randomization is an essential component of sound clinical trials which prevents selection biases and helps in blinding the allocations. randomization is a process by which subsequent subjects enrolled into trial groups only by chance which is essentially eliminates selection biases. a serious consequence of randomization is severe imbalance among treatment groups with respect to some prognostic...

We consider the alternating proximal gradient method (APGM) proposed to solve a convex minimization model with linear constraints and separable objective function which is the sum of two functions without coupled variables. Inspired by Peaceman-Rachford splitting method (PRSM), a nature idea is to extend APGM to the symmetric alternating proximal gradient method (SAPGM), which can be viewed as ...

We propose a solution to the image deconvolution problem where the convolution operator or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the uncertainty in a high dimensional space. Specifically, we assume the image is sparse corresponding to the natural sparsity of m...

Principal Component Analysis (PCA; Pearson, 1901) is a widely used method for data compression. The goal is to find the best low rank approximation of a given matrix, as judged by minimization of the `2 norm of the difference between the original matrix and the low rank approximation. However, the classical method is not resistant to corruption of individual input data points. Recently, a robus...

We provide a comprehensive study of out-of-sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. ...

Recently, self-calibration algorithms that use only the information in the image have been actively researched. But most algorithms require bundle adjustment in the projective reconstruction or in the nonlinear minimization. We propose a practical self-calibration algorithm that only requires a linear projective reconstruction. We overcome the sensitivity of the algorithm due to image noises by...

– The problem of analog system design for a minimal computer time has been formulated as the functional minimization problem of the control theory. The design process in this case is formulated as the controllable dynamic system. The optimal sequence of the control vector switch points was determined as a principal characteristic of the minimal-time system design algorithm. The conception of th...

Recently, self-calibration algorithms that use only the information in the image have been actively researched. But most algorithms require bundle adjustment in the projective reconstruction or in the nonlinear minimization. We propose a practical self-calibration algorithm that only requires a linear projective reconstruction. We overcome the sensitivity of the algorithm due to image noises by...

The Low-Emittance Transport (LET) region of a linear collider includes all of the beamlines between the exit of the main damping ring and the interaction point. Generically, the LET includes the bunch compressor, main linac, and beam delivery sections of the linear collider. The principal luminosity requirements of the LET are: preservation of the small emittances generated by the main damping ...

Statistical shape analysis techniques employed in the medical imaging community, such as Active Shape Models or Active Appearance Models, rely on Principal Component Analysis (PCA) to decompose shape variability into a reduced set of interpretable components. Our model uses point distribution models (PDM) for the representation of shapes. The point association is initialized using shape-based s...