Assessment of dryland condition using spatial anomalies of vegetation index values

A landscape's long‐term capacity to retain, utilize and recycle local resources is an objective basis for assessing its ecological functionality or condition. In subtropical and tropical drylands, where plant growth is moisture‐limited for much of the time, land condition is reflected in the local water balance. The ratio of long‐term actual evapotranspiration and precipitation (E _a/P) is proposed as an objective indicator of dryland condition. A spatial modelling framework is developed for the quantification of E _a/P over large areas using remotely sensed vegetation density patterns. Model parameters are defined by two particular situations: (i) non‐vegetated sites, where E _a/P depends on the long‐term runoff coefficient of bare soil surfaces (RC_bs), and (ii) non‐degraded sites with a vegetation density close to the potential value for which E _a/P?1.0. Specht's evaporative coefficient is used as an independent variable for the prediction of the potential vegetation density, whereas RC_bs is estimated with the curve number method.

The performance of the method was evaluated in a 900 km² area in south‐east Spain, where predicted land condition was found to be in good agreement with qualitative field observations on the nature and intensity of land degradation processes.     

Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming

The model complexity reduction problem of large chemical reaction networks under isobaric and isothermal conditions is considered. With a given detailed kinetic mechanism and measured data of the key species over a finite time horizon, the complexity reduction is formulated in the form of a mixed-integer quadratic optimization problem where the objective function is derived from the parametric sensitivity matrix. The proposed method sequentially eliminates reactions from the mechanism and simultaneously tunes the remaining parameters until the pre-specified tolerance limit in the species concentration space is reached. The computational efficiency and numerical stability of the optimization are improved by a pre-reduction step followed by suitable scaling and initial conditioning of the Hessian involved. The proposed complexity reduction method is illustrated using three well-known case studies taken from reaction kinetics literature.     

Parallelization of the FMM on distributed-memory GPGPU systems for acoustic-scattering prediction

In this work, we carry out the parallelization of the single level Fast Multipole Method (FMM) for solving acoustic-scattering problems (using the Helmholtz equation) on distributed-memory GPGPU systems. With the aim of enlarging the scope of feasible simulations, the presented solution combines the techniques developed for our distributed-memory CPU solver with our shared-memory GPGPU solver. The performance of the developed solution is proved using two different GPGPU clusters: the first one consists of two workstations with NVIDIA GTX 480 GPUs linked by a Gigabit Ethernet network, and the second one comprises four nodes with NVIDIA Tesla M2090 GPUs linked by an Infiniband network.     

From latex specifications to parallel codes

The advent of multicore systems, joined to the potential acceleration of the graphics processing units, has given us a low cost computation capability unprecedented. The new systems alleviate some well-known important architectural problems at the expense of a considerable increment of the programmability wall. Heterogeneity, at both the architectural and programming levels, poses a great challenge to programmers. As a contribution, we propose a development methodology for the automatic source-to-source transformation on specific domains. This methodology is successfully instantiated as a framework to solve Dynamic Programming problems. As a result of applying our framework, the end user (a physicist, a mathematician or a biologist) can express her problem through a latex equation and automatically derive efficient parallel codes for current homogeneous or heterogeneous architectures. The approach allows an easy portability to new emergent architectures.     

Efficient Geodesic Attribute Thinnings Based on the Barycentric Diameter

An attribute opening is an idempotent, anti-extensive and increasing operator, which removes from an image connected components which do not fulfil a given criterion. When the increasingness property is dropped, we obtain a—more general—attribute thinning. In this paper, we propose efficient grey scale thinnings based on geodesic attributes. Given that the geodesic diameter is time consuming, we propose a new geodesic attribute, the barycentric diameter to speed up the computation time. Then, we give the theoretical error bound between these two attributes, and we note that in practice, the barycentric diameter gives very similar results in comparison with the geodesic diameter. Finally, we present the algorithm with further optimisations, to obtain a 60× speed up. We illustrate the use of these thinnings in automated non-destructive material inspection: the detection of cracks. We discuss the advantages of these operators over other methods such as path openings or the supremum of openings with segments.     

Analyzing probabilistic pushdown automata

The paper gives a summary of the existing results about algorithmic analysis of probabilistic pushdown automata and their subclasses.     

Evaluation in Music Information Retrieval

The field of Music Information Retrieval has always acknowledged the need for rigorous scientific evaluations, and several efforts have set out to develop and provide the infrastructure, technology and methodologies needed to carry out these evaluations. The community has enormously gained from these evaluation forums, but we have reached a point where we are stuck with evaluation frameworks that do not allow us to improve as much and as well as we want. The community recently acknowledged this problem and showed interest in addressing it, though it is not clear what to do to improve the situation. We argue that a good place to start is again the Text IR field. Based on a formalization of the evaluation process, this paper presents a survey of past evaluation work in the context of Text IR, from the point of view of validity, reliability and efficiency of the experiments. We show the problems that our community currently has in terms of evaluation, point to several lines of research to improve it and make various proposals in that line.     

Semitransparent monocrystalline solar cells manufactured by laser cutting and anisotropic etching

This paper presents two possible technologies used to manufacture semitransparent monocrystalline building integrated solar cells: laser cutting and anisotropic etching. Nd:YAG 1,064 nm laser cut sidewalls are rough and contain molten residues, resulting in shunts across the through-holes confirmed by I–V characteristic measurement and reverse bias measurement results. Tetramethylammonium hydroxide etched edges are uniform with smooth sidewalls. Carrier lifetime and Kelvin probe measurements reveal the flaws occurring in the two technologies.     

Ordinal and nominal classification of wind speed from synoptic pressurepatterns

Wind speed reconstruction is a challenging problem in areas (mainly wind farms) where there are not direct wind measures available. Different approaches have been applied to this reconstruction, such as measure-correlate-predict algorithms, approaches based on physical models such as reanalysis methods, or more recently, indirect measures such as pressure, and its relation to wind speed. This paper adopts the latter method, and deals with wind speed estimation in wind farms from pressure measures, but including different novelties in the problem treatment. Existing synoptic pressure-based indirect approaches for wind speed estimation are based on considering the wind speed as a continuous target variable, estimating then the corresponding wind series of continuous values. However, the exact wind speed is not always needed by wind farm managers, and a general idea of the level of speed is, in the majority of cases, enough to set functional operations for the farm (such as wind turbines stop, for example). Moreover, the accuracy of the models obtained is usually improved for the classification task, given that the problem is simplified. Thus, this paper tackles the problem of wind speed prediction from synoptic pressure patterns by considering wind speed as a discrete variable and, consequently, wind speed prediction as a classification problem, with four wind level categories: low, moderate, high or very high. Moreover, taking into account that these four different classes are associated to four values in an ordinal scale, the problem can be considered as an ordinal regression problem. The performance of several ordinal and nominal classifiers and the improvement achieved by considering the ordering information are evaluated. The results obtained in this paper present the support vector machine as the best tested classifier for this task. In addition, the use of the intrinsic ordering information of the problem is shown to significantly improve ranks with respect to nominal classification, although differences in accuracy aresmall.