Disease progression models infer group-level temporal trajectories of change in patients' features as a chronic degenerative condition plays out.

For these systems, a common issue is the difficulty of collecting sufficiently refined timescale data.

Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones. Existing cost-sensitive-based methods often rely on strong assumptions that examples with an observed positive label were selected entirely at random. In fact, the uneven distribution of labels is prevalent in real-world PU problems, indicating that most actual positive and unlabeled data are subject to selection bias. In this paper, we propose a PU learning enhancement (PUe) algorithm based on causal inference theory, which employs normalized propensity scores and normalized inverse probability weighting (NIPW) techniques to reconstruct the loss function, thus obtaining a consistent, unbiased estimate of the classifier and enhancing the model's performance. Moreover, we investigate and propose a method for estimating propensity scores in deep learning using regularization techniques when the labeling mechanism is unknown. Our experiments on three benchmark datasets demonstrate the proposed PUe algorithm significantly improves the accuracy of classifiers on non-uniform label distribution datasets compared to advanced cost-sensitive PU methods.

Table 2 shows theresults, likewisewithUMPIREasbest.

Multimodal learning combines information from multiple data modalities to improve predictive performance. However, modalities often contribute unequally and in a data dependent way, making it unclear which data modalities are genuinely informative and to what extent their contributions can be trusted. Quantifying modality level importance together with uncertainty is therefore central to interpretable and reliable multimodal learning. We introduce conformal Shapley intervals, a framework that combines Shapley values with conformal inference to construct uncertainty-aware importance intervals for each modality. Building on these intervals, we propose a modality selection procedure with a provable op-timality guarantee: conditional on the observed features, the selected subset of modalities achieves performance close to that of the optimal subset. We demonstrate the effectiveness of our approach on multiple datasets, showing that it provides meaningful uncertainty quantification and strong predictive performance while relying on only a small number of informative modalities.

He Went to Prison for Gene-Editing Babies. Now He's Planning to Do It Again Chinese scientist He Jiankui wants to end Alzheimer's and thinks Silicon Valley is conducting a "Nazi eugenic experiment." In 2018, a nervous-looking He Jiankui took the stage at a scientific conference in Hong Kong. A hush settled over the packed auditorium as the soft-spoken Chinese scientist adjusted his microphone and confirmed the circulating media reports: He had created the world's first gene-edited babies . Three little girls were born with modifications to their genomes that were intended to protect them against HIV. The changes he'd made to their DNA were permanent and heritable, meaning they could be passed down to future generations.