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Related Concept Videos

Types of Selection01:46

Types of Selection

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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Multimachine Stability01:25

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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An Approximate Approach to Automatic Kernel Selection.

Lizhong Ding, Shizhong Liao

    IEEE Transactions on Cybernetics
    |April 6, 2016
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    Summary
    This summary is machine-generated.

    This study introduces approximate kernel selection for regression using multilevel circulant matrices. The novel algorithms offer quasi-linear complexity and demonstrate effectiveness in experiments.

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    Area of Science:

    • Machine Learning
    • Computational Statistics

    Background:

    • Kernel selection is crucial for kernel-based learning.
    • Existing methods can be computationally intensive.

    Purpose of the Study:

    • To develop an efficient, approximate approach for automatic kernel selection in regression.
    • To leverage kernel matrix approximation for computational gains.

    Main Methods:

    • Introduction of multilevel circulant matrices into kernel selection.
    • Development of two approximate kernel selection algorithms exploiting these matrices.
    • Analysis of approximation error bounds and convergence properties.

    Main Results:

    • Proposed algorithms achieve quasi-linear time complexity with respect to the number of data points.
    • Theoretical analysis confirms convergence of approximate hypotheses to accurate ones.
    • Experimental validation on benchmark datasets shows significant effectiveness.

    Conclusions:

    • The approximate kernel selection method using multilevel circulant matrices is computationally efficient.
    • The approach provides a reliable and effective alternative for automatic kernel selection in regression tasks.