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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Related Experiment Video

Updated: May 23, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Transductive multi-view zero-shot learning.

Yanwei Fu, Timothy M Hospedales, Tao Xiang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 7, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new framework to improve zero-shot learning by addressing projection domain shift and prototype sparsity. The novel approach enhances recognition accuracy on image and video datasets.

    Related Experiment Videos

    Last Updated: May 23, 2026

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Existing zero-shot learning methods use transfer learning with a shared semantic space.
    • These methods suffer from projection domain shift and prototype sparsity.

    Purpose of the Study:

    • To propose a novel framework to overcome limitations in current zero-shot learning approaches.
    • To address projection domain shift and prototype sparsity for improved recognition.

    Main Methods:

    • Developed a transductive multi-view embedding framework.
    • Formulated a heterogeneous multi-view hypergraph label propagation method.
    • Utilized complementary information from multiple semantic representations and manifold structures.

    Main Results:

    • Successfully rectified the projection shift between auxiliary and target domains.
    • Effectively exploited the complementarity of multiple semantic representations.
    • Significantly outperformed existing methods in zero-shot and N-shot recognition.

    Conclusions:

    • The proposed approach offers a robust solution for zero-shot learning challenges.
    • Demonstrated superior performance on benchmark image and video datasets.
    • Enabled novel cross-view annotation tasks.