Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Space Trusses01:25

Space Trusses

1.3K
A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. The space truss is widely used in various construction projects due to its adaptability and capacity to withstand complex loads.
At the core of a space truss lies the fundamental unit known as the tetrahedron. This structure is composed of six members that form a three-dimensional shape...
1.3K
State Space Representation01:27

State Space Representation

610
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
610
Space Trusses: Problem Solving01:29

Space Trusses: Problem Solving

916
A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. Due to its adaptability and capacity to withstand complex loads, the space truss is widely used in various construction projects.
Consider a tripod consisting of a tetrahedral space truss with a ball-and-socket joint at C. Suppose the height and lengths of the horizontal and vertical...
916
Transfer Function to State Space01:23

Transfer Function to State Space

818
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
818
State Space to Transfer Function01:21

State Space to Transfer Function

595
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
595
Rocket Propulsion in Empty Space - I01:13

Rocket Propulsion in Empty Space - I

3.8K
The driving force for the motion of any vehicle is friction, but in the case of rocket propulsion in space, the friction force is not present. The motion of a rocket changes its velocity (and hence its momentum) by ejecting burned fuel gases, thus causing it to accelerate in the direction opposite to the velocity of the ejected fuel. In this situation, the mass and velocity of the rocket constantly change along with the total mass of ejected gases. Due to conservation of momentum, the...
3.8K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Subspace Method of Moments for <i>Ab Initio</i> 3-D Single Particle Cryo-EM Reconstruction.

SIAM journal on imaging sciences·2026
Same author

Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis.

Acta crystallographica. Section D, Structural biology·2026
Same author

FAST EXPANSION INTO HARMONICS ON THE BALL.

SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics·2026
Same author

Bayesian Perspective for Orientation Determination in Cryo-EM with Application to Structural Heterogeneity Analysis.

bioRxiv : the preprint server for biology·2025
Same author

The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge.

bioRxiv : the preprint server for biology·2025
Same author

Image-processing methods for electron microscopy of biological specimens.

Acta crystallographica. Section D, Structural biology·2025

Video Experimental Relacionado

Updated: Feb 12, 2026

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
07:54

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

Published on: September 20, 2012

14.2K

Aprendizaje múltiple en espacios métricos.

Liane Xu1, Amit Singer2

  • 1Program in Applied and Computational Mathematics, Princeton University, USA.

Applied and computational harmonic analysis
|February 11, 2026
PubMed
Resumen

Este estudio introduce un marco generalizado para el aprendizaje múltiple en espacios métricos, que se extiende más allá de la distancia euclidiana. Investiga las condiciones para la convergencia laplaciana del gráfico con métricas alternativas como la distancia de Wasserstein.

Palabras clave:
Los mapas propios laplacianos.Aprendizaje múltiple.El espacio Wasserstein es el espacio de Wasserstein.mapas de difusión de los mapas de difusión.gráfico el laplaciano.

Más Videos Relacionados

A Metric Test for Assessing Spatial Working Memory in Adult Rats Following Traumatic Brain Injury
05:53

A Metric Test for Assessing Spatial Working Memory in Adult Rats Following Traumatic Brain Injury

Published on: May 7, 2021

3.9K
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.2K

Videos de Experimentos Relacionados

Last Updated: Feb 12, 2026

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
07:54

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

Published on: September 20, 2012

14.2K
A Metric Test for Assessing Spatial Working Memory in Adult Rats Following Traumatic Brain Injury
05:53

A Metric Test for Assessing Spatial Working Memory in Adult Rats Following Traumatic Brain Injury

Published on: May 7, 2021

3.9K
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.2K

Área de la Ciencia:

  • Aprendizaje automático Aprendizaje automático.
  • Ciencia de datos Ciencia de datos.
  • Topología de la topología.

Sus antecedentes:

  • Los métodos basados en Laplacio son ampliamente utilizados para la reducción de dimensionalidad de datos en el espacio euclidiano (RN).
  • Las garantías teóricas para estos métodos a menudo se basan en la distancia euclidiana que se aproxima a las distancias geodésicas en subvariedades de datos.
  • Las métricas de distancia alternativas, como la distancia de Wasserstein, pueden ser más adecuadas para ciertos conjuntos de datos que la distancia euclidiana.

Objetivo del estudio:

  • Para generalizar el aprendizaje múltiple a espacios métricos arbitrarios.
  • Establecer las condiciones teóricas para la convergencia de los grafos laplacianos cuando se utilizan métricas no euclidianas.
  • Explorar la aplicabilidad de métricas más allá de la distancia euclidiana en la reducción de dimensionalidad.

Principales métodos:

  • Desarrollo de un marco teórico generalizado para el aprendizaje múltiple en espacios métricos.
  • Análisis de la convergencia puntual del gráfico con el operador laplaciano.
  • Investigación de las propiedades métricas requeridas para las garantías de convergencia.

Principales resultados:

  • Se presenta un marco que extiende el aprendizaje múltiple a espacios métricos generales.
  • Se identifican condiciones suficientes para la convergencia puntual del gráfico laplaciano en estas configuraciones generalizadas.
  • El estudio demuestra la posibilidad teórica de utilizar métricas como la distancia de Wasserstein para la reducción de dimensionalidad.

Conclusiones:

  • El marco propuesto amplía la aplicabilidad de las técnicas de reducción de dimensionalidad basadas en Laplacio.
  • Los hallazgos proporcionan bases teóricas para el uso de diversas métricas de distancia en el aprendizaje múltiple.
  • Esta investigación abre caminos para aplicar la reducción de dimensionalidad avanzada a estructuras de datos complejas donde la distancia euclidiana es subóptima.