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 (Sr1−x_{1-x}Cax_{x})3_{3}Ir4_{4}Sn13_{13} and Ca3_{3}Rh4_{4}Sn13_{13}

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 (Sr$_{1-x}$Ca$_{x}$)$_{3}$Ir$_{4}$Sn$_{13}$ ($x$ = 0, 0.5, 1) and Ca$_{3}$Rh$_{4}$Sn$_{13}$ show some unusual properties similar to the Fe-pnictides, through $^{119}$Sn nuclear magnetic resonance (NMR) measurements. In (Sr$_{1-x}$Ca$_{x}$)$_{3}$Ir$_{4}$Sn$_{13}$, the NMR linewidth increases below a temperature $T^*$ that is higher than the structural phase transition temperature $T_{\rm s}$. The spin-lattice relaxation rate ($1/T_1$) divided by temperature ($T$), $1/T_1T$, and the Knight shift $K$ increase with decreasing $T$ down to $T^*$, but start to decrease below $T^*$ and followed by more distinct changes at $T_{\rm s}$. In contrast, none of the anomalies was observed in Ca$_{3}$Rh$_{4}$Sn$_{13}$ 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 Ca$_{3}$Ir$_{4}$Sn$_{13}$, $1/T_{1}$ decays as ${\rm exp}(-\Delta/k_{\rm B}T)$ with a large gap $\Delta = 2.21 k_{\rm B}T_{\rm c}$, 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 $10^6M_{\odot}$, 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 $10^5 M_{\odot}$ 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 $10^5 M_{\odot}$. 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