Search results for "V2"

showing 10 items of 446 documents

"Appendix 1.3" of "Measurement of the higher-order anisotropic flow coefficients for identified hadrons in Au$+$Au collisions at $\sqrt{s_{_{NN}}}$ =…

2021

Azimuthal anisotropy $v_n$ via the event-plane method for charge-combined $K^{\pm}$ in 40%���50% central Au+Au collisions at $\sqrt{s_{NN}} =$ 200 GeV.

V2V4AUAU --> CHRAGED XV3Elliptic FlowNUCLEUS NUCLEUS --> CHARGED XQuadrangular FlowNuclear Experiment200Triangular Flow
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"Appendix 1.1" of "Measurement of the higher-order anisotropic flow coefficients for identified hadrons in Au$+$Au collisions at $\sqrt{s_{_{NN}}}$ =…

2021

Azimuthal anisotropy $v_n$ via the event-plane method for charge-combined $K^{\pm}$ in 20%���30% central Au+Au collisions at $\sqrt{s_{NN}} =$ 200 GeV.

V2V4AUAU --> CHRAGED XV3Elliptic FlowNUCLEUS NUCLEUS --> CHARGED XQuadrangular FlowNuclear Experiment200Triangular Flow
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"Figure 1.2.1.0" of "Measurement of the higher-order anisotropic flow coefficients for identified hadrons in Au$+$Au collisions at $\sqrt{s_{_{NN}}}$…

2021

Azimuthal anisotropy $v_2$ and $v_3$ via the two-particle correlation method for charge-combined $p\bar{p}$ in 0%���50% central Au+Au collisions at $\sqrt{s_{NN}} =$ 200 GeV.

V2V4AUAU --> CHRAGED XV3Elliptic FlowNUCLEUS NUCLEUS --> CHARGED XQuadrangular FlowNuclear Experiment200Triangular Flow
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"Figure 2.2" of "Measurements of Higher-Order Flow Harmonics in Au+Au Collisions at sqrt(s_NN) = 200 GeV"

2020

Charged hadron azimuthal anisotropy $v_2$, $v_3$, and $v_4$ vs $p_T$ in 20-30% central Au+Au collisions at 200 GeV. The mean $$ in each $p_T$ bins used for the $v_n$ measurement is shown in Fig.2.6.

V2V4V3pTNUCLEUS NUCLEUS --> CHARGED XQuadrangular Flow200.0Triangular FlowInclusiveElliptic FlowTransverse MomentumAU AU --> CHARGED XHigh Energy Physics::ExperimentNuclear Experiment
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"Figure 2.4" of "Measurements of Higher-Order Flow Harmonics in Au+Au Collisions at sqrt(s_NN) = 200 GeV"

2020

Charged hadron azimuthal anisotropy $v_2$, $v_3$, and $v_4$ vs $p_T$ in 40-50% central Au+Au collisions at 200 GeV. The mean $$ in each $p_T$ bins used for the $v_n$ measurement is shown in Fig.2.6.

V2V4V3pTNUCLEUS NUCLEUS --> CHARGED XQuadrangular Flow200.0Triangular FlowInclusiveElliptic FlowTransverse MomentumAU AU --> CHARGED XHigh Energy Physics::ExperimentNuclear Experiment
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"Figure 2.3" of "Measurements of Higher-Order Flow Harmonics in Au+Au Collisions at sqrt(s_NN) = 200 GeV"

2020

Charged hadron azimuthal anisotropy $v_2$, $v_3$, and $v_4$ vs $p_T$ in 30-40% central Au+Au collisions at 200 GeV. The mean $$ in each $p_T$ bins used for the $v_n$ measurement is shown in Fig.2.6.

V2V4V3pTNUCLEUS NUCLEUS --> CHARGED XQuadrangular Flow200.0Triangular FlowInclusiveElliptic FlowTransverse MomentumAU AU --> CHARGED XHigh Energy Physics::ExperimentNuclear Experiment
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"Table 24" of "Investigations of anisotropic flow using multi-particle azimuthal correlations in pp, p-Pb, Xe-Xe, and Pb-Pb collisions at the LHC"

2019

$v_2\{4\}$ with 3-subevent method in Xe-Xe collisions at $\sqrt{s_{NN}} = 5.44$ TeV.

Xe Xe --> CHARGED X5440.0v24_3sub
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"Table 23" of "Investigations of anisotropic flow using multi-particle azimuthal correlations in pp, p-Pb, Xe-Xe, and Pb-Pb collisions at the LHC"

2019

$v_2\{4\}$ in Xe-Xe collisions at $\sqrt{s_{NN}} = 5.44$ TeV.

Xe Xe --> CHARGED Xv245440.0
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Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks

2023

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning-based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases th…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]5GBIoV[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI]Zero-day attacksSécurité5G V2X IoV Sécurité Attaques Détection Apprentissage Fédéré[INFO] Computer Science [cs]Intrusion DetectionDétectionAttaquesSecurityV2XApprentissage FédéréFederated Learning5GConnected and Automated Vehicles[INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]
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Salīdzinošā analīze attēlu aizpildīšanas uzdevumam, izmantojot GAN neironu tīklus

2022

Aizpildīšanas algoritmi tiek izmantoti attēlu apstrādē, lai labotu attēlu bojātās vai trūkstošās krāsojuma daļas. Šajā darbā tika salīdzināti trīs dziļās mašīnmācīšanās modeļi trūkstošo reģionu aizpildīšanai. Tika sniegti kvalitatīvs salīdzinājums, kvantitatīvi rezultāti un lietotāja izpēte DeepFill v2, CoModGAN un LaMa attēlu izpildīšanas algoritmiem. Tiek izmantotas Places2 un CelebA-HQ datu kopas, lai norādītu vairākas problēmas ar kurām sastopas attēlu atjaunošanā.

attēlu aizpildīšanaDeepFill v2CoModGANLaMaDatorzinātneGAN
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