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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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Related Experiment Video

Updated: May 24, 2025

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Toward Efficient Target-Level Machine Unlearning Based on Essential Graph.

Heng Xu, Tianqing Zhu, Lefeng Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Machine unlearning enables trained models to forget data, addressing privacy and regulations. This study introduces "target unlearning" to remove specific data points within instances, improving model performance and data privacy.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Privacy

    Background:

    • Machine unlearning is crucial for privacy, regulatory compliance, and data management.
    • Current methods primarily address instance-level or class-level data removal.
    • These methods are insufficient for granular data removal within instances.

    Purpose of the Study:

    • To develop an effective and efficient machine unlearning scheme for partial target removal.
    • To address the limitations of instance-level unlearning in complex scenarios.
    • To ensure model performance is maintained post-unlearning.

    Main Methods:

    • Proposed a novel "target unlearning" approach.
    • Constructed an essential graph data structure based on model explanation.
    • Utilized a pruning-based unlearning method to remove specific target information.

    Main Results:

    • Demonstrated the effectiveness of target unlearning across various datasets and models.
    • Showcased improved model performance compared to direct migration of instance-level methods.
    • Successfully removed specific target information without complete data erasure.

    Conclusions:

    • Target unlearning offers a more effective and efficient solution for partial data removal in machine learning.
    • The proposed method preserves model utility while ensuring data privacy.
    • This approach advances the field of machine unlearning for fine-grained data management.