Supervised learning usually requires a large amount of labeled data. However, attaining ground-truth labels is costly for many tasks. Alternatively, weakly supervised methods learn with cheap weak signals that only approximately label some data.
View Article and Find Full Text PDFIn the complex dynamics of modern information systems such as e-commerce and streaming services, managing uncertainty and leveraging information theory are crucial in enhancing session-aware recommender systems (SARSs). This paper presents an innovative approach to SARSs that combines static long-term and dynamic short-term preferences within a counterfactual causal framework. Our method addresses the shortcomings of current prediction models that tend to capture spurious correlations, leading to biased recommendations.
View Article and Find Full Text PDFWater evaporation-induced electricity generators are considered a promising green energy-harvesting technology to alleviate the increasingly serious fossil energy crisis. Previous water evaporation-induced electricity generators mainly focused on single component carbon black, limiting the improvements in energy output. At present, there are relatively few studies on multi-component carbon black for improving electricity-generation performance.
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