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Local Anesthetics: Adverse Effects01:12

Local Anesthetics: Adverse Effects

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While local anesthetics are generally safe and well-tolerated, they can occasionally cause adverse effects that vary in severity. Local anesthetics can induce toxicity at two distinct levels. They can either produce local effects through direct contact with the neural elements or be absorbed into the bloodstream from the injection site, leading to systemic effects.
Once absorbed into the systemic circulation, local anesthetics can affect the organs that depend on the functioning of sodium...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
329
Local Anesthetics: Chemistry and Structure-Activity Relationship01:30

Local Anesthetics: Chemistry and Structure-Activity Relationship

6.3K
Local anesthetics (LAs) are drugs that induce a temporary loss of sensation in a limited body area, preventing pain. Cocaine was the first local anesthetic discovered in the late 19th century. Cocaine is a benzoic acid ester obtained from the leaves of coca shrubs and was often used for its psychotropic effects. Cocaine was first isolated in 1860 by Albert Niemann. Sigmund Freud studied the physiological actions of cocaine. Carl Koller later introduced it into clinical practice in 1884 as a...
6.3K
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

1.0K
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
1.0K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

318
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Updated: Jan 8, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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Spurious Local Minima Provably Exist for Deep CNNs: Theory and Application.

Bo Liu, Keyi Fu, Tongtong Yuan

    IEEE Transactions on Neural Networks and Learning Systems
    |December 17, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Spurious local minima exist in deep convolutional neural networks (CNNs). Researchers developed a method to escape these minima, improving accuracy across various architectures and datasets.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Deep neural networks, particularly convolutional neural networks (CNNs), often exhibit complex loss landscapes.
    • The presence of spurious local minima can hinder the training process and prevent models from reaching optimal performance.

    Purpose of the Study:

    • To prove the existence of a general family of spurious local minima in CNNs with specific properties.
    • To develop a deterministic optimization method to escape these spurious local minima.

    Main Methods:

    • Construction of spurious local minima by perturbing parameter space and strategically grouping data samples.
    • Addressing challenges posed by convolutional layers to ensure targeted perturbation effects.
    • Designing a deterministic optimization algorithm based on the spurious local minima construction.

    Main Results:

    • Demonstrated the general existence of spurious local minima applicable to arbitrary CNN architectures.
    • Experimental validation on CIFAR-10, CIFAR-100, and ImageNet-1k datasets confirmed theoretical findings.
    • The proposed optimization method consistently outperformed Stochastic Gradient Descent (SGD) and Adam, achieving an average accuracy improvement of 0.27% across architectures.

    Conclusions:

    • Spurious local minima are a general phenomenon in deep learning models like CNNs.
    • The developed optimization technique offers a reliable method to escape these minima and enhance model accuracy.
    • The findings have broad applicability to various neural network architectures including CNNs, ResNets, MLPs, and transformers.