Performance threat is a situation in which something is obviously wrong or has the potential to go wrong.
Answer:
corpus callosum
Explanation:
Yolanda, who is 5 years old, has improved dramatically in her ability to throw and catch a baseball. The growth of the corpus callosum has contributed significantly to her improved abilities by enhancing communication between the left and right hemispheres of the brain.
Answer:
confounding variable; lowered.
Explanation:
In the field of statistical analysis, a <u>confounding variable</u> is one that influences both the independent variable and the dependent variable. When an experimented is designed, the researcher wants to study the effect the independent variable has on the dependent variable. However, if there's a third variable that can influence them, it can cause a spurious correlation.
The psychologist wanted to test the effects using the new computer program (independent variable) had in helping students learn math (dependent variable). But when she divided the group in two, separating them by gender, she introduced a third variable (confounding variable) that wasn't accounted for when designing the experiment and that can influence either variable. <u>Because of this, the internal validity of the study has been </u><u>lowered</u><u>.</u>
Markerless motion capture and understanding of professional non-daily human movements is an important yet unsolved task, which suffers from complex motion patterns and severe self-occlusion, especially for the monocular setting. In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.
About SportCaps :
SportsCap proposes a challenging sports dataset called Sports Motion and Recognition Tasks (SMART) dataset, which contains per-frame action labels, manually annotated pose, and action assessment of various challenging sports video clips from professional referees.
Their approaches :
This is especially prevalent in non-daily action classes like fitness and sports domains. This can be mitigated, for example, by annotating domain-specific datasets , but that requires a considerable amount of manual annotation efforts, financial resources, and 3D annotations can only be obtained in controlled conditions. Therefore, we propose to learn domain-specific pose-sensitive representations from unlabeled videos, which can be fine tuned using only a small labeled dataset. ...
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