Quantum order by disorder in a semiclassical spin ice

We study the S>1 nearest-neighbor Heisenberg model with a ferromagnetic interaction J and a large non-collinear easy-axis anisotropy D on a pyrochlore lattice. For a finite D>>|J|, the low-energy physics is described by a Ising model with second- and third-neighbor exchange interactions. The extensive degeneracy of the ground state manifold in the infinite anisotropic limit is lifted by the transverse quantum fluctuations, and a q=0 ordered state is selected via the quantum order by disorder machanism, through a first-order phase transition at low temperature.Comment: 4 pages, 5 figure

iForgot: a model of forgetting in robotic memories

Much effort has focused in recent years on developing more life-like robots. In this paper we propose a model of memory for robots, based on human digital memories, though our model incorporates an element of forgetting to ensure that the robotic memory appears more human and therefore can address some of the challenges for human-robot interaction

Linear system identification via backward-time observer models

Presented here is an algorithm to compute the Markov parameters of a backward-time observer for a backward-time model from experimental input and output data. The backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) for the backward-time system identification. The identified backward-time system Markov parameters are used in the Eigensystem Realization Algorithm to identify a backward-time state-space model, which can be easily converted to the usual forward-time representation. If one reverses time in the model to be identified, what were damped true system modes become modes with negative damping, growing as the reversed time increases. On the other hand, the noise modes in the identification still maintain the property that they are stable. The shift from positive damping to negative damping of the true system modes allows one to distinguish these modes from noise modes. Experimental results are given to illustrate when and to what extent this concept works

Robust eigensystem assignment for second-order estimators

An approach for the robust eigensystem assignment of flexible structures using full state or output feedback is developed. Using the second-order dynamic equations, the approach can assign the eigenvalues of the system via velocity and displacement feedbacks, or acceleration and velocity feedbacks. The eigenvalues and eigenvectors of the system are assigned, via the second-order eigenvalue problem for the structural system, in two steps. First, an orthonormal basis spanning the attainable closed-loop eigenvector space corresponding to each desired closed-loop eigenvalue is generated using the Singular Value or QR decompositions. Second, a sequential procedure is used to choose a set of closed-loop eigenvectors that are as close as possible to the column space of a well-conditioned target matrix. Among the possible choices of the target matrix, the closest unitary matrix to the open-loop eigenvector matrix appears to be a suitable choice. A numerical example is given to illustrate the proposed algorithm

Accurate computation of low-temperature thermodynamics for quantum spin chains

We apply the biorthonormal transfer-matrix renormalization group (BTMRG) [Phys. Rev. E 83, 036702 (2011)] to study low-temperature properties of quantum spin chains. Simulation on isotropic Heisenberg spin-1/2 chain demonstrates that the BTMRG outperforms the conventional transfer-matrix renormalization group (TMRG) by successfully accessing far lower temperature unreachable by conventional TMRG, while retaining the same level of accuracy. The power of the method is further illustrated by the calculation of the low-temperature specific heat for a frustrated spin chain.Comment: 5 pages, 4 figure