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The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification; 2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data. Code will be released., Comment: Accepted by Artificial Intelligence Journal with minor revision

In GUT-scale constrained (GUTc) supersymmetric (SUSY) models, the mass of smuon $\tilde{\mu}_1$ is typically heavier than that of stau $\tilde{\tau}_1$, and stau co-annihilation is a typical annihilation mechanism of dark matter. However, light smuon is more favored by the muon $g-2$ anomaly, thus smuon-neutralino loop contribution to muon $g-2$ is usually smaller than that of sneutrino-chargino. Inspired by the latest muon $g-2$ results, we take the GUTc- Next-to-Minimal Supersymmetric Model (NMSSM) as an example, where the gaugino (Higgs) masses are not unified to the usual parameter $M_{1/2}$ ($M_0$), exploring its possibility of light smuon and its contribution to muon $g-2$. After complicated calculations and discussions, we conclude that in GUTc-NMSSM the smuon can be lighter than stau. In this light-smuon scenario, the contribution of smuon-neutralino loop to the muon $g-2$ can be larger than that of the sneutrino-chargino loop. The annihilation mechanisms of dark matter are dominated by multiple slepton or chargino co-annihilation. In our calculations, we consider also other latest related constraints like Higgs data, SUSY searches, dark matter relic density and direct detections, etc., Comment: 12 pages, 5 figures

The correlation between Higgs-like scalars and light dark matter is an interesting topic, especially now that a $125 GeV$ Higgs was discovered and dark matter (DM) searches got negative results. The $95 GeV$ excess reported by the CMS collaboration with $132 fb^{-1}$ data recently, and the DM search results by XENONnT and LZ collaborations motivate us to revise that. In this work, we study that in the GUT-scale constrained (GUTc) Next-to-Minimal Supersymmetric Model (NMSSM), where most parameters are input at the GUT scale, but with scalar and gaugino masses not unified there. In the calculation we also consider other recent experimental constraints, such as Higgs data, Supersymmetry (SUSY) searches, DM relic density, etc. After detailed analysis and discussion, we find that: (i) The light DM can be bino- or singlino-dominated, but can be mixed with minor components of Higgsino. (ii) Both cases can get right relic density and sizable Higgs invisible decay, by adjusting the dimensionless parameters $\lambda, \kappa$, or suitably mixing with Higgsino. (iii) Both cases can have four funnel annihilation mechanisms, i.e., annihilating through $Z, a_1, h_2, h_1$. (iv) Samples with right relic density usually get weak signal of Higgs invisible decay at future lepton collider, but the $95 GeV$ scalar can have sizable $b\bar{b}$ signal., Comment: 12 pages, 3 figures

In 2018, the CMS collaboration reported a di-photon excess around 95.3 GeV with a local significance of 2.8 $\sigma$. Interestingly, the CMS collaboration also reported a di-tau excess recently at 95 $\sim$ 100 GeV with a local significance of 2.6 $\sim$ 3.1 $\sigma$. Besides, a $b\bar{b}$ excess at 98 GeV with a 2.3 $\sigma$ local significance was reported with LEP data about twenty years ago. In this work, we consider interpreting these excesses together with a light Higgs boson in the next-to-minimal supersymmetric standard model (NMSSM). We conclude that in NMSSM the 95 $\sim$ 100 GeV excesses are difficult to be satisfied simultaneously (not possible globally at $1\sigma$ level, or simultaneously at $2\sigma$ level), and we analyze two partial-satisfied scenarios: the globally $2\sigma$ scenario and small di-photon scenario. An approximate equation of global fit to the three excesses is derived, and two representative types of surviving samples are analyzed in detail. Since the mass regions of these excesses are near the Z boson, we also consider checking the light Higgs boson in the $t\bar{t}$-associated channels. The detailed results may be useful for further checking the low-mass-region excesses in the future., Comment: 9 pages, 5 figures, 2 tables

Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multi-stage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intra-class variations while maintaining inter-class separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework., Comment: accepted to IEEE Transactions on Cybernetics

Link failures and cable miswirings are not uncommon in building data center networks, which prevents the existing automatic address configuration methods from functioning correctly. However, accurately detecting such malfunctions is not an easy task because there could be no observable node degree changes. Fixing or correcting such malfunctions is even harder as almost no work can provide accurate fixation suggestions now. To solve the problems, we design and implement A3, an automatic topology-aware malfunction detection and fixation system. A3 innovatively formulates the problem of finding minimal fixation to the problem of computing minimum graph difference (NP-hard) and solves it in O(k^6) and O(k^3) for any less than k/2 and k/4 undirected link malfunctions for FatTree, respectively. Our evaluation demonstrates that for less than k/2 undirected link malfunctions, A3 is 100% accurate for malfunction detection and provides the minimum fixation result. For greater or equal to k/2 undirected link malfunctions, A3 still has accuracy of about 100% and provides the near optimal fixation result., Comment: The poster version is published as a SIGCOMM 2019 poster, and the 5 pages' version is under submission