Determination of the trap-assisted recombination strength in polymer light emitting diodes

The recombination processes in poly(p-phenylene vinylene) based polymer light-emitting diodes (PLEDs) are investigated. Photogenerated current measurements on PLED device structures reveal that next to the known Langevin recombination also trap-assisted recombination is an important recombination channel in PLEDs, which has not been considered until now. The dependence of the open-circuit voltage on light intensity enables us to determine the strength of this process. Numerical modeling of the current-voltage characteristics incorporating both Langevin and trap-assisted recombination yields a correct and consistent description of the PLED, without the traditional correction of the Langevin prefactor. At low bias voltage the trap-assisted recombination rate is found to be dominant over the free carrier recombination rate.

A Reinforcement Learning Agent for Minutiae Extraction from Fingerprints

In this paper we show that reinforcement learning can be used for minutiae detection in fingerprint matching. Minutiae are characteristic features of fingerprints that determine their uniqueness. Classical approaches use a series of image processing steps for this task, but lack robustness because they are highly sensitive to noise and image quality. We propose a more robust approach, in which an autonomous agent walks around in the fingerprint and learns how to follow ridges in the fingerprint and how to recognize minutiae. The agent is situated in the environment, the fingerprint, and uses reinforcement learning to obtain an optimal policy. Multi-layer perceptrons are used for overcoming the difficulties of the large state space. By choosing the right reward structure and learning environment, the agent is able to learn the task. One of the main difficulties is that the goal states are not easily specified, for they are part of the learning task as well. That is, the recognition of minutiae has to be learned in addition to learning how to walk over the ridges in the fingerprint. Results of successful first experiments are presented

Determining the Anisotropic Exchange Coupling of CrO_2 via First-Principles Density Functional Theory Calculations

We report a study of the anisotropic exchange interactions in bulk CrO_2 calculated from first principles within density functional theory. We determine the exchange coupling energies, using both the experimental lattice parameters and those obtained within DFT, within a modified Heisenberg model Hamiltonian in two ways. We employ a supercell method in which certain spins within a cell are rotated and the energy dependence is calculated and a spin-spiral method that modifies the periodic boundary conditions of the problem to allow for an overall rotation of the spins between unit cells. Using the results from each of these methods, we calculate the spin-wave stiffness constant D from the exchange energies using the magnon dispersion relation. We employ a Monte Carlo method to determine the DFT-predicted Curie temperature from these calculated energies and compare with accepted values. Finally, we offer an evaluation of the accuracy of the DFT-based methods and suggest implications of the competing ferro- and antiferromagnetic interactions.Comment: 10 pages, 13 figure

Symmetric separable convex resource allocation problems with structured disjoint interval bound constraints

Motivated by the problem of scheduling electric vehicle (EV) charging with a minimum charging threshold in smart distribution grids, we introduce the resource allocation problem (RAP) with a symmetric separable convex objective function and disjoint interval bound constraints. In this RAP, the aim is to allocate an amount of resource over a set of $n$ activities, where each individual allocation is restricted to a disjoint collection of $m$ intervals. This is a generalization of classical RAPs studied in the literature where in contrast each allocation is only restricted by simple lower and upper bounds, i.e., $m=1$. We propose an exact algorithm that, for four special cases of the problem, returns an optimal solution in $O \left(\binom{n+m-2}{m-2} (n \log n + nF) \right)$ time, where the term $nF$ represents the number of flops required for one evaluation of the separable objective function. In particular, the algorithm runs in polynomial time when the number of intervals $m$ is fixed. Moreover, we show how this algorithm can be adapted to also output an optimal solution to the problem with integer variables without increasing its time complexity. Computational experiments demonstrate the practical efficiency of the algorithm for small values of $m$ and in particular for solving EV charging problems.Comment: 20 pages, 4 figure

