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A composite body is a body made up of multiple parts, connected to form a larger, unified object. Each part has its own weight and center of gravity, which must be considered to determine the center of gravity of the composite body. In cases where the density or specific weight is constant, the center of gravity coincides with the centroid.
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Updated: Jul 13, 2025

Creation of a High-Fidelity, Low-Cost, Intraosseous Line Placement Task Trainer via 3D Printing
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Finding the Right Moment: Human-Assisted Trailer Creation via Task Composition.

Pinelopi Papalampidi, Frank Keller, Mirella Lapata

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    Summary
    This summary is machine-generated.

    Researchers developed a novel method to identify movie trailer moments by modeling films as graphs. This approach, combining narrative structure and sentiment analysis, outperforms existing automatic and manual techniques for effective trailer creation.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Computational Linguistics

    Background:

    • Movie trailers are crucial for film promotion, introducing narrative, mood, and style to attract audiences.
    • Trailer creation is complex due to the need to balance multiple communicative functions.
    • Identifying optimal 'trailer moments' (shots for trailers) is a key challenge in automated or semi-automated trailer generation.

    Purpose of the Study:

    • To develop and evaluate a novel method for identifying optimal trailer moments within a movie.
    • To improve the efficiency and quality of trailer creation through automated and human-in-the-loop approaches.

    Main Methods:

    • Movies are represented as graphs, with shots as nodes and semantic relations as edges, learned via joint contrastive training on screenplay data.
    • The approach decomposes trailer moment selection into narrative structure identification and sentiment prediction.
    • An unsupervised graph traversal algorithm selects trailer moments based on learned semantic relations.

    Main Results:

    • The unsupervised algorithm's selected trailer moments were preferred by human judges over those from supervised methods.
    • An interactive tool, utilizing interpretable criteria, enables users to select superior trailer shots in under 30 minutes.
    • The tool's output is comparable to expert manual selection and superior to fully automatic methods.

    Conclusions:

    • The proposed graph-based method effectively identifies compelling trailer moments by integrating narrative and sentiment analysis.
    • The interpretable, human-in-the-loop tool enhances trailer creation efficiency and quality, democratizing expert-level selection.
    • This research offers a significant advancement in automated video summarization and content selection for media marketing.