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Related Concept Videos

Fischer Projections02:18

Fischer Projections

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Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines. While...
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Coordinates and Map Projections01:29

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Coordinates and map projections are essential tools in accurately representing the Earth's surface for various applications, ranging from navigation to spatial analysis. The latitude and longitude coordinate system is a universally recognized framework for defining locations. Latitude specifies the distance of a point north or south of the equator, measured in degrees from 0° at the equator to 90° at the poles. Longitude indicates a location's position east or west of the prime meridian,...
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Collisions in Multiple Dimensions: Problem Solving01:06

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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.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Newman Projections02:06

Newman Projections

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Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
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Collisions in Multiple Dimensions: Introduction01:05

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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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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Related Experiment Video

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection.

Ping Luo, Liang Lin, Xiaobai Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |June 19, 2015
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    Summary

    This study introduces a new shape model using multiple distance metrics to handle shape distortions. The model effectively addresses various deformations, improving performance on challenging datasets.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Shape Analysis

    Background:

    • Accurate shape modeling is crucial for image analysis and computer vision tasks.
    • Existing models often struggle with diverse shape deformations and background clutter.

    Purpose of the Study:

    • To develop a novel compositional contour-based shape model.
    • To enhance robustness against various shape distortions and deformations using multiple distance metrics.

    Main Methods:

    • Contour feature generation by sampling local prototype contour segments.
    • Description of segments using three diverse and complementary distance metrics.
    • Generative learning algorithm employing an information projection principle for model pursuit.

    Main Results:

    • Demonstrated effectiveness on public, challenging datasets.
    • Successfully handled various shape deformations, articulations, and background clutter.
    • Integration of multiple distance metrics significantly boosted system performance.

    Conclusions:

    • The proposed compositional shape model effectively accounts for diverse shape variations.
    • The use of multiple distance metrics enhances model adaptability and performance.
    • This approach offers a robust solution for shape analysis in complex scenarios.