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Related Concept Videos

Weak Base Solutions03:21

Weak Base Solutions

25.2K
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

Weak Acid Solutions

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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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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.
As a result, there is no simple...
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Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

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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:
49.3K
Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Structural Joints: Fibrous Joints01:03

Structural Joints: Fibrous Joints

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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
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SPFTN: A Joint Learning Framework for Localizing and Segmenting Objects in Weakly Labeled Videos.

Dingwen Zhang, Junwei Han, Le Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 17, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a novel Self-Paced Fine-Tuning Network (SPFTN) for joint object localization and segmentation in videos with weak labels. The framework effectively handles ambiguity, improving performance in weakly supervised learning scenarios.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • Object localization and segmentation in videos are crucial but challenging tasks.
    • Existing methods primarily use fully supervised learning, lacking approaches for weak supervision.
    • Weakly supervised learning in this domain suffers from significant ambiguity due to limited annotations.

    Purpose of the Study:

    • To propose a novel joint learning framework for simultaneous object localization and segmentation in weakly labeled videos.
    • To address the ambiguity issue inherent in learning these tasks under weak supervision.
    • To develop a method that leverages task-specific prior knowledge and inferred confident samples.

    Main Methods:

    • Introduced the Self-Paced Fine-Tuning Network (SPFTN), a deep learning framework.
    • Employed a novel multi-task self-paced learning objective.
    • Leveraged task-specific priors and inferred confident training samples to mitigate ambiguity.
    • Aggregated confident knowledge from individual tasks to mine reliable patterns and learn deep features.

    Main Results:

    • The SPFTN framework effectively addresses ambiguity in weakly supervised learning.
    • Achieved superior performance compared to state-of-the-art methods and baselines.
    • Demonstrated effectiveness on large-scale datasets like YouTube-Objects and DAVIS.

    Conclusions:

    • The proposed SPFTN framework offers a robust solution for joint object localization and segmentation in weakly labeled videos.
    • The multi-task self-paced learning objective is key to overcoming the challenges of weak supervision.
    • The approach shows significant potential for advancing video understanding under limited annotation conditions.