone and atmosphere through and along imperfectly sealed hydrocarbon wells. Field tests in hydrocarbon-producing regions are routinely conducted for detecting serious leakage to prevent environmental pollution. The challenge is that testing is costly, time-consuming, and sometimes labor-intensive. In this study, machine learning approaches were applied to predict serious leakage with uncertainty quantification for wells that have not been field tested in Alberta, Canada. An improved imputation technique was developed by Cholesky factorization of the covariance matrix between features, where missing data are imputed via conditioning of available values. The uncertainty in imputed values was quantified and incorporated into the final prediction to improve decision-making. Next, a wide range of predictive algorithms and various performance metrics were considered to achieve the most reliable classifier. However, a highly skewed distribution of field tests toward the negative class (nonserious leakage) forces predictive models to unrealistically underestimate the minority class (serious leakage). To address this issue, a combination of oversampling, undersampling, and ensemble learning was applied. By investigating all the models on never-before-seen data, an optimum classifier with minimal false negative prediction was determined. The developed methodology can be applied to identify the wells with the highest likelihood for serious fluid leakage within producing fields. This information is of key importance for optimizing field test operations to achieve economic and environmental benefits.

The distribution of human leukocyte antigens in the population assists in matching solid organ donors and recipients when the typing methods used do not provide sufficiently precise information. This is made possible by linkage disequilibrium (LD), where alleles co-occur more often than random chance would suggest. There is a trade-off between the high bias and low variance of a broad sample from the population and the low bias but high variance of a focused sample. Some of this trade-off could be alleviated if sub-populations shared LD despite having different allele frequencies. These experiments show that Bayesian estimation can balance bias and variance by tuning the effective sample size of the reference panel, but the LD as represented by an additive or multiplicative copula is not shared.

Imputing missing values is an important preprocessing step in data analysis, but the literature offers little guidance on how to choose between imputation models. This letter suggests adopting the imputation model that generates a density of imputed values most similar to those of the observed values for an incomplete variable after balancing all other covariates. We recommend stable balancing weights as a practical approach to balance covariates whose distribution is expected to differ if the values are not missing completely at random. After balancing, discrepancy statistics can be used to compare the density of imputed and observed values. We illustrate the application of the suggested approach using simulated and real-world survey data from the American National Election Study, comparing popular imputation approaches including random forests, hot-deck, predictive mean matching, and multivariate normal imputation. An R package implementing the suggested approach accompanies this letter.

There is a long tradition of imputation studies looking at how abstainers would vote if they had to. This is crucial for democracies because when abstainers and voters have different preferences, the electoral outcome ceases to reflect the will of the people. In this paper, we apply a non-parametric method to revisit old evidence. We impute the vote of abstainers in 15 European countries using Coarsened Exact Matching (CEM). While traditional imputation methods rely on the choice of voters that are on average like abstainers, and simulate full turnout, CEM only imputes the vote of the abstainers that are similar to voters, and allows to simulate an electoral outcome under varying levels of turnout, including levels that credibly simulate compulsory voting. We find that higher turnout would benefit social democratic parties while imposing substantial losses to extreme left and green parties.

It has been argued that Kant’s practical philosophy cannot allow for degrees of responsibility for one’s actions. However, it would be uncompromising to allow for only two possibilities: either full responsibility or none. Moreover, in the Metaphysics of Morals Kant himself claims that there can be degrees of responsibility, depending on the magnitude of the obstacles that have to be overcome when acting. I will show that this claim is consistent with Kant’s theory as a whole and thereby make transparent how degrees of responsibility are possible for Kant. The solution is based on the distinction between two senses of responsibility: taking oneself to be an accountable person is an all-or-nothing affair, whereas praise- or blameworthiness for a particular action can still be a matter of degree.

Kant’s conception of conscience has been relatively neglected by Kant scholars and the secondary literature offers no explanation of whether (and if so, how) his treatments of conscience fit together. To achieve a fuller understanding of Kant’s general position on conscience, I question the widespread assumption that conscience is a feeling and account for the nature of conscience and its multiple functions. On my reading, conscience is ‘the internal judge’ whose verdict triggers certain emotional reactions. Through the moral self-evaluative activities of this inner judge, we come to know our character better. In the judgements of conscience, we take account of various psychological conditions while judging both whether these conditions stand in the way of our establishing moral maxims and whether we hold ourselves accountable for our actions. By arousing certain feelings, these judgements also move us to moral action.

Imputation of high-density genotypes from low- or medium-density platforms is a promising way to enhance the efficiency of whole-genome selection programs at low cost. In this study, we compared the efficiency of three widely used imputation algorithms (fastPHASE, BEAGLE and findhap) using Chinese Holstein cattle with Illumina BovineSNP50 genotypes. A total of 2108 cattle were randomly divided into a reference population and a test population to evaluate the influence of the reference population size. Three bovine chromosomes, BTA1, 16 and 28, were used to represent large, medium and small chromosome size, respectively. We simulated different scenarios by randomly masking 20%, 40%, 80% and 95% single-nucleotide polymorphisms (SNPs) on each chromosome in the test population to mimic different SNP density panels. Illumina Bovine3K and Illumina BovineLD (6909 SNPs) information was also used. We found that the three methods showed comparable accuracy when the proportion of masked SNPs was low. However, the difference became larger when more SNPs were masked. BEAGLE performed the best and was most robust with imputation accuracies >90% in almost all situations. fastPHASE was affected by the proportion of masked SNPs, especially when the masked SNP rate was high. findhap ran the fastest, whereas its accuracies were lower than those of BEAGLE but higher than those of fastPHASE. In addition, enlarging the reference population improved the imputation accuracy for BEAGLE and findhap, but did not affect fastPHASE. Considering imputation accuracy and computational requirements, BEAGLE has been found to be more reliable for imputing genotypes from low- to high-density genotyping platforms.

Traditional Protestant accounts of Paul's theology are often criticized for their inability to relate justification by faith and the participatory categories of Paul's thought. The two are driven apart by sharp distinctions between declaring and making righteous, between justification as a once for all external act and regeneration as an internal lifelong process. The way is left open for justification to be treated as a legal fiction. Contrary to popular misconceptions, these difficulties do not stem from Martin Luther. In his exegesis of Paul, Luther intimately connects justification by faith and participation in Christ, integrating the two effectively. This article explores the manner in which Luther does so, evaluating his exegetical conclusions and assessing their relevance for contemporary attempts to interpret Paul's theology.