arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 1,261 records · Page 70Linked to original sources

arXiv2013

Pomeranchuk cooling of the SU($2N$) ultra-cold fermions in optical lattices

We investigate the thermodynamic properties of a half-filled SU(2N) Fermi-Hubbard model in the two-dimensional square lattice using the determinantal quantum Monte Carlo simulation, which is free of the fermion "sign problem". The large number of hyperfine-spin components enhances spin fluctuations, which facilitates the Pomeranchuk cooling to temperatures comparable to the superexchange energy scale at the case of SU$(6)$. Various quantities including entropy, charge fluctuation, and spin correlations have been calculated.

cond-mat.quant-gas↗

arXiv2013

Comment on: "Classical signature of quantum annealing"

In a recent preprint (arXiv:1305.4904) entitled "Classical signature of quantum annealing" Smolin and Smith point out that a bimodal distribution presented in (arXiv:1304.4595) for the success probability in the D-Wave device does not in itself provide sufficient evidence for quantum annealing, by presenting a classical model that also exhibits bimodality. Here we analyze their model and in addition present a similar model derived from the semi-classical limit of quantum spin dynamics, which also exhibits a bimodal distribution. We find that in both cases the correlations between the success probabilities of these classical models and the D-Wave device are weak compared to the correlations between a simulated quantum annealer and the D-Wave device. Indeed, the evidence for quantum annealing presented in arXiv:1304.4595 is not limited to the bimodality, but relies in addition on the success probability correlations between the D-Wave device and the simulated quantum annealer. The Smolin-Smith model and our semi-classical spin model both fail this correlation test.

quant-ph↗

arXiv2013

Direct measurement of topological invariants in optical lattices

We propose an experimental technique for classifying the topology of band structures realized in optical lattices, based on a generalization of topological charge pumping in quantum Hall systems to cold atom in optical lattices. Time-of-flight measurement along one spatial direction combined with in situ detection along the transverse direction provide a direct measure of the system's Chern number, as we illustrate by calculations for the Hofstadter lattice. Based on an analogy with Wannier functions techniques of topological band theory, the method is very general and also allows the measurement of other topological invariants, such as the $Z_2$ topological invariant of time-reversal symmetric insulators.

cond-mat.quant-gas↗

arXiv2013

The LAMOST Survey of Background Quasars in the Vicinity of the Andromeda and Triangulum Galaxies -- II. Results from the Commissioning Observations and the Pilot Surveys

We present new quasars discovered in the vicinity of the Andromeda and Triangulum galaxies with the LAMOST during the 2010 and 2011 observational seasons. Quasar candidates are selected based on the available SDSS, KPNO 4 m telescope, XSTPS optical, and WISE near infrared photometric data. We present 509 new quasars discovered in a stripe of ~135 sq. deg from M31 to M33 along the Giant Stellar Stream in the 2011 pilot survey datasets, and also 17 new quasars discovered in an area of ~100 sq. deg that covers the central region and the southeastern halo of M31 in the 2010 commissioning datasets. These 526 new quasars have i magnitudes ranging from 15.5 to 20.0, redshifts from 0.1 to 3.2. They represent a significant increase of the number of identified quasars in the vicinity of M31 and M33. There are now 26, 62 and 139 known quasars in this region of the sky with i magnitudes brighter than 17.0, 17.5 and 18.0 respectively, of which 5, 20 and 75 are newly-discovered. These bright quasars provide an invaluable collection with which to probe the kinematics and chemistry of the ISM/IGM in the Local Group of galaxies. A total of 93 quasars are now known with locations within 2.5 deg of M31, of which 73 are newly discovered. Tens of quasars are now known to be located behind the Giant Stellar Stream, and hundreds behind the extended halo and its associated substructures of M31. The much enlarged sample of known quasars in the vicinity of M31 and M33 can potentially be utilized to construct a perfect astrometric reference frame to measure the minute PMs of M31 and M33, along with the PMs of substructures associated with the Local Group of galaxies. Those PMs are some of the most fundamental properties of the Local Group.

astro-ph.GA↗

arXiv2013

How does group interaction and its severance affect life expectancy?

The phenomenon of apoptosis observed in cell cultures consists in the fact that unless cells permanently receive a "Stay alive" signal from their neighbors, they are bound to die. A natural question is whether manifestations of this apoptosis paradigm can also be observed in other organizations of living organisms. In this paper we report results from a two-year long campaign of experiments on three species of ants and one species of (tephritid) fruit flies. In these experiments individuals were separated from their colony and kept in isolation either alone or in groups of 10 individuals. The overall conclusion is that "singles" have a shorter life expectancy than individuals in the groups of 10. This observation holds for ants as well as for fruit flies. The paper also provides compelling evidence of a similar effect in married versus unmarried (i.e. single, widowed or divorced) people. A natural question concerns the dynamic of the transition between the two regimes. Observation suggests an abrupt (rather than smooth) transition and this conclusion seems to hold for ants, fruit flies and humans as well. We call it a shock transition. In addition, for red fire ants Solenopsis invicta, it was observed that individuals in groups of 10 that also comprise one queen, die much faster than those in similar groups without queens. The paper also examines the corresponding survivorship curves from the perspective of the standard classification into 3 types. The survivorship curves of ants (whether single or in groups of 10) are found to be of type II whereas those of the fruit fly Bactrocera dorsalis are rather of type III. In this connection it is recalled that the survivorship curve of the fruit fly Drosophila melanogaster is of type I, i.e. of same type as for humans.

q-bio.PE↗

arXiv2013

Quantum magnetic properties of the SU(2N) Hubbard model in the square lattice: a quantum Monte Carlo study

We employ the determinant projector quantum Monte-Carlo method to investigate the ground state magnetic properties in the Mott insulating states of the half-filled SU(4) and SU(6) Fermi-Hubbard model in the 2D square lattice, which is free of the sign problem. The long-range antiferromagnetic Neel order is found for the SU(4) case with a small residual Neel moment. Quantum fluctuations are even stronger in the SU(6) case. Numeric results are consistent with either a vanishing or even weaker Neel ordering than that of SU(4).

cond-mat.str-el↗

arXiv2013

Low-Complexity Adaptive Set-Membership Reduced-rank LCMV Beamforming

This paper proposes a new adaptive algorithm for the implementation of the linearly constrained minimum variance (LCMV) beamformer. The proposed algorithm utilizes the set-membership filtering (SMF) framework and the reduced-rank joint iterative optimization (JIO) scheme. We develop a stochastic gradient (SG) based algorithm for the beamformer design. An effective time-varying bound is employed in the proposed method to adjust the step sizes, avoid the misadjustment and the risk of overbounding or underbounding. Simulations are performed to show the improved performance of the proposed algorithm in comparison with existing full-rank and reduced-rank methods.

cs.IT↗

arXiv2013

Adaptive Low-rank Constrained Constant Modulus Beamforming Algorithms using Joint Iterative Optimization of Parameters

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The scheme provides an efficient way to deal with filters with large number of elements. It consists of a bank of full-rank adaptive filters that forms a transformation matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. The transformation matrix projects the received vector onto a low-dimension vector, which is processed by the reduced-rank filter to estimate the desired signal. The expressions of the transformation matrix and the reduced-rank weight vector are derived according to the constrained constant modulus (CCM) criterion. Two novel low-complexity adaptive algorithms are devised for the implementation of the proposed scheme with respect to different constrained conditions. Simulations are performed to show superior performance of the proposed algorithms in comparison with the existing methods.

cs.IT↗

arXiv2013

Robust Auxiliary Vector Filtering with Constrained Constant Modulus Design for Beamforming

This paper proposes an auxiliary vector filtering (AVF) algorithm based on a constrained constant modulus (CCM) design for robust adaptive beamforming. This scheme provides an efficient way to deal with filters with a large number of elements. The proposed beamformer decomposes the adaptive filter into a constrained (reference vector filters) and an unconstrained (auxiliary vector filters) components. The weight vector is iterated by subtracting the scaling auxiliary vector from the reference vector. The scalar factor and the auxiliary vector depend on each other and are jointly calculated according to the CCM criterion. The proposed robust AVF algorithm provides an iterative exchange of information between the scalar factor and the auxiliary vector and thus leads to a fast convergence and an improved steady-state performance over the existing techniques. Simulations are performed to show the performance and the robustness of the proposed scheme and algorithm in several scenarios.

cs.IT↗

arXiv2013

Topological phase transition in a generalized Kane-Mele-Hubbard model: A combined Quantum Monte Carlo and Green's function study

We study a generalized Kane-Mele-Hubbard model with third-neighbor hopping, an interacting two-dimensional model with a topological phase transition as a function of third-neighbor hopping, by means of the determinant projector Quantum Monte Carlo (QMC) method. This technique is essentially numerically exact on models without a fermion sign problem, such as the one we consider. We determine the interaction-dependence of the Z2 topological insulator/trivial insulator phase boundary by calculating the Z2 invariants directly from the single-particle Green's function. The interactions push the phase boundary to larger values of third-neighbor hopping, thus stabilizing the topological phase. The observation of boundary shifting entirely stems from quantum °uctuations. We also identify qualitative features of the single-particle Green's function which are computationally useful in numerical searches for topological phase transitions without the need to compute the full topological invariant.

cond-mat.str-el↗

arXiv2013

Low-Complexity Constrained Constant Modulus SG-based Beamforming Algorithms with Variable Step Size

In this paper, two low-complexity adaptive step size algorithms are investigated for blind adaptive beamforming. Both of them are used in a stochastic gradient (SG) algorithm, which employs the constrained constant modulus (CCM) criterion as the design approach. A brief analysis is given for illustrating their properties. Simulations are performed to compare the performances of the novel algorithms with other well-known methods. Results indicate that the proposed algorithms achieve superior performance, better convergence behavior and lower computational complexity in both stationary and non-stationary environments.

cs.IT↗

arXiv2013

An Efficient Dynamic Programming Algorithm for the Generalized LCS Problem with Multiple Substring Exclusion Constrains

In this paper, we consider a generalized longest common subsequence problem with multiple substring exclusion constrains. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and a set of $d$ constrains $P=\{P_1,...,P_d\}$ of total length $r$, the problem is to find a common subsequence $Z$ of $X$ and $Y$ excluding each of constrain string in $P$ as a substring and the length of $Z$ is maximized. The problem was declared to be NP-hard\cite{1}, but we finally found that this is not true. A new dynamic programming solution for this problem is presented in this paper. The correctness of the new algorithm is proved. The time complexity of our algorithm is $O(nmr)$.

cs.DS↗

arXiv2013

Blind Adaptive Beamforming Based on Constrained Constant Modulus RLS Algorithm for Smart Antennas

In this paper, we study the performance of blind adaptive beamforming algorithms for smart antennas in realistic environments. A constrained constant modulus (CCM) design criterion is described and used for deriving a recursive least squares (RLS) type optimization algorithm. Furthermore, two kinds of scenarios are considered in the paper for analyzing its performance. Simulations are performed to compare the performance of the proposed method to other well-known methods for blind adaptive beamforming. Results indicate that the proposed method has a significant faster convergence rate, better robustness to changeable environments and better tracking capability.

cs.IT↗

arXiv2013

Reduced-rank Adaptive Constrained Constant Modulus Beamforming Algorithms based on Joint Iterative Optimization of Filters

This paper proposes a reduced-rank scheme for adaptive beamforming based on the constrained joint iterative optimization of filters. We employ this scheme to devise two novel reduced-rank adaptive algorithms according to the constant modulus (CM) criterion with different constraints. The first devised algorithm is formulated as a constrained joint iterative optimization of a projection matrix and a reduced-rank filter with respect to the CM criterion subject to a constraint on the array response. The constrained constant modulus (CCM) expressions for the projection matrix and the reduced-rank weight vector are derived, and a low-complexity adaptive algorithm is presented to jointly estimate them for implementation. The second proposed algorithm is extended from the first one and implemented according to the CM criterion subject to a constraint on the array response and an orthogonal constraint on the projection matrix. The Gram-Schmidt (GS) technique is employed to achieve this orthogonal constraint and improve the performance. Simulation results are given to show superior performance of the proposed algorithms in comparison with existing methods.

cs.IT↗

arXiv2013

Reduced-Rank DOA Estimation based on Joint Iterative Subspace Optimization and Grid Search

In this paper, we propose a novel reduced-rank algorithm for direction of arrival (DOA) estimation based on the minimum variance (MV) power spectral evaluation. It is suitable to DOA estimation with large arrays and can be applied to arbitrary array geometries. The proposed DOA estimation algorithm is formulated as a joint optimization of a subspace projection matrix and an auxiliary reduced-rank parameter vector with respect to the MV and grid search. A constrained least squares method is employed to solve this joint optimization problem for the output power over the grid. The proposed algorithm is described for problems of large number of users' direction finding with or without exact information of the number of sources, and does not require the singular value decomposition (SVD). The spatial smoothing (SS) technique is also employed in the proposed algorithm for dealing with correlated sources problem. Simulations are conducted with comparisons against existent algorithms to show the improved performance of the proposed algorithm in different scenarios.

cs.IT↗

arXiv2013

Set-Membership Conjugate Gradient Constrained Adaptive Filtering Algorithm for Beamforming

We introduce a new linearly constrained minimum variance (LCMV) beamformer that combines the set-membership (SM) technique with the conjugate gradient (CG) method, and develop a low-complexity adaptive filtering algorithm for beamforming. The proposed algorithm utilizes a CG-based vector and a variable forgetting factor to perform the data-selective updates that are controlled by a time-varying bound related to the parameters. For the update, the CG-based vector is calculated iteratively (one iteration per update) to obtain the filter parameters and to avoid the matrix inversion. The resulting iterations construct a space of feasible solutions that satisfy the constraints of the LCMV optimization problem. The proposed algorithm reduces the computational complexity significantly and shows an enhanced convergence and tracking performance over existing algorithms.

cs.IT↗

arXiv2013

Adaptive Set-Membership Reduced-Rank Least Squares Beamforming Algorithms

This paper presents a new adaptive algorithm for the linearly constrained minimum variance (LCMV) beamformer design. We incorporate the set-membership filtering (SMF) mechanism into the reduced-rank joint iterative optimization (JIO) scheme to develop a constrained recursive least squares (RLS) based algorithm called JIO-SM-RLS. The proposed algorithm inherits the positive features of reduced-rank signal processing techniques to enhance the output performance, and utilizes the data-selective updates (around 10-15%) of the SMF methodology to save the computational cost significantly. An effective time-varying bound is imposed on the array output as a constraint to circumvent the risk of overbounding or underbounding, and to update the parameters for beamforming. The updated parameters construct a set of solutions (a membership set) that satisfy the constraints of the LCMV beamformer. Simulations are performed to show the superior performance of the proposed algorithm in terms of the convergence rate and the reduced computational complexity in comparison with the existing methods.

cs.IT↗

arXiv2013

An Efficient Dual Approach to Distance Metric Learning

Distance metric learning is of fundamental interest in machine learning because the distance metric employed can significantly affect the performance of many learning methods. Quadratic Mahalanobis metric learning is a popular approach to the problem, but typically requires solving a semidefinite programming (SDP) problem, which is computationally expensive. Standard interior-point SDP solvers typically have a complexity of $O(D^{6.5})$ (with $D$ the dimension of input data), and can thus only practically solve problems exhibiting less than a few thousand variables. Since the number of variables is $D (D+1) / 2 $, this implies a limit upon the size of problem that can practically be solved of around a few hundred dimensions. The complexity of the popular quadratic Mahalanobis metric learning approach thus limits the size of problem to which metric learning can be applied. Here we propose a significantly more efficient approach to the metric learning problem based on the Lagrange dual formulation of the problem. The proposed formulation is much simpler to implement, and therefore allows much larger Mahalanobis metric learning problems to be solved. The time complexity of the proposed method is $O (D ^ 3) $, which is significantly lower than that of the SDP approach. Experiments on a variety of datasets demonstrate that the proposed method achieves an accuracy comparable to the state-of-the-art, but is applicable to significantly larger problems. We also show that the proposed method can be applied to solve more general Frobenius-norm regularized SDP problems approximately.

cs.LG↗