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Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

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...
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Physiology of Emotion01:20

Physiology of Emotion

The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences physiological...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
Cognitive Theories: Lazarus Mediational Theory of Emotion01:17

Cognitive Theories: Lazarus Mediational Theory of Emotion

Richard Lazarus' cognitive mediational theory highlights the pivotal role of cognitive appraisal in shaping emotional responses. According to this theory, the evaluation of a stimulus — based on personal values, goals, beliefs, and expectations — mediates the emotional response. This appraisal process is immediate and often occurs unconsciously, influencing the intensity and nature of the resulting emotion.
Cognitive Appraisal and Emotional Response
Lazarus proposed that emotions are not solely...

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Related Experiment Videos

Incomplete Multimodal Probability Flow Recovery for Emotion Recognition.

Yuanzhi Wang, Zhen Cui, Mengyi Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 15, 2026
    PubMed
    Summary

    This study introduces a novel framework for multimodal emotion recognition (MER) to effectively handle missing data. The proposed method enhances recovery fidelity, improving performance in real-world applications.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Multimodal emotion recognition (MER) uses diverse data types but struggles with missing modalities in real-world scenarios.
    • Existing methods for recovering missing modalities in MER face issues like poor control, low fidelity, and inter-modal inconsistencies.

    Purpose of the Study:

    • To develop an advanced framework, Incomplete Multimodal Probability Flow Recovery (IM-PFR), to enhance emotion recognition when modalities are missing.
    • To address limitations of current recovery techniques by offering controllable restoration and improved fidelity.

    Main Methods:

    • Proposed the Incomplete Multimodal Probability Flow Recovery (IM-PFR) framework, unifying prior approaches into an autoencoder-like paradigm.
    • Introduced a Reversible Probability Flow Transformation (RPFT) using non-stochastic ordinary differential equations (ODEs) for controllable, continuous-time modeling.
    • Incorporated a time-dependent aligner for coordinated multimodal generation and a cross-modal high-order ODE solver to minimize error accumulation.

    Main Results:

    • Demonstrated state-of-the-art performance on various MER datasets.
    • Achieved superior recovery fidelity across diverse missing-modality conditions, confirmed by quantitative metrics and qualitative analyses.
    • Enabled tractable prior learning with inherent reversibility without explicit constraints.

    Conclusions:

    • The IM-PFR framework effectively boosts performance in modality-missed emotion recognition.
    • The proposed RPFT, coupled with specialized components, offers a robust solution for handling missing data in MER.
    • The method shows significant improvements in recovery fidelity and consistency, paving the way for more reliable real-world MER applications.