Fermion Pairing across a Dipolar Interaction Induced Resonance

It is known from the solution of the two-body problem that an anisotropic dipolar interaction can give rise to s-wave scattering resonances, which are named as dipolar interaction induced resonaces (DIIR). In this letter, we study zero-temperature many-body physics of a two-component Fermi gas across a DIIR. In the low-density regime, it is very striking that the resulting pairing order parameter is a nearly isotropic singlet pairing and the physics can be well described by an s-wave resonant interaction potential with finite range corrections, despite of the anisotropic nature of dipolar interaction. The pairing energy is as strong as a unitary Fermi gas nearby a magnetic Feshbach resonance. In the high density regime, the anisotropic effect plays an important role. We find phase transitions from singlet pairing to a state with mixed singlet and triplet pairing, and then from mixed pairing to pure triplet pairing. The state with mixed pairing spontaneously breaks the time-reversal symmetry.Comment: 4.5 pages, 4 figures, figures updated, minor changes in tex

s-Wave Scattering Resonances Induced by Dipolar Interactions of Polar Molecules

We show that s-wave scattering resonances induced by dipolar interactions in a polar molecular gas have a universal large and positive effective range, which is very different from Feshbach resonances realized in cold atoms before, where the effective range is either negligible or negative. Such a difference has important consequence in many-body physics. At high temperature regime, a positive effective range gives rise to stronger repulsive interaction energy for positive scattering length, and weaker attractive interaction energy for negative scattering length. While at low-temperatures, we study polaron problem formed by single impurity molecule, and we find that the polaron binding energy increases at the BEC side and decreases at the BCS side. All these effects are in opposite to narrow Feshbach resonances where the effective range is negative.Comment: 5 pages, 3 figures, published versio

Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.Comment: 10 pages. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, United Kingdom, ACM, 201

TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering

Unsupervised commonsense question answering requires mining effective commonsense knowledge without the rely on the labeled task data. Previous methods typically retrieved from traditional knowledge bases or used pre-trained language models (PrLMs) to generate fixed types of knowledge, which have poor generalization ability. In this paper, we aim to address the above limitation by leveraging the implicit knowledge stored in PrLMs and propose a two-stage prompt-based unsupervised commonsense question answering framework (TSGP). Specifically, we first use knowledge generation prompts to generate the knowledge required for questions with unlimited types and possible candidate answers independent of specified choices. Then, we further utilize answer generation prompts to generate possible candidate answers independent of specified choices. Experimental results and analysis on three different commonsense reasoning tasks, CommonsenseQA, OpenBookQA, and SocialIQA, demonstrate that TSGP significantly improves the reasoning ability of language models in unsupervised settings. Our code is available at: https://github.com/Yueqing-Sun/TSGP.Comment: Findings of EMNLP202

Semileptonic Decays of BcB_c Meson to a P-Wave Charmonium State χc\chi_c or hch_c

The semileptonic decays of meson $B_c$ to a P-wave charmonium state $\chi_c(^3P_J)$ or $h_c(^1P_1)$ are computed. The results show that the decays are sizable so they are accessible in Tevatron and in LHC, especially, with the detectors LHCB and BTeV in the foreseeable future, and of them, the one to the $^1P_1$ charmonium state potentially offers us a novel window to see the unconfirmed $h_c$ particle. In addition, it is pointed out that since the two charmonium radiative decays $\chi_c(^3P_{1,2}) \to J/\psi+\gamma$ have sizable branching ratios, the cascade decays of the concerned decays and the charmonium radiative decays may affect the result of the observing the $B_c$ meson through the semileptonic decays $B_{c}\to {J/\psi}+{l}+\nu_{l}$ substantially.Comment: 8 pages, 2 figure