0000000001176468

AUTHOR

Yifei Zhang

showing 13 related works from this author

Experimental Evidence for an Attractive p-φ Interaction

2021

Physical review letters 127(17), 172301 (2021). doi:10.1103/PhysRevLett.127.172301

:Kjerne- og elementærpartikkelfysikk: 431 [VDP]ProtonGeneral Physics and Astronomy01 natural sciencesHigh Energy Physics - ExperimentALICEscattering [p p][PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]correlation functionNuclear ExperimentPhysicsstrong interactionVDP::Kjerne- og elementærpartikkelfysikk: 431:Nuclear and elementary particle physics: 431 [VDP]VDP::Nuclear and elementary particle physics: 431nuclear matterPHOTOPRODUCTIONParticle Physics - Experimentcorrelation: two-particleQCD SUM-RULES; VECTOR-MESONS; COLLISIONS; PARTICLES; PHOTOPRODUCTIONCOLLISIONSParticle physicsp p: scatteringMesonStrong interactionCorrelation function (quantum field theory)[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]Physics and Astronomy(all)530114 Physical sciencessymmetry: chiralQCD SUM-RULES; VECTOR-MESONS; COLLISIONS; PARTICLES; PHOTOPRODUCTION;QCD SUM-RULES0103 physical sciencesPARTICLEScorrelation: two-particle ; symmetry: chiral ; p p: scattering ; scattering length ; Phi(1020) ; coupling constant ; correlation function ; strong interaction ; ALICE ; nuclear matter ; effective range ; experimental results ; 13000 GeV-cms/nucleonNuclear Physics - Experimentddc:530phi meson particle physics ALICE010306 general physicstwo-particle [correlation]Coupling constantchiral [symmetry]010308 nuclear & particles physicsScatteringPhi(1020)coupling constantScattering lengthNuclear matter13000 GeV-cms/nucleonscattering lengthStrong Interactioneffective rangeHigh Energy Physics::ExperimentVECTOR-MESONSexperimental results
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Incorporating depth information into few-shot semantic segmentation

2021

International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Artificial neural networkComputer sciencebusiness.industry[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020206 networking & telecommunications02 engineering and technologyImage segmentationSemanticsVisualization[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingMetric (mathematics)0202 electrical engineering electronic engineering information engineeringEmbeddingRGB color modelSegmentationComputer visionArtificial intelligencebusiness
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Kaon-proton strong interaction at low relative momentum via femtoscopy in Pb-Pb collisions at the LHC

2021

Physics letters / B 822, 136708 (2021). doi:10.1016/j.physletb.2021.136708

atom: exoticheavy ion: scatteringnucleon: paircorrelation [momentum]exoticheavy ion scatteringmomentum correlationmeasurement methodsHadron01 natural sciencesHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)effective field theoryALICE[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]effective field theory: chiralNuclear Experiment (nucl-ex)Nuclear ExperimentNuclear Experimentchiral [effective field theory]effective field theory chiralPhysicsatom exoticSPECTROSCOPYatomstrong interactionPhysicsnucleontwo-particleheavy ion3. Good healthCERN LHC Collkinematicsforce CoulombScattering theoryNucleonforceCoulomb [force]Particle Physics - ExperimentParticle physicsNuclear and High Energy Physicsstrong interaction [K p]QC1-999FOS: Physical sciencesmomentum[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]530K p: strong interaction ; heavy ion: scattering ; momentum: correlation ; force: Coulomb ; effective field theory: chiral ; atom: exotic ; nucleon: pair ; heavy ion scattering ; momentum correlation ; force Coulomb ; effective field theory chiral ; atom exotic ; nucleon pair ; CERN LHC Coll ; two-particle ; measurement methods ; sensitivity ; strong interaction ; ALICE ; kinematics ; TeV ; scattering length ; experimental results ; 5020 GeV-cms/nucleon ; hadron114 Physical sciencesscattering [heavy ion]0103 physical sciencesTeVSCATTERINGNuclear Physics - Experimentddc:5305020 GeV-cms/nucleonSensitivity (control systems)010306 general physicsexotic [atom]Exotic atomK p: strong interaction010308 nuclear & particles physicsScatteringforce: Coulombpairpair [nucleon]momentum: correlationScattering lengthHeavy Ions ExperimentsLOW-ENERGY K; DA-PHI-NE; SCATTERING; SPECTROSCOPYsensitivityLOW-ENERGY KchiralALICE heavy-ion collisions nuclear physicscorrelationscattering lengthCoulombHigh Energy Physics::ExperimenthadronDA-PHI-NEnucleon pairEnergy (signal processing)experimental results
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Multiscale Attention-Based Prototypical Network For Few-Shot Semantic Segmentation

