arXiv subjects
Publications and source records attributed to Peng Zhang.
We show that the nucleon spectrum in a hard-wall AdS/QCD model can be improved by use of a relatively large IR cutoff. All of the spin-1/2 nucleon masses listed in PDG can be fit quite well within 11%. The average error is remarkably only 4.66%.
By using a new parametrization of the dilaton field and including a cubic term in the bulk scalar potential, we realize linear confinement in both meson and nucleon sectors within the framework of soft-wall AdS/QCD. At the same time this model also correctly incorporate chiral symmetry breaking. We compare our resulting mass spectra with experimental data and find good agreement between them.
When an off-resonant light field is coupled with atomic spins, its polarization can rotate depending on the direction of the spins via a Faraday rotation which has been used for monitoring and controlling the atomic spins. We observed Faraday rotation by an angle of more than 10 degrees for a single 1/2 nuclear spin of 171Yb atom in a high-finesse optical cavity. By employing the coupling between the single nuclear spin and a photon, we have also demonstrated that the spin can be projected or weakly measured through the projection of the transmitted single ancillary photon.
We constructed a cavity QED system with a diamagnetic atom of 171Yb and performed projective measurements on a single nuclear spin. Since Yb has no electronic spin and has 1/2 nuclear spin, the procedure of spin polarization and state verification can be dramatically simplified compared with the pseudo spin-1/2 system. By enhancing the photon emission rate of the 1S0-3P1 transition, projective measurement is implemented for an atom with the measurement time of T_meas = 30us. Unwanted spin flip as well as dark counts of the detector lead to systematic error when the present technique is applied for the determination of diagonal elements of an unknown spin state, which is delta|beta|^2 < 2 * 10^-2. Fast measurement on a long-lived qubit is key to the realization of large-scale one-way quantum computing.
In density estimation task, maximum entropy model (Maxent) can effectively use reliable prior information via certain constraints, i.e., linear constraints without empirical parameters. However, reliable prior information is often insufficient, and the selection of uncertain constraints becomes necessary but poses considerable implementation complexity. Improper setting of uncertain constraints can result in overfitting or underfitting. To solve this problem, a generalization of Maxent, under Tsallis entropy framework, is proposed. The proposed method introduces a convex quadratic constraint for the correction of (expected) Tsallis entropy bias (TEB). Specifically, we demonstrate that the expected Tsallis entropy of sampling distributions is smaller than the Tsallis entropy of the underlying real distribution. This expected entropy reduction is exactly the (expected) TEB, which can be expressed by a closed-form formula and act as a consistent and unbiased correction. TEB indicates that the entropy of a specific sampling distribution should be increased accordingly. This entails a quantitative re-interpretation of the Maxent principle. By compensating TEB and meanwhile forcing the resulting distribution to be close to the sampling distribution, our generalized TEBC Maxent can be expected to alleviate the overfitting and underfitting. We also present a connection between TEB and Lidstone estimator. As a result, TEB-Lidstone estimator is developed by analytically identifying the rate of probability correction in Lidstone. Extensive empirical evaluation shows promising performance of both TEBC Maxent and TEB-Lidstone in comparison with various state-of-the-art density estimation methods.
We present the X-ray variability properties of the X-ray and TeV bright blazar Mrk 421 with a ~60 ks long XMM-Newton observation performed on November 9-10, 2005. The source experienced a pronounced flare, of which the inter-band time lags were determined with a very high confidence level. The soft (0.6-0.8 keV) X-ray variations lagged the hard (4-10 keV) ones by 1.09 ks, and the soft lag increases with increasing difference in the photon energy. The energy-dependent soft lags can be well fitted with the difference of the energy-dependent cooling timescales of the relativistic electron distribution responsible for the observed X-ray emission, which constrains the magnetic field strength and Doppler factor of the emitting region to be Bδ^(1/3)~1.78 Gauss.
We consider a trapped two-component atomic Bose-Einstein condensate (BEC), where each atom with three energy-levels is coupled to an optical cavity field and an external classical optical field as well as a microwave field to form the so-called $Δ$-type configuration. After adiabatically eliminating the atomic excited state, an extended JC-Dicke model is derived under the rotating-wave approximation. The scaled ground-state energy and the phase diagram of this model Hamiltonian are investigated in the framework of mean-field approach. A new phase transition is revealed when the amplitude of microwave field changes its sign.
Manifold learning is a hot research topic in the field of computer science and has many applications in the real world. A main drawback of manifold learning methods is, however, that there is no explicit mappings from the input data manifold to the output embedding. This prohibits the application of manifold learning methods in many practical problems such as classification and target detection. Previously, in order to provide explicit mappings for manifold learning methods, many methods have been proposed to get an approximate explicit representation mapping with the assumption that there exists a linear projection between the high-dimensional data samples and their low-dimensional embedding. However, this linearity assumption may be too restrictive. In this paper, an explicit nonlinear mapping is proposed for manifold learning, based on the assumption that there exists a polynomial mapping between the high-dimensional data samples and their low-dimensional representations. As far as we know, this is the first time that an explicit nonlinear mapping for manifold learning is given. In particular, we apply this to the method of Locally Linear Embedding (LLE) and derive an explicit nonlinear manifold learning algorithm, named Neighborhood Preserving Polynomial Embedding (NPPE). Experimental results on both synthetic and real-world data show that the proposed mapping is much more effective in preserving the local neighborhood information and the nonlinear geometry of the high-dimensional data samples than previous work.
