Pathology of Schwinger boson mean field theory for Heisenberg spin models

We have re-analyze the Schwinger boson mean field theory (SBMFT) for Heisenberg spin models on the cubic lattice. We find that the second order phase transition point for magnetic ordering previously reported corresponds to a local maximum of the free energy functional. For both ferromagnetic and antiferromagnetic Heisenberg models with spin $S \geq S_C$, where $S_C < 1/2$, the mean field transitions are first order from the magnetically long-ranged ordered phase to the completely uncorrelated phase. In addition to erroneously giving a first order transition for magnetic ordering, the mean field theory does not include a phase with finite short-range correlation, thus negating one of the prime advantages of SBMFT. The relevance of these pathologies to other situations beyond the cubic lattice is discussed.Comment: 15 pages including 6 postscript figure

Theory for spin and orbital orderings in high temperature phase in YVO3YVO_3

Motivated by the recent neutron diffraction experiment on $YVO_3$, we consider a microscopic model where each $V^{3+}$ ion is occupied by two 3d electrons of parallel spins with two fold degenerate orbital configurations. The mean field classical solutions of the spin-orbital superexchange model predicts an antiferro-orbital ordering at a higher temperature followed by a C-type antiferromagnetic spin ordering at a lower temperature. Our results are qualitatively consistent with the observed orbital phase transition at $\sim 200K$ and the spin phase transition at $\sim 114K$ in $YVO_3$.Comment: 7 pages, 3 figures and 2 tables. Accepted to be published in PR

Dynamically generated dimension reduction and crossover in a spin orbital model

We study a spin orbital model in which the spin-spin interaction couples linearly to the orbital isospin. Fluctuations drive the transition from paramagnetic state to C type ordered state into a strongly first order one, as observed in $V_2O_3$. At T=0, there is a FOCS to FOGS transition. Close to the transition point, the system shows dynamically generated dimension reduction and crossover, resulting in one or more spin reentrant transitions.Comment: Submitted to PRL. 4 pages and one figur

Experimental high-intensity three-photon entangled source

We experimentally realize a high-intensity three-photon Greenberger-Horne-Zeilinger (GHZ) entanglement source directly following the proposal by Rarity and Tapster [J. G. Rarity and P. R. Tapster, Phys. Rev. A 59, R35 (1999)]. The threefold coincidence rate can be more than 200 Hz with a fidelity of 0.811, and the intensity can be further improved with moderate fidelity degradation. The GHZ entanglement is characterized by testing the Bell-Mermin inequality and using an entanglement witness operator. To optimize the polarization-entangled source, we theoretically analyze the relationship between the mean photon number of the single-photon source and the probability of parametric down-conversion.Comment: 4 pages, 4 figure

Application of Neural-Like P Systems With State Values for Power Coordination of Photovoltaic/Battery Microgrids

The power coordination control of a photovoltaic/battery microgrid is performed with a novel bio-computing model within the framework of membrane computing. First, a neural-like P system with state values (SVNPS) is proposed for describing complex logical relationships between different modes of Photovoltaic (PV) units and energy storage units. After comparing the objects in the neurons with the thresholds, state values will be obtained to determine the con guration of the SVNPS. Considering the characteristics of PV/battery microgrids, an operation control strategy based on bus voltages of the point of common coupling and charging/discharging statuses of batteries is proposed. At rst, the SVNPS is used to construct the complicated unit working modes; each unit of the microgrid can adjust the operation modes automatically. After that, the output power of each unit is reasonably coordinated to ensure the operation stability of the microgrid. Finally, a PV/battery microgrid, including two PV units, one storage unit, and some loads are taken into consideration, and experimental results show the feasibility and effectiveness of the proposed control strategy and the SVNPS-based power coordination control models

Optimizing semiconductor devices by self-organizing particle swarm

A self-organizing particle swarm is presented. It works in dissipative state by employing the small inertia weight, according to experimental analysis on a simplified model, which with fast convergence. Then by recognizing and replacing inactive particles according to the process deviation information of device parameters, the fluctuation is introduced so as to driving the irreversible evolution process with better fitness. The testing on benchmark functions and an application example for device optimization with designed fitness function indicates it improves the performance effectively.Comment: Congress on Evolutionary Computation, 2004. CEC2004. Volume: 2, On page(s): 2017- 2022 Vol.

Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space

The periodic mode is analyzed together with two conventional boundary handling modes for particle swarm. By providing an infinite space that comprises periodic copies of original search space, it avoids possible disorganizing of particle swarm that is induced by the undesired mutations at the boundary. The results on benchmark functions show that particle swarm with periodic mode is capable of improving the search performance significantly, by compared with that of conventional modes and other algorithms.Comment: Congress on Evolutionary Computation, 2004. CEC2004. Volume: 2, On page(s): 2307- 2311 Vol.

An Adaptive Fast Solver for a General Class of Positive Definite Matrices Via Energy Decomposition

In this paper, we propose an adaptive fast solver for a general class of symmetric positive definite (SPD) matrices which include the well-known graph Laplacian. We achieve this by developing an adaptive operator compression scheme and a multiresolution matrix factorization algorithm which achieve nearly optimal performance on both complexity and well-posedness. To develop our adaptive operator compression and multiresolution matrix factorization methods, we first introduce a novel notion of energy decomposition for SPD matrix $A$ using the representation of energy elements. The interaction between these energy elements depicts the underlying topological structure of the operator. This concept of decomposition naturally reflects the hidden geometric structure of the operator which inherits the localities of the structure. By utilizing the intrinsic geometric information under this energy framework, we propose a systematic operator compression scheme for the inverse operator $A^{-1}$. In particular, with an appropriate partition of the underlying geometric structure, we can construct localized basis by using the concept of interior and closed energy. Meanwhile, two important localized quantities are introduced, namely, the error factor and the condition factor. Our error analysis results show that these two factors will be the guidelines for finding the appropriate partition of the basis functions such that prescribed compression error and acceptable condition number can be achieved. By virtue of this insight, we propose the patch pairing algorithm to realize our energy partition framework for operator compression with controllable compression error and condition number

A Fast Hierarchically Preconditioned Eigensolver Based on Multiresolution Matrix Decomposition

In this paper we propose a new iterative method to hierarchically compute a relatively large number of leftmost eigenpairs of a sparse symmetric positive matrix under the multiresolution operator compression framework. We exploit the well-conditioned property of every decomposition component by integrating the multiresolution framework into the implicitly restarted Lanczos method. We achieve this combination by proposing an extension-refinement iterative scheme, in which the intrinsic idea is to decompose the target spectrum into several segments such that the corresponding eigenproblem in each segment is well-conditioned. Theoretical analysis and numerical illustration are also reported to illustrate the efficiency and effectiveness of this algorithm

Deviation analysis of rotational inertia measurement based on torsion bar method

The test of moment of inertia has a wide range of applications in aerospace, vehicle engineering, precision machinery, motors and other fields, moment of inertia directly affects the reliability and performance of components or equipment, it is very essential to test the moment of inertia. By analyzing the principle of moment of inertia test, we could come to the conclusion that the theoretical value, the inertia of the disk, the period of the torsion swing of the standard body and the period of the empty disk of the moment of inertia and the moment of inertia of the standard body. By analyzing the measurement error, position error and damping during the test, we could reach the following conclusion that the test accuracy of the moment of inertia can reach 0.1 %