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Showing results 20309 to 20328 of 37132
   m-order integrals and generalized Itô's formula; the case of a fractional Brownian motion with any Hurst index; Nourdin, Ivan  ; et alin Annales de l'Institut Henri Poincare (B) Probability & Statistics (2005), 41(4), 781-806 Detailed reference viewed: 74 (1 UL)M-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form SolutionKrivochiza, Jevgenij ; Merlano Duncan, Juan Carlos ; Chatzinotas, Symeon et alScientific 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 ▲] Detailed reference viewed: 50 (5 UL)M. A. Larsen: The Making and Shaping of the Victorian Teacher.Priem, Karin in H-Soz-u-Kult (2013) Detailed reference viewed: 70 (10 UL)The M2 muscarinic receptor antagonist methoctramine activates mast cells via pertussis toxin-sensitive G proteins; ; Bueb, Jean-Luc et alin 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 ▲] Detailed reference viewed: 105 (0 UL) M2 World Ocean Tide from Tide Gauge MeasurementsFrancis, Olivier ; in Geophysical Research Letters (1991), 18(6), 1167-1170 Detailed reference viewed: 77 (4 UL)M2 World Ocean Tides from Tide Gauge and Gravity Loading MeasurementsFrancis, Olivier ; in Paquet, Paul; Flick, Johnny; Ducarme, Bernard (Eds.) GPS for Geodesy and (1990) Detailed reference viewed: 86 (7 UL)M3: A Hardware/Operating-System Co-Design to Tame Heterogeneous Manycores; Volp, Marcus ; et alin Architectural Support for Programming Languages and Operating Systems (ASPLOS) (2016, April) Detailed reference viewed: 272 (30 UL) “Ma misi me per l’alto mare aperto”Roelens, Nathalie Scientific Conference (2017) Detailed reference viewed: 34 (0 UL)Mabuchi and Aubin-Yau functionals over complex manifoldsLi, Yi E-print/Working paper (2010) Detailed reference viewed: 31 (3 UL)Mabuchi and Aubin-Yau functionals over complex surfacesLi, Yi in Journal of Mathematical Analysis and Applications (2014), 416(1), 81-98 Detailed reference viewed: 65 (1 UL)Mabuchi and Aubin-Yau functionals over complex three-foldsLi, Yi E-print/Working paper (2010) Detailed reference viewed: 36 (1 UL)Macbeth and the joystick: Evidence for moral cleansing after playing a violent video game; Melzer, André in Journal of Experimental Social Psychology (2012), 48 Detailed reference viewed: 223 (5 UL)Machbarkeitsstudie "Betreuungsatlas Schweiz": Die Geographie betreuter KindheitNeumann, Sascha ; ; Hekel, Nicole et alReport (2015) Detailed reference viewed: 46 (3 UL)Machine learning analysis of metabolomics and neuro-imaging data for Parkinson’s diseaseGlaab, Enrico Presentation (2019, January) Detailed reference viewed: 132 (11 UL)Machine learning and natural language processing on the patent corpus: data, tools, and new measuresBalsmeier, Benjamin ; ; et alin Journal of Economics & Management Strategy (2018), 27 Detailed reference viewed: 346 (18 UL)Machine Learning based Antenna Selection and Power Allocation in Multi-user MISO SystemsVu, Thang Xuan ; Lei, Lei ; Chatzinotas, Symeon et alin 2019 IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) (2019, June) Detailed reference viewed: 6 (0 UL)Machine Learning for Data-Driven Smart Grid ApplicationsGlauner, Patrick ; Meira, Jorge Augusto ; State, Radu 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 ▲] Detailed reference viewed: 352 (10 UL)Machine Learning for Reliable Network Attack Detection in SCADA Systems; Adamsky, Florian ; Soua, Ridha et alin 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (IEEE TrustCom-18) (2018) Detailed reference viewed: 334 (40 UL)Machine learning in nanoscience: big data at small scales; ; Maccaferri, Nicolò et alin 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 ▲] Detailed reference viewed: 48 (0 UL) |
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