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In the constituent quark model, each quark inside a baryon carries 1/3 unit of the baryon number. An alternative picture exists where the center of a Y-shaped topology of gluon fields, called the baryon junction, carries a unit baryon number. Studying baryon transport over a large rapidity gap ($\delta y$) in nuclear collisions provides a possible tool to distinguish these two pictures. A recent analysis of global data on net-proton yield at mid-rapidity in Au+Au collisions showed an exponential dependence on $\delta y$ and the exponential slope does not vary with event centrality, favoring the baryon junction picture. Since junctions are flavor blind, hyperons -- baryons containing valence strange quarks -- are expected to exhibit a similar behavior as the proton. This study aims to test this prediction by analyzing hyperon yields in Au+Au collisions at various energies. We observe that net-hyperon yields, after correcting for the strangeness production suppression, adhere to the expected exponential form. The extracted slope parameters for net-$\Lambda$, net-$\Xi$ and net-$\Omega$ are consistent with each other and with those of net-proton within uncertainties, and exhibit no centrality dependence, further substantiating the baryon junction picture. Various implementations of the \texttt{PYTHIA} event generator, primarily based on valence quarks for baryon transport, are unable to simultaneously describe the slope parameters for all baryons.

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full graphs is often ignored in such process, which restricts the expressive power of GNNs. Various graph data augmentation methods that enable the message passing with richer structure knowledge have been introduced as one main way to tackle this issue, but they are often focused on individual structure features and difficult to scale up with more structure features. In this work we propose a novel approach, namely collective structure knowledge-augmented graph neural network (CoS-GNN), in which a new message passing method is introduced to allow GNNs to harness a diverse set of node- and graph-level structure features, together with original node features/attributes, in augmented graphs. In doing so, our approach largely improves the structural knowledge modeling of GNNs in both node and graph levels, resulting in substantially improved graph representations. This is justified by extensive empirical results where CoS-GNN outperforms state-of-the-art models in various graph-level learning tasks, including graph classification, anomaly detection, and out-of-distribution generalization.

One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and imbalanced learning loss functions, can be adopted for enabling graph representation learning models to cope with this challenge. However, these methods often directly operate on the graph representations, ignoring rich discriminative information within the graphs and their interactions. To tackle this issue, we introduce a novel multi-scale oversampling graph neural network (MOSGNN) that learns expressive minority graph representations based on intra- and inter-graph semantics resulting from oversampled graphs at multiple scales - subgraph, graph, and pairwise graphs. It achieves this by jointly optimizing subgraph-level, graph-level, and pairwise-graph learning tasks to learn the discriminative information embedded within and between the minority graphs. Extensive experiments on 16 imbalanced graph datasets show that MOSGNN i) significantly outperforms five state-of-the-art models, and ii) offers a generic framework, in which different advanced imbalanced learning loss functions can be easily plugged in and obtain significantly improved classification performance.

In relativistic heavy-ion collisions, a hot and dense state of matter, called the Quark-Gluon Plasma (QGP), is produced. Semi-inclusive jets recoiling from trigger hadrons of high transverse momenta ($p_{\mathrm{T}}$) can serve as an effective probe of the QGP properties, as they are expected to experience jet quenching when traversing the QGP. Recent experimental results on the ratio of recoil jet yields normalized by the trigger counts in heavy-ion collisions to that in $p$+$p$ collisions ($I_{\mathrm{AA}}$) pose an unexpected challenge in its interpretation. It is observed that $I_{\mathrm{AA}}$ rises with the jet $p_{\mathrm{T}}$ and possibly exceeds unity at high $p_{\mathrm{T}}$, while traditionally it is expected that jet quenching would lead to $I_{\mathrm{AA}} < 1$. To address this challenge, we utilize the Linear Boltzmann Transport (LBT) model to simulate jet transport in the QGP, and study the effect of jet quenching for high-$p_{\mathrm{T}}$ triggers and recoil jets separately on $I_{\mathrm{AA}}$. We find that the quenching of the colored triggers alone is responsible for the rising trend and larger-than-unity value observed experimentally., Comment: 4 pages, 4 figures

Heavy quarks, produced at early stages of heavy-ion collisions, are an excellent probe of the Quark-Gluon Plasma (QGP) also created in these collisions. Electrons from open heavy-flavor hadron decays (HFE) are good proxies for heavy quarks, and have been measured extensively in the last two decades to study QGP properties. These measurements are traditionally carried out by subtracting all known background sources from the inclusive electron sample. More recently, a significant enhancement of $e^+e^-$ pair production at very low transverse momenta was observed in peripheral heavy-ion collisions. The production characteristics is consistent with coherent photon-photon interactions, which should also constitute a background source to the HFE measurements. In this article, we provide theoretical predictions for the contribution of coherent electron production to HFE as a function of transverse momentum, centrality and collision energy in Au+Au and Pb+Pb collisions.

Baryon numbers are carried by valence quarks in the standard QCD picture of the baryon structure, while some theory proposed an alternative baryon number carrier, a non-perturbative Y-shaped configuration of the gluon field, called the baryon junction in the 1970s. However, neither of the theories has been verified experimentally. It was recently suggested to search for the baryon junction by investigating the correlation of net-charge and net-baryon yields at midrapidity in heavy-ion collisions. This paper presents studies of such correlations in collisions of various heavy ions from Oxygen to Uranium with the UrQMD Monte Carlo model. The UrQMD model implements valence quark transport as the primary means of charge and baryon stopping at midrapidity. Detailed study are also carried out for isobaric $_{40}^{96}\mathrm{Zr}$ + $_{40}^{96}\mathrm{Zr}$ and $_{44}^{96}\mathrm{Ru}$ + $_{44}^{96}\mathrm{Ru}$ collisions. We found a universal trend of the charge stopping with respect to the baryon stopping, and that the charge stopping is always more than the baryon stopping. This study provides a model baseline in valence quark transport for what is expected in net-charge and net-baryon yields at midrapidity of relativistic heavy-ion collisions., Comment: 6 pages, 7 figures

During the early development of Quantum Chromodynamics, it was proposed that baryon number could be carried by a non-perturbative Y-shaped topology of gluon fields, called the gluon junction, rather than by the valence quarks as in the QCD standard model. A puzzling feature of ultra-relativistic nucleus-nucleus collisions is the apparent substantial baryon excess in the midrapidity region that could not be adequately accounted for in most conventional models of quark and diquark transport. The transport of baryonic gluon junctions is predicted to lead to a characteristic exponential distribution of net-baryon density with rapidity and could resolve the puzzle. In this context we point out that the rapidity density of net-baryons near midrapidity indeed follows an exponential distribution with a slope of $-0.61\pm0.03$ as a function of beam rapidity in the existing global data from A+A collisions at AGS, SPS and RHIC energies. To further test if quarks or gluon junctions carry the baryon quantum number, we propose to study the absolute magnitude of the baryon vs. charge stopping in isobar collisions at RHIC. We also argue that semi-inclusive photon-induced processes ($\gamma+p$/A) at RHIC kinematics provide an opportunity to search for the signatures of the baryon junction and to shed light onto the mechanisms of observed baryon excess in the mid-rapidity region in ultra-relativistic nucleus-nucleus collisions. Such measurements can be further validated in A+A collisions at the LHC and $e+p$/A collisions at the EIC., Comment: 12 pages, 6 figures, Submitted to EPJC

Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations. The random distillation is achieved by training one GNN to predict another GNN with randomly initialized network weights. Extensive experiments on 16 real-world graph datasets from diverse domains show that our model significantly outperforms seven state-of-the-art models. Code and datasets are available at https://git.io/GLocalKD., Comment: Accepted to WSDM 2022

Deep neural networks (DNNs) have achieved extraordinary success in numerous areas. However, to attain this success, DNNs often carry a large number of weight parameters, leading to heavy costs of memory and computation resources. Overfitting is also likely to happen in such network when the training data are insufficient. These shortcomings severely hinder the application of DNNs in resource-constrained platforms. In fact, many network weights are known to be redundant and can be removed from the network without much loss of performance. To this end, we introduce a new non-convex integrated transformed $\ell_1$ regularizer to promote sparsity for DNNs, which removes both redundant connections and unnecessary neurons simultaneously. To be specific, we apply the transformed $\ell_1$ to the matrix space of network weights and utilize it to remove redundant connections. Besides, group sparsity is also employed as an auxiliary to remove unnecessary neurons. An efficient stochastic proximal gradient algorithm is presented to solve the new model at the same time. To the best of our knowledge, this is the first work to utilize a non-convex regularizer in sparse optimization based method to promote sparsity for DNNs. Experiments on several public datasets demonstrate the effectiveness of the proposed method.

In these proceedings, an overview of recent quarkonium measurements in nuclear collisions carried out at both RHIC and LHC is presented. In p+p collisions, despite theoretical progresses made in understanding the production mechanisms for quarkonia, a complete picture is still yet to be achieved. In p+A collisions where measurements are done to quantify the cold nuclear matter effects, significant suppression of quarkonium production is observed for low transverse momentum region at both forward- and mid-rapidities. Furthermore, results from A+A collisions show the interplay of dissociation and regeneration contributions for charmonium at different collision energies, while a correspondence between the suppression level and the binding energy is observed for bottomonium family. Comparisons to experimental data provide stringent tests to model calculations, and help constrain the temperature of the deconfined medium created in heavy-ion collisions., Comment: 7 pages, 18 figures, quarkonium overview proceedings for QM2018