11,085 research outputs found
FDRC: Flow-Driven Rule Caching Optimization in Software Defined Networking
With the sharp growth of cloud services and their possible combinations, the
scale of data center network traffic has an inevitable explosive increasing in
recent years. Software defined network (SDN) provides a scalable and flexible
structure to simplify network traffic management. It has been shown that
Ternary Content Addressable Memory (TCAM) management plays an important role on
the performance of SDN. However, previous literatures, in point of view on rule
placement strategies, are still insufficient to provide high scalability for
processing large flow sets with a limited TCAM size. So caching is a brand new
method for TCAM management which can provide better performance than rule
placement. In this paper, we propose FDRC, an efficient flow-driven rule
caching algorithm to optimize the cache replacement in SDN-based networks.
Different from the previous packet-driven caching algorithm, FDRC is
characterized by trying to deal with the challenges of limited cache size
constraint and unpredictable flows. In particular, we design a caching
algorithm with low-complexity to achieve high cache hit ratio by prefetching
and special replacement strategy for predictable and unpredictable flows,
respectively. By conducting extensive simulations, we demonstrate that our
proposed caching algorithm significantly outperforms FIFO and least recently
used (LRU) algorithms under various network settings
Enhanced entanglement of two optical modes in optomechanical systems via an optical parametric amplifier
We investigate the effect of a degenerate optical parametric amplifier (OPA)
placed inside an optomechanical cavity on the steady-state entanglement of two
cavity modes, which jointly interact with a mechanical resonator. Two cavity
modes are respectively driven at the red and blue sideband associated with the
mechanical resonator, which generates entanglement between them in the limit of
resolved sideband. The OPA gives rise to single-mode squeezing of the cavity
fields, which results in significant improvement of the two-mode entanglement.
It is found that an optimal nonlinear gain of the OPA exists, depending on the
system temperatures, which yields the maximum entanglement. The improvement is
particularly remarkable for the system at cryogenic temperatures.Comment: 14 pages, 5 figures, to appear in J. Phys.
Image Stitching Based on Planar Region Consensus
Image stitching for two images without a global transformation between them
is notoriously difficult. In this paper, noticing the importance of planar
structure under perspective geometry, we propose a new image stitching method
which stitches images by allowing for the alignment of a set of matched
dominant planar regions. Clearly different from previous methods resorting to
plane segmentation, the key to our approach is to utilize rich semantic
information directly from RGB images to extract planar image regions with a
deep Convolutional Neural Network (CNN). We specifically design a new module to
make fully use of existing semantic segmentation networks to accommodate planar
segmentation. To train the network, a dataset for planar region segmentation is
contributed. With the planar region knowledge, a set of local transformations
can be obtained by constraining matched regions, enabling more precise
alignment in the overlapping area. We also use planar knowledge to estimate a
transformation field over the whole image. The final mosaic is obtained by a
mesh-based optimization framework which maintains high alignment accuracy and
relaxes similarity transformation at the same time. Extensive experiments with
quantitative comparisons show that our method can deal with different
situations and outperforms the state-of-the-arts on challenging scenes.Comment: 15 pages, 9 figure
Review of Text Style Transfer Based on Deep Learning
Text style transfer is a hot issue in recent natural language
processing,which mainly studies the text to adapt to different specific
situations, audiences and purposes by making some changes. The style of the
text usually includes many aspects such as morphology, grammar, emotion,
complexity, fluency, tense, tone and so on. In the traditional text style
transfer model, the text style is generally relied on by experts knowledge and
hand-designed rules, but with the application of deep learning in the field of
natural language processing, the text style transfer method based on deep
learning Started to be heavily researched. In recent years, text style transfer
is becoming a hot issue in natural language processing research. This article
summarizes the research on the text style transfer model based on deep learning
in recent years, and summarizes, analyzes and compares the main research
directions and progress. In addition, the article also introduces public data
sets and evaluation indicators commonly used for text style transfer. Finally,
the existing characteristics of the text style transfer model are summarized,
and the future development trend of the text style transfer model based on deep
learning is analyzed and forecasted.Comment: There are some nonstandard problems in current paper
Genus one GW invariants of quintic threefolds via MSP localization
The moduli stack of Mixed Spin P-fields (MSP) provides an effective algorithm
to evaluate all genus Gromov-Witten invariants of quintic Calabi-Yau
threefolds. This paper is to apply the algorithm in genus one case. We use the
localization formula, the proposed algorithm in [CLLL1, CLLL2], and Zinger's
packaging technique to compute the genus one Gromov-Witten invariants of
quintic Calabi-Yau threefolds. New hypergeometric series identities are also
discovered in the process
Learning Actor Relation Graphs for Group Activity Recognition
Modeling relation between actors is important for recognizing group activity
in a multi-person scene. This paper aims at learning discriminative relation
between actors efficiently using deep models. To this end, we propose to build
a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture
the appearance and position relation between actors. Thanks to the Graph
Convolutional Network, the connections in ARG could be automatically learned
from group activity videos in an end-to-end manner, and the inference on ARG
could be efficiently performed with standard matrix operations. Furthermore, in
practice, we come up with two variants to sparsify ARG for more effective
modeling in videos: spatially localized ARG and temporal randomized ARG. We
perform extensive experiments on two standard group activity recognition
datasets: the Volleyball dataset and the Collective Activity dataset, where
state-of-the-art performance is achieved on both datasets. We also visualize
the learned actor graphs and relation features, which demonstrate that the
proposed ARG is able to capture the discriminative relation information for
group activity recognition.Comment: Accepted by CVPR 201
Structural phase transition, precursory electronic anomaly and strong-coupling superconductivity in quasi-skutterudite (SrCa)IrSn and CaRhSn
The interplay between superconductivity and structural phase transition has
attracted enormous interests in recent years. For example, in Fe-pnictide high
temperature superconductors, quantum fluctuations in association with
structural phase transition have been proposed to lead to many novel physical
properties and even the superconductivity itself. Here we report a finding that
the quasi-skutterudite superconductors
(SrCa)IrSn ( = 0, 0.5, 1) and
CaRhSn show some unusual properties similar to the
Fe-pnictides, through Sn nuclear magnetic resonance (NMR) measurements.
