Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Transformers in Distribution System

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...

Energy Losses in Transformers

Energy Losses in Transformers

In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality, the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be the high resistance of the...

Transformers with Off-Nominal Turns Ratios

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...

You might also read

Related Articles

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

Sort by

Same author

Circular inference in visual cortical hierarchies in schizophrenia.

Psychiatry and clinical neurosciences·2026

Same author

Behavioral and psychological factors associated with sleep-related symptoms and daytime sleepiness in Japanese Sports and Health Sciences undergraduates: a cross-sectional study.

Sleep and biological rhythms·2026

Same author

Developmental oligodendrocytes regulate brain function through the mediation of synchronized spontaneous activity.

eLife·2026

Same author

IL-6 and TNFα are associated with depressive symptoms among men in a community-based cohort, with a tentative trend for IL-17A: Findings from the Shika Study in Japan.

Neuro endocrinology letters·2026

Same author

Predictors and Procedural Outcomes of Additional Balloon Dilatation Success Following Vessel Preparation Failure in Femoropopliteal Lesions.

Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists·2026

Same author

Dietary omega-3 polyunsaturated fatty acids relate to lower liver lipids and adipose-tissue insulin resistance, modulated by fatty acid desaturase 1 rs174546 in type 2 diabetes.

Clinical nutrition (Edinburgh, Scotland)·2026

Search research articles

Related Experiment Video

Updated: Sep 15, 2025

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Data stream-pairwise bottleneck transformer for engagement estimation from video conversation.

Keita Suzuki¹, Nobukatsu Hojo¹, Kazutoshi Shinoda¹

¹NTT Human Informatics Laboratories, NTT Corporation, Yokosuka, Japan.

Frontiers in Artificial Intelligence

|July 14, 2025

Summary

This study introduces a new joint model for analyzing multiparty conversations using video and audio. The model effectively captures participant engagement by processing all data streams together, outperforming previous methods.

Keywords:

classification engagement global token multimodal multiparty conversation transformer

More Related Videos

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Related Experiment Videos

Last Updated: Sep 15, 2025

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Modeling participant engagement in multiparty conversations requires effective handling of multimodal data streams (video, audio).
Previous methods using global token-based transformers faced challenges with redundancy in participant-feature estimation across modalities and participants.

Purpose of the Study:

To develop and evaluate a novel joint model for assessing participant engagement in multiparty conversations.
To address the redundancy issue in standard cross-attention transformers for multimodal interaction modeling.

Main Methods:

A joint model utilizing global token-based transformers was proposed, treating all data streams (video, audio) without distinguishing cross-modal or cross-participant interactions.
The model was experimented on the RoomReader corpus to assess its performance.

Main Results:

The proposed joint model demonstrated superior performance compared to previous approaches.
Achieved accuracy scores ranging from 0.720 to 0.763.
Weighted F1 scores ranged from 0.733 to 0.771, and macro F1 scores ranged from 0.236 to 0.277.

Conclusions:

The developed joint model effectively models interactions among all data streams for assessing participant engagement.
The findings suggest that a unified approach to multimodal interaction modeling can improve performance in analyzing complex conversational dynamics.