arXiv subjects

Tao Zhou

Publications and source records attributed to Tao Zhou.

At least 523 records · Page 29Linked to original sources

arXiv2009

Emergence of target waves in paced populations of cyclically competing species

We investigate the emergence of target waves in a cyclic predator-prey model incorporating a periodic current of the three competing species in a small area situated at the center of the square lattice. The periodic current acts as a pacemaker, trying to impose its rhythm on the overall spatiotemporal evolution of the three species. We show that the pacemaker is able to nucleate target waves that eventually spread across the whole population, whereby three routes leading to this phenomenon can be distinguished depending on the mobility of the three species and the oscillation period of the localized current. First, target waves can emerge due to the synchronization between the periodic current and oscillations of the density of the three species on the spatial grid. The second route is similar to the first, the difference being that the synchronization sets in only intermittently. Finally, the third route towards target waves is realized when the frequency of the pacemaker is much higher than that characterizing the oscillations of the overall density of the three species. By considering mobility and the frequency of the current as variable parameters, we thus provide insights into the mechanisms of pattern formation resulting from the interplay between local and global dynamics in systems governed by cyclically competing species.

q-bio.PE↗

arXiv2009

Effective Personalized Recommendation in Collaborative Tagging Systems

Recently, collaborative tagging systems have attracted more and more attention and have been widely applied in web systems. Tags provide highly abstracted information about personal preferences and item content, and are therefore potential to help in improving better personalized recommendations. In this paper, we propose a tag-based recommendation algorithm considering the personal vocabulary and evaluate it in a real-world dataset: Del.icio.us. Experimental results demonstrate that the usage of tag information can significantly improve the accuracy of personalized recommendations.

cs.IR↗

arXiv2009

Effective leadership in competition

Among natural biological flocks/swarms or even mass social activities, when the collective behaviors of the followers has been dominated by the moving direction or opinion of one leader group, it seems very difficult for later-coming leaders to reverse the orientation of the mass followers, especially when they are in quantitative minority. This Letter reports a counter-intuitive phenomenon, Following the Later-coming Minority, provided that the late-comers obey a favorable distribution pattern which enables them to spread their influence to as many followers as possible in a given time and to accumulate enough power to govern these followers. We introduce a discriminant index to quantify the whole group's orientation under competing leadership, which helps to design an economic way for the minority later-coming leaders to defeat the dominating majority leaders solely by optimizing their distribution pattern. Our investigation provides new insights into the effective leadership in biological systems, with meaningful implication to social and industrial applications.

physics.soc-ph↗

arXiv2009

Degree correlation effect of bipartite network on personalized recommendation

In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the proposed algorithm, the degree correlation between users and objects is taken into account and embedded into the similarity index by a tunable parameter. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the MCF, measured by the average ranking score, is further improved by 18.19% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%.

physics.data-an↗

arXiv2009

Ultra accurate personalized recommendation via eliminating redundant correlations

In this paper, based on a weighted projection of bipartite user-object network, we introduce a personalized recommendation algorithm, called the \emph{network-based inference} (NBI), which has higher accuracy than the classical algorithm, namely \emph{collaborative filtering}. In the NBI, the correlation resulting from a specific attribute may be repeatedly counted in the cumulative recommendations from different objects. By considering the higher order correlations, we design an improved algorithm that can, to some extent, eliminate the redundant correlations. We test our algorithm on two benchmark data sets, \emph{MovieLens} and \emph{Netflix}. Compared with the NBI, the algorithmic accuracy, measured by the ranking score, can be further improved by 23% for \emph{MovieLens} and 22% for \emph{Netflix}, respectively. The present algorithm can even outperform the \emph{Latent Dirichlet Allocation} algorithm, which requires much longer computational time. Furthermore, most of the previous studies considered the algorithmic accuracy only, in this paper, we argue that the diversity and popularity, as two significant criteria of algorithmic performance, should also be taken into account. With more or less the same accuracy, an algorithm giving higher diversity and lower popularity is more favorable. Numerical results show that the present algorithm can outperform the standard one simultaneously in all five adopted metrics: lower ranking score and higher precision for accuracy, larger Hamming distance and lower intra-similarity for diversity, as well as smaller average degree for popularity.

