Displaying similar documents to “TeX in a Nutshell”

Wildfires identification: Semantic segmentation using support vector machine classifier

Pecha, Marek, Langford, Zachary, Horák, David, Tran Mills, Richard

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This paper deals with wildfire identification in the Alaska regions as a semantic segmentation task using support vector machine classifiers. Instead of colour information represented by means of BGR channels, we proceed with a normalized reflectance over 152 days so that such time series is assigned to each pixel. We compare models associated with 𝓁 1 -loss and 𝓁 2 -loss functions and stopping criteria based on a projected gradient and duality gap in the presented benchmarks.

Mobile TeX: Porting TeX to the iPad

Arthur Reutenauer (2011)

Zpravodaj Československého sdružení uživatelů TeXu

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The paper presents the achievement of Richard Koch, amongst others author of TeXShop and MacTeX developer, who has successfully compiled and used TeX on Apple's iPad.

TeX Live

CSTUG editorial board (1996)

Zpravodaj Československého sdružení uživatelů TeXu

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On the least common multiple of Lucas subsequences

Shigeki Akiyama, Florian Luca (2013)

Acta Arithmetica

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We compare the growth of the least common multiple of the numbers u a 1 , . . . , u a n and | u a 1 ⋯ u a n | , where ( u n ) n ≥ 0 is a Lucas sequence and ( a n ) n ≥ 0 is some sequence of positive integers.

A depth-based modification of the k-nearest neighbour method

Ondřej Vencálek, Daniel Hlubinka (2021)

Kybernetika

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We propose a new nonparametric procedure to solve the problem of classifying objects represented by d -dimensional vectors into K ≥ 2 groups. The newly proposed classifier was inspired by the k nearest neighbour (kNN) method. It is based on the idea of a depth-based distributional neighbourhood and is called k nearest depth neighbours (kNDN) classifier. The kNDN classifier has several desirable properties: in contrast to the classical kNN, it can utilize global properties of the considered...

Theoretical analysis for ℓ 1 - ℓ 2 minimization with partial support information

Haifeng Li, Leiyan Guo (2025)

Applications of Mathematics

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We investigate the recovery of k -sparse signals using the ℓ 1 - ℓ 2 minimization model with prior support set information. The prior support set information, which is believed to contain the indices of nonzero signal elements, significantly enhances the performance of compressive recovery by improving accuracy, efficiency, reducing complexity, expanding applicability, and enhancing robustness. We assume k -sparse signals 𝐱 with the prior support T which is composed of g true indices and b wrong...

Seasonal time-series imputation of gap missing algorithm (STIGMA)

Eduardo Rangel-Heras, Pavel Zuniga, Alma Y. Alanis, Esteban A. Hernandez-Vargas, Oscar D. Sanchez (2023)

Kybernetika

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This work presents a new approach for the imputation of missing data in weather time-series from a seasonal pattern; the seasonal time-series imputation of gap missing algorithm (STIGMA). The algorithm takes advantage from a seasonal pattern for the imputation of unknown data by averaging available data. We test the algorithm using data measured every 10 minutes over a period of 365 days during the year 2010; the variables include global irradiance, diffuse irradiance, ultraviolet irradiance,...

On the distribution of consecutive square-free primitive roots modulo p

Huaning Liu, Hui Dong (2015)

Czechoslovak Mathematical Journal

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A positive integer n is called a square-free number if it is not divisible by a perfect square except 1 . Let p be an odd prime. For n with ( n , p ) = 1 , the smallest positive integer f such that n f ≡ 1 ( mod p ) is called the exponent of n modulo p . If the exponent of n modulo p is p - 1 , then n is called a primitive root mod p . Let A ( n ) be the characteristic function of the square-free primitive roots modulo p . In this paper we study the distribution ∑ n ≤ x A ( n ) A ( n + 1 ) , and give an asymptotic formula by using properties of character...