2021

International audience; Deep learning-based image understanding techniques require a large number of labeled images for training. Few-shot semantic segmentation, on the contrary, aims at generalizing the segmentation ability of the model to new categories given only a few labeled samples. To tackle this problem, we propose a novel prototypical network (MAPnet) with multiscale feature attention. To fully exploit the representative features of target classes, we firstly extract rich contextual information of labeled support images via a multiscale feature enhancement module. The learned prototypes from support features provide further semantic guidance on the query image. Then we adaptively i…

business.industryComputer scienceDeep learningFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognition02 engineering and technologyImage segmentation010501 environmental sciencesSemantics01 natural sciencesImage (mathematics)[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Minimum bounding boxFeature (computer vision)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentationArtificial intelligencebusiness0105 earth and related environmental sciences
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Λc+ Production and Baryon-to-Meson Ratios in pp and p -Pb Collisions at sNN=5.02  TeV at the LHC

2021

The prompt production of the charm baryon Λ c + and the Λ c + / D 0 production ratios were measured at midrapidity with the ALICE detector in p p and p -Pb collisions at s NN = 5.02 TeV . These new measurements show a clear decrease of the Λ c + / D 0 ratio with increasing transverse momentum ( p T ) in both collision systems in the range 2 p T 12 GeV / c , exhibiting similarities with the light-flavor baryon-to-meson ratios p / π and Λ / K S 0 . At low p T , predictions that include additional color-reconnection mechanisms beyond the leading-color approximation, assume the existence of additional higher-mass charm-baryon states, or include hadronization via coalescence can describe the dat…

PhysicsParticle physicsLarge Hadron ColliderMeson010308 nuclear & particles physicsNuclear TheoryHigh Energy Physics::PhenomenologyHadronGeneral Physics and Astronomy01 natural sciencesHadronizationBaryon0103 physical sciencesTransverse momentumHigh Energy Physics::ExperimentNuclear Experiment010306 general physicsPhysical Review Letters
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Outdoor Scenes Pixel-wise Semantic Segmentation using Polarimetry and Fully Convolutional Network

2019

International audience; In this paper, we propose a novel method for pixel-wise scene segmentation application using polarimetry. To address the difficulty of detecting highly reflective areas such as water and windows, we use the angle and degree of polarization of these areas, obtained by processing images from a polarimetric camera. A deep learning framework, based on encoder-decoder architecture, is used for the segmentation of regions of interest. Different methods of augmentation have been developed to obtain a sufficient amount of data, while preserving the physical properties of the polarimetric images. Moreover, we introduce a new dataset comprising both RGB and polarimetric images…

Ground truthModality (human–computer interaction)reflective areasPixelbusiness.industryComputer scienceDeep learningsegmentationPolarimetryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONdeep learning[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineering02 engineering and technologyMarket segmentationaugmentation0202 electrical engineering electronic engineering information engineeringRGB color model020201 artificial intelligence & image processingComputer visionSegmentationArtificial intelligencebusinesspolarimetryProceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
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Deep multimodal fusion for semantic image segmentation: A survey

2021

International audience; Recent advances in deep learning have shown excellent performance in various scene understanding tasks. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. A variety of studies have demonstrated that deep multimodal fusion for semantic image segmentation achieves significant performance improvement. These fusion approaches take the benefits of multiple information sources and generate an optimal joint prediction automatically. This paper describes the essential background concepts of deep multimodal fusion and the relevant applications in compute…

Computer science02 engineering and technologyMachine learningcomputer.software_genre0202 electrical engineering electronic engineering information engineeringImage fusionSegmentationmutimodal fusionImage segmentationImage fusionHeuristicbusiness.industryDeep learning[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Deep learning020207 software engineeringImage segmentationSemantic segmentationVariety (cybernetics)Multi-modal[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Signal ProcessingBenchmark (computing)020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencePerformance improvementbusinesscomputerImage and Vision Computing
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Observation of the Dalitz decayη′→γe+e−

2015

We report the first observation of the Dalitz decay eta' -> gamma e(+)e(-), based on a data sample of 1.31 billion J/psi events collected with the BESIII detector. The eta' mesons are produced via the J/psi -> gamma eta' decay process. The ratio (eta' -> gamma e(+)e(-))/Gamma (eta' -> gamma gamma) is measured to be (2.13 +/- 0.09(stat) +/- 0.07(sys)) x 10(-2). This corresponds to a branching fraction B(eta' -> gamma e(+)e(-)) = (4.69 +/- 0.20(stat) +/- 0.23(sys)) x 10(-4). The transition form factor is extracted and different expressions are compared to the measured dependence on the e(+)e(-) invariant mass. The results are consistent with the prediction of the vector meson dominance model.

