Este lunes comenzó el Web Summit en Lisboa, Portugal, uno de los congresos de tecnología más destacados a nivel global. Con la participación de más de 3.000 empresas de 160 países y la asistencia de más de 1.000 inversores, el evento se perfila como un epicentro para el sector tecnológico mundial, reuniendo a un público […]

Artículo publicado en : Invest in Bogota Presenta Agenda de Trabajo en Web Summit de Portugal para Atraer Inversionistas

El ejecutivo portugués ha logrado convertir a su grupo en el primer fabricante de vehículos en nuestro país. Y debería aprobar dos nuevos megaproyectos que suman 4.000 millones de euros. Leer

El presidente de Aragón ha compartido, durante casi 3 horas, un discurso centrado en las familias y la prosperidad, en el que ha mostrado su firme rechazo a las políticas del Ejecutivo nacional Leer

El ministro tacha a José Luis Sanz de "mentiroso" y dice que no piensa recibirle. Hace sólo un mes también insultó a la alcaldesa de Huelva por acudir al Senado Leer

Panelistas consideran que el gobierno no ha logrado administrar la casa. Creen que estas polémicas desvían la atención de los temas importantes.

Read the in depth Review of Inversion Gaming. Know detailed info about Inversion configuration, design and performance quality along with pros & cons, Digit rating, verdict based on user opinions/feedback.

Düsseldorf : düsseldorf university press, [2021]

Indicated for an Expanded List of Back Pain Related Conditions

Bill George and Mihir Desai, professors at Harvard Business School, explain why our corporate tax code is driving American business overseas.

We use the classical Fourier analysis to introduce analytic families of weighted differential operators on the unit sphere. These operators are polynomial functions of the usual Beltrami-Laplace operator. New inversion formulas are obtained for totally geodesic Funk transforms on the sphere and the correpsonding lambda-cosine transforms.

Dense kernel matrices $Theta in mathbb{R}^{N imes N}$ obtained from point evaluations of a covariance function $G$ at locations ${ x_{i} }_{1 leq i leq N} subset mathbb{R}^{d}$ arise in statistics, machine learning, and numerical analysis. For covariance functions that are Green's functions of elliptic boundary value problems and homogeneously-distributed sampling points, we show how to identify a subset $S subset { 1 , dots , N }^2$, with $# S = O ( N log (N) log^{d} ( N /epsilon ) )$, such that the zero fill-in incomplete Cholesky factorisation of the sparse matrix $Theta_{ij} 1_{( i, j ) in S}$ is an $epsilon$-approximation of $Theta$. This factorisation can provably be obtained in complexity $O ( N log( N ) log^{d}( N /epsilon) )$ in space and $O ( N log^{2}( N ) log^{2d}( N /epsilon) )$ in time, improving upon the state of the art for general elliptic operators; we further present numerical evidence that $d$ can be taken to be the intrinsic dimension of the data set rather than that of the ambient space. The algorithm only needs to know the spatial configuration of the $x_{i}$ and does not require an analytic representation of $G$. Furthermore, this factorization straightforwardly provides an approximate sparse PCA with optimal rate of convergence in the operator norm. Hence, by using only subsampling and the incomplete Cholesky factorization, we obtain, at nearly linear complexity, the compression, inversion and approximate PCA of a large class of covariance matrices. By inverting the order of the Cholesky factorization we also obtain a solver for elliptic PDE with complexity $O ( N log^{d}( N /epsilon) )$ in space and $O ( N log^{2d}( N /epsilon) )$ in time, improving upon the state of the art for general elliptic operators.

Bernstein polynomials, long a staple of approximation theory and computational geometry, have also increasingly become of interest in finite element methods. Many fundamental problems in interpolation and approximation give rise to interesting linear algebra questions. When attempting to find a polynomial approximation of boundary or initial data, one encounters the Bernstein-Vandermonde matrix, which is found to be highly ill-conditioned. Previously, we used the relationship between monomial Bezout matrices and the inverse of Hankel matrices to obtain a decomposition of the inverse of the Bernstein mass matrix in terms of Hankel, Toeplitz, and diagonal matrices. In this paper, we use properties of the Bernstein-Bezout matrix to factor the inverse of the Bernstein-Vandermonde matrix into a difference of products of Hankel, Toeplitz, and diagonal matrices. We also use a nonstandard matrix norm to study the conditioning of the Bernstein-Vandermonde matrix, showing that the conditioning in this case is better than in the standard 2-norm. Additionally, we use properties of multivariate Bernstein polynomials to derive a block $LU$ decomposition of the Bernstein-Vandermonde matrix corresponding to equispaced nodes on the $d$-simplex.

