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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

Detailed reference viewed: 32 (1 UL)
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 (in press)

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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 of accurate energy-conserving molecular force fields
Chmiela, Stefan; Tkatchenko, Alexandre UL; Sauceda, Huziel et al

in Science Advances (2017), 3

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems— we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate ... [more ▼]

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems— we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. [less ▲]

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See detailMachine learning of molecular electronic properties in chemical compound space
Montavon, Gregoire; Rupp, Matthias; Gobre, Vivekanand et al

in NEW JOURNAL OF PHYSICS (2013), 15

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful ... [more ▼]

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy polarizability, frontier orbital eigenvalues, ionization potential electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e. nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a `quantum machine' is similar, and sometimes superior, to modern quantum-chemical methods-at negligible computational cost. [less ▲]

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See detailMachine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space
Hansen, K.; Biegler, F.; Ramakrishnan, R. et al

in Journal of Physical Chemistry Letters (2015), 6(12), 2326-2331

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical ... [more ▼]

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the "holy grail" of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. © 2015 American Chemical Society. [less ▲]

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See detailMachine learning techniques for atmospheric pollutant monitoring
Sainlez, Matthieu UL; Heyen, Georges

Poster (2012, January 27)

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