| Proposed is a theoretically exact formula for inversion of data obtained by a spiral CT scan with a 2D detector array. The detector array is supposed to be of limited extent in the axial direction. The main property of the formula is that it can be implemented in a truly ltered backprojection fashion. First, one performs shift-invariant ltering of a derivative of the cone beam projections, an...

The productivity of the computation systems is determined to a high degree by the speed of multiplication, which is а basic element in multiple applications for digital signal processing, quantum computations, etc. The various approaches for design of asymptotically rapid algorithms for multiplication have developed in parallel with the evolution of the effective multiplicator architectures. Th...

Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout. This...

The resolution of fluorescence microscopes is limited by diffraction, which determines the extension of their point spread functions. We propose and study numerically a simple method, based on a combination of subtraction microscopy with regular and annular excitation beams, which permits to double the resolution compared to wide-field microscopy. When combined with the fluorescence saturation ...

This paper shows that a turbo-coded communication system can be made secure with a little bit of complexity cost. The classical permutation ciphers are revisited and analyzed. Firstly, the ideal stream permutation ciphers are shown to be asymptotically information-theoretically secure in the sense that the channel from plaintext to ciphertext has a vanished capacity, while the practical stream ...

Reinforcement learning for embodied agents is a challenging problem. The accumulated reward to be optimized is often a very rugged function, and gradient methods are impaired by many local optimizers. We demonstrate, in an experimental setting, that incorporating an intrinsic reward can smoothen the optimization landscape while preserving the global optimizers of interest. We show that policy g...

The massive amount of data and large variety of data distributions in the big data era call for access methods that are efficient in both query processing and index bulk-loading, and over both practical and worst-case workloads. To address this need, we revisit a classic multidimensional access method – the R-tree. We propose a novel R-tree packing strategy that produces R-trees with an asympto...