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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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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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Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Stereotype Content Model02:16

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Related Experiment Video

Updated: Aug 4, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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Balanced Federated Semisupervised Learning With Fairness-Aware Pseudo-Labeling.

Xiao-Xiang Wei, Hua Huang

    IEEE Transactions on Neural Networks and Learning Systems
    |April 5, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Federated semisupervised learning (FSSL) faces fairness issues due to imbalanced data. A new fairness-aware pseudo-labeling (FAPL) strategy balances data, improving model performance across all clients and classes.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Science

    Background:

    • Federated semisupervised learning (FSSL) leverages labeled and unlabeled data in decentralized settings.
    • Non-identically distributed data across clients causes imbalanced training and performance disparities.
    • Existing FSSL methods struggle with fairness issues, leading to inconsistent model performance.

    Purpose of the Study:

    • To address the fairness challenge in federated semisupervised learning.
    • To improve model performance and consistency across different classes and clients.
    • To introduce a novel balanced FSSL method using a fairness-aware pseudo-labeling strategy.

    Main Methods:

    • Developed a fairness-aware pseudo-labeling (FAPL) strategy for balanced FSSL.
    • Globally balanced the number of unlabeled data samples for training.
    • Decomposed global restrictions into personalized local restrictions for each client's pseudo-labeling.

    Main Results:

    • The proposed FAPL strategy effectively mitigates fairness issues in FSSL.
    • Achieved a more equitable federated model performance across all clients.
    • Demonstrated superior performance compared to state-of-the-art FSSL methods on image classification tasks.

    Conclusions:

    • The fairness-aware pseudo-labeling strategy is a viable solution for balanced FSSL.
    • This approach enhances model fairness and overall performance in federated learning.
    • The method shows significant improvements on real-world datasets, validating its effectiveness.