Off-Shell NN Potential and Triton Binding Energy

The NONLOCAL Bonn-B potential predicts 8.0 MeV binding energy for the triton (in a charge-dependent 34-channel Faddeev calculation) which is about 0.4 MeV more than the predictions by LOCAL NN potentials. We pin down origin and size of the nonlocality in the Bonn potential, in analytic and numeric form. The nonlocality is due to the use of the correct off-shell Feynman amplitude of one-boson-exchange avoiding the commonly used on-shell approximations which yield the local potentials. We also illustrate how this off-shell behavior leads to more binding energy. We emphasize that the increased binding energy is not due to on-shell differences (differences in the fit of the NN data or phase shifts). In particular, the Bonn-B potential reproduces accurately the $\epsilon_1$ mixing parameter up to 350 MeV as determined in the recent Nijmegen multi-energy NN phase-shift analysis. Adding the relativistic effect from the relativistic nucleon propagators in the Faddeev equations, brings the Bonn-B result up to 8.2 MeV triton binding. This leaves a difference of only 0.3 MeV to experiment, which may possibly be explained by refinements in the treatment of relativity and the inclusion of other nonlocalities (e.~g., quark-gluon exchange at short range). Thus, it is conceivable that a realistic NN potential which describes the NN data up to 300 MeV correctly may explain the triton binding energy without recourse to 3-N forces; relativity would play a major role for this result.Comment: 5 pages LaTeX and 2 figures (hardcopies, available upon reqest), UI-NTH-940

Quantum correction for electron transfer rates. Comparison of polarizable versus nonpolarizable descriptions of solvent

The electron transfer rate constant is treated using the spin-boson Hamiltonian model. The spectral density is related to the experimentally accessible data on the dielectric dispersion of the solvent, using a dielectric continuum approximation. On this basis the quantum correction for the ferrous–ferric electron transfer rate is found to be a factor 9.6. This value is smaller than the corresponding result (36) of Chandler and co-workers in their pioneering quantum simulation using a molecular model of the system [J. S. Bader, R. A. Kuharski, and D. Chandler, J. Chem. Phys. 93, 230 (1990)]. The likely reason for the difference lies in use of a rigid water molecular model in the simulation, since we find that other models for water in the literature which neglect the electronic and vibrational polarizability also give a large quantum effect. Such models are shown to overestimate the dielectric dispersion in one part of the quantum mechanically important region and to underestimate it in another part. It will be useful to explore a polarizable molecular model which reproduces the experimental dielectric response over the relevant part of the frequency spectrum

Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production

Multi-modal Image Processing based on Coupled Dictionary Learning

In real-world scenarios, many data processing problems often involve heterogeneous images associated with different imaging modalities. Since these multimodal images originate from the same phenomenon, it is realistic to assume that they share common attributes or characteristics. In this paper, we propose a multi-modal image processing framework based on coupled dictionary learning to capture similarities and disparities between different image modalities. In particular, our framework can capture favorable structure similarities across different image modalities such as edges, corners, and other elementary primitives in a learned sparse transform domain, instead of the original pixel domain, that can be used to improve a number of image processing tasks such as denoising, inpainting, or super-resolution. Practical experiments demonstrate that incorporating multimodal information using our framework brings notable benefits.Comment: SPAWC 2018, 19th IEEE International Workshop On Signal Processing Advances In Wireless Communication

The self-trapped hole in caesium halides

The equilibrium lattice configuration, electronic excitation energies and activation energies for hopping motion are calculated for a self-trapped hole in simple cubic CsCl, CsBr and CsI. The defect is regarded as a X2- molecular ion (X=Cl, Br, I) whose bond-length has been modified by the crystalline environment. Agreement with the experimental ultraviolet transition energies is good. Excitation energies deduced from measurement of g-shifts in CsBr and CsI are too low, a feature common to all alkali bromides and iodides, and attributed to the approximations involved in their deviation. The initial calculations predict lower activation energies of 90 degrees jumps than for 180 degrees jumps, in contrast with what is observed in CsI. An alternative model is presented, which reproduces the correct trend. Comparison of the actual numbers with experiment is hampered by the fact that the latter are done at low temperature (60-90K), the calculations being done in the high-temperature limit