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Base Excision Repair01:54

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One of the common DNA damages is the chemical alteration of single bases by alkylation, oxidation, or deamination. The altered bases cause mispairing and strand breakage during replication. This type of damage causes minimal change to the DNA double helix structure and can be repaired by the base excision repair (BER) pathways. BER corrects damaged DNA sequences by removing the damaged base and restoring the original base sequence using the complementary strand as a template.
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Some compounds produce hydroxide ions when dissolved by chemically reacting with water molecules. In all cases, these compounds react only partially and so are classified as weak bases. These types of compounds are also abundant in nature and important commodities in various technologies. For example, global production of the weak base ammonia is typically well over 100 metric tons annually, being widely used as an agricultural fertilizer, a raw material for chemical synthesis of other...
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Investigating Nuisances in DCNN-based Face Recognition.

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    Deep Convolutional Neural Networks (DCNNs) significantly advance face recognition by learning features directly from data. This study analyzes DCNN architecture, data preprocessing, and similarity measures for optimal performance in real-world scenarios.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep Convolutional Neural Networks (DCNNs) have revolutionized face recognition, surpassing traditional hand-crafted feature methods.
    • DCNNs effectively handle variations in illumination, pose, expression, and occlusion by learning representations directly from raw image data.

    Purpose of the Study:

    • To conduct a comprehensive analysis of factors influencing DCNN performance in face recognition.
    • To investigate the impact of network architecture, similarity measures, data characteristics, and preprocessing techniques.

    Main Methods:

    • Evaluated DCNN performance from the perspectives of network architecture and similarity measures.
    • Assessed the influence of data source, quality, bounding box, and alignment preprocessing.

    Main Results:

    • Identified key aspects impacting DCNN-based face recognition.
    • Demonstrated that competitive, state-of-the-art performance can be achieved with standard DCNN architectures and pipelines.

    Conclusions:

    • Provided practical insights for designing, training, and testing DCNNs for face recognition.
    • Highlighted the importance of data characteristics and preprocessing alongside network design for robust face recognition systems.