The global geometrical property of jet events in high-energy nuclear collisions

We present the first theoretical study of medium modifications of the global geometrical pattern, i.e., transverse sphericity ($S_{\perp}$) distribution of jet events with parton energy loss in relativistic heavy-ion collisions. In our investigation, POWHEG+PYTHIA is employed to make an accurate description of transverse sphericity in the p+p baseline, which combines the next-to-leading order (NLO) pQCD calculations with the matched parton shower (PS). The Linear Boltzmann Transport (LBT) model of the parton energy loss is implemented to simulate the in-medium evolution of jets. We calculate the event normalized transverse sphericity distribution in central Pb+Pb collisions at the LHC, and give its medium modifications. An enhancement of transverse sphericity distribution at small $S_{\perp}$ region but a suppression at large $S_{\perp}$ region are observed in A+A collisions as compared to their p+p references, which indicates that in overall the geometry of jet events in Pb+Pb becomes more pencil-like. We demonstrate that for events with 2 jets in the final-state of heavy-ion collisions, the jet quenching makes the geometry more sphere-like with medium-induced gluon radiation. However, for events with $\ge 3$~jets, parton energy loss in the QCD medium leads to the events more pencil-like due to jet number reduction, where less energetic jets may lose their energies and then fall off the jet selection kinematic cut. These two effects offset each other and in the end result in more jetty events in heavy-ion collisions relative to that in p+p.Comment: 9 pages, 9 figure

Quenching of jets tagged with WW bosons in high-energy nuclear collisions

We carry out the first detailed calculations of jet production associated with $W$ gauge bosons in Pb+Pb collisions at the Large Hadron Collider (LHC). In our calculations, the production of $W$+jet in p+p collisions as a reference is obtained by Sherpa, which performs next-to-leading-order matrix element calculations matched to the resummation of parton shower simulations, while jet propagation and medium response in the quark-gluon plasma are simulated with the Linear Boltzmann Transport (LBT) model. We provide numerical predictions on seven observables of $W$+jet production with jet quenching in Pb+Pb collisions: the medium modification factor for the tagged jet cross sections $I_{AA}$, the distribution in invariant mass between the two leading jets in $N_{jets}\ge 2$ events $m_{jj}$, the missing $p_T$ or the vector sum of the lepton and jet transverse momentum $|\vec{p}_T^{Miss}|$, the summed scalar $p_T$ of all the jets in an event $S_T$, transverse momentum imbalance $x_{jW}$, average number of jets per $W$ boson $R_{jW}$, and azimuthal angle between the $W$ boson and jets $\Delta \phi_{jW}$. The distinct nuclear modifications of these seven observables in Pb+Pb relative to that in p+p collisions are presented with detailed discussions.Comment: 19 pages,12 figure

Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition

Current methods for skeleton-based human action recognition usually work with complete skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy skeletons, which could significantly deteriorate the performance of current methods when some informative joints are occluded or disturbed. To improve the robustness of action recognition models, a multi-stream graph convolutional network (GCN) is proposed to explore sufficient discriminative features spreading over all skeleton joints, so that the distributed redundant representation reduces the sensitivity of the action models to non-standard skeletons. Concretely, the backbone GCN is extended by a series of ordered streams which is responsible for learning discriminative features from the joints less activated by preceding streams. Here, the activation degrees of skeleton joints of each GCN stream are measured by the class activation maps (CAM), and only the information from the unactivated joints will be passed to the next stream, by which rich features over all active joints are obtained. Thus, the proposed method is termed richly activated GCN (RA-GCN). Compared to the state-of-the-art (SOTA) methods, the RA-GCN achieves comparable performance on the standard NTU RGB+D 60 and 120 datasets. More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN.Comment: Accepted by IEEE T-CSVT, 11 pages, 6 figures, 10 table