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Modern digital connectivity has necessitated the creation of robust methods for securely storing and transferring data. At the heart of all security infrastructure is the random number generator (RNG). While random numbers find use in a variety of applicat ...
In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...
In this paper we present proofs for the new biases in RC4 which were experimentally found and listed out (without theoretical justifi- cations and proofs) in a paper by Vanhoef et al. in USENIX 2015. Their purpose was to exploit the vulnerabilities of RC4 ...
The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...
In this paper we present proofs for the new biases in RC4 which were experimentally found and listed out (without theoretical justifications and proofs) in a paper by Vanhoef et al. in USENIX 2015. Their purpose was to exploit the vulnerabilities of RC4 in ...
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG2018
Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum fü ...
We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real-time at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class ...
The classical multivariate extreme-value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max-stable distributions. In this work, the classical theory is extended to the case where aggregated data, such as max ...
We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/overparameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under b ...
Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...