PhysicsNuclear and High Energy PhysicsParticle physicsMeson010308 nuclear & particles physicsBranching fractionAstrophysics::High Energy Astrophysical PhenomenaForm factor (quantum field theory)Vector meson dominance01 natural sciencesGamma gammaNuclear physics0103 physical sciencesHigh Energy Physics::ExperimentInvariant massVector mesonNuclear Experiment010306 general physicsPhysical Review D
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Study of very forward energy and its correlation with particle production at midrapidity in pp and p-Pb collisions at the LHC

2022

Journal of high energy physics 08(8), 86 (2022). doi:10.1007/JHEP08(2022)086

perturbation theory [quantum chromodynamics]p p: scatteringNuclear and High Energy Physics:Kjerne- og elementærpartikkelfysikk: 431 [VDP]FOS: Physical scienceshiukkasfysiikkatransverse momentum[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]530114 Physical sciencesHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)ALICEHeavy Ion Experimentsscattering [p p]Heavy Ion Experiments ; calorimeter: forward spectrometer ; p: fragmentation ; quantum chromo ; dynamics: perturbation theory ; pp: scattering ; p nucleus: scattering ; parton: interaction ; CERN LHC Coll ; PYTHIA ; correlation ; Monte Carlo ; underlying event ; ALICE ; transverse momentum ; rapidity ; experimental results ; 13000 GeV-cms/nucleon ; 8160 GeV-cms/nucleon[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Nuclear Physics - Experimentddc:530p: fragmentationquantum chromodynamics: perturbation theoryNuclear Experiment (nucl-ex)parton: interactionNuclear ExperimentNuclear Experimentp nucleus: scatteringMonte Carlointeraction [parton]calorimeter: forward spectrometerunderlying eventscattering [p nucleus]8160 GeV-cms/nucleonfragmentation [p]forward spectrometer [calorimeter]:Nuclear and elementary particle physics: 431 [VDP]CERN LHC Collrapiditycorrelation13000 GeV-cms/nucleonPYTHIAParticle Physics - Experimentexperimental results
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Analyse et fusion d’images multimodales pour la navigation autonome

2021

Robust semantic scene understanding is challenging due to complex object types, as well as environmental changes caused by varying illumination and weather conditions. This thesis studies the problem of deep semantic segmentation with multimodal image inputs. Multimodal images captured from various sensory modalities provide complementary information for complete scene understanding. We provided effective solutions for fully-supervised multimodal image segmentation and few-shot semantic segmentation of the outdoor road scene. Regarding the former case, we proposed a multi-level fusion network to integrate RGB and polarimetric images. A central fusion framework was also introduced to adaptiv…

Multi-ModalApprentissage profond[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Multimodalite[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Image fusionDeep learningSemantic segmentationSegmentation semantiqueFusion d’images
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First study of the two-body scattering involving charm hadrons

2022

Physical review / D 106(5), 052010 (2022). doi:10.1103/PhysRevD.106.052010

:Kjerne- og elementærpartikkelfysikk: 431 [VDP]charmed mesoncorrelation [momentum]heavy flavourhiukkasfysiikkaHigh Energy Physics - ExperimentnukleonitHigh Energy Physics - Experiment (hep-ex)ALICENucleon-scatteringscattering [p p][PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]BaryonsCollisionsNuclear Experiment (nucl-ex)EXCHANGENuclear ExperimentNuclear ExperimentBARYONSMesonscharm hadronsstrong interactionnucleonVDP::Kjerne- og elementærpartikkelfysikk: 431MESONSexperimental results ; pp: scattering ; momentum: correlation ; nucleon ; isospin ; charmed meson ; D- ; D+ ; strong interaction ; ALICE ; scattering length ; correlation: two-particle ; Coulomb:Nuclear and elementary particle physics: 431 [VDP]PARTIAL-WAVE ANALYSISVDP::Nuclear and elementary particle physics: 431D+D-Particle Physics - ExperimentHeavy-ion physics heavy flavour charm hadronscorrelation: two-particleCOLLISIONSp p: scatteringNuclear and High Energy PhysicsFOS: Physical sciencesPARTIAL-WAVE ANALYSIS; NUCLEON-SCATTERING; COLLISIONS; EXCHANGE; BARYONS; MESONS[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]530114 Physical sciencesheavy-ion physics; charm; heavy flavourNUCLEON-SCATTERINGisospinHeavy Ion ExperimentsPartial-wave analysisNuclear Physics - Experimentsirontaddc:530two-particle [correlation]Nuclear PhysicsHigh Energy Physics::Phenomenologymomentum: correlationExchangeHeavy-ion physicsscattering lengthCoulombcharmexperimental results
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Charm-quark fragmentation fractions and production cross section at midrapidity in pp collisions at the LHC

