Traffic flow and efficient routing on scale-free networks: A survey

Recently, motivated by the pioneer works in revealing the small-world effect and scale-free property of various real-life networks, many scientists devote themselves to studying complex networks. In this paper, we give a brief review on the studies of traffic flow and efficient routing on scale-free networks, including the traffic dynamics based on global routing protocol, Traffic dynamics based on local routing protocol, and the critical phenomena and scaling behaviors of real and artificial traffic. Finally, perspectives and some interesting problems are proposed.Comment: A brief review on recent progress of network traffi

Asymptotic analysis of the linearized Boltzmann collision operator from angular cutoff to non-cutoff

We give quantitative estimates on the asymptotics of the linearized Boltzmann collision operator and its associated equation from angular cutoff to non cutoff. On one hand, the results disclose the link between the hyperbolic property resulting from the Grad's cutoff assumption and the smoothing property due to the long-range interaction. On the other hand, with the help of the localization techniques in the phase space, we observe some new phenomenon in the asymptotic limit process. As a consequence, we give the affirmative answer to the question that there is no jump for the property that the collision operator with cutoff does not have the spectrum gap but the operator without cutoff does have for the moderate soft potentials

Improving Raw Image Storage Efficiency by Exploiting Similarity

To improve the temporal and spatial storage efficiency, researchers have intensively studied various techniques, including compression and deduplication. Through our evaluation, we find that methods such as photo tags or local features help to identify the content-based similar- ity between raw images. The images can then be com- pressed more efficiently to get better storage space sav- ings. Furthermore, storing similar raw images together enables rapid data sorting, searching and retrieval if the images are stored in a distributed and large-scale envi- ronment by reducing fragmentation. In this paper, we evaluated the compressibility by designing experiments and observing the results. We found that on a statistical basis the higher similarity photos have, the better com- pression results are. This research helps provide a clue for future large-scale storage system design

Good, Better, Best: Choosing Word Embedding Context

We propose two methods of learning vector representations of words and phrases that each combine sentence context with structural features extracted from dependency trees. Using several variations of neural network classifier, we show that these combined methods lead to improved performance when used as input features for supervised term-matching

On universal α\alpha-central extensions of Hom-preLie algebras

We introduce the notion of Hom-co-represention and low-dimensional chain complex. We study universal central extensions of Hom-preLie algebras and generlize some classical results. As the same time, we introduce $\alpha$-central extensions, universal $\alpha$-central extensions and $\alpha$-perfect Hom-preLie algebras. We construct universal ($\alpha$)-central extensions of Hom-preLie algebras.Comment: arXiv admin note: substantial text overlap with arXiv:1209.5887 and arXiv:1209.6266 by other author

Degree-layer theory of network topology

The network topology can be described by the number of nodes and the interconnections among them. The degree of a node in a network is the number of connections it has to other nodes and the degree distribution is the probability distribution of these degrees over the whole network. Therefore, the degree is very important structural parameter of network topology. However, given the number of nodes and the degree of each node in a network, the topology of the network cannot be determined. Therefore, we propose the degree-layer theory of network topology to describe deeply the network topology. First, we propose the concept of degree-tree with the breadth-first search tree. The degrees of all nodes are layered and have a hierarchical structure. Second,the degree-layer theory is described in detail. Two new concepts are defined in the theory. An index is proposed to quantitatively distinguish the two network topologies. It also can quantitatively measure the stability of network topology built by a model mechanism. One theorem is given and proved, furthermore, and one corollary is derived directly from the theorem. Third, the applications of the degree-layer theory are discussed in the ER random network, WS small world network and BA scale-free network, and the influences of the degree distribution on the stability of network topology are studied in the three networks. In conclusion, the degree-layer theory is helpful for accurately describing the network topology, and provides a new starting point for researching the similarity and isomorphism between two network topologies.Comment: 6 pages, 4 figure

Field-free Magnetization Switching by Utilizing the Spin Hall Effect and Interlayer Exchange Coupling of Iridium

Magnetization switching by spin-orbit torque (SOT) via spin Hall effect represents as a competitive alternative to that by spin-transfer torque (STT) used for magnetoresistive random access memory (MRAM), as it does not require high-density current to go through the tunnel junction. For perpendicular MRAM, however, SOT driven switching of the free layer requires an external in-plane field, which poses limitation for viability in practical applications. Here we demonstrate field-free magnetization switching of a perpendicular magnet by utilizing an Iridium (Ir) layer. The Ir layer not only provides SOTs via spin Hall effect, but also induce interlayer exchange coupling with an in-plane magnetic layer that eliminates the need for the external field. Such dual functions of the Ir layer allows future build-up of magnetoresistive stacks for memory and logic applications. Experimental observations show that the SOT driven field-free magnetization reversal is characterized as domain nucleation and expansion. Micromagnetic modeling is carried out to provide in-depth understanding of the perpendicular magnetization reversal process in the presence of an in-plane exchange coupling field

Synchronization on complex networks with different sorts of communities

In this paper, inspired by the idea that many real networks are composed by different sorts of communities, we investigate the synchronization property of oscillators on such networks. We identify the communities by the intrinsic frequencies probability density $g(\omega)$ of Kuramoto oscillators. That is to say, communities in different sorts are functional different. For a network containing two sorts of communities, when the community strength is strong, only the oscillators in the same community synchronize. With the weakening of the community strength, an interesting phenomenon, \emph{Community Grouping}, appears: although the global synchronization is not achieved, oscillators in the same sort of communities will synchronize. Global synchronization will appear with the further reducing of the community strength, and the oscillators will rotate around the average frequency.Comment: 5 pages, 6 figure

Minimal Gated Unit for Recurrent Neural Networks

Recently recurrent neural networks (RNN) has been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN is a difficult task, partly because there are many competing and complex hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN's applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU's properties theoretically and empirically

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence's representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNN achieves state-of-the-art performance on AS, PI and TE tasks.Comment: TACL Camera-read