Search results for "VERTEX"

showing 10 items of 225 documents

Probing nucleons with photons at the quark level

2014

The description of electromagnetic interactions with hadrons from the quark level requires knowledge of the underlying quark-gluon ingredients. I discuss some properties of the quark-photon vertex and quark Compton vertex, along with the role of electromagnetic gauge invariance and vector-meson dominance. A simple parametrization for the quark-photon vertex is given.

Vertex (graph theory)PhysicsQuarkParticle physicsPhotonNuclear Theory010308 nuclear & particles physicsHigh Energy Physics::LatticeHadronNuclear TheoryHigh Energy Physics::PhenomenologyGeneral Physics and AstronomyFOS: Physical sciences01 natural sciencesNuclear Theory (nucl-th)High Energy Physics - PhenomenologyHigh Energy Physics - Phenomenology (hep-ph)0103 physical sciencesHigh Energy Physics::ExperimentVector mesonGauge theory010306 general physicsNucleonNuclear ExperimentParametrization
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QSPR/QSAR Studies of 2-Furylethylenes Using Bond-Level Quadratic Indices and Comparison with Other Computational Approaches

2017

The recently introduced, non-stochastic and stochastic quadratic indices (Marrero-Ponce <em>et al. J. Comp. Aided Mol. Des.</em> 2006, 20, 685-701) were applied to QSAR/QSPR studies of heteroatomic molecules. These novel bond-based molecular descriptors (MDs) were used for the prediction of the partition coefficient (log P), and the antibacterial activity of 34 derivatives of 2-furylethylenes. Two statistically significant QSPR models using non-stochastic and stochastic bond-based quadratic indices were obtained (R<sup>2</sup> = 0.971, s = 0.137 and R<sup>2</sup> = 0.986, s = 0.096). These models showed good stability to data variation in leave-one-out (L…

Vertex (graph theory)Quantitative structure–activity relationshipQuadratic equationDiscriminantMolecular descriptorStochastic matrixMoleculeGeneral ChemistryBiological systemStability (probability)MathematicsJournal of the Mexican Chemical Society
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Prediction of Molecular Volume and Surface of Alkanes by Molecular Topology.

2003

Molecular volume and molecular surface are expressed as a function of topological degree in alkane graphs. This allows not only a straightforward approach to calculate such physicochemical magnitudes but also an interpretation of the role of the local vertex invariant (LOVI) or valence degree, delta, as well as the connectivity indices in the prediction of physicochemical properties. The interpretation is based on the concept of molecular accessibility (as introduced by Estrada, J. Phys. Chem. A 2002, 106, 9085) for which precise mathematical definitions are provided.

Vertex (graph theory)Surface (mathematics)Alkanechemistry.chemical_classificationValence (chemistry)Degree (graph theory)ChemistryGeometryGeneral ChemistryGeneral MedicineFunction (mathematics)Computer Science ApplicationsInterpretation (model theory)Computational Theory and MathematicsStatistical physicsMolecular topologyInvariant (mathematics)Information SystemsMathematicsChemInform
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Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs. 14 Electrodes EEG Data

2012

As nonnegative tensor factorization (NTF) is particularly useful for the problem of underdetermined linear transform model, we performed NTF on the EEG data recorded from 14 electrodes to extract the multi-domain feature of N170 which is a visual event-related potential (ERP), as well as 5 typical electrodes in occipital-temporal sites for N170 and in frontal-central sites for vertex positive potential (VPP) which is the counterpart of N170, respectively. We found that the multi-domain feature of N170 from 5 electrodes was very similar to that from 14 electrodes and more discriminative for different groups of participants than that of VPP from 5 electrodes. Hence, we conclude that when the …

Vertex (graph theory)Underdetermined systemDiscriminative modelFeature (computer vision)business.industryEvent-related potentialElectrodeFeature extractionPattern recognitionArtificial intelligenceNonnegative tensor factorizationbusinessMathematics
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QSPR Modeling of Hydrocarbon Dipole Moments by Means of Correlation Weighting of Local Graph Invariants

2003

Hydrocarbon dipole moments are calculated by means of correlation weighting of local graph invariants within the context of QSPR theory. This sort of flexible topological descriptor is used for several parameters: local invariants of k th vertex in the labeled hydrogen filled graph extended connectivity of zero-, first- and second-orders, number of paths of length 2 at k th vertex and valence shell of the k th vertex. The models predict hydrocarbon dipole moments in a quite sensible way. The best model is that one based upon numbers of path length 2 correlation weighting.

