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In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Curvilinear Motion: Rectangular Components01:23

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Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
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Vector Algebra: Method of Components01:08

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
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Related Experiment Video

Updated: Oct 10, 2025

High-speed Particle Image Velocimetry Near Surfaces
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A Variational EM Acceleration for Efficient Clustering at Very Large Scales.

Florian Hirschberger, Dennis Forster, Jorg Lucke

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 9, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a novel variational approach for optimizing Gaussian mixture models (GMMs), significantly reducing computational complexity for large-scale clustering tasks. The efficient algorithm achieves substantial speedups, enabling analysis at unprecedented data scales.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Computational Statistics

    Background:

    • Efficiently clustering large datasets with high dimensionality is a significant challenge in data analysis.
    • Gaussian Mixture Models (GMMs) are powerful tools for clustering but can be computationally intensive.

    Purpose of the Study:

    • To develop a novel variational approach for optimizing GMMs to enable efficient large-scale clustering.
    • To reduce the computational complexity and improve the scalability of GMM optimization.

    Main Methods:

    • Utilized a variational method approximating Expectation Maximization (EM) with truncated posteriors and partial E-steps.
    • Incorporated coresets to reduce data size, leading to a reduced run time complexity per iteration.
    • Developed a parallelized and efficient clustering algorithm based on the reduced complexity.

    Main Results:

    • Reduced run time complexity per EM iteration from O(NCD) to O(N'G^2D) using coresets.
    • Demonstrated sublinear scaling of overall clustering times with the number of clusters (C) and dataset size (N).
    • Achieved substantial wall-clock speedups compared to existing efficient methods on large-scale benchmarks.

    Conclusions:

    • The proposed variational approach offers a highly efficient and scalable solution for GMM-based clustering.
    • The algorithm's sublinear scaling enables applications on massive datasets where other methods fail.
    • Successfully demonstrated applicability on the YFCC100M benchmark with 50,000 clusters and 150 million parameters.