See Figure 1 for an illustration of the different SNR thresholds and Section 2.1 for more

Ahumansupervisorcaninterpret and understand the label predictions by inspecting the concept predictions.

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.

RFK Jr. Says Americans Need More Protein. A 30-second Super Bowl ad featuring boxing legend Mike Tyson and paid for by the nonprofit MAHA Center encourages viewers to avoid processed foods and visit Realfood.gov . The government website, which Health and Human Services secretary Robert F. Kennedy Jr. is promoting, provides resources on the administration's new dietary guidelines, released in January, and encourages people to use Elon Musk's AI chatbot Grok to "get real answers about real food." I decided to see how Grok's advice aligns with the administration's recommendations, particularly around protein intake. The new guidelines say to get 1.2 to 1.6 grams of protein per kilogram of body weight per day--more than what was previously advised--while the new inverted food pyramid prominently features steak and other animal products.

To this end, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modalities.