Composition and decomposition in bimanual dynamic learning.

Our ability to skillfully manipulate an object often involves the motor system learning to compensate for the dynamics of the object. When the two arms learn to manipulate a single object they can act cooperatively, whereas when they manipulate separate objects they control each object independently. We examined how learning transfers between these two bimanual contexts by applying force fields to the arms. In a coupled context, a single dynamic is shared between the arms, and in an uncoupled context separate dynamics are experienced independently by each arm. In a composition experiment, we found that when subjects had learned uncoupled force fields they were able to transfer to a coupled field that was the sum of the two fields. However, the contribution of each arm repartitioned over time so that, when they returned to the uncoupled fields, the error initially increased but rapidly reverted to the previous level. In a decomposition experiment, after subjects learned a coupled field, their error increased when exposed to uncoupled fields that were orthogonal components of the coupled field. However, when the coupled field was reintroduced, subjects rapidly readapted. These results suggest that the representations of dynamics for uncoupled and coupled contexts are partially independent. We found additional support for this hypothesis by showing significant learning of opposing curl fields when the context, coupled versus uncoupled, was alternated with the curl field direction. These results suggest that the motor system is able to use partially separate representations for dynamics of the two arms acting on a single object and two arms acting on separate objects.

Catalyst composition and impurity-dependent kinetics of nanowire heteroepitaxy.

The mechanisms and kinetics of axial Ge-Si nanowire heteroepitaxial growth based on the tailoring of the Au catalyst composition via Ga alloying are studied by environmental transmission electron microscopy combined with systematic ex situ CVD calibrations. The morphology of the Ge-Si heterojunction, in particular, the extent of a local, asymmetric increase in nanowire diameter, is found to depend on the Ga composition of the catalyst, on the TMGa precursor exposure temperature, and on the presence of dopants. To rationalize the findings, a general nucleation-based model for nanowire heteroepitaxy is established which is anticipated to be relevant to a wide range of material systems and device-enabling heterostructures.

Paraphrastic language models and combination with neural network language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, paraphrastic LMs were proposed in previous research and successfully applied to a US English conversational telephone speech transcription task. In order to exploit the complementary characteristics of paraphrastic LMs and neural network LMs (NNLM), the combination between the two is investigated in this paper. To investigate paraphrastic LMs' generalization ability to other languages, experiments are conducted on a Mandarin Chinese broadcast speech transcription task. Using a paraphrastic multi-level LM modelling both word and phrase sequences, significant error rate reductions of 0.9% absolute (9% relative) and 0.5% absolute (5% relative) were obtained over the baseline n-gram and NNLM systems respectively, after a combination with word and phrase level NNLMs. © 2013 IEEE.

Stabilization of a complex perovskite superstructure under ambient conditions: Influence of cation composition and ordering, and evaluation as an SOFC cathode

Ba1.6Ca2.3Y1.1Fe5O13 is an Fe3+ oxide adopting a complex perovskite superstructure, which is an ordered intergrowth between the Ca2Fe2O5 and YBa2Fe3O8 structures featuring octahedral, square pyramidal, and tetrahedral B sites and three distinct A site environments. The distribution of A site cations was evaluated by combined neutron and X-ray powder diffraction. Consistent with the Fe3+ charge state, the material is an antiferromagnetic insulator with a Néel temperature of 480-485 °C and has a relatively low d.c. conductivity of 2.06 S cm-1 at 700 °C. The observed area specific resistance in symmetrical cell cathodes with the samarium-doped ceria electrolyte is 0.87 Ω cm2 at 700 °C, consistent with the square pyramidal Fe3+ layer favoring oxide ion formation and mobility in the oxygen reduction reaction. Density functional theory calculations reveal factors favoring the observed cation ordering and its influence on the electronic structure, in particular the frontier occupied and unoccupied electronic states. © 2010 American Chemical Society.

Effect of size, composition, and morphology on magnetic performance: First-order reversal curves evaluation of iron oxide nanoparticles

© 2014 AIP Publishing LLC. Superparamagnetic nanoparticles are employed in a broad range of applications that demand detailed magnetic characterization for superior performance, e.g., in drug delivery or cancer treatment. Magnetic hysteresis measurements provide information on saturation magnetization and coercive force for bulk material but can be equivocal for particles having a broad size distribution. Here, first-order reversal curves (FORCs) are used to evaluate the effective magnetic particle size and interaction between equally sized magnetic iron oxide (Fe2O3) nanoparticles with three different morphologies: (i) pure Fe2O3, (ii) Janus-like, and (iii) core/shell Fe2O3/SiO2synthesized using flame technology. By characterizing the distribution in coercive force and interaction field from the FORC diagrams, we find that the presence of SiO2in the core/shell structures significantly reduces the average coercive force in comparison to the Janus-like Fe2O3/SiO2and pure Fe2O3particles. This is attributed to the reduction in the dipolar interaction between particles, which in turn reduces the effective magnetic particle size. Hence, FORC analysis allows for a finer distinction between equally sized Fe2O3particles with similar magnetic hysteresis curves that can significantly influence the final nanoparticle performance.