7,581 research outputs found
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 Meson to a P-Wave Charmonium State or
The semileptonic decays of meson to a P-wave charmonium state
or 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
charmonium state potentially offers us a novel window to see the
unconfirmed particle. In addition, it is pointed out that since the two
charmonium radiative decays have sizable
branching ratios, the cascade decays of the concerned decays and the charmonium
radiative decays may affect the result of the observing the meson through
the semileptonic decays substantially.Comment: 8 pages, 2 figure
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