University of Paderborn

About: University of Paderborn is a education organization based out in Paderborn, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Control reconfiguration & Software. The organization has 6684 authors who have published 16929 publications receiving 323154 citations.

Sampling in dynamic data streams and applications

Abstract: A dynamic geometric data stream is a sequence of m Add/Remove operations of points from a discrete geometric space (1,...,Δ)d [21]. Add(p) inserts a point p from (1,...,Δ)d into the current point set, Remove(p) deletes p from P. We develop low-storage data structures to (i) maintain e-approximations of range spaces of P with constant VC-dimension and (ii) maintain an e-approximation of the weight of the Euclidean minimum spanning tree of P. Our data structures use O(log3e • log3(1/e) • log(1/e)/e2) and O(log (1/δ) • (log Δ/e)O(d)) bits of memory, respectively (we assume that the dimension d is a constant), and they are correct with probability 1-δ. These results are based on a new data structure that maintains a set of elements chosen (almost) uniformly at random from P.

Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques

Abstract: Once a popular theme of futuristic science fiction or far-fetched technology forecasts, digital home assistants with a spoken language interface have become a ubiquitous commodity today. This success has been made possible by major advancements in signal processing and machine learning for so-called far-field speech recognition, where the commands are spoken at a distance from the sound-capturing device. The challenges encountered are quite unique and different from many other use cases of automatic speech recognition (ASR). The purpose of this article is to describe, in a way that is amenable to the nonspecialist, the key speech processing algorithms that enable reliable, fully hands-free speech interaction with digital home assistants. These technologies include multichannel acoustic echo cancellation (MAEC), microphone array processing and dereverberation techniques for signal enhancement, reliable wake-up word and end-of-interaction detection, and high-quality speech synthesis as well as sophisticated statistical models for speech and language, learned from large amounts of heterogeneous training data. In all of these fields, deep learning (DL) has played a critical role.

A new approach toward boundedness in a two-dimensional parabolic chemotaxis system with singular sensitivity

Abstract: We consider the parabolic chemotaxis model in a smooth, bounded, convex two-dimensional domain and show global existence and boundedness of solutions for χ∈(0,χ0) for some χ0>1, thereby proving that the value χ = 1 is not critical in this regard. Our main tool is consideration of the energy functional for a > 0, b≥0, where using nonzero values of b appears to be new in this context. Copyright © 2015 John Wiley & Sons, Ltd.

Partial key exposure attacks on RSA up to full size exponents

Abstract: We present several attacks on RSA that factor the modulus in polynomial time under the condition that a fraction of the most significant bits or least significant bits of the private exponent is available to the attacker. Our new attacks on RSA are the first attacks of this type that work up to full size public or private exponent.

Density of phonon states in iron at high pressure

Abstract: The lattice dynamics of the hexagonal close-packed (hcp) phase of iron was studied with nuclear inelastic absorption of synchrotron radiation at pressures from 20 to 42 gigapascals. A variety of thermodynamic parameters were derived from the measured density of phonon states for hcp iron, such as Debye temperatures, Gruneisen parameter, mean sound velocities, and the lattice contribution to entropy and specific heat. The results are of geophysical interest, because hcp iron is considered to be a major component of Earth's inner core.