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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Related Experiment Videos

DAR-Prompt: Dynamic Regulation in Prompt Tuning for Multi-Label Zero-Shot Learning.

Yiwen Liang, Hui Chen, Zijia Lin

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Dynamic Regulation in Prompt Tuning (DAR-Prompt) enhances multi-label classification by addressing class imbalance and prompt interactions. This method improves generalization and achieves state-of-the-art performance in zero-shot learning tasks.

    Related Experiment Videos

    Area of Science:

    • Natural Language Processing
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Prompt tuning excels in multi-label zero-shot classification, leveraging multiple prompts for knowledge acquisition.
    • Existing methods face challenges with class imbalance and suboptimal prompt interactions, limiting generalization.
    • Current solutions for class imbalance can worsen the issue by over-suppressing minority classes.

    Purpose of the Study:

    • To introduce Dynamic Regulation in Prompt Tuning (DAR-Prompt), a novel framework to overcome limitations in prompt tuning for multi-label classification.
    • To address class imbalance and enhance prompt interactions for improved generalization.
    • To achieve state-of-the-art performance in multi-label zero-shot classification tasks.

    Main Methods:

    • DAR-Prompt incorporates a semantic regulator and a debiased regulator to dynamically manage class imbalance.
    • Contrastive gradient regularization is applied to optimize prompt interactions and enhance feature separation.
    • Class-adaptive thresholds and rectified overconfident predictions are utilized to compensate for tail classes and biased learning.

    Main Results:

    • DAR-Prompt demonstrates state-of-the-art performance across various benchmarks.
    • The proposed dynamic components effectively address class imbalance issues.
    • Enhanced feature distinctiveness and improved generalization capabilities were observed.

    Conclusions:

    • DAR-Prompt offers a superior approach to prompt tuning for multi-label zero-shot classification.
    • The framework effectively mitigates class imbalance and optimizes prompt interactions.
    • The method shows significant potential for advancing natural language processing tasks.