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

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.4K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.4K
Classification of Systems-II01:31

Classification of Systems-II

241
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
241
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

3.6K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
3.6K
Classification of Systems-I01:26

Classification of Systems-I

301
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
301
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.6K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.6K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

35.7K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
35.7K

You might also read

Related Articles

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

Sort by
Same author

Generation of Interactive Knowledge Graphs to Enable Research of the Effects of Trauma Center Organization on Patient Outcomes.

Knowledge graphs and semantic web : 7th international conference, KGSWC 2025, Leipzig, Germany, November 26-28, 2025 : proceedings. KGSWC (Conference) (7th : 2025 : Leipzig, Germany)·2026
Same author

Diurnal Patterns of Accelerometer-Measured Physical Activity and Sleep in Relation to Cancer Risk: Findings From a Nationally Representative Study.

International journal of cancer·2026
Same author

The 100 K protein of adenovirus: character, location, and function.

Archives of virology·2026
Same author

Tendon-Inspired, Fatigue-Resistant Conductive Organohydrogels via Solvent-Exchange-Assisted Mechanical Training.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

A tri-modal contrastive learning framework for protein representation learning.

Cell reports methods·2026
Same author

The triangular drivers of bone aging: mechanistic insights and therapeutic targets in cellular senescence, estrogen deficiency, and gut microenvironment dysregulation.

Frontiers in cell and developmental biology·2026

Related Experiment Video

Updated: Sep 11, 2025

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

1.2K

MuCHEx: A Multimodal Conversational Debugging Tool for Interactive Visual Exploration of Hierarchical Object

Reza Shahriari, Yichi Yang, Danish Nisar Ahmed Tamboli

    IEEE Computer Graphics and Applications
    |August 12, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Debugging complex hierarchical object classification models is challenging. MuCHEx, a multimodal conversational system, uses natural language and visual interaction for easier, context-aware debugging of fine-grained object recognition systems.

    More Related Videos

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
    11:38

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

    Published on: August 23, 2017

    9.9K
    Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
    11:19

    Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

    Published on: March 20, 2018

    10.5K

    Related Experiment Videos

    Last Updated: Sep 11, 2025

    Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
    08:28

    Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

    Published on: March 3, 2023

    1.2K
    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
    11:38

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

    Published on: August 23, 2017

    9.9K
    Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
    11:19

    Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

    Published on: March 20, 2018

    10.5K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Object recognition, especially fine-grained classification, is a core computer vision challenge.
    • Increasing model complexity and data scale in hierarchical classification tasks complicates debugging.
    • Existing debugging methods lack flexibility and adaptive explanations for diverse user needs.

    Purpose of the Study:

    • To introduce MuCHEx, a novel multimodal conversational system for interactive debugging.
    • To enhance the debugging process for hierarchical object classification models.
    • To enable flexible, context-aware exploration during model debugging.

    Main Methods:

    • MuCHEx integrates natural language processing with visual interaction.
    • The system provides adaptive explanations, tailoring information to the user's task.
    • It supports flexible, high-level queries and direct manipulation for debugging.

    Main Results:

    • MuCHEx facilitates more intuitive and efficient debugging of complex models.
    • The multimodal approach combines language expressiveness with visual precision.
    • Context-aware exploration is enabled through adaptive, relevant information surfacing.

    Conclusions:

    • MuCHEx offers a significant advancement in debugging hierarchical object classification systems.
    • The blend of natural language and visual interaction improves user experience and debugging effectiveness.
    • This system addresses the growing complexity of debugging large-scale, fine-grained classification models.