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It has been observed in both natural and man-made materials that volume sometimes decreases with increasing temperature. Though mechanistic understanding has been gained for some individual materials, a general answer to the question “Why does volume sometimes decrease with the increase of temperature?” remains lacking. Based on the thermodynamic relation that the derivative of volume with respect to temperature, i.e., thermal expansion, is equal to the negative derivative of entropy with respect to pressure, we developed a general theory in terms of multiscale entropy to understand and predict the change of volume as a function of temperature, which is termed as zentropy theory in the present work. It is shown that a phase at high temperatures is a statistical representation of the ground-state stable and multiple nonground-state metastable configurations. It is demonstrated that when the volumes of the nonground-state configurations with high probabilities are smaller than that of the ground-state configuration, the volume of the phase may decrease with the increase of temperature in certain ranges of temperature-pressure combinations, depicting the negative divergency of thermal expansion at the critical point. As examples, positive and negative divergencies of thermal expansion are predicted at the critical points of Ce and Fe3Pt, respectively, along with the temperature and pressure ranges for abnormally positive and negative thermal expansions. The authors believe that the zentropy theory is applicable to predict anomalies of other physical properties of phases because the change of entropy drives the responses of a system to external stimuli.
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Spontaneous suppression in dating couples: Social and physiological correlates of suppressing negative and positive emotions during negative and positive conversations
- Award ID(s):
- 1941868
- PAR ID:
- 10407333
- Date Published:
- Journal Name:
- International Journal of Psychophysiology
- Volume:
- 178
- Issue:
- C
- ISSN:
- 0167-8760
- Page Range / eLocation ID:
- 60 to 70
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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We devised and evaluated a multi-modal machine learning-based system to analyze videos of school classrooms for "positive climate" and "negative climate", which are two dimensions of the Classroom Assessment Scoring System (CLASS). School classrooms are highly cluttered audiovisual scenes containing many overlapping faces and voices. Due to the difficulty of labeling them (reliable coding requires weeks of training) and their sensitive nature (students and teachers may be in stressful or potentially embarrassing situations), CLASS- labeled classroom video datasets are scarce, and their labels are sparse (just a few labels per 15-minute video dip). Thus, the overarching challenge was how to harness modern deep perceptual architectures despite the paucity of labeled data. Through training low-level CNN-based facial attribute detectors (facial expression & adult/child) as well as a direct audio-to- climate regressor, and by integrating low-level information over time using a Bi-LSTM, we constructed automated detectors of positive and negative classroom climate with accuracy (10- fold cross-validation Pearson correlation on 241 CLASS-labeled videos) of 0.40 and 0.51, respectively. These numbers are superior to what we obtained using shallower architectures. This work represents the first automated system designed to detect specific dimensions of the CLASS.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.ijpsycho.2022.06.001
