In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks. Based on tensor factorization theory and kernel methods, we propose a novel Kernelized Support Tensor Machine (KSTM) which integrates kernelized tensor factorization with maximum-...

Humans show a natural tendency to discount bad news while incorporating good news into beliefs (the "good news-bad news effect"), an effect that may help explain seemingly irrational risk taking. Understanding how this bias develops with age is important because adolescents are prone to engage in risky behavior; thus, educating them about danger is crucial. We reveal a striking valence-dependen...

The use of machine learning is attractive in the design of autonomous robots, since it enables robots to adapt to the unforeseen. However, one of the key bottlenecks of current machine learning algorithms is the enormous sample complexity, which appears to prohibit their usage in all but the simplest of robotic domains. To make machine learning more practical in such domains, more powerful algo...

Research Findings: Evolution by natural selection is often relegated to the high school curriculum on the assumption that younger students cannot grasp its complexity. We sought to test that assumption by teaching children ages 4–12 (n = 96) a selection-based explanation for biological adaptation and comparing their success to that of adults (n = 30). Participants provided explanations before a...