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

Encoding01:19

Encoding

882
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
882
Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)

1.7K
Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
The central atom need not be NMR-active because its electrons are affected by the electron polarization of the spin-active atoms. However, spin information is transmitted less effectively than in one-bond coupling, and 2J values are usually weaker than 1J values. The energy of...
1.7K
Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)01:22

Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)

1.5K
Vicinal or three-bond coupling is commonly observed between protons attached to adjacent carbons. Here, nuclear spin information is primarily transferred via electron spin interactions between adjacent C‑H bond orbitals. This generally favors the antiparallel arrangement of spins, so 3J values are usually positive.
The extent of coupling depends on the C‑C bond length, the two H‑C‑C angles, any electron-withdrawing substituents, and the dihedral angle between the involved orbitals. The...
1.5K
G-protein Coupled Receptors01:21

G-protein Coupled Receptors

132.2K
G-protein coupled receptors are ligand binding receptors that indirectly affect changes in the cell. The actual receptor is a single polypeptide that transverses the cell membrane seven times creating intracellular and extracellular loops. The extracellular loops create a ligand specific pocket which binds to neurotransmitters or hormones. The intracellular loops holds onto the G-protein.
132.2K
Spin–Spin Coupling: One-Bond Coupling01:17

Spin–Spin Coupling: One-Bond Coupling

1.5K
Coupling interactions are strongest between NMR-active nuclei bonded to each other, where spin information can be transmitted directly through the pair of bonding electrons. While nuclei polarize their electrons to the opposite spins, the bonding electron pair has opposite spins. Configurations with antiparallel nuclear spins are expected to be lower in energy. When coupling makes antiparallel states more favorable, J is considered to have a positive value. The one-bond coupling constant, 1J,...
1.5K
Couple01:29

Couple

1.0K
A couple is a pair of parallel forces equal in magnitude but in opposite directions. The forces are separated by a perpendicular distance, known as the couple's arm. The couple causes a rotation force or moment that rotates the body about an axis perpendicular to the plane of the forces. The resulting moment is referred to as the couple moment. The SI unit of a couple moment is the Newton-meter (N-m).
A typical example to understand this concept is tightening a bolt with a lug wrench. A...
1.0K

You might also read

Related Articles

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

Sort by
Same author

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same author

Uncovering Hierarchical Asymmetries in Artificial Intelligence Transformation: Navigating the Bright and Dark Sides Across Organizational Levels.

Journal of visualized experiments : JoVE·2026
Same author

Effects of divalent cations on diffusion dynamics of biological water confined between lipid membranes.

The Journal of chemical physics·2026
Same author

Neural Network Copulas for Generating Synthetic Test Data Preserving Psychometric Properties.

Journal of Intelligence·2026
Same author

Composite marginal likelihood estimation of higher-order diagnostic classification models under high dimensionality.

The British journal of mathematical and statistical psychology·2026
Same author

A Landmark-Guided Dual-Stream Synergistic Framework for Automated Intracranial Aneurysm Detection in Magnetic Resonance Angiography.

Journal of imaging informatics in medicine·2026
Same journal

A practical design of backdoor trigger under frequency-based orthogonality constraints.

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

EEG fine-grained visual semantic decoding via a multimodal framework.

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

Collaborative-adversarial jailbreaking: A propagation-aware attack framework for multi-agent code generation systems.

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

Theoretical analysis of the denoising autoencoder using Tweedie's formula.

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

Frequency-based cross-attention fusion network for RGB-D salient object detection.

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

HTNet: A self-supervised heterogeneous triple network for multi-modal data.

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

Related Experiment Video

Updated: Feb 14, 2026

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors
16:16

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors

Published on: September 13, 2013

15.8K

Coupled generative adversarial stacked Auto-encoder: CoGASA.

Mohammad Ahangar Kiasari1, Dennis Singh Moirangthem1, Minho Lee1

  • 1School of Electronics Engineering, IT1, Kyungpook National University, 80 Daehakro, Bukgu, Daegu - 41566, South Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|February 12, 2018
PubMed
Summary
This summary is machine-generated.

We introduce Coupled Generative Adversarial Stacked Auto-encoder (CoGASA) for efficient cross-domain image transformation. This method handles noisy data and reduces computation time, outperforming existing models.

Keywords:
Coupled GANGenerative adversarial networksImage transformationStacked Auto-encoders

More Related Videos

Evaluating Skilled Prehension in Mice Using an Auto-Trainer
05:01

Evaluating Skilled Prehension in Mice Using an Auto-Trainer

Published on: September 12, 2019

6.1K
Scaling of Engineered Vascular Grafts Using 3D Printed Guides and the Ring Stacking Method
09:38

Scaling of Engineered Vascular Grafts Using 3D Printed Guides and the Ring Stacking Method

Published on: March 27, 2017

8.8K

Related Experiment Videos

Last Updated: Feb 14, 2026

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors
16:16

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors

Published on: September 13, 2013

15.8K
Evaluating Skilled Prehension in Mice Using an Auto-Trainer
05:01

Evaluating Skilled Prehension in Mice Using an Auto-Trainer

Published on: September 12, 2019

6.1K
Scaling of Engineered Vascular Grafts Using 3D Printed Guides and the Ring Stacking Method
09:38

Scaling of Engineered Vascular Grafts Using 3D Printed Guides and the Ring Stacking Method

Published on: March 27, 2017

8.8K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Coupled Generative Adversarial Network (CoGAN) models joint distributions but struggles with noisy data and computational efficiency.
  • Existing methods for cross-domain image transformation are often inefficient and sensitive to input noise.

Purpose of the Study:

  • To propose a novel method, Coupled Generative Adversarial Stacked Auto-encoder (CoGASA), for robust and efficient cross-domain image transformation.
  • To address the limitations of CoGAN in handling noisy data and reducing computational cost.

Main Methods:

  • Developed the Coupled Generative Adversarial Stacked Auto-encoder (CoGASA) architecture.
  • Implemented a direct data transfer mechanism between different domains.
  • Evaluated the model on MNIST and CelebA datasets.

Main Results:

  • CoGASA demonstrates robustness to noise in input data.
  • The proposed method significantly reduces computation time compared to CoGAN.
  • Achieved highly competitive performance in cross-domain image transformation tasks.

Conclusions:

  • CoGASA offers an efficient and noise-robust solution for cross-domain image transformation.
  • The model facilitates easy transfer of images into target domains with minimal effort.
  • CoGASA presents a promising advancement in generative adversarial networks for practical applications.