4,668 research outputs found
Tensor network and (-adic) AdS/CFT
We use the tensor network living on the Bruhat-Tits tree to give a concrete
realization of the recently proposed -adic AdS/CFT correspondence (a
holographic duality based on the -adic number field ). Instead
of assuming the -adic AdS/CFT correspondence, we show how important features
of AdS/CFT such as the bulk operator reconstruction and the holographic
computation of boundary correlators are automatically implemented in this
tensor network.Comment: 59 pages, 18 figures; v3: improved presentation, added figures and
reference
Improper Ferroelectric Polarisation in a Perovskite driven by Inter-site Charge Transfer and Ordering
It is of great interest to design and make materials in which ferroelectric
polarisation is coupled to other order parameters such as lattice, magnetic and
electronic instabilities. Such materials will be invaluable in next-generation
data storage devices. Recently, remarkable progress has been made in
understanding improper ferroelectric coupling mechanisms that arise from
lattice and magnetic instabilities. However, although theoretically predicted,
a compact lattice coupling between electronic and ferroelectric (polar)
instabilities has yet to be realised. Here we report detailed crystallographic
studies of a novel perovskite
HgMnMnO that is
found to exhibit a polar ground state on account of such couplings that arise
from charge and orbital ordering on both the A' and B-sites, which are
themselves driven by a highly unusual Mn-Mn inter-site charge
transfer. The inherent coupling of polar, charge, orbital and hence magnetic
degrees of freedom, make this a system of great fundamental interest, and
demonstrating ferroelectric switching in this and a host of recently reported
hybrid improper ferroelectrics remains a substantial challenge.Comment: 9 pages, 7 figure
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The Association between Virus Prevalence and Intercolonial Aggression Levels in the Yellow Crazy Ant, Anoplolepis Gracilipes (Jerdon).
The recent discovery of multiple viruses in ants, along with the widespread infection of their hosts across geographic ranges, provides an excellent opportunity to test whether viral prevalence in the field is associated with the complexity of social interactions in the ant population. In this study, we examined whether the association exists between the field prevalence of a virus and the intercolonial aggression of its ant host, using the yellow crazy ant (Anoplolepis gracilipes) and its natural viral pathogen (TR44839 virus) as a model system. We delimitated the colony boundary and composition of A. gracilipes in a total of 12 study sites in Japan (Okinawa), Taiwan, and Malaysia (Penang), through intercolonial aggression assay. The spatial distribution and prevalence level of the virus was then mapped for each site. The virus occurred at a high prevalence in the surveyed colonies of Okinawa and Taiwan (100% infection rate across all sites), whereas virus prevalence was variable (30%-100%) or none (0%) at the sites in Penang. Coincidentally, colonies in Okinawa and Taiwan displayed a weak intercolonial boundary, as aggression between colonies is generally low or moderate. Contrastingly, sites in Penang were found to harbor a high proportion of mutually aggressive colonies, a pattern potentially indicative of complex colony composition. Our statistical analyses further confirmed the observed correlation, implying that intercolonial interactions likely contribute as one of the effective facilitators of/barriers to virus prevalence in the field population of this ant species
Weakly-supervised Caricature Face Parsing through Domain Adaptation
A caricature is an artistic form of a person's picture in which certain
striking characteristics are abstracted or exaggerated in order to create a
humor or sarcasm effect. For numerous caricature related applications such as
attribute recognition and caricature editing, face parsing is an essential
pre-processing step that provides a complete facial structure understanding.
However, current state-of-the-art face parsing methods require large amounts of
labeled data on the pixel-level and such process for caricature is tedious and
labor-intensive. For real photos, there are numerous labeled datasets for face
parsing. Thus, we formulate caricature face parsing as a domain adaptation
problem, where real photos play the role of the source domain, adapting to the
target caricatures. Specifically, we first leverage a spatial transformer based
network to enable shape domain shifts. A feed-forward style transfer network is
then utilized to capture texture-level domain gaps. With these two steps, we
synthesize face caricatures from real photos, and thus we can use parsing
ground truths of the original photos to learn the parsing model. Experimental
results on the synthetic and real caricatures demonstrate the effectiveness of
the proposed domain adaptation algorithm. Code is available at:
https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at
https://github.com/ZJULearning/CariFaceParsin
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