Jove
Visualize
Contact Us
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 Concept Videos

COPD: Pathogenesis and Clinical Features01:20

COPD: Pathogenesis and Clinical Features

1.8K
Chronic obstructive pulmonary disease (COPD) is a group of lung conditions that progressively worsen over time, including chronic bronchitis and emphysema. This cluster of diseases collectively leads to a gradual and irreversible decline in lung function over time.
The primary cause for the onset of COPD is cigarette smoking and exposure to air pollution. These hazardous factors initiate a chain reaction within the lungs, resulting in chronic inflammation, damage to the airways, and a...
1.8K
Special Features of Adaptive Immunity01:20

Special Features of Adaptive Immunity

3.0K
The adaptive immune system, a crucial component of the overall immune response, offers a highly specialized defense against pathogens. It involves specific cell types and features, enabling it to combat infections effectively and efficiently.
The primary cell types involved in adaptive immunity are T cells and B cells. Each type has a unique role in defending the body against pathogens. T cells are responsible for cell-mediated immunity. They identify and eliminate infected cells directly,...
3.0K
Esophageal Strictures-II: Clinical Features and Management01:26

Esophageal Strictures-II: Clinical Features and Management

605
Patients with esophageal strictures often experience a range of symptoms. Initially, they may have difficulty swallowing solid foods, which can progress to include liquids. Additional symptoms may involve chest pain or discomfort, regurgitating food and fluids, heartburn, unintentional weight loss, coughing or choking during meals, and hoarseness.
Healthcare providers should gather a comprehensive medical history and conduct a physical examination for diagnosis. If esophageal stricture is...
605
Endocarditis II: Clinical Features of Infective Endocarditis01:25

Endocarditis II: Clinical Features of Infective Endocarditis

473
Endocarditis can present various clinical features depending on the causative organism and the patient's underlying health conditions. Initially, the clinical features of infective endocarditis develop gradually, presenting with nonspecific symptoms that can be easily mistaken for other illnesses.General SymptomsEarly symptoms of infective endocarditis are fever, chills, weakness, malaise, fatigue, and weight loss. These symptoms reflect the systemic nature of the infection and the body's...
473
Pericarditis II: Clinical Features and Diagnostic Tests01:19

Pericarditis II: Clinical Features and Diagnostic Tests

319
Pericarditis is distinguished by inflammation of the pericardium, the fibrous sac that encases the heart. It can be acute, lasting less than six weeks, or chronic, persisting for over three months. Understanding its clinical manifestations and diagnostic findings is crucial for timely and effective management.Clinical ManifestationsWhile pericarditis can be asymptomatic, it usually presents with characteristic symptoms such as:Chest Pain: The most characteristic symptom of pericarditis is chest...
319
Esophageal Varices-II: Clinical Features and Management01:28

Esophageal Varices-II: Clinical Features and Management

543
Esophageal varices often manifest as gastrointestinal bleeding episodes, presenting symptoms like hematemesis (vomiting of blood), hematochezia (passing fresh blood via the rectum), and melena (black, tarry stools). Other signs can include weight loss, anorexia, abdominal discomfort, jaundice, pruritus, altered mental status, and muscle cramps.
In the initial assessment, a thorough review of the patient's medical history is vital to identify risk factors such as liver disease, alcohol...
543

You might also read

Related Articles

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

Sort by
Same author

Agreement Between an Artificial Intelligence-Based Meal Image Recognition System and the Weighed Dietary Record for Estimating Energy and Nutrient Intakes.

Nutrients·2026
Same author

360CityGML: Realistic and Interactive Urban Visualization System Integrating CityGML Model and 360$^{\circ }$ Videos.

IEEE transactions on visualization and computer graphics·2025
Same author

Validity of Digital Photographic Images for Dietary Assessment of Participants with Low Frequency of Home-Made Meal Intake.

Journal of nutritional science and vitaminology·2025
Same author

Statistical characteristics of comic panel viewing times.

Scientific reports·2023
Same author

Field-of-View IoU for Object Detection in 360° Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same author

Elastic shell theory for plant cell wall stiffness reveals contributions of cell wall elasticity and turgor pressure in AFM measurement.

Scientific reports·2022

Related Experiment Video

Updated: Jan 26, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

830

Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features.

Shota Horiguchi, Daiki Ikami, Kiyoharu Aizawa

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 17, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Distance metric learning (DML) and softmax-based networks are compared for feature extraction in computer vision. This study provides objective comparisons between these two feature extraction approaches using identical network architectures.

    More Related Videos

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.4K
    Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
    05:00

    Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

    Published on: August 9, 2024

    1.9K

    Related Experiment Videos

    Last Updated: Jan 26, 2026

    Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
    09:09

    Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

    Published on: September 27, 2024

    830
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.4K
    Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
    05:00

    Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

    Published on: August 9, 2024

    1.9K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • End-to-end distance metric learning (DML) is utilized for extracting features in computer vision.
    • Previous studies lack equitable comparisons between DML-based and softmax-based network features.

    Purpose of the Study:

    • To present objective comparisons between DML and softmax-based feature extraction methods.
    • To evaluate feature performance under identical network architectures.

    Main Methods:

    • Utilized a consistent network architecture for both DML and softmax approaches.
    • Performed objective comparative analysis of extracted features.

    Main Results:

    • Features extracted via DML and softmax methods were compared.
    • Performance differences were analyzed under controlled conditions.

    Conclusions:

    • The study provides a clear comparison of DML vs. softmax features.
    • Findings contribute to understanding feature extraction in computer vision.