Jove
Visualize
Contact Us

Related Concept Videos

Weak Base Solutions03:21

Weak Base Solutions

25.1K
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...
25.1K
Weak Acid Solutions04:02

Weak Acid Solutions

43.0K
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...
43.0K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Titration of a Weak Acid with a Weak Base01:08

Titration of a Weak Acid with a Weak Base

4.9K
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...
4.9K
Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

49.2K
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:
49.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Assessment and counseling to get the best efficiency and effectiveness of the assistive technology (MATCH): Study protocol.

PloS one·2022
Same author

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities.

International journal of environmental research and public health·2021
Same author

Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor.

Sensors (Basel, Switzerland)·2019
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 28, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.7K

Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation.

Carolina Redondo-Cabrera, Marcos Baptista-Rios, Roberto J Lopez-Sastre

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 26, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new weakly-supervised semantic segmentation model that learns from simple image tags, eliminating the need for expensive pixel-level annotations. The model effectively generates segmentation masks using image recognition priors, outperforming existing methods.

    More Related Videos

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    10.7K
    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
    09:16

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

    Published on: June 18, 2020

    7.3K

    Related Experiment Videos

    Last Updated: Jan 28, 2026

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.7K
    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    10.7K
    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
    09:16

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

    Published on: June 18, 2020

    7.3K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Semantic segmentation models typically require extensive pixel-level annotations, which are costly and time-consuming to acquire.
    • Image-level tags are significantly easier and cheaper to collect, offering a potential alternative for training segmentation models.

    Purpose of the Study:

    • To develop a novel weakly-supervised semantic segmentation model that learns solely from image-level labels.
    • To introduce a methodology for generating accurate class-specific segmentation masks without external algorithms.
    • To integrate this mask generation into an end-to-end trainable framework for joint image classification and segmentation.

    Main Methods:

    • Leveraged prior knowledge from a pre-trained image recognition network as an attention mechanism to identify semantic regions.
    • Developed a novel strategy to generate class-specific segmentation masks from identified regions.
    • Implemented an end-to-end training process for joint classification and segmentation using generated masks.

    Main Results:

    • The proposed model successfully learns semantic segmentation from image tags alone.
    • Generated segmentation masks were accurate and did not require external objectness or saliency algorithms.
    • The end-to-end learning process with generated masks outperformed recent weakly-supervised methods on the PASCAL VOC 2012 dataset.

    Conclusions:

    • Weakly-supervised semantic segmentation can be effectively achieved using only image-level tags.
    • The proposed attention-based mask generation and end-to-end training offer a more efficient approach to semantic segmentation.
    • This method demonstrates superior performance compared to existing weakly-supervised techniques, even those with additional supervision.