We develop a method of simultaneous and independent control of different scattering lengths in ultracold multicomponent atomic gases, such as $ \mathrm{40}}$K or $^{\mathrm{40}} $K-$^{\mathrm{6}}$Li mixture. Our method can be used to engineer multi-component quantum phases and Efimov trimer states.
Many real-world networks display a natural bipartite structure. Investigating it based on the original structure is helpful to get deep understanding about the networks. In this paper, some real-world bipartite networks are collected and divided into two types, dependence bipartite networks and independence bipartite networks, according to the different relation of two sets of nodes. By analyzing them, the results show that the actors nodes have scale-free property in the dependence networks, and there is no accordant degree distribution in the independence networks for both two types of nodes. In order to understand the scale-free property of actors in dependence networks, two growing bipartite models without the preferential attachment principle are proposed. The models show the scale-free phenomena in actors' degree distribution. It also gives well qualitatively consistent behavior with the empirical results.
In this paper, a new comparative definition for community in networks is proposed and the corresponding detecting algorithm is given. A community is defined as a set of nodes, which satisfy that each node's degree inside the community should not be smaller than the node's degree toward any other community. In the algorithm, the attractive force of a community to a node is defined as the connections between them. Then employing attractive force based self-organizing process, without any extra parameter, the best communities can be detected. Several artificial and real-world networks, including Zachary Karate club network and College football network are analyzed. The algorithm works well in detecting communities and it also gives a nice description for network division and group formation.
Based on signaling process on complex networks, a method for identification community structure is proposed. For a network with $n$ nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source once to inspire the whole network by exciting its neighbors and then the source node is endowed a $n$d vector which recording the effects of signaling process. So by this process, the topological relationship of nodes on networks could be transferred into the geometrical structure of vectors in $n$d Euclidian space. Then the best partition of groups is determined by $F$-statistic and the final community structure is given by Fuzzy $C$-means clustering method (FCM). This method can detect community structure both in unweighted and weighted networks without any extra parameters. It has been applied to ad hoc networks and some real networks including Zachary Karate Club network and football team network. The results are compared with that of other approaches and the evidence indicates that the algorithm based on signaling process is effective.
Many real-world networks display a natural bipartite structure. It is necessary and important to study the bipartite networks by using the bipartite structure of the data. Here we propose a modification of the clustering coefficient given by the fraction of cycles with size four in bipartite networks. Then we compare the two definitions in a special graph, and the results show that the modification one is better to character the network. Next we define a edge-clustering coefficient of bipartite networks to detect the community structure in original bipartite networks.
In this paper we consider the ZZ brane decay in a D-dimensional background with a linear dilaton and a Liouville potential switched on. We mainly calculate the closed string emission rate during the decay process. For the case of a spacelike dilaton we find a similar Hagedorn behavior, in the closed string UV region, with the brane decay in the usual 26d and 2d bosonic string theory. This means that all of the energy of the original brane converts into outgoing closed strings. In the IR region the result is finite. We also give some comments about the case that the dilaton is null.
In this note we make a test of the open topological string version of the OSV conjecture, proposed in hep-th/0504054, in the toric Calabi-Yau manifold $X= O(-3)\to\mathbf{P}^2$ with background D4-branes wrapped on Lagrangian submanifolds. The D-brane partition function reduces to an expectation value of some inserted operators of a q-deformed Yang-Mills theory living on a chain of $\mathbf{P}^1$'s in the base $\mathbf{P}^2$ of $X$. At large $N$ this partition function can be written as a sum over squares of chiral blocks, which are related to the open topological string amplitudes in the local $\mathbf{P}^2$ geometry with branes at both the outer and inner edges of the toric diagram. This is in agreement with the conjecture.
The role of weight on the weighted networks is investigated by studying the effect of weight on community structures. We use weighted modularity $Q^w$ to evaluate the partitions and Weighted Extremal Optimization algorithm to detect communities. Starting from idealized and empirical weighted networks, the distribution or matching between weights and edges are disturbed. Using dissimilarity function $D$ to distinguish the difference between community structures, it is found that the redistribution of weights does strongly affect the community structure especially in dense networks. This indicates that the community structure in networks is a suitable property to reflect the role of weight.
Based on brief review of approaches for community identification and measurement for sensitivity characterization, the accuracy and precision of several approaches for detecting communities in weighted networks are investigated. In weighted networks, the community structure should take both links and link weights into account and the partition of networks should be evaluated by weighted modularity $Q^w$. The results reveal that link weight has important effects on communities especially in dense networks. Potts model and Weighted Extremal Optimization (WEO) algorithm work well on weighted networks. Then Potts model and WEO algorithms are used to detect communities in Rhesus monkey network. The results gives nice understanding for real community structure.
In this paper, we study an exactly solvable model of IIB superstring in a time-dependent plane-wave backgound with a constant self-dual Ramond-Ramond 5-form field strength and a linear dilaton in the light-like direction. This background keeps sixteen supersymmetries. In the light-cone gauge, the action is described by the two-dimensional free bosons and fermions with time-dependent masses. The model could be canonically quantized and its Hamiltonian is time-dependent with vanishing zero point energy. The spectrum of the excitations is symmetric between the bosonic and fermionic sector. The string mode creation turns out to be very small.