M wie Mitte

Article for general public (2012)

M-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution

Scientific Conference (2019, August)

In this paper, we derive a closed-form algorithm of the computationally efficient Symbol-Level Precoding (SLP) for power efficient communications when using M-QAM modulated waveforms. The channel state ... [more ▼]

In this paper, we derive a closed-form algorithm of the computationally efficient Symbol-Level Precoding (SLP) for power efficient communications when using M-QAM modulated waveforms. The channel state information (CSI) based and data-aided SLP technique optimizes power efficiency by solving a non-negative convex quadratic optimization problem per time frame of transmitted symbols. The optimization combines constructive inter-user interference to minimize the sum power of precoded symbols at the transmitter side under constraints for minimum SNR at the receiver side. The SLP implementation incurs extra computational complexity of the transmitter. We propose a convex quadratic optimization problem for M-QAM constellations and derive a closed-form algorithm with a fixed number of iterations to solve the problem. [less ▲]

The M2 muscarinic receptor antagonist methoctramine activates mast cells via pertussis toxin-sensitive G proteins

in Naunyn-Schmiedeberg's Archives of Pharmacology (1998), 357(4), 357-62

Methoctramine, a selective M2 muscarinic cholinergic receptor antagonist, has been reported to activate phosphoinositide breakdown at high concentrations. Its polyamine structure suggests a putative ... [more ▼]

Methoctramine, a selective M2 muscarinic cholinergic receptor antagonist, has been reported to activate phosphoinositide breakdown at high concentrations. Its polyamine structure suggests a putative activation of guanine nucleotide-binding proteins (G proteins). Incubation of methoctramine with rat peritoneal mast cells resulted in a dose-dependent noncytotoxic histamine release, with an EC50 of 20 microM and a maximum effect at 1 mM. Atropine, pirenzepine and HHSiD neither inhibited methoctramine-induced histamine release nor stimulated histamine release. Histamine release and inositol phosphates generation induced by methoctramine were both inhibited by pertussis toxin pretreatment. Benzalkonium chloride, a selective inhibitor of histamine secretion induced by basic secretagogues, inhibited the secretory response to methoctramine. [p-Glu5, D-Trp7,9,l0]-SPs5-11 (GPAnt-2), a well-characterized antagonist of G proteins, blocked the methoctramine-induced histamine release when the antagonist was allowed to reach its intracellular target by streptolysin O-permeabilization. The response to methoctramine was prevented by the hydrolysis of sialic acid residues of the cell surface by neuraminidase. The response of mast cells was restored by permeabilization of the plasma membrane. These results demonstrate that methoctramine, following its entry into the cell and the involvement of pertussis toxin-sensitive G proteins, activates phosphoinositide hydrolysis leading to mast cell exocytosis. [less ▲]

M2 World Ocean Tides from Tide Gauge and Gravity Loading Measurements

in Paquet, Paul; Flick, Johnny; Ducarme, Bernard (Eds.) GPS for Geodesy and (1990)

“Ma misi me per l’alto mare aperto”

Scientific Conference (2017)

Mabuchi and Aubin-Yau functionals over complex surfaces

in Journal of Mathematical Analysis and Applications (2014), 416(1), 81-98

Macbeth and the joystick: Evidence for moral cleansing after playing a violent video game

in Journal of Experimental Social Psychology (2012), 48

Machine Argumentation. Can We Replace Taxi Drivers by Robots?

in Natural Argument, A tribute to John Woods (2019)

We need ethical non-monotonic action logics to control machines which interact heavily with humans. Such logics face special problems and require features which we need to recognise and to address. We ... [more ▼]

We need ethical non-monotonic action logics to control machines which interact heavily with humans. Such logics face special problems and require features which we need to recognise and to address. We believe that injecting argumentation methods into action pre-conditions is possibly the way to proceed to model what is needed. To get an idea of what is needed we must investigate a typical problem of replacing a human with a robot operating in a highly interactive environment. This paper focuses on replacing a human taxi driver by a robot. Robot driven cars are already under production and so there is an urgent need for modelling the kind of Artificial Intelligence/Logic/Norms/Ethics which is to be involved and installed in the mind of the Robot. This is research in progress. [less ▲]

Machine learning and natural language processing on the patent corpus: data, tools, and new measures

in Journal of Economics & Management Strategy (2018), 27

Machine Learning for Data-Driven Smart Grid Applications

Scientific Conference (2018)

The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns ... [more ▼]

The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns can then be applied to new data in order to make predictions. Machine Learning also allows to automatically adapt to changes in the data without amending the underlying model. We deal every day dozens of times with Machine Learning applications such as when doing a Google search, using spam filters, face detection, speaking to voice recognition software or when sitting in a self-driving car. In recent years, machine learning methods have evolved in the smart grid community. This change towards analyzing data rather than modeling specific problems has lead to adaptable, more generic methods, that require less expert knowledge and that are easier to deploy in a number of use cases. This is an introductory level course to discuss what machine learning is and how to apply it to data-driven smart grid applications. Practical case studies on real data sets, such as load forecasting, detection of irregular power usage and visualization of customer data, will be included. Therefore, attendees will not only understand, but rather experience, how to apply machine learning methods to smart grid data. [less ▲]