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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
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Complex zeros are the solutions to polynomial equations that include imaginary numbers, specifically, numbers of the form a + bi, where a and b are real numbers and i is the imaginary unit defined by i2=-1. These zeros satisfy the equation P(x) = 0, where P(x) is a polynomial with real or complex coefficients. Since the complex number system includes all real numbers, it provides a complete framework for analyzing all possible roots of a polynomial.Every polynomial of degree n≥1 can be...
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Region of Convergence of Laplace Tarnsform01:20

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The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
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Setting Limits on Supersymmetry Using Simplified Models
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Truncamiento de redes tensoriales anulares mediante fijación de gauge de modo cero

Ihor Sokolov1,2, Yintai Zhang1,3, Jacek Dziarmaga1,2

  • 1Jagiellonian University, Institute of Theoretical Physics, Faculty of Physics, Astronomy and Applied Computer Science, ul. Łojasiewicza 11, 30-348 Kraków, Poland.

Physical review. E
|December 23, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Las redes tensoriales anulares se comprimen de manera ineficiente debido a correlaciones internas. Este estudio presenta un método de optimización local de enlaces que mejora la compresión al utilizar correlaciones de bucles para una mejor truncación, superando la inicialización estándar.

Palabras clave:
redes tensorialescompresiónoptimización de enlacescorrelaciones de buclesiPEPSpMPSTRG

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Área de la Ciencia:

  • Física de la materia condensada
  • Teoría de la información cuántica
  • Métodos numéricos en física

Sus antecedentes:

  • Las redes tensoriales anulares, como los estados de pares de entrelazamiento proyectados infinitos (iPEPS) y los estados de matrices de matrices periódicas (pMPS), exhiben correlaciones internas complejas.
  • Estas correlaciones a menudo conducen a ineficiencias en las técnicas de compresión estándar, lo que dificulta las simulaciones numéricas precisas.

Objetivo del estudio:

  • Desarrollar un método de compresión mejorado para redes tensoriales anulares.
  • Mejorar la eficiencia y precisión de los algoritmos de redes tensoriales optimizando las dimensiones de los enlaces.

Principales métodos:

  • Se propone una novedosa técnica de optimización local de enlaces que aprovecha las correlaciones de bucles locales.
  • El método implica cortar enlaces para definir estados, utilizando su dependencia lineal para la truncación.
  • La dependencia lineal se resuelve utilizando los modos cero del tensor métrico de los estados.

Principales resultados:

  • El método propuesto demuestra errores de truncación iniciales mejorados en comparación con las técnicas de inicialización estándar.
  • Aplicación exitosa a estados de pares de entrelazamiento proyectados infinitos (iPEPS) y estados de matrices de matrices periódicas (pMPS) dentro del contexto del grupo de renormalización tensorial (TRG).
  • Validación a través de varios ejemplos ilustrativos que muestran un rendimiento superior.

Conclusiones:

  • La optimización local de enlaces ofrece un enfoque más eficaz para comprimir redes tensoriales anulares.
  • El método proporciona una mejora significativa en la precisión de la truncación inicial para los estados de redes tensoriales.
  • Esta técnica mejora la eficiencia de las simulaciones numéricas que involucran sistemas cuánticos complejos.