Answer:
The new industries and businesses affected the way many american children lived, because due to the Industrial Revolution and the emergence of new industries and businesses since 19th century, many children worked long hours in factories, mills or mines from an early age (some from 6 years old). They had to work strenuous working days of more than 54 hours per week, which caused the children to be forced to leave their studies and their infantile activities to carry out the work. As a result of that many children suffered accidents that several times seriously harmed them.Explanation:
Answer:
True statements are,
A, B, D, E , and F
Step-by-step explanation:
<u>Properties of a square</u>
1) All sides are equal
2) All angles are equal to 90°
3) Opposite sides are parallel
<u>A. WXYZ is a parallelogram</u>
True (property 3)
<u>B. <W is right angle </u>
True (Property 2)
<u>C. WXYZ is a trapezoid</u>
False
<u>D. WX ≅ XY</u>
True (Property 1)
<u>E. <W congruent to <Y</u>
True (Property 1)
<u>F. <W is supplementary to <Y</u>
True (Property 2)
True statements are,
A, B, D, E , and F
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. ...
Learn more about Monocular 3 D Human :
brainly.com/question/21602764
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Him and his friend were sent home for organizing student-lead protests and manifestations. This foreshadowed his devotion to activism and wanting to do what he could to make a change that was important to him.