2022

Physical review / D 105(1), L011103 (2022). doi:10.1103/PhysRevD.105.L011103

Physics and Astronomy (miscellaneous)electron p: interactionPROTON-PROTON COLLISIONSMESON PRODUCTIONROOT-S=5.02 TEVmeasured [cross section]hiukkasfysiikka2760 GeV-cms/nucleon01 natural sciencesHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)ALICEscattering [p p]ground state [charm]Charm; p-p collisions[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Nuclear Experiment (nucl-ex)Nuclear ExperimentNuclear Experimentcharm: ground statep-p collisionsMeson productioninteraction [electron p]CERN LHC Coll7000 GeV-cms/nucleonParticle Physics - Experimentperturbation theory [quantum chromodynamics]p p: scatteringCharmcharm: fragmentation ; p p: scattering ; electron p: interaction ; charm: ground state ; quantum chromodynamics: perturbation theory ; cross section: measured ; hadron hadron: interaction ; CERN LHC Coll ; rapidity ; ALICE ; experimental results ; 2760 GeV-cms/nucleon ; 5020 GeV-cms/nucleon ; 7000 GeV-cms/nucleoneducationFOS: Physical sciencesfragmentation [charm][PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]530114 Physical sciencesProton-proton collisions0103 physical sciencescharm fragmentation fractions nuclear physicsddc:5305020 GeV-cms/nucleonNuclear Physics - Experimentcharm: fragmentationD-0quantum chromodynamics: perturbation theory010306 general physicshadron hadron: interactionPROTON-PROTON COLLISIONS; MESON PRODUCTION; ROOT-S=5.02 TEV; QCD; D-0interaction [hadron hadron]010308 nuclear & particles physicsHigh Energy Physics::Phenomenologycross section: measuredRoot-s=5.02 tevQCDQcdrapidityHigh Energy Physics::Experimentkvanttiväridynamiikkaexperimental results
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Characterizing the initial conditions of heavy-ion collisions at the LHC with mean transverse momentum and anisotropic flow correlations

2022

Physics letters / B 834, 137393 (2022). doi:10.1016/j.physletb.2022.137393

:Kjerne- og elementærpartikkelfysikk: 431 [VDP]heavy ion: scatteringNuclear Experiment; Nuclear Experiment; High Energy Physics - Experimenthiukkasfysiikkanucl-exElliptic-flowHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)ALICE5020: 5440 GeV-cms/nucleon[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Quark-gluon plasma elliptic flow Pb collisionsNuclear Experiment (nucl-ex)Nuclear ExperimentViscosityelliptic flowQuark-gluon plasmaheavy ion: scattering ; flow: anisotropy ; gluon: saturation ; correlation: higher-order ; initial state ; transverse momentum ; ALICE ; boundary condition ; CERN LHC Coll ; hydrodynamics ; color glass condensate ; numerical calculations ; experimental results ; 5020: 5440 GeV-cms/nucleonflow: anisotropyHigh Energy Heavy Ion Collisions:Nuclear and elementary particle physics: 431 [VDP]CERN LHC CollPerspectiveydinfysiikkahigher-order [correlation]Particle Physics - Experimentanisotropy [flow]Nuclear and High Energy PhysicsEvolutionFOS: Physical sciencesPb collisionstransverse momentum[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]114 Physical sciences530scattering [heavy ion]Nuclear Physics - Experimentddc:530saturation [gluon]numerical calculationsinitial statehep-exkvarkki-gluoniplasmaheavy-ion collisions nuclear physics correlations LHCcorrelation: higher-orderboundary condition5440 GeV-cms/nucleon [5020]hydrodynamicsgluon: saturationcolor glass condensateexperimental results
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