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  2. File:Intensity profiles of Laguerre-Gaussian modes.pdf ...

    en.wikipedia.org/wiki/File:Intensity_profiles_of...

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  3. Total variation denoising - Wikipedia

    en.wikipedia.org/wiki/Total_variation_denoising

    The regularization parameter plays a critical role in the denoising process. When =, there is no smoothing and the result is the same as minimizing the sum of squares.As , however, the total variation term plays an increasingly strong role, which forces the result to have smaller total variation, at the expense of being less like the input (noisy) signal.

  4. Restricted isometry property - Wikipedia

    en.wikipedia.org/wiki/Restricted_isometry_property

    In linear algebra, the restricted isometry property (RIP) characterizes matrices which are nearly orthonormal, at least when operating on sparse vectors. The concept was introduced by Emmanuel Candès and Terence Tao [1] and is used to prove many theorems in the field of compressed sensing. [2]

  5. File:Gaussian and Logistic Normal pdfs.pdf - Wikipedia

    en.wikipedia.org/wiki/File:Gaussian_and_Logistic...

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  6. 4D reconstruction - Wikipedia

    en.wikipedia.org/wiki/4D_reconstruction

    It is sometimes referred to as "4D Gaussian splatting"; however, this naming convention implies the use of 4D Gaussian primitives (parameterized by a 4×4 mean and a 4×4 covariance matrix). Most work in this area still employs 3D Gaussian primitives, applying temporal constraints as an extra parameter of optimization.

  7. Density estimation - Wikipedia

    en.wikipedia.org/wiki/Density_Estimation

    The density estimates are kernel density estimates using a Gaussian kernel. That is, a Gaussian density function is placed at each data point, and the sum of the density functions is computed over the range of the data. From the density of "glu" conditional on diabetes, we can obtain the probability of diabetes conditional on "glu" via Bayes ...

  8. Difference of Gaussians - Wikipedia

    en.wikipedia.org/wiki/Difference_of_Gaussians

    The Laplacian of Gaussian is useful for detecting edges that appear at various image scales or degrees of image focus. The exact values of sizes of the two kernels that are used to approximate the Laplacian of Gaussian will determine the scale of the difference image, which may appear blurry as a result.

  9. TURBOMOLE - Wikipedia

    en.wikipedia.org/wiki/TURBOMOLE

    Gaussian basis sets are used in Turbomole. The functionality of the program concentrates extensively on the electronic structure methods with effective cost-performance characteristics such as density functional theory , [ 3 ] second–order Møller-Plesset [ 4 ] [ 5 ] and coupled cluster theory .