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

Machines: Problem Solving II01:30

Machines: Problem Solving II

774
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
774
Machines: Problem Solving I01:22

Machines: Problem Solving I

829
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
829
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.7K
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...
5.7K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.5K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.5K
The Representativeness Heuristic02:13

The Representativeness Heuristic

17.1K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
17.1K
Machines01:19

Machines

736
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
736

You might also read

Related Articles

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

Sort by
Same author

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images.

Journal of healthcare engineering·2017
Same author

Brain tumor segmentation with Deep Neural Networks.

Medical image analysis·2016
Same author

The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions.

IEEE transactions on pattern analysis and machine intelligence·2015
Same author

[Quality by design based high shear wet granulation process development for the microcrystalline cellulose].

Yao xue xue bao = Acta pharmaceutica Sinica·2015
Same author

Health and climate change: policy responses to protect public health.

Lancet (London, England)·2015
Same author

Effect of ear-acupoint pressing and Ear Apex (HX6,7) bloodletting on haemorheology in chloasma patients with Gan depression pattern.

Chinese journal of integrative medicine·2015
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Apr 18, 2026

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

3.2K

Challenges in representation learning: a report on three machine learning contests.

Ian J Goodfellow1, Dumitru Erhan2, Pierre Luc Carrier1

  • 1Université de Montréal, Montréal QC H3T 1N8, Canada.

Neural Networks : the Official Journal of the International Neural Network Society
|January 24, 2015
PubMed
Summary
This summary is machine-generated.

The ICML 2013 Workshop on Representation Learning highlighted key challenges in machine learning, including black box, facial expression, and multimodal learning. Datasets and competition results were summarized, offering insights for future machine learning challenges.

Keywords:
CompetitionDatasetRepresentation learning

Related Experiment Videos

Last Updated: Apr 18, 2026

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

3.2K

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision

Background:

  • The International Conference on Machine Learning (ICML) 2013 hosted a workshop focused on critical challenges in representation learning.
  • Representation learning is crucial for developing effective machine learning models.

Purpose of the Study:

  • To detail the datasets created for the ICML 2013 Representation Learning challenges.
  • To summarize the outcomes of the competitions held during the workshop.
  • To offer recommendations for future machine learning challenge organizers.

Main Methods:

  • Description of datasets curated for three specific challenges: black box learning, facial expression recognition, and multimodal learning.
  • Analysis of competition results from participants addressing these challenges.

Main Results:

  • Summary of performance across the different representation learning challenges.
  • Identification of trends and insights derived from the competition data.

Conclusions:

  • The workshop provided valuable datasets and insights into representation learning.
  • Machine learning competitions offer a platform for advancing research and knowledge in the field.
  • Recommendations are provided for enhancing future machine learning challenges.