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See detailM wie Mitte
Heimböckel, Dieter UL

Article for general public (2012)

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See detailm-order integrals and generalized Itô's formula; the case of a fractional Brownian motion with any Hurst index
Gradinaru, Mihai; Nourdin, Ivan UL; Russo, Francesco et al

in Annales de l'Institut Henri Poincare (B) Probability & Statistics (2005), 41(4), 781-806

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See detailM-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution
Krivochiza, Jevgenij UL; Merlano Duncan, Juan Carlos UL; Chatzinotas, Symeon UL et al

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 ▲]

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See detailM. A. Larsen: The Making and Shaping of the Victorian Teacher.
Priem, Karin UL

in H-Soz-u-Kult (2013)

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See detailThe M2 muscarinic receptor antagonist methoctramine activates mast cells via pertussis toxin-sensitive G proteins
Chahdi, A.; Daeffler, L.; Bueb, Jean-Luc UL et al

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 ▲]

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See detailM2 World Ocean Tide from Tide Gauge Measurements
Francis, Olivier UL; Mazzega, P.

in Geophysical Research Letters (1991), 18(6), 1167-1170

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See detailM2 World Ocean Tides from Tide Gauge and Gravity Loading Measurements
Francis, Olivier UL; Mazzega, Pierre

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

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See detailM3: A Hardware/Operating-System Co-Design to Tame Heterogeneous Manycores
Asmussen, Nils; Volp, Marcus UL; Nöthen, Benedikt et al

in Architectural Support for Programming Languages and Operating Systems (ASPLOS) (2016, April)

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See detail“Ma misi me per l’alto mare aperto”
Roelens, Nathalie UL

Scientific Conference (2017)

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See detailMabuchi and Aubin-Yau functionals over complex manifolds
Li, Yi UL

E-print/Working paper (2010)

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See detailMabuchi and Aubin-Yau functionals over complex surfaces
Li, Yi UL

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

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See detailMabuchi and Aubin-Yau functionals over complex three-folds
Li, Yi UL

E-print/Working paper (2010)

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See detailMacbeth and the joystick: Evidence for moral cleansing after playing a violent video game
Gollwitzer, Mario; Melzer, André UL

in Journal of Experimental Social Psychology (2012), 48

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See detailMachbarkeitsstudie "Betreuungsatlas Schweiz": Die Geographie betreuter Kindheit
Neumann, Sascha UL; Tinguely, Luzia; Hekel, Nicole UL et al

Report (2015)

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See detailMachine learning and natural language processing on the patent corpus: data, tools, and new measures
Balsmeier, Benjamin UL; Li, Guan-Cheng; Assaf, Mohamad et al

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

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See detailMachine Learning based Antenna Selection and Power Allocation in Multi-user MISO Systems
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in 2019 IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) (2019, June)

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See detailMachine Learning for Data-Driven Smart Grid Applications
Glauner, Patrick UL; Meira, Jorge Augusto UL; State, Radu UL

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 ▲]

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See detailMachine Learning for Reliable Network Attack Detection in SCADA Systems
Lopez Perez, Rocio; Adamsky, Florian UL; Soua, Ridha UL et al

in 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (IEEE TrustCom-18) (2018)

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See detailMachine learning in nanoscience: big data at small scales
Brown, Keith A.; Brittman, Sarah; Maccaferri, Nicolò UL et al

in Nano Letters (in press)

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with ... [more ▼]

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML’s advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this mini-review, which is not able to be comprehensive, we highlight some recent efforts to connect the ML and nanoscience communities focusing on three types of interaction: (1) using ML to analyze and extract new information from large nanoscience data sets, (2) applying ML to accelerate materials discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers. [less ▲]

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