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Ecological thresholds and large carnivores conservation: Implications for the Amur tiger and leopard in China
The ecological threshold concept describes how changes in one or more factors at thresholds can result in a large shift in the state of an ecosystem. This concept focuses attention on limiting factors that affect the tolerance of systems or organisms and changes in them. Accumulating empirical evidence for the existence of ecological thresholds has created favorable conditions for practical application to wildlife conservation. Applying the concept has the potential to enhance conservation of two large carnivores, Amur tiger and leopard, and the knowledge gained could guide the construction of a proposed national park. In this review, ecological thresholds that result from considering a paradigm of bottom-up control were evaluated for their potential to contribute to the conservation of Amur tiger and leopard. Our review highlights that large carnivores, as top predators, are potentially affected by ecological thresholds arising from changes in climate (or weather), habitat, vegetation, prey, competitors, and anthropogenic disturbances. What's more, interactions between factors and context dependence need to be considered in threshold research and conservation practice, because they may amplify the response of ecosystems or organisms to changes in specific drivers. Application of the threshold concept leads to a more thorough evaluation of conservation needs, and could be used to guide future Amur tiger and leopard research and conservation in China. Such application may inform the conservation of other large carnivores worldwide
Single chargino production via gluon-gluon fusion in a supersymmetric theory with an explicit R-parity violation
We studied the production of single chargino
accompanied by lepton via gluon-gluon fusion at the LHC. The
numerical analysis of their production rates is carried out in the mSUGRA
scenario with some typical parameter sets. The results show that the cross
sections of the productions via gluon-gluon
collision are in the order of femto barn quantitatively at the
CERN LHC, and can be competitive with production mechanism via quark-antiquark
annihilation process.Comment: LaTex file, 18 pages, 4 EPS file
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Visual media are powerful means of expressing emotions and sentiments. The
constant generation of new content in social networks highlights the need of
automated visual sentiment analysis tools. While Convolutional Neural Networks
(CNNs) have established a new state-of-the-art in several vision problems,
their application to the task of sentiment analysis is mostly unexplored and
there are few studies regarding how to design CNNs for this purpose. In this
work, we study the suitability of fine-tuning a CNN for visual sentiment
prediction as well as explore performance boosting techniques within this deep
learning setting. Finally, we provide a deep-dive analysis into a benchmark,
state-of-the-art network architecture to gain insight about how to design
patterns for CNNs on the task of visual sentiment prediction.Comment: Preprint of the paper accepted at the 1st Workshop on Affect and
Sentiment in Multimedia (ASM), in ACM MultiMedia 2015. Brisbane, Australi
Residual proton-neutron interactions and the scheme
We investigate the correlation between integrated proton-neutron interactions
obtained by using the up-to-date experimental data of binding energies and the
, the product of valence proton number and valence neutron
number with respect to the nearest doubly closed nucleus. We make corrections
on a previously suggested formula for the integrated proton-neutron
interaction. Our results demonstrate a nice, nearly linear, correlation between
the integrated p-n interaction and , which provides us
with a firm foundation of the applicability of the scheme
to nuclei far from the stability line.Comment: four pages, three figures, Physical Review C, in pres
Polarization Induced Switching Effect in Graphene Nanoribbon Edge-Defect Junction
With nonequilibrium Green's function approach combined with density
functional theory, we perform an ab initio calculation to investigate transport
properties of graphene nanoribbon junctions self-consistently. Tight-binding
approximation is applied to model the zigzag graphene nanoribbon (ZGNR)
electrodes, and its validity is confirmed by comparison with GAUSSIAN03 PBC
calculation of the same system. The origin of abnormal jump points usually
appearing in the transmission spectrum is explained with the detailed
tight-binding ZGNR band structure. Transport property of an edge defect ZGNR
junction is investigated, and the tunable tunneling current can be sensitively
controlled by transverse electric fields.Comment: 18 pages, 8 figure
Two Higgs Bosons at the Tevatron and the LHC?
The best fit to the Tevatron results in the bb channel and the mild excesses
at CMS in the gamma-gamma channel at 136 GeV and in the tau-tau channel above
132 GeV can be explained by a second Higgs state in this mass range, in
addition to the one at 125 GeV recently discovered at the LHC. We show that a
scenario with two Higgs bosons at 125 GeV and 136 GeV can be consistent with
practically all available signal rates, including a reduced rate in the tau-tau
channel around 125 GeV as reported by CMS. An example in the parameter space of
the general NMSSM is given where, moreover, the signal rates of the 125 GeV
Higgs boson in the gamma-gamma channels are enhanced relative to the
expectation for a SM Higgs boson of this mass.Comment: 13 pages, 4 Table
Spatio-Temporal Sentiment Hotspot Detection Using Geotagged Photos
We perform spatio-temporal analysis of public sentiment using geotagged photo
collections. We develop a deep learning-based classifier that predicts the
emotion conveyed by an image. This allows us to associate sentiment with place.
We perform spatial hotspot detection and show that different emotions have
distinct spatial distributions that match expectations. We also perform
temporal analysis using the capture time of the photos. Our spatio-temporal
hotspot detection correctly identifies emerging concentrations of specific
emotions and year-by-year analyses of select locations show there are strong
temporal correlations between the predicted emotions and known events.Comment: To appear in ACM SIGSPATIAL 201
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