physics.soc-ph↗

arXiv2009

Quantum phase transition in the Fulde-Ferrell-Larkin-Ovchinnikov states of a two dimensional d-wave superconductor upon increasing the exchange field

We studied the phase diagram for a two-dimensional d-wave superconducting system under an in-plane magnetic field or an exchange field. According to the spatial configuration of the order parameter, we show that there exists quantum phase transitions in which the uniform phase transforms to the one-dimensional Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, and then to two-dimensional FFLO state upon increasing the exchange field. The local density of states are calculated and suggested to be signatures to distinguish these phases.

cond-mat.supr-con↗

arXiv2009

Collaborative filtering based on multi-channel diffusion

In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user-object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain rating and a user having voted an object will be connected to the channel corresponding to the rating. Diffusion process taking place on such a user-channel bipartite graph gives a new similarity measure of user pairs, which is further demonstrated to be more accurate than the classical Pearson correlation coefficient under the standard collaborative filtering framework.

cs.IR↗

arXiv2009

Predicting Missing Links via Local Information

Missing link prediction of networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely common neighbors, has the best overall performance, and the Adamic-Adar index performs the second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbors. It is found that many links are assigned same scores if only the information of the nearest neighbors is used. We therefore design another new measure exploited information of the next nearest neighbors, which can remarkably enhance the prediction accuracy.

physics.data-an↗

arXiv2009

Reducing the Heterogeneity of Payoffs: an Effective Way to Promote Cooperation in Prisoner's Dilemma Game

In this paper, the total payoff of each agent is regulated to reduce the heterogeneity of the distribution of the total payoffs. It is found there is an optimal regulation strength where the fraction of cooperation is prominently promoted, too weak or too strong of the strength will have little effects or result in the disappearance of the cooperators. It is also found that most of the cooperators are not distributed in isolation but form the cooperator clusters, and to promote the cooperation the only way is to enlarge the size of the cooperator clusters. Finally, we try to explain the emergence of larger clusters and prove the existence of the optimal regulation strength. Our works provide insight into the understanding of the relations between the distribution of payoffs and the cooperative behaviors.

physics.soc-ph↗

arXiv2009

Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs

Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this paper, we propose a recommendation algorithm based on an integrated diffusion on user-item-tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.

cs.IR↗

arXiv2009

Optimal View Angle in Collective Dynamics of Self-propelled Agents

We study a system of self-propelled agents in which each agent has a part of omnidirectional or panoramic view of its sensor disc, the field of vision of the agent in this case is only a sector of a disc bounded by two radii and the included arc. The inclination of these two radii is characterized as the view angle. Contrary to our intuition, we find that, the non-omnidirectional-view for swarm agents with periodic boundary conditions in noiseless Vicsek model can accelerate the transient process of the emergence of the ordered state. One consequent implication is that, there are generally superfluous communications in the Vicsek Model, which may even obstruct the possible fast swarm emergence. This phenomenon may invoke further efforts and attentions to explore the underlying mechanism of the emergence in self-propelled agents.

nlin.AO↗

arXiv2009

Finding Community Structure Based on Subgraph Similarity

Community identification is a long-standing challenge in the modern network science, especially for very large scale networks containing millions of nodes. In this paper, we propose a new metric to quantify the structural similarity between subgraphs, based on which an algorithm for community identification is designed. Extensive empirical results on several real networks from disparate fields has demonstrated that the present algorithm can provide the same level of reliability, measure by modularity, while takes much shorter time than the well-known fast algorithm proposed by Clauset, Newman and Moore (CNM). We further propose a hybrid algorithm that can simultaneously enhance modularity and save computational time compared with the CNM algorithm.