In (SrCa)IrSn, the NMR linewidth increases
below a temperature that is higher than the structural phase transition
temperature . The spin-lattice relaxation rate () divided by
temperature (), , and the Knight shift increase with decreasing
down to , but start to decrease below and followed by more
distinct changes at . In contrast, none of the anomalies was
observed in CaRhSn that does not undergo a structural phase
transition. The precursory phenomenon above structural phase transition
resembles that occurs in Fe-pnictides. In the superconducting state of
CaIrSn, decays as
with a large gap , yet without a
Hebel-Slichter coherence peak, which indicate strong-coupling
superconductivity. Our results provide new insight into the relationship
between superconductivity and the electronic-structure change associated with
structural phase transition.Comment: Chin. Phys. B (in press
Thermal conduction across a boron nitride and silicon oxide interface
The needs for efficient heat removal and superior thermal conduction in
nano/micro devices have triggered tremendous studies in low-dimensional
materials with high thermal conductivity. Hexagonal boron nitride (h-BN) is
believed to be one of the candidates for thermal management and heat
dissipation due to its novel physical properties, i.e. thermal conductor and
electrical insulator. Here we reported interfacial thermal resistance between
few-layer h-BN and its silicon oxide substrate using differential 3 omega
method. The measured interfacial thermal resistance is around ~1.6*10-8 m2K/W
for monolayer h-BN and ~3.4*10-8 m2K/W for 12.8nm-thick h-BN in metal/h-BN/SiO2
interfaces. Our results suggest that the voids and gaps between substrate and
thick h-BN flakes limit the interfacial thermal conduction. This work provides
a deeper understanding of utilizing h-BN flake as lateral heat spreader in
electronic and optoelectronic nano/micro devices with further miniaturization
and integration.Comment: 9 pages, 6 figure
Probing satellite galaxies in the Local Group by using FAST
The abundance of neutral hydrogen (HI) in satellite galaxies in the Local
Group is important for studying the formation history of our Local Group. In
this work, we generated mock HI satellite galaxies in the Local Group using the
high mass resolution hydrodynamic \textsc{apostle} simulation. The simulated HI
mass function agrees with the ALFALFA survey very well above ,
although there is a discrepancy below this scale because of the observed flux
limit. After carefully checking various systematic elements in the
observations, including fitting of line width, sky coverage, integration time,
and frequency drift due to uncertainty in a galaxy's distance, we predicted the
abundance of HI in galaxies in a future survey that will be conducted by FAST.
FAST has a larger aperture and higher sensitivity than the Arecibo telescope.
We found that the HI mass function could be estimated well around if the integration time is 40 minutes. Our results indicate that
there are 61 HI satellites in the Local Group, and 36 in the FAST field above
. This estimation is one order of magnitude better than the
current data, and will put a strong constraint on the formation history of the
Local Group. Also more high resolution simulated samples are needed to achieve
this target.Comment: 12 pages, 9 figures, 1 table, submitted to RA
Modeling citation networks based on vigorousness and dormancy
In citation networks, the activity of papers usually decreases with age and
dormant papers may be discovered and become fashionable again. To model this
phenomenon, a competition mechanism is suggested which incorporates two
factors: vigorousness and dormancy. Based on this idea, a citation network
model is proposed, in which a node has two discrete stage: vigorous and
dormant. Vigorous nodes can be deactivated and dormant nodes may be activated
and become vigorous. The evolution of the network couples addition of new nodes
and state transitions of old ones. Both analytical calculation and numerical
simulation show that the degree distribution of nodes in generated networks
displays a good right-skewed behavior. Particularly, scale-free networks are
obtained as the deactivated vertex is target selected and exponential networks
are realized for the random-selected case. Moreover, the measurement of four
real-world citation networks achieves a good agreement with the stochastic
model.Comment: ws-tex, 11 pages, 5 figure
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