Predictions of the Strange partner of TccT_{cc} in the quark delocalization color screening model

Inspired by the detection of $T_{cc}$ tetraquark state by LHCb Collaboration, we preform a systemical investigation of the low-lying doubly heavy charm tetraquark states with strangeness in the quark delocalization color screening model in the present work. Two kinds of configurations, the meson-meson configuration and diquark-antidiquark configuration, are considered in the calculation. Our estimations indicate that the coupled channel effects play important role in the multiquark system, and a bound state with $J^{P}=1^{+}$ and a resonance state with $J^{P}=0^{+}$ have been predicted. The mass of the bound state is evaluated to be $(3971\sim3975)$ MeV, while the mass and width of the resonance are determined to be $(4113\sim4114)$ MeV and $(14.3\sim 16.1)$ MeV, respectively.Comment: 12 pages, 4 figure, 7 table

Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits

Probabilistic Circuits (PCs) are a general and unified computational framework for tractable probabilistic models that support efficient computation of various inference tasks (e.g., computing marginal probabilities). Towards enabling such reasoning capabilities in complex real-world tasks, Liu et al. (2022) propose to distill knowledge (through latent variable assignments) from less tractable but more expressive deep generative models. However, it is still unclear what factors make this distillation work well. In this paper, we theoretically and empirically discover that the performance of a PC can exceed that of its teacher model. Therefore, instead of performing distillation from the most expressive deep generative model, we study what properties the teacher model and the PC should have in order to achieve good distillation performance. This leads to a generic algorithmic improvement as well as other data-type-specific ones over the existing latent variable distillation pipeline. Empirically, we outperform SoTA TPMs by a large margin on challenging image modeling benchmarks. In particular, on ImageNet32, PCs achieve 4.06 bits-per-dimension, which is only 0.34 behind variational diffusion models (Kingma et al., 2021)