Jove
Visualize
Contact Us

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

252
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
252
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

140
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
140
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.7K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.7K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

708
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
708

You might also read

Related Articles

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

Sort by
Same author

Zero-Shot Evolutionary Architecture Search for Low-Rank Adaptation.

International journal of neural systems·2026
Same author

Graph Embedding Comparator for Evolutionary Neural Architecture Search with Isomorphic Multi-Comparison.

International journal of neural systems·2026
Same author

Differentiable Generative Adversarial Network Architecture Search Guided by Efficient Attention and Fréchet Distance.

International journal of neural systems·2026
Same author

Evolutionary Channel Pruning for Style-Based Generative Adversarial Networks.

International journal of neural systems·2025
Same author

A Compound-Eye-Inspired Multi-Scale Neural Architecture with Integrated Attention Mechanisms.

International journal of neural systems·2025
Same author

Dominant Classifier-assisted Hybrid Evolutionary Multi-objective Neural Architecture Search.

International journal of neural systems·2025
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026
Same journal

Exploratory Study of Color Influence During an Academic Auditory Sustained Attention Test and the Implications for Educational Environments.

International journal of neural systems·2026
Same journal

Abnormal Temporal Variability Organization Associated with Compromised Verbal Memory and Psychotic Symptoms in Schizophrenia Patients and Their First-Degree Relatives.

International journal of neural systems·2026
See all related articles
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 Experiment Video

Updated: Sep 9, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

Lightweight Diffusion Models Based on Multi-Objective Evolutionary Neural Architecture Search.

Yu Xue1, Chunxiao Jiao1, Yong Zhang2

  • 1School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China.

International Journal of Neural Systems
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

We developed Lightweight Diffusion Models based on Multi-Objective Evolutionary Search (LDMOES) to create efficient diffusion models. LDMOES significantly reduces computational costs while maintaining or improving image generation quality.

Keywords:
Lightweight diffusion modelsknowledge distillationmulti-objective evolutionary algorithmneural architecture search

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.0K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

Related Experiment Videos

Last Updated: Sep 9, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.0K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • Diffusion models excel at image generation but suffer from high computational costs and long inference times.
  • Existing acceleration methods primarily focus on inference steps, neglecting diffusion model architecture optimization.
  • Optimizing diffusion model architectures is crucial for developing computationally efficient generative models.

Purpose of the Study:

  • To propose LDMOES (Lightweight Diffusion Models based on Multi-Objective Evolutionary Search), a novel framework for designing efficient UNet-based diffusion models.
  • To leverage multi-objective evolutionary neural architecture search and knowledge distillation for optimizing diffusion model architectures.
  • To reduce the computational complexity of diffusion models without compromising image generation quality.

Main Methods:

  • Implemented a framework combining multi-objective evolutionary neural architecture search with knowledge distillation.
  • Utilized a modular search space within LDMOES to decouple architecture components and enhance search efficiency.
  • Validated the proposed method on diverse datasets including CIFAR-10, Tiny-ImageNet, CelebA-HQ, and LSUN-church.

Main Results:

  • LDMOES achieved approximately 40% reduction in multiply-accumulate operations (MACs) in pixel space, outperforming the teacher model.
  • On the Tiny-ImageNet dataset, the model generated high-quality images with a competitive FID score of 4.16, demonstrating strong generalization.
  • In latent space, MACs were reduced by ~50% with negligible performance loss, and nearly 60% reduction in computational cost on LSUN-church.

Conclusions:

  • LDMOES effectively designs lightweight and efficient UNet-based diffusion models through multi-objective evolutionary search and knowledge distillation.
  • The proposed method significantly reduces computational costs (MACs) in both pixel and latent spaces while maintaining or improving generation quality.
  • LDMOES demonstrates strong effectiveness and transferability across various datasets, offering a promising direction for efficient generative AI.