cs.NI↗

arXiv2009

Information filtering based on transferring similarity

In this Brief Report, we propose a new index of user similarity, namely the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarkably higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach its optimal value when the parameter, contained in the definition of transferring similarity, gets close to its critical value, before which the series expansion of transferring similarity is convergent and after which it is divergent. Our study is complementary to the one reported in [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E {\bf 73} 026120 (2006)], and is relevant to the missing link prediction problem.

physics.data-an↗

arXiv2009

Consensus of self-driven agents with avoidance of collisions

In recent years, many efforts have been addressed on collision avoidance of collectively moving agents. In this paper, we propose a modified version of the Vicsek model with adaptive speed, which can guarantee the absence of collisions. However, this strategy leads to an aggregated state with slowly moving agents. We therefore further introduce a certain repulsion, which results in both faster consensus and longer safe distance among agents, and thus provides a powerful mechanism for collective motions in biological and technological multi-agent systems.

physics.data-an↗

arXiv2009

Effect of spin excitations on the property of quasiparticles in electron-doped cuprates

It is proposed that the 50-70 meV dispersion anomaly (kink) in electron-doped cuprates revealed by recent angle-resolved photoemission spectroscopy experiments is caused by coupling with the spin fluctuation. We elaborate that the kink exists both along nodal and antinodal directions, and both in the superconducting and normal state. The renormalized effect for the density of states is also studied and the hump feature outside the superconducting coherent peak is established, consistent with recent scanning tunnelling microscopy experiments.

cond-mat.supr-con↗

arXiv2008

Empirical analysis on a keyword-based semantic system

Keywords in scientific articles have found their significance in information filtering and classification. In this article, we empirically investigated statistical characteristics and evolutionary properties of keywords in a very famous journal, namely Proceedings of the National Academy of Science of the United States of America (PNAS), including frequency distribution, temporal scaling behavior, and decay factor. The empirical results indicate that the keyword frequency in PNAS approximately follows a Zipf's law with exponent 0.86. In addition, there is a power-low correlation between the cumulative number of distinct keywords and the cumulative number of keyword occurrences. Extensive empirical analysis on some other journals' data is also presented, with decaying trends of most popular keywords being monitored. Interestingly, top journals from various subjects share very similar decaying tendency, while the journals of low impact factors exhibit completely different behavior. Those empirical characters may shed some light on the in-depth understanding of semantic evolutionary behaviors. In addition, the analysis of keyword-based system is helpful for the design of corresponding recommender systems.

physics.data-an↗

arXiv2008

Accelerating consensus of self-driven swarm via adaptive speed

In resent years, Vicsek model has attracted more and more attention and been well developed. However, the in-depth analysis on the convergence time are scarce thus far. In this paper, we study some certain factors that mainly govern the convergence time of Vicsek model. By extensively numerical simulations, we find the convergence time scales in a power law with $r^2\ln N$ in the noise-free case, where $r$ and $N$ are horizon radius and the number of particles. Furthermore, to accelerate the convergence, we propose a new model in which the speed of each particle is variable. The convergence time can be remarkably shortened compared with the standard Vicsek model.

cond-mat.dis-nn↗

arXiv2008

How target waves emerge in population dynamics

Based on a multi-agent model, we investigate how target waves emerge from a population dynamics with cyclical interactions among three species. We show that the periodically injecting source in a small central area can generate target waves in a two-dimensional lattice system. By detecting the temporal period of species' concentration at the central area, three modes of target waves can be distinguished. Those different modes result from the competition between local and global oscillations induced by cyclical interactions: Mode A corresponds to a synchronization of local and global oscillations, Mode B results from an intermittent synchronization, and Mode C corresponds to the case when the frequency of the local oscillation is much higher than that of the global oscillation. This work provides insights into pattern formation in biologic and ecologic systems that are totally different from the extensively studied diffusion systems driven by chemical reactions.

physics.bio-ph↗