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  1. Order is one of the most important concepts to interpret various phenomena such as the emergence of turbulence and the life-evolution process. The generation of laser can also be treated as an ordering process in which the interaction between the laser beam and the gain medium leads to the correlation between photons in the output optical field. Here, we demonstrate experimentally in a hybrid Raman-laser-optomechanical system that an ordered Raman laser can be generated from an entropy-absorption process by a chaotic optomechanical resonator. When the optomechanical resonator is chaotic or disordered enough, the Raman-laser field is in an ordered lasing mode. This can be interpreted by the entropy transfer from the Raman-laser mode to the chaotic motion mediated by optomechanics. Different order parameters, such as the box-counting dimension, the maximal Lyapunov exponent, and the Kolmogorov entropy, are introduced to quantitatively analyze this entropy transfer process, by which we can observe the order transfer between the Raman-laser mode and the optomechanical resonator. Our study presents a new mechanism of laser generation and opens up new dimensions of research such as the modulation of laser by optomechanics.

     
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  2. Recent NLP literature has seen growing interest in improving model interpretability. Along this direction, we propose a trainable neural network layer that learns a global interaction graph between words and then selects more informative words using the learned word interactions. Our layer, we call WIGRAPH, can plug into any neural network-based NLP text classifiers right after its word embedding layer. Across multiple SOTA NLP models and various NLP datasets, we demonstrate that adding the WIGRAPH layer substantially improves NLP models' interpretability and enhances models' prediction performance at the same time.

     
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    Free, publicly-accessible full text available June 27, 2024
  3. The present study tested the learning avoidance model by examining the degree to which learning avoidance in various afterschool settings mediated the negative association between math anxiety and math achievement. Participants consisted of 207 third to sixth graders. Using a path model, findings showed that students’ math anxiety was negatively associated with both standardized math achievement test scores and parent-reported math school grades. Additionally, higher math anxiety was associated with more negative homework behaviors and less frequent participation in math-related extracurricular activities. Finally, the association between math anxiety and math achievement was partially mediated by negative math homework behaviors and participation in math extracurricular activities. Effort in math exam preparation did not contribute to explaining the association between math anxiety and math achievement. Overall, these findings support the learning avoidance model and suggest that avoidance behaviors in everyday learning in the afterschool setting may contribute to explaining the undesired math achievement among highly math anxious students.

     
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  4. Free, publicly-accessible full text available November 1, 2024
  5. Gas-phase oxygenated organic molecules (OOMs) can contribute significantly to both atmospheric new particle growth and secondary organic aerosol formation. Precursor apportionment of atmospheric OOMs connects them with volatile organic compounds (VOCs). Since atmospheric OOMs are often highly functionalized products of multistep reactions, it is challenging to reveal the complete mapping relationships between OOMs and their precursors. In this study, we demonstrate that the machine learning method is useful in attributing atmospheric OOMs to their precursors using several chemical indicators, such as O/C ratio and H/C ratio. The model is trained and tested using data acquired in controlled laboratory experiments, covering the oxidation products of four main types of VOCs (isoprene, monoterpenes, aliphatics, and aromatics). Then, the model is used for analyzing atmospheric OOMs measured in both urban Beijing and a boreal forest environment in southern Finland. The results suggest that atmospheric OOMs in these two environments can be reasonably assigned to their precursors. Beijing is an anthropogenic VOC dominated environment with ∼64% aromatic and aliphatic OOMs, and the other boreal forested area has ∼76% monoterpene OOMs. This pilot study shows that machine learning can be a promising tool in atmospheric chemistry for connecting the dots. 
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