Factors influencing the long-term competitiveness of commercial milk producers: evidence from panel data in East Griqualand, South Africa

This study investigates factors influencing the long-term competitiveness of 11 commercial milk producers from East Griqualand (EG), South Africa using unbalanced panel data for the period 1990 to 2006. Results of a ridge regression analysis show that dairy herd size, the level of farm debt, annual production per cow, technology and policy changes over time, and the ratio of trading income to total milk income influence the long-term competitiveness of these milk producers. To enhance their competitiveness in a deregulated dairy market, relatively small and profitable EG milk producers should consider increasing their herd sizes, as the importance of herd size in explaining competitiveness suggests that size economies exist. All EG milk producers should consider utilising more pasture- and forage-based production systems to lower feed costs and select dairy cattle of superior genetic merit to improve milk yields on pasture.Commercial milk production, competitiveness, panel data, Production Economics,

The vortex dynamics of a Ginzburg-Landau system under pinning effect

It is proved that the vortices are attracted by impurities or inhomogeities in the superconducting materials. The strong H^1-convergence for the corresponding Ginzburg-Landau system is also proved.Comment: 23page

Metal-Insulator-Like Behavior in Semimetallic Bismuth and Graphite

When high quality bismuth or graphite crystals are placed in a magnetic field directed along the c-axis (trigonal axis for bismuth) and the temperature is lowered, the resistance increases as it does in an insulator but then saturates. We show that the combination of unusual features specific to semimetals, i.e., low carrier density, small effective mass, high purity, and an equal number of electrons and holes (compensation), gives rise to a unique ordering and spacing of three characteristic energy scales, which not only is specific to semimetals but which concomitantly provides a wide window for the observation of apparent field induced metal-insulator behavior. Using magnetotransport and Hall measurements, the details of this unusual behavior are captured with a conventional multi-band model, thus confirming the occupation by semimetals of a unique niche between conventional metals and semiconductors.Comment: 4 pages, 4 figs, data and discussion on bismuth added, final published versio

Exploiting temporal information for 3D pose estimation

In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D space. They also showed that a low-dimensional representation like 2D locations of a set of joints can be discriminative enough to estimate 3D pose with high accuracy. However, estimation of 3D pose for individual frames leads to temporally incoherent estimates due to independent error in each frame causing jitter. Therefore, in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses. We designed a sequence-to-sequence network composed of layer-normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training. We found that the knowledge of temporal consistency improves the best reported result on Human3.6M dataset by approximately $12.2\%$ and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails

Design Rules for Self-Assembly of 2D Nanocrystal/Metal-Organic Framework Superstructures.

We demonstrate the guiding principles behind simple two dimensional self-assembly of MOF nanoparticles (NPs) and oleic acid capped iron oxide (Fe3 O4 ) NCs into a uniform two-dimensional bi-layered superstructure. This self-assembly process can be controlled by the energy of ligand-ligand interactions between surface ligands on Fe3 O4 NCs and Zr6 O4 (OH)4 (fumarate)6 MOF NPs. Scanning transmission electron microscopy (TEM)/energy-dispersive X-ray spectroscopy and TEM tomography confirm the hierarchical co-assembly of Fe3 O4 NCs with MOF NPs as ligand energies are manipulated to promote facile diffusion of the smaller NCs. First-principles calculations and event-driven molecular dynamics simulations indicate that the observed patterns are dictated by combination of ligand-surface and ligand-ligand interactions. This study opens a new avenue for design and self-assembly of MOFs and NCs into high surface area assemblies, mimicking the structure of supported catalyst architectures, and provides a thorough fundamental understanding of the self-assembly process, which could be a guide for designing functional materials with desired structure

Experimentally obtaining the Likeness of Two Unknown Quantum States on an NMR Quantum Information Processor

Recently quantum states discrimination has been frequently studied. In this paper we study them from the other way round, the likeness of two quantum states. The fidelity is used to describe the likeness of two quantum states. Then we presented a scheme to obtain the fidelity of two unknown qubits directly from the integral area of the spectra of the assistant qubit(spin) on an NMR Quantum Information Processor. Finally we demonstrated the scheme on a three-qubit quantum information processor. The experimental data are consistent with the theoretical expectation with an average error of 0.05, which confirms the scheme.Comment: 3 pages, 4 figure

Automatic 3D facial model and texture reconstruction from range scans

This paper presents a fully automatic approach to fitting a generic facial model to detailed range scans of human faces to reconstruct 3D facial models and textures with no manual intervention (such as specifying landmarks). A Scaling Iterative Closest Points (SICP) algorithm is introduced to compute the optimal rigid registrations between the generic model and the range scans with different sizes. And then a new template-fitting method, formulated in an optmization framework of minimizing the physically based elastic energy derived from thin shells, faithfully reconstructs the surfaces and the textures from the range scans and yields dense point correspondences across the reconstructed facial models. Finally, we demonstrate a facial expression transfer method to clone facial expressions from the generic model onto the reconstructed facial models by using the deformation transfer technique

On Two Theorems About Symplectic Reflection Algebras

We give a new proof and an improvement of two Theorems of J. Alev, M.A. Farinati, T. Lambre and A.L. Solotar : the first one about Hochschild cohomology spaces of some twisted bimodules of the Weyl algebra W and the second one about Hochschild cohomology spaces of the smash product G * W (G a finite subgroup of SP(2n)), and as an application, we then give a new proof of a Theorem of P. Etingof and V. Ginzburg, which shows that the Symplectic Reflection Algebras are deformations of G * W (and, in fact, all possible ones).Comment: corrected typo