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Self-Awareness and Its Effects01:21

Self-Awareness and Its Effects

318
Self-awareness is a psychological state in which the individual becomes the focal point of their attention. This inward focus transforms the self into an object of contemplation and assessment, influencing how individuals perceive their actions and their alignment with personal and societal standards.Triggers and Contexts for Self-AwarenessSelf-awareness can be activated by external stimuli that make individuals visually or audibly aware of themselves, such as mirrors, cameras, or recordings.
318
Altered States of Awareness01:06

Altered States of Awareness

1.2K
Altered states of consciousness represent significant deviations from one's normal mental state. These deviations can range from subtle changes in awareness to profound transformations in perception, thought processes, and sensory experiences. Altered states of consciousness can be triggered by various factors, including drug use, meditation, hypnosis, illness, or even intense fatigue.
The ingestion of substances like stimulants or hallucinogens leads to chemical alterations in the brain...
1.2K
Subconsciousness and No Awareness01:15

Subconsciousness and No Awareness

721
The concept of subconscious awareness refers to the processing of information below the level of conscious thought, which significantly influences both behaviors and decisions. It is also known as waking subconscious awareness. This complex level of cognition operates without the direct awareness of the individual, facilitating rapid and simultaneous handling of multiple information streams.
An illustrative example of subconscious processing is its role in problem-solving. Often, individuals...
721
Passive Filters01:27

Passive Filters

1.0K
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
1.0K
High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

786
Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
786
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

17.8K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Updated: Feb 12, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
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Eficiencia en el uso de recursos y conocimiento de la interdependencia de capas CNN poda aprovechando el reemplazo de

S Tofigh, M Askarizadeh, M Omair Ahmad

    IEEE transactions on neural networks and learning systems
    |February 10, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio introduce el reemplazo de filtros (FR) para la poda de la red neuronal convolucional (CNN), mejorando la precisión y la eficiencia. El nuevo método libre de datos mejora la eficiencia de los recursos en los modelos de CNN.

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    Área de la Ciencia:

    • Ciencias de la computación Ciencias de la computación
    • La inteligencia artificial es inteligencia artificial.
    • Aprendizaje automático Aprendizaje automático.

    Sus antecedentes:

    • Los métodos tradicionales de poda de redes neuronales convolucionales (CNN) a menudo usan criterios heurísticos, lo que lleva a un rendimiento inconsistente y una generalización limitada.
    • Las técnicas de poda existentes pueden carecer de eficiencia y adaptabilidad para los modelos modernos de aprendizaje profundo.

    Objetivo del estudio:

    • Introducir un nuevo marco de reemplazo de filtros (FR) para la poda de CNN, tratando la poda como el reemplazo de filtros con filtros cero.
    • Desarrollar un algoritmo de poda eficiente, libre de datos y de baja complejidad derivando un límite de error y definiendo una función de importancia submodular.
    • Ampliar el marco de FR con reemplazos óptimos de filtros distintos de cero e introducir una métrica de eficiencia de recursos (ER).

    Principales métodos:

    • Propuso un marco de reemplazo de filtros (FR) para la poda de CNN.
    • Derivado un límite superior en el error absoluto para definir una eficiente, $\gamma $- débilmente submodular función de importancia.
    • Desarrolló un algoritmo oblivious libre de datos para la selección de filtros y amplió el FR con técnicas de mejor aproximación para el reemplazo de filtros.
    • Se introdujo una métrica de eficiencia de recursos (ER) para evaluar los métodos de poda.

    Principales resultados:

    • Logró resultados de vanguardia en redes de referencia y conjuntos de datos.
    • Demostró una reducción del 25,5% en los parámetros de red para ResNet-50 en ImageNet, mejorando la precisión del 75,15% al 76,52%.
    • El método de poda consciente de la interdependencia de capas (LIAP, por sus siglas en inglés) mostró una eficiencia hasta 1011 veces mayor en comparación con las técnicas existentes.

    Conclusiones:

    • El marco propuesto de reemplazo de filtros (FR) ofrece un enfoque efectivo y eficiente para la poda de CNN.
    • El algoritmo libre de datos y de baja complejidad y los reemplazos optimizados de filtros logran un rendimiento superior y una eficiencia de recursos superior.
    • Este trabajo establece un nuevo estándar para la poda de CNN consciente de los recursos, equilibrando la precisión y la compresión del modelo.