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Distillation: Vapor–Liquid Equilibria01:01

Distillation: Vapor–Liquid Equilibria

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Distillation is a separation technique that takes advantage of the boiling point properties of disparate elements in a mixture. To perform distillation, we begin by heating a miscible mixture of two liquids with a significant difference in boiling points (at least 20°C). As the solution heats up and reaches the bubble point of the more volatile component, some molecules of the more volatile component transition into the gas phase and travel upward into the condenser, which is a glass tube...
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pH Scale02:41

pH Scale

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Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
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Scaling01:26

Scaling

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In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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Thermometers and Temperature Scales01:22

Thermometers and Temperature Scales

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Any physical property that depends consistently and reproducibly on temperature can be used as the basis of a thermometer. For example, volume increases with temperature for most substances. This property is the basis for the common alcohol thermometer and the original mercury thermometers. Other properties used to measure temperature include electrical resistance, color, and the emission of infrared radiation.
As many physical properties depend on temperature, the variety of thermometers is...
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Labeling Emotion01:20

Labeling Emotion

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Gas Thermometers and the Kelvin Scale01:22

Gas Thermometers and the Kelvin Scale

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The definition of temperature in terms of molecular motion suggests that there should be a lowest possible temperature, where the average kinetic energy of molecules is zero (or the minimum allowed by quantum mechanics). Experiments confirm the existence of such a temperature, called absolute zero. An absolute temperature scale is one whose zero point is absolute zero. Such scales are convenient in science because several physical quantities, such as the volume of an ideal gas, are directly...
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A User-friendly and Powerful R Analysis of Large-scale Datasets
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Poda y Cuantificación de Etiquetas Suaves para la Destilación de Conjuntos de Datos a Gran Escala

Lingao Xiao, Yang He

    IEEE transactions on pattern analysis and machine intelligence
    |February 13, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    La destilación de conjuntos de datos enfrenta desafíos de almacenamiento debido a las etiquetas suaves grandes. Nuestro método LPQLD reduce significativamente el tamaño de las etiquetas y mejora la precisión para conjuntos de datos a gran escala como ImageNet.

    Palabras clave:
    Destilación de conjuntos de datosPoda de etiquetasCuantificación de etiquetasCompresión de datosVisión por computadoraAprendizaje automático

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    Área de la Ciencia:

    • Visión por Computadora
    • Aprendizaje Automático
    • Compresión de Datos

    Sus antecedentes:

    • La destilación de conjuntos de datos a gran escala requiere un almacenamiento sustancial para etiquetas suaves auxiliares, a menudo cientos de veces más grandes que las imágenes condensadas.
    • Los métodos existentes se ven obstaculizados por una diversidad insuficiente de imágenes y una diversidad de supervisión, lo que lleva a una degradación del rendimiento a altas tasas de compresión.

    Objetivo del estudio:

    • Abordar los problemas de almacenamiento y rendimiento en la destilación de conjuntos de datos a gran escala.
    • Proponer un método novedoso, Poda y Cuantificación de Etiquetas para Destilación a Gran Escala (LPQLD), para una compresión eficiente de conjuntos de datos.

    Principales métodos:

    • Mejora de la diversidad de imágenes a través de la agrupación por clases y la supervisión de la Normalización por Lotes (BN) durante la generación de datos sintéticos.
    • Mejora de la diversidad de supervisión a través de la Poda de Etiquetas con Reutilización Dinámica del Conocimiento y la Cuantificación de Etiquetas con Alineación Calibrada de Estudiante-Profesor.

    Principales resultados:

    • Reducción del almacenamiento de etiquetas suaves 78 veces en ImageNet-1K y 500 veces en ImageNet-21K.
    • Logro de mejoras en la precisión de hasta 7,2% en ImageNet-1K y 2,8% en ImageNet-21K.
    • Demostración de superioridad en varias arquitecturas de red y en comparación con otros métodos de destilación.

    Conclusiones:

    • LPQLD supera eficazmente las limitaciones del gran almacenamiento de etiquetas suaves en la destilación de conjuntos de datos.
    • El método propuesto logra relaciones de compresión significativas al tiempo que mejora la precisión del modelo.
    • LPQLD representa un enfoque superior para la destilación eficiente de conjuntos de datos a gran escala.