The multiferroic phase of DyFeO3_{3}:an ab--initio study

By performing accurate ab-initio density functional theory calculations, we study the role of $4f$ electrons in stabilizing the magnetic-field-induced ferroelectric state of DyFeO$_{3}$. We confirm that the ferroelectric polarization is driven by an exchange-strictive mechanism, working between adjacent spin-polarized Fe and Dy layers, as suggested by Y. Tokunaga [Phys. Rev. Lett, \textbf{101}, 097205 (2008)]. A careful electronic structure analysis suggests that coupling between Dy and Fe spin sublattices is mediated by Dy-$d$ and O-$2p$ hybridization. Our results are robust with respect to the different computational schemes used for $d$ and $f$ localized states, such as the DFT+$U$ method, the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional and the GW approach. Our findings indicate that the interaction between the $f$ and $d$ sublattice might be used to tailor ferroelectric and magnetic properties of multiferroic compounds.Comment: 6 pages, 4 figures-Revised versio

Seasonality of low flows and dominant processes in the Rhine River

Low flow forecasting is crucial for sustainable cooling water supply and planning of river navigation in the Rhine River. The first step in reliable low flow forecasting is to understand the characteristics of low flow. In this study, several methods are applied to understand the low flow characteristics of Rhine River basin. In 108 catchments of the Rhine River, winter and summer low flow regions are determined with the seasonality ratio (SR) index. To understand whether different numbers of processes are acting in generating different low flow regimes in seven major sub-basins (namely, East Alpine, West Alpine, Middle Rhine, Neckar, Main, Mosel and Lower Rhine) aggregated from the 108 catchments, the dominant variable concept is adopted from chaos theory. The number of dominant processes within the seven major sub-basins is determined with the correlation dimension analysis. Results of the correlation dimension analysis show that the minimum and maximum required number of variables to represent the low flow dynamics of the seven major sub-basins, except the Middle Rhine and Mosel, is 4 and 9, respectively. For the Mosel and Middle Rhine, the required minimum number of variables is 2 and 6, and the maximum number of variables is 5 and 13, respectively. These results show that the low flow processes of the major sub-basins of the Rhine could be considered as non-stochastic or chaotic processes. To confirm this conclusion, the rescaled range analysis is applied to verify persistency (i.e. non-randomness) in the processes. The estimated rescaled range statistics (i.e. Hurst exponents) are all above 0.5, indicating that persistent long-term memory characteristics exist in the runoff processes. Finally, the mean values of SR indices are compared with the nonlinear analyses results to find significant relationships. The results show that the minimum and maximum numbers of required variables (i.e. processes) to model the dynamic characteristics for five out of the seven major sub-basins are the same, but the observed low flow regimes are different (winter low flow regime and summer low flow regime). These results support the conclusion that a few interrelated nonlinear variables could yield completely different behaviour (i.e. dominant low flow regime)

Bulk de novo mitogenome assembly from pooled total DNA elucidates the phylogeny of weevils (Coleoptera: Curculionoidea)

Complete mitochondrial genomes have been shown to be reliable markers for phylogeny reconstruction among diverse animal groups. However, the relative difficulty and high cost associated with obtaining de novo full mitogenomes have frequently led to conspicuously low taxon sampling in ensuing studies. Here, we report the successful use of an economical and accessible method for assembling complete or near-complete mitogenomes through shot-gun next-generation sequencing of a single library made from pooled total DNA extracts of numerous target species. To avoid the use of separate indexed libraries for each specimen, and an associated increase in cost, we incorporate standard polymerase chain reaction-based “bait” sequences to identify the assembled mitogenomes. The method was applied to study the higher level phylogenetic relationships in the weevils (Coleoptera: Curculionoidea), producing 92 newly assembled mitogenomes obtained in a single Illumina MiSeq run. The analysis supported a separate origin of wood-boring behavior by the subfamilies Scolytinae, Platypodinae, and Cossoninae. This finding contradicts morphological hypotheses proposing a close relationship between the first two of these but is congruent with previous molecular studies, reinforcing the utility of mitogenomes in phylogeny reconstruction. Our methodology provides a technically simple procedure for generating densely sampled trees from whole mitogenomes and is widely applicable to groups of animals for which bait sequences are the only required prior genome knowledge