A vibratory device comprises a rotor and an eccentric weight. The eccentric weight is mounted to the shaft and has a center of mass that is offset from the shaft axis. The vibratory device is configured such that the eccentric weight is in a first rotational orientation relative to the shaft and rotates with the shaft when the shaft rotates in a first direction about the shaft axis and such that the eccentric weight is in a second rotational orientation relative to the shaft when the shaft rotates in an opposite second direction about the shaft axis. The vibratory device is further configured such that the eccentric weight automatically transitions from the first rotational orientation to the second rotational orientation when the shaft is switched from rotating in the first direction to rotating in the second direction.

"We investigate the potential of using high-resolution seismic methods for rock characterization and for targeting of gold deposits at the St. Ives gold camp. The application of seismic methods in hard-rock environments is challenged by complex structures, intrinsically low signal-to-noise ratio, regolith distortions, and access restrictions. If these issues can be addressed, then the unparalleled resolving power of reflection seismic can be used for mineral exploration. Appropriate spatial sampling of the wavefield combined with a survey geometry design and rigorous data processing to incorporate high fold and long offsets are necessary for creation of high-quality seismic images. In the hard-rock environment of Western Australia, accurate static corrections and multiphase velocity analysis are essential processing steps. This is followed by a rigorous quality control following each processing step. In such a case, we show that the role of reflection seismic could be lifted from mere identification of first-order structures to refined lithological analyses. Five deep boreholes with sonic logs and core sample test data wer eused to calibrate 2D seismic images. Despite seismic images were produced with relatively robust scaling it was possible to achieve reasonably high seismic-log correlation across three of the tightly spaced boreholes using a single composite wavelet. Amplitude-versus-offset (AVO) analysis indicated that gold-bearing structures may be related to elevated AVO effect and increased reflectivity. Consequently, partial stack analysis and acoustic and elastic inversions were conducted. These results and impedance crossplots were then evaluated against known gold occurrences. While still in the preliminary stages, hard-rock seismic imaging, inversion, and the application of AVO techniques indicated significant potential for targeting mineral reserves" -- Summary.

Christian Rau.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 549--557.

Abstract:
We revisit a shape inversion formula derived by Panaretos in the context of a particle density estimation problem with unknown rotation of the particle. A distribution is presented which imitates, or “fakes”, the uniformity or Haar distribution that is part of that formula.

Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from model-based techniques in that they provide only posterior predictions for quantities (time series) of interest, not posterior models with calibrated parameters. DSI methods require a large number of flow simulations to first be performed on prior geological realizations. Given observed data, posterior predictions can then be generated directly. DSI operates in a Bayesian setting and provides posterior samples of the data vector. In this work we develop and evaluate a new approach for data parameterization in DSI. Parameterization reduces the number of variables to determine in the inversion, and it maintains the physical character of the data variables. The new parameterization uses a recurrent autoencoder (RAE) for dimension reduction, and a long-short-term memory (LSTM) network to represent flow-rate time series. The RAE-based parameterization is combined with an ensemble smoother with multiple data assimilation (ESMDA) for posterior generation. Results are presented for two- and three-phase flow in a 2D channelized system and a 3D multi-Gaussian model. The RAE procedure, along with existing DSI treatments, are assessed through comparison to reference rejection sampling (RS) results. The new DSI methodology is shown to consistently outperform existing approaches, in terms of statistical agreement with RS results. The method is also shown to accurately capture derived quantities, which are computed from variables considered directly in DSI. This requires correlation and covariance between variables to be properly captured, and accuracy in these relationships is demonstrated. The RAE-based parameterization developed here is clearly useful in DSI, and it may also find application in other subsurface flow problems.

Actors and celebrities have been busy honing new skills during the lockdown, and television actress Karishma Tanna is no different. She has aced the complicated inversion pose of yoga, going by her new social media post. In a photograph shared on Instagram, Karishma can be seen in hot pants and a sports bra pulling off the pose with ease. "Sometimes you have to let life turn you upside down, so u can learn how to live right side up!" she captioned the image.

 

 

 

View this post on Instagram

Sometimes you have to let life turn you upside down, so u can learn how to live right side up! • . #thoughts #yogi #mood #intezaar #quarantine

A post shared by Karishma Tanna (@karishmaktanna) onApr 20, 2020 at 6:45am PDT

Karishma's efforts left her friends stunned, who rooted for her at the comment section. Actress Lauren Gottlieb wrote: "Get it girl". Actress Amruta Khanvilkar said: "Whoaaaaaaaaa so good". While Aamna Sharif mentioned: "Wowww"

The pose was recently tried by Jawaani Jaaneman actress Alaya F. She got the complicated pose right after a few failed attempts, she informed, adding that she was inspired by Jacqueline Fenrnandez and celebrity fitness trainer Kuldeep Shashi.

On the work front, Karishma is currently the part of the action reality show "Fear Factor: Khatron Ke Khiladi 10" (KKK 10).

The new season is shot in Bulgarian landscapes with actors Ada Khan, Amruta Khanvilkar Karan Patel, Shivin Narang, Tejashwi Prakash, Amruta Khanvilkar and Rani Chatterjee, along with comedian Balraj Syal, RJ Malishka and choreographer Dharmesh Yelande.

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