Vertex (graph theory)chemistry.chemical_classificationMathematical analysisComputer Science ApplicationsWeightingCorrelationDipoleHydrocarbonComputational Theory and MathematicsPath lengthchemistryComputational chemistrysortPhysical and Theoretical ChemistryValence electronMathematicsJournal of Theoretical and Computational Chemistry
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Vertex Distinguishing Edge- and Total-Colorings of Cartesian and other Product Graphs

2012

International audience; This paper studies edge- and total-colorings of graphs in which (all or only adjacent) vertices are distinguished by their sets of colors. We provide bounds for the minimum number of colors needed for such colorings for the Cartesian product of graphs along with exact results for generalized hypercubes. We also present general bounds for the direct, strong and lexicographic products.

[INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM]total coloringadjacent vertex-distinguishingvertex-distinguishingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONedge-coloring[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM]graphgraph productsAMS 05C15[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]total adjacent vertex-distinguishingMathematicsofComputing_DISCRETEMATHEMATICS
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The b-chromatic number of power graphs

2003

The b-chromatic number of a graph G is defined as the maximum number k of colors that can be used to color the vertices of G, such that we obtain a proper coloring and each color i, with 1 ≤ i≤ k, has at least one representant x_i adjacent to a vertex of every color j, 1 ≤ j ≠ i ≤ k. In this paper, we discuss the b-chromatic number of some power graphs. We give the exact value of the b-chromatic number of power paths and power complete binary trees, and we bound the b-chromatic number of power cycles.

b-chromatic numberGeneral Computer Science[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]power graphTheoretical Computer ScienceCombinatoricsComputer Science::Discrete MathematicsDiscrete Mathematics and CombinatoricsChromatic scaleGraph coloringcoloringMathematicscycle and complete binary treeMathematics::CombinatoricsBinary treelcsh:Mathematicscycle and complete binary tree.path[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM]Complete coloringlcsh:QA1-939Vertex (geometry)Brooks' theorem[INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM]Edge coloringFractional coloringDiscrete Mathematics & Theoretical Computer Science
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Blind Robust 3-D Mesh Watermarking Based on Mesh Saliency and QIM Quantization for Copyright Protection

2019

International audience; Due to the recent demand of 3-D models in several applications like medical imaging, video games, among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased considerably. The majority of robust 3-D watermark-ing techniques have essentially focused on the robustness against attacks while the imperceptibility of these techniques is still a real issue. In this context, a blind robust 3-D mesh watermarking method based on mesh saliency and Quantization Index Modulation (QIM) for Copyright protection is proposed. The watermark is embedded by quantifying the vertex norms of the 3-D mesh using QIM scheme since it offe…

business.industryComputer scienceWatermark robustness[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingData_MISCELLANEOUS[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringQuantization index modulationWatermark02 engineering and technologyVertex (geometry)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionArtificial intelligencebusinessQuantization (image processing)Digital watermarkingSmoothingComputingMilieux_MISCELLANEOUS
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Performance of $b$-Jet Identification in the ATLAS Experiment

2016

We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT an…

detector-systems performancePerformance of High Energy Physics Detectorsecondary [vertex]Elementary particle01 natural sciencesPARTONlaw.inventionSubatomär fysikCHANNELcluster findingscattering [p p]impact parameterGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)протон-протонные столкновенияQBLarge detector-systems performanceHigh energy physics detectorLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; Instrumentation; Mathematical Physicstrack data analysisQUARK PAIR PRODUCTIONbottom [jet]CERN LHC CollPattern recognition cluster finding calibration and fitting method7000 GeV-cmscolliding beams [p p]performanceHADRONIC COLLISIONSCiências Naturais::Ciências FísicasLarge detectorFitting methodHigh energy physicATLAS LHC High Energy Physics510 MathematicsmuonDISTRIBUTIONSUncertainty analysis Astroparticle physicHigh Energy Physics010306 general physicsSystematic uncertainties AlgorithmsAstroparticle physicsCalibration and fitting methodsScience & Technology010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsParticle acceleratorRangingPerformance of High Energy PhysicsCOLLIDERScorrelationExperimental High Energy PhysicsPerformance of High Energy Physics DetectorshadronATLAS детекторБольшой адронный коллайдерcharm [jet]Elementary particleHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)lawSubatomic Physics[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Detectors and Experimental TechniquesInstrumentationUncertainty analysisMathematical PhysicsPhysicsPattern recognition cluster finding calibration and fitting methods4. EducationATLAS experimentSettore FIS/01 - Fisica SperimentaleDetectorsflavor [jet]calibration and fitting methodsATLASLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large; detector-systems performance; Pattern recognition cluster finding; calibration and fitting methods; Performance of High Energy Physics; Detectors; PRODUCTION CROSS-SECTION; QUARK PAIR PRODUCTION; ROOT-S=7 TEV; PARTON; DISTRIBUTIONS; HADRONIC COLLISIONS; MATRIX-ELEMENTS; LHC; COLLIDERS; DETECTOR; CHANNEL8. Economic growthCalibrationparticle identification [bottom]LHCImpact parameterParticle Physics - ExperimentParticle physicsdata analysis method530 Physics:Ciências Físicas [Ciências Naturais]FOS: Physical sciences530MATRIX-ELEMENTSparticle identification [charm]on-line [trigger]Pattern recognition0103 physical sciencesComplementary methodddc:610DETECTORROOT-S=7 TEVCluster findingFísicaLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics DetectorsPattern recognition systemcalibrationtracksPRODUCTION CROSS-SECTIONefficiencyHadronLarge detector systems for particle and astroparticle physicLargeHigh Energy Physics::ExperimentStatistical correlationstatisticalexperimental results
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The Large Hadron–Electron Collider at the HL-LHC

2021

The Large Hadron-Electron Collider (LHeC) is designed to move the field of deep inelastic scattering (DIS) to the energy and intensity frontier of particle physics. Exploiting energy-recovery technology, it collides a novel, intense electron beam with a proton or ion beam from the High-Luminosity Large Hadron Collider (HL-LHC). The accelerator and interaction region are designed for concurrent electron-proton and proton-proton operations. This report represents an update to the LHeC's conceptual design report (CDR), published in 2012. It comprises new results on the parton structure of the proton and heavier nuclei, QCD dynamics, and electroweak and top-quark physics. It is shown how the LH…

energy recoverylepton nucleus: scatteringparton: distribution functionhiukkasfysiikka7. Clean energy01 natural sciencesaccelerator physicsHigh Energy Physics - Phenomenology (hep-ph)HEAVY FLAVOR CONTRIBUTIONSenergy-recovery- linacNuclear Experimentcolliding beams [electron p]deep-inelastic scatteringtop and electroweak physicsnew physicsPhysicsSTRUCTURE-FUNCTION RATIOSMonte Carlo [numerical calculations]buildingsprimary [vertex]High Energy Physics - Phenomenologyelectron p: colliding beamskinematicsNuclear Physics - Theoryfinal state: hadronicp: distribution functionbeyond Standard Modelvertex: primarynumerical calculations: Monte Carlodistribution function [parton]High-lumiLHCSTRUCTURE-FUNCTION F-2(X[PHYS.NUCL]Physics [physics]/Nuclear Theory [nucl-th]ion: beam[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]114 Physical sciencesNuclear Theory (nucl-th)deep inelastic scatteringquantum chromodynamicsddc:530010306 general physicsdeep-inelastic scattering; high-lumi LHC; QCD; Higgs; top and electroweak physics; nuclear physics; beyond standard Model; energy-recovery- linac; accelerator physics010308 nuclear & particles physicshigh-lumi LHCresolutionscattering [electron p]structure function [nucleus]sensitivitybeam [electron]energy-recovery-linacHiggsacceptanceNuclear TheoryHIGH-ENERGY FACTORIZATIONdistribution function [p]density [parton]Higgs; High-lumi LHCHigh Energy Physics - Experimentdesign [detector]High Energy Physics - Experiment (hep-ex)electron: linear acceleratorelectron hadron: scatteringCERN LHC Coll: upgrade[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]scattering [electron hadron]FCCelectron: beamNuclear Experiment (nucl-ex)linear accelerator [electron]Nuclear ExperimentlatticesuperconductivityEnergy-recoverylinacBeyond Standard ModeNuclear physics; QCDelectron nucleus: colliding beamsparton: densitycolliding beams [electron nucleus]Particle Physics - ExperimentNUCLEON STRUCTURE FUNCTIONSNuclear and High Energy Physicsscattering [lepton nucleus]beam [ion]FOS: Physical sciencesnucleus: structure functionhadronic [final state]electron p: scatteringTRANSVERSE-MOMENTUM DEPENDENCEnuclear physics0103 physical sciencesNuclear Physics - Experimentstructureupgrade [CERN LHC Coll]detector: designParticle Physics - PhenomenologyDEEP-INELASTIC-SCATTERINGelectroweak interaction3-LOOP SPLITTING FUNCTIONSCLASSICAL RADIATION ZEROScalibrationAccelerators and Storage RingsQCDmagnethigh [current]13. Climate action[PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph]LHeCPhysics::Accelerator PhysicsJET CROSS-SECTIONSHigh Energy Physics::Experimentcurrent: highJournal of Physics G: Nuclear and Particle Physics
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