Pair density wave characterized by a hidden string order parameter

A composite pairing structure of superconducting state is revealed by density matrix renormalization group study in a two-leg $t$-$J$ model. The pairing order parameter is composed of a pairing amplitude and a phase factor, in which the latter explicitly depends on the spin background with an analytic form identified in the anisotropic limit as the interchain hopping integral $t_{\perp}\rightarrow 0$. Such a string-like phase factor is responsible for a pair density wave (PDW) induced by spin polarization with a wavevector $Q_{\mathrm {PDW}}=2\pi m$ ($m$ the magnetization). By contrast, the pairing amplitude remains smooth, unchanged by the PDW. In particular, a local spin polarization can give rise to a sign change of the order parameter across the local defect. Unlike in an Fulde-Ferrell-Larkin-Ovchinnikov state, the nonlocal phase factor here plays a role as the new order parameter characterizing the PDW, whose origin can be traced back to the essential sign structure of the doped Mott insulator.Comment: 10 pages, 8 figure

The effects of the little Higgs models on ttˉh0t\bar{t} h^0 production via γγ\gamma \gamma collision at linear colliders

In the frameworks of the littlest Higgs($LH$) model and its extension with T-parity($LHT$), we studied the associated $t\bar th^0$ production process $e^+ e^- \to \gamma\gamma \to t \bar t h^0$ at the future $e^+e^-$ linear colliders up to QCD next-to-leading order. We present the regions of $\sqrt{s}-f$ parameter space in which the $LH$ and $LHT$ effects can and cannot be discovered with the criteria assumed in this paper. The production rates of process $\gamma\gamma \to t \bar t h^0$ in different photon polarization collision modes are also discussed. We conclude that one could observe the effects contributed by the $LH$ or $LHT$ model on the cross section for the process $e^+ e^- \to \gamma\gamma \to t \bar t h^0$ in a reasonable parameter space, or might put more stringent constraints on the $LH$/$LHT$ parameters in the future experiments at linear colliders.Comment: 22 pages, 25 figures, version to appear in Phys. Rev.

Does Graph Distillation See Like Vision Dataset Counterpart?

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condensation methods primarily focus on optimizing the feature matrices of condensed graphs while overlooking the impact of the structure information from the original graphs. To investigate the impact of the structure information, we conduct analysis from the spectral domain and empirically identify substantial Laplacian Energy Distribution (LED) shifts in previous works. Such shifts lead to poor performance in cross-architecture generalization and specific tasks, including anomaly detection and link prediction. In this paper, we propose a novel Structure-broadcasting Graph Dataset Distillation (SGDD) scheme for broadcasting the original structure information to the generation of the synthetic one, which explicitly prevents overlooking the original structure information. Theoretically, the synthetic graphs by SGDD are expected to have smaller LED shifts than previous works, leading to superior performance in both cross-architecture settings and specific tasks. We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98.6% test accuracy of training on the original graph dataset with 1,000 times saving on the scale of the graph. Moreover, we empirically evaluate there exist 17.6% ~ 31.4% reductions in LED shift crossing 9 datasets. Extensive experiments and analysis verify the effectiveness and necessity of the proposed designs. The code is available in the GitHub repository: https://github.com/RingBDStack/SGDD.Comment: Accepted by NeurIPS 202