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

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

You might also read

Related Articles

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

Sort by
Same author

UncerTrans: uncertainty-aware temporal transformer for early action prediction.

Scientific reports·2026
Same author

Uncovering the inherent link between pseudo-graphite structure and closed-pore formation in hard carbon.

Journal of colloid and interface science·2025
Same author

Shape memory bone screws loading L-arginine and Ca<sup>2+</sup> propagate mechanical stimulation, energize bone cells and augment bone regeneration.

Bioactive materials·2025
Same author

Subdivision method for rational ANCF circular elements.

Scientific reports·2025
Same author

Gut-bone axis perturbation: Mechanisms and interventions via gut microbiota as a primary driver of osteoporosis.

Journal of orthopaedic translation·2025
Same author

Unlocking the future potential of SWATH-MS: Advancing non-target screening workflow for the qualitative and quantitative analysis of emerging contaminants.

Water research·2025
Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026
Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026
Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026
Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026
Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026
Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Learning multi-granularity skeleton representations via hierarchical graph contrastive learning.

Yaxiong Liu1, Xianfeng Zhai2,3

  • 1Qinghai Normal University, Qinghai, 810000, China.

Scientific Reports
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

Hierarchical Graph Contrastive Learning (HGCL) enhances skeleton-based action recognition by addressing label reliance and data augmentation issues. This method improves accuracy and reduces feature distances, outperforming existing models in various scenarios.

Related Experiment Videos

Last Updated: Jun 7, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Skeleton-based action recognition is robust to environmental variations but struggles with data requirements and augmentation.
  • Existing methods often fail to effectively utilize joint confidence and can disrupt action topology during augmentation.

Purpose of the Study:

  • To introduce Hierarchical Graph Contrastive Learning (HGCL) for improved skeleton-based action recognition.
  • To address challenges of label reliance, topological destruction during augmentation, and underutilized joint confidence in a unified framework.

Main Methods:

  • HGCL employs a novel augmentation strategy with topology constraints and adaptive perturbation.
  • A hierarchical contrastive objective balances global and part-level motion semantics.
  • Joint detection confidence is integrated into graph convolution and representation aggregation.

Main Results:

  • HGCL achieved 91.2% (UCF101) and 66.8% (HMDB51) Top-1 accuracy, surpassing InfoGCN.
  • Significant improvements were observed in few-shot learning (21.6 pp on UCF101) and under noisy conditions.
  • Feature analysis showed reduced intra-class (54.5%) and increased inter-class (90.3%) distances.

Conclusions:

  • HGCL offers a robust and effective approach to skeleton-based action recognition.
  • The unified framework successfully tackles key limitations of previous methods.
  • HGCL demonstrates strong performance in few-shot learning and noisy environments, validating its self-supervised representation learning capabilities.