Metaheuristics based on decision hierarchies for the traveling purchaser problem

In this paper we address the traveling purchaser problem, an NP‐hard problem that generalizes the traveling salesman problem. We present several metaheuristics that combine genetic algorithms and local search. The genetic algorithms are induced by different hierarchic orderings of the decision making regarding the route and the acquisition of the items. Computational experiments were carried out with benchmark instances and the results show that the proposed metaheuristics are a suitable tool to solve high‐dimensioned instances for which the exact methods do not provide solutions within a reasonable CPU time. For several instances, best new upper bounds for the optimum value of the objective function were obtained.     

Organic Single Crystal Patterning Method for Micrometric Photosensors

Light detection technologies are of interest due to their applications in energy conversion and optical communications. Single-crystal organic semiconductors, such as rubrene, present high detectivities and charge carrier mobility, making them attractive for light-sensing applications. Growth of high crystallinity organic crystals is achieved using vapor processes, forming crystals of arbitrary shapes and orientations and requiring posterior patterning processes. However, patterning the organic semiconductors using industry-standard microfabrication techniques is not straightforward, as these often cause irreversible damage to the crystals. Here the fabrication of patterned micrometric rubrene photosensors is demonstrated through a combination of photolithography and Reactive Ion Etching steps. Protective layers during microfabrication minimize degradation of optoelectronic properties of the organic single crystals during fabrication. Crystals undergoing the patterning process presented a survival rate of 39%. Photoresponse values of up to 41 mA W⁻¹ are obtained under illumination at 500 nm. This opens a route for the industrial-scale fabrication process of high-performance optoelectronic devices based on organic crystals semiconductors.     

Visualizing Thermally Activated Memristive Switching in Percolating Networks of Solution-Processed 2D Semiconductors

Memristive systems present a low-power alternative to silicon-based electronics for neuromorphic and in-memory computation. 2D materials have been increasingly explored for memristive applications due to their novel biomimetic functions, ultrathin geometry for ultimate scaling limits, and potential for fabricating large-area, flexible, and printed neuromorphic devices. While the switching mechanism in memristors based on single 2D nanosheets is similar to conventional oxide memristors, the switching mechanism in nanosheet composite films is complicated by the interplay of multiple physical processes and the inaccessibility of the active area in a two-terminal vertical geometry. Here, the authors report thermally activated memristors fabricated from percolating networks of diverse solution-processed 2D semiconductors including MoS₂, ReS₂, WS₂, and InSe. The mechanisms underlying threshold switching and negative differential resistance are elucidated by designing large-area lateral memristors that allow the direct observation of filament and dendrite formation using in situ spatially resolved optical, chemical, and thermal analyses. The high switching ratios (up to 10³) that are achieved at low fields (≈4 kV cm⁻¹) are explained by thermally assisted electrical discharge that preferentially occurs at the sharp edges of 2D nanosheets. Overall, this work establishes percolating networks of solution-processed 2D semiconductors as a platform for neuromorphic architectures.     

A survey of artificial intelligence strategies for automatic detection of sexually explicit videos

Digital forensics and analysis have emerged as a discipline to fight against cyber and computer-assisted crime. In particular, taking into account the increasing of unconstrained pornographic content over Internet and the spreading cases of Child Sex Abuse material distribution, there is a growing need of efficient computational tools to automatically detect or/and block pornographic videos. The primary objective of this study is to review the different strategies available in the literature for pornography detection in videos and identify research gaps. This survey shows that deep learning based techniques detect videos with sexually explicit content more accurately compared with other conventional detection strategies. The accuracy of the strategies reported in this work, is found to be dependent on features extraction techniques, architecture, and learning algorithms. Finally, further research areas in pornographic video detection are outlined.

     

Improving scientific application execution on android mobile devices via code refactorings

The increasing number of mobile devices with ever‐growing capabilities makes them useful for running scientific applications. However, these applications have high computational demands, whereas mobile devices have limited capabilities when compared with non‐mobile devices. More importantly, mobile devices rely on batteries for their power supply. We initially measure the battery consumption of different versions of known micro‐benchmarks representing common programming primitives found in scientific applications. Then, we analyze the performance of such micro‐benchmarks in CPU‐intensive mobile applications. We apply good programming practices and code refactorings to reduce battery consumption of scientific mobile applications. Our results show the reduction in energy usage from applying these refactorings to three scientific applications, and we consequently propose guidelines for high‐performance computing applications. Our focus is on Android, the dominant mobile operating system. As a long‐term contribution, our results represent one more step in the progress towards hybrid distributed infrastructures comprising fixed and mobile nodes, that is, the so‐called mobile grids. Copyright © 2016 John Wiley & Sons, Ltd.     

Cloud parameter retrievals from Meteosat and their effects on the shortwave radiation at the surface

A method based on Spinning Enhanced Visible and Infrared Imager (SEVIRI) measured reflectance at 0.6 and 3.9 µm is used to retrieve the cloud optical thickness (COT) and cloud effective radius (r_e) over the Iberian Peninsula. A sensitivity analysis of simulated retrievals to the input parameters demonstrates that the cloud top height is an important factor in satellite retrievals of COT and r_e with uncertainties around 10% for small values of COT and r_e; for water clouds these uncertainties can be greater than 10% for small values of r_e. The uncertainties found related with geometries are around 3%. The COT and r_e are assessed using well-known satellite cloud products, showing that the method used characterize the cloud field with more than 80% (82%) of the absolute differences between COT (r_e) mean values of all clouds (water plus ice clouds) centred in the range from ±10 (±10 µm), with absolute bias lower than 2 (2 μm) for COT (r_e) and root mean square error values lower than 10 (8 μm) for COT (r_e). The cloud water path (CWP), derived from satellite retrievals, and the shortwave cloud radiative effect at the surface (CRE_SW) are related for high fractional sky covers (F_sc >0.8), showing that water clouds produce more negative CRE_SW than ice clouds. The COT retrieved was also related to the cloud modification factor, which exhibits reductions and enhancements of the surface SW radiation of the order of 80% and 30%, respectively, for COT values lower than 10. A selected case study shows, using a ground-based sky camera that some situations classified by the satellite with high F_sc values correspond to situations of broken clouds where the enhancements actually occur. For this case study, a closure between the liquid water path (LWP) obtained from the satellite retrievals and the same cloud quantity obtained from ground-based microwave measurements was performed showing a good agreement between both LWP data set values.     

Attribute‐preserving gamut mapping of measured BRDFs

Reproducing the appearance of real‐world materials using current printing technology is problematic. The reduced number of inks available define the printer's limited gamut, creating distortions in the printed appearance that are hard to control. Gamut mapping refers to the process of bringing an out‐of‐gamut material appearance into the printer's gamut, while minimizing such distortions as much as possible. We present a novel two‐step gamut mapping algorithm that allows users to specify which perceptual attribute of the original material they want to preserve (such as brightness, or roughness). In the first step, we work in the low‐dimensional intuitive appearance space recently proposed by Serrano et al. [ SGM*16 ], and adjust achromatic reflectance via an objective function that strives to preserve certain attributes. From such intermediate representation, we then perform an image‐based optimization including color information, to bring the BRDF into gamut. We show, both objectively and through a user study, how our method yields superior results compared to the state of the art, with the additional advantage that the user can specify which visual attributes need to be preserved. Moreover, we show how this approach can also be used for attribute‐preserving material editing.     

Selective A<Emphasis Type="Italic">n</Emphasis>DE for large data learning: a low-bias memory constrained approach

Learning from data that are too big to fit into memory poses great challenges to currently available learning approaches. Averaged n-Dependence Estimators (AnDE) allows for a flexible learning from out-of-core data, by varying the value of n (number of super parents). Hence, AnDE is especially appropriate for learning from large quantities of data. Memory requirement in AnDE, however, increases combinatorially with the number of attributes and the parameter n. In large data learning, number of attributes is often large and we also expect high n to achieve low-bias classification. In order to achieve the lower bias of AnDE with higher n but with less memory requirement, we propose a memory constrained selective AnDE algorithm, in which two passes of learning through training examples are involved. The first pass performs attribute selection on super parents according to available memory, whereas the second one learns an AnDE model with parents only on the selected attributes. Extensive experiments show that the new selective AnDE has considerably lower bias and prediction error relative to A\(n'\)DE, where \(n' = n-1\), while maintaining the same space complexity and similar time complexity. The proposed algorithm works well on categorical data. Numerical data sets need to be discretized first.     

A metaheuristic approach to fisheries survey route planning

Every autumn, a research vessel carries out a sampling survey tour to estimate the abundance of groundfish species of the Portuguese continental waters. The sampling operations are carried out at predefined geographical locations, the fishing stations, within predefined multiple time windows. The vessel route starts and ends at the port of Lisbon, and must visit all fishing stations. According to a predefined periodicity, the vessel must enter a port to supply food, refuel, and/or change crew. Given the geographical locations of the fishing stations/ports and current weather conditions, the objective is to minimize the total traveled distance and completion time. We present a mixed integer linear program to describe the problem and propose two sequential heuristic approaches that combine genetic algorithms and adaptive large neighborhood search to solve it. Computational experience with real data shows that the proposed heuristics are suitable tools to solve the problem.     

Influence of Precipitation Changes on the SPI and Related Drought Severity. An Analysis Using Long-Term Data Series

Drought indices, such as the Standardized Precipitation Index (SPI) are used to quantify drought severity. Due to the SPI probabilistic and standardized nature, a given value of SPI computed in distinct time periods or locations indicates the same relative drought severity but corresponds to different amounts of precipitation. Thus, the present study aims at contributing for a comprehensive analysis of the influence of long-term precipitation variability on drought assessment by the SPI. Long records of monthly precipitation, spanning from 1863 to 2007 in several locations across Portugal, were divided into 30 years sub-periods and the SPI with 12-month time scale (SPI-12) was computed for each sub-period and for the entire period of records. The probability distributions adjusted to precipitation in those different time periods were compared envisaging to detect the SPI sensitivity to the reference period and, therefore, to changes in precipitation. Precipitation thresholds relative to the upper limits of SPI-12 drought categories were obtained and the influence of the time period was investigated. Results have shown that when SPI values derived from the full data record for a recent time period are lