The effect of magnetic dipolar interactions on the interchain spin wave dispersion in CsNiF_3

Inelastic neutron scattering measurements were performed on the ferromagnetic chain system CsNiF_3 in the collinear antiferromagnetic ordered state below T_N = 2.67K. The measured spin wave dispersion was found to be in good agreement with linear spin wave theory including dipolar interactions. The additional dipole tensor in the Hamiltonian was essential to explain some striking phenomena in the measured spin wave spectrum: a peculiar feature of the dispersion relation is a jump at the zone center, caused by strong dipolar interactions in this system. The interchain exchange coupling constant and the planar anisotropy energy were determined within the present model to be J'/k_B = -0.0247(12)K and A/k_B = 3.3(1)K. This gives a ratio J/J' \approx 500, using the previously determined intrachain coupling constant J/k_B = 11.8$. The small exchange energy J' is of the same order as the dipolar energy, which implies a strong competition between the both interactions.Comment: 18 pages, TeX type, 7 Postscript figures included. To be published in Phys. Rev.

Efficient Constellation-Based Map-Merging for Semantic SLAM

Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments. When ambiguous measurements arise, conservatism often mandates that the measurement is discarded or a new landmark is initialized rather than risking an incorrect association. To address the inevitable `duplicate' landmarks that arise, we present an efficient map-merging framework to detect duplicate constellations of landmarks, providing a high-confidence loop-closure mechanism well-suited for object-level SLAM. This approach uses an incrementally-computable approximation of landmark uncertainty that only depends on local information in the SLAM graph, avoiding expensive recovery of the full system covariance matrix. This enables a search based on geometric consistency (GC) (rather than full joint compatibility (JC)) that inexpensively reduces the search space to a handful of `best' hypotheses. Furthermore, we reformulate the commonly-used interpretation tree to allow for more efficient integration of clique-based pairwise compatibility, accelerating the branch-and-bound max-cardinality search. Our method is demonstrated to match the performance of full JC methods at significantly-reduced computational cost, facilitating robust object-based loop-closure over large SLAM problems.Comment: Accepted to IEEE International Conference on Robotics and Automation (ICRA) 201

Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM

Sparsity has been widely recognized as crucial for efficient optimization in graph-based SLAM. Because the sparsity and structure of the SLAM graph reflect the set of incorporated measurements, many methods for sparsification have been proposed in hopes of reducing computation. These methods often focus narrowly on reducing edge count without regard for structure at a global level. Such structurally-naive techniques can fail to produce significant computational savings, even after aggressive pruning. In contrast, simple heuristics such as measurement decimation and keyframing are known empirically to produce significant computation reductions. To demonstrate why, we propose a quantitative metric called elimination complexity (EC) that bridges the existing analytic gap between graph structure and computation. EC quantifies the complexity of the primary computational bottleneck: the factorization step of a Gauss-Newton iteration. Using this metric, we show rigorously that decimation and keyframing impose favorable global structures and therefore achieve computation reductions on the order of $r^2/9$ and $r^3$, respectively, where $r$ is the pruning rate. We additionally present numerical results showing EC provides a good approximation of computation in both batch and incremental (iSAM2) optimization and demonstrate that pruning methods promoting globally-efficient structure outperform those that do not.Comment: Pre-print accepted to ICRA 201

Comparing persistence diagrams through complex vectors

The natural pseudo-distance of spaces endowed with filtering functions is precious for shape classification and retrieval; its optimal estimate coming from persistence diagrams is the bottleneck distance, which unfortunately suffers from combinatorial explosion. A possible algebraic representation of persistence diagrams is offered by complex polynomials; since far polynomials represent far persistence diagrams, a fast comparison of the coefficient vectors can reduce the size of the database to be classified by the bottleneck distance. This article explores experimentally three transformations from diagrams to polynomials and three distances between the complex vectors of coefficients.Comment: 11 pages, 4 figures, 2 table

The Development of Learning Tasks for those Central Washington State College Freshmen Prospective Teacher Candidates Participating in Field Experiences in the Ellensburg Public Schools

It was the purpose of this study to describe and to explore the factors in the development and the planning and organization of appropriate learning tasks in the field experience for the Central Washington State College Pre-Professional Students within the Ellensburg Public Schools. The new experience for these freshmen raised the question: What are appropriate learning tasks or competencies that these freshmen should or should not have? Thus this study: (1) examined various learning tasks; (2) developed a rationale for appropriate competencies; and (3) developed and listed appropriate tasks in behavioral terms for these freshmen