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Related Concept Videos

Weak Base Solutions03:21

Weak Base Solutions

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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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Weak Acid Solutions04:02

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Few compounds act as strong acids. A far greater number of compounds behave as weak acids and only partially react with water, leaving a large majority of dissolved molecules in their original form and generating a relatively small amount of hydronium ions. Weak acids are commonly encountered in nature, being the substances partly responsible for the tangy taste of citrus fruits, the stinging sensation of insect bites, and the unpleasant smells associated with body odor. A familiar example of a...
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Conservation of Protein Domains Over Different Proteins02:26

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Titration of a Weak Acid with a Weak Base01:08

Titration of a Weak Acid with a Weak Base

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Weak acids and bases do not undergo dissociation completely, and titrations between these two are rarely studied. When such studies are performed, say, for the titration of a weak acid with a weak base, the titration curve plots the change in pH as a function of the volume of base added. Take the titration of acetic acid with ammonia, for instance. During the titration, these two species form ammonium acetate and water, but the pH change is slow and gradual.
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Titration Calculations: Weak Acid - Strong Base03:55

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Calculating pH for Titration Solutions: Weak Acid/Strong Base
For the titration of 25.00 mL of 0.100 M CH3CO2H with 0.100 M NaOH, the reaction can be represented as:
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Measurement: Standard Units03:38

Measurement: Standard Units

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Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
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Weakly Supervised Facial Action Unit Recognition With Domain Knowledge.

Shangfei Wang, Guozhu Peng, Shiyu Chen

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    |October 2, 2018
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    This study introduces a novel weakly supervised method for facial action unit (AU) recognition, using expression labels instead of costly AU annotations. The approach leverages domain knowledge to accurately identify AUs, improving upon existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Current facial action unit (AU) recognition relies heavily on fully annotated image datasets, which are expensive and time-consuming to create.
    • Individual differences and facial nuances make AU annotation a challenging and error-prone task.
    • Facial expressions are simpler to label globally, and inherent dependencies exist between AUs and expressions (domain knowledge).

    Purpose of the Study:

    • To propose a novel weakly supervised facial action unit (AU) recognition method that bypasses the need for direct AU annotations.
    • To leverage inherent domain knowledge, specifically the relationship between facial expressions and AUs, for improved recognition.
    • To develop an efficient learning algorithm for jointly learning multiple AU classifiers using only expression labels.

    Main Methods:

    • Summarized expression-dependent AU ranking using domain knowledge of conditional probabilities.
    • Formulated weakly supervised AU recognition as a multi-label ranking problem.
    • Developed an efficient learning algorithm to solve the formulated problem and extended it to a semi-supervised scenario.

    Main Results:

    • The proposed weakly supervised method effectively utilizes domain knowledge for multi-AU recognition.
    • Experimental results on three benchmark databases show superior performance compared to state-of-the-art weakly supervised methods.
    • The semi-supervised extension also demonstrated significant improvements when partial AU-labeled data was available.

    Conclusions:

    • Weakly supervised learning, by leveraging domain knowledge, offers a viable and efficient alternative to fully supervised AU recognition.
    • The proposed method successfully exploits the relationship between facial expressions and AUs, reducing annotation burden.
    • This approach has the potential to significantly advance the field of facial behavior analysis with less reliance on extensive manual annotation.