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First Pass Effect01:12

First Pass Effect

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Presystemic elimination, or the first-pass effect, is the metabolism of drugs that reduces their effective concentration at the site of action. Apart from the first-pass effect, the systemic bioavailability of the drug is also reduced by other factors, including incomplete absorption or chemical degradation of drugs.
Depending on the route of administration, drugs can be metabolized in the liver, intestine, lungs, and vasculature. Orally administered drugs are first absorbed through the...
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Adjusting a Traverse01:12

Adjusting a Traverse

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In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
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Crossover Experiments01:16

Crossover Experiments

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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
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Reflex Activity01:08

Reflex Activity

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A reflex activity is an automatic, involuntary response to specific stimuli. It is a part of our survival mechanism, designed to protect us from potential harm. For example, when a bright light suddenly shines into our eyes, we instinctively close them or look away. This is a simple reflex activity orchestrated by the nervous system without conscious thought or effort.
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Introspection01:29

Introspection

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Introspection, long upheld as a reliable route to self-knowledge, involves examining one's thoughts, emotions, and mental processes. It underpins many psychological practices, from mindfulness meditation to psychotherapy and self-help strategies. However, empirical evidence challenges the accuracy of introspection as a means of understanding oneself.Limitations of Introspective InsightSeminal work by Nisbett and Wilson demonstrated that individuals are frequently unaware of the true causes...
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Homologous Recombination02:31

Homologous Recombination

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Video Experimental Relacionado

Updated: Nov 16, 2025

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
06:17

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Published on: January 26, 2024

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Primero regresa, luego explora.

Adrien Ecoffet1,2, Joost Huizinga3,4, Joel Lehman5,6

  • 1Uber AI Labs, San Francisco, CA, USA. adrienecoffet@gmail.com.

Nature
|February 25, 2021
PubMed
Resumen
Este resumen es generado por máquina.

Los algoritmos Go-Explore mejoran el aprendizaje por refuerzo recordando los estados y volviendo a ellos antes de explorar. Este enfoque resuelve juegos no resueltos previamente y avanza en las capacidades de exploración de IA.

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Área de la Ciencia:

  • Inteligencia artificial
  • Aprendizaje automático
  • La robótica

Sus antecedentes:

  • El aprendizaje por refuerzo (RL) tiene como objetivo la toma de decisiones autónoma, pero lucha con señales de recompensa escasas o engañosas.
  • La exploración eficaz del medio ambiente es crucial para RL, pero sigue siendo un desafío importante.
  • Los algoritmos de RL existentes a menudo olvidan los estados visitados anteriormente o no los revisan antes de explorar otros nuevos.

Objetivo del estudio:

  • Para hacer frente a los desafíos de desprendimiento y descarrilamiento en la exploración de aprendizaje por refuerzo.
  • Para introducir un nuevo algoritmo, Go-Explore, diseñado para una exploración más eficaz del entorno.
  • Para demostrar la eficacia de Go-Explore en juegos complejos y tareas de robótica.

Principales métodos:

  • Introdujo Go-Explore, una familia de algoritmos que hace hincapié en recordar los estados prometedores y volver a ellos antes de la exploración.
  • Aplicado Go-Explore a juegos de Atari no resueltos anteriormente y puntos de referencia de exploración dura.
  • Probado Go-Explore en una tarea robótica escasa de recoger y colocar.
  • Políticas integradas condicionadas por objetivos para mejorar aún más la eficiencia de la exploración y gestionar la estocasticidad.

Principales resultados:

  • Go-Explore resolvió con éxito todos los juegos de Atari no resueltos anteriormente.
  • Logró un rendimiento de vanguardia en juegos de exploración dura, con mejoras significativas en la venganza de Montezuma y Pitfall.
  • Demostró su aplicabilidad práctica en una tarea robótica de escasa recompensa.
  • Las políticas condicionadas a objetivos mejoraron la eficiencia y la robustez de la exploración de Go-Explore hasta la estocasticidad.

Conclusiones:

  • Los principios de recordar, regresar y explorar desde los estados son un enfoque poderoso y general para la exploración de RL.
  • Go-Explore ofrece ganancias sustanciales de rendimiento, lo que sugiere una vía crítica hacia agentes de aprendizaje más inteligentes.
  • Los hallazgos ponen de relieve la importancia de las estrategias de exploración estructuradas para superar las limitaciones de RL.