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Related Concept Videos

Cohesion01:07

Cohesion

58.1K
Cohesion is the attraction between molecules of the same type, such as water molecules. Water molecules have an overall neutral charge but are polar molecule. An oxygen atom in one water molecule has a partial negative charge that can bind to a hydrogen atom with a partial positive charge in a second water molecule, forming a hydrogen bond. Each water molecule can form up to four hydrogen bonds with other water molecules. Hydrogen bonds are responsible for water's cohesive nature.
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Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

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The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
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Cohesins02:20

Cohesins

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Cohesin protein complexes are a molecular glue that holds two sister chromatids together. They play an important role both in mitosis and meiosis. In mitosis, all cohesin complexes present on the chromosomes are removed before the start of the anaphase stage.
Cohesin complexes in Meiotic Division
Meiosis involves two distinct rounds of chromosomal segregation and cell divisions— Meiosis I followed by Meiosis II – producing four daughter cells. Meiosis I includes the separation of...
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Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

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Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
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Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

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In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
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Stability of structures01:14

Stability of structures

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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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Coherency Sensitive Hashing.

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    Coherency Sensitive Hashing (CSH) quickly finds matching image patches, outperforming PatchMatch by being faster and more accurate. This novel method leverages hashing and image coherence for efficient patch matching, especially in complex textures.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Locality Sensitive Hashing (LSH) uses hashing to find similar image patches.
    • PatchMatch utilizes image coherence for efficient patch matching.
    • Existing methods face challenges in speed and accuracy, particularly in textured regions.

    Purpose of the Study:

    • To introduce Coherency Sensitive Hashing (CSH) as an advancement over LSH and PatchMatch.
    • To improve the speed and accuracy of matching patches between images.
    • To explore CSH's effectiveness on large-scale datasets and various extensions.

    Main Methods:

    • CSH combines hashing for initial patch matching with image coherence for match propagation.
    • Hashing allows information propagation based on appearance similarity and spatial neighborhood.
    • The method was validated on 133 image pairs and tested with extensions like k-nearest neighbor search and 3D video patch matching.

    Main Results:

    • CSH is 3-4 times faster than PatchMatch.
    • CSH demonstrates higher accuracy, especially in textured image regions.
    • The approach shows promise for various applications, including rotation invariance and 3D data.

    Conclusions:

    • CSH offers a significant improvement in patch matching efficiency and accuracy.
    • The integration of hashing and coherence effectively accelerates information propagation.
    • CSH presents a robust solution for image reconstruction and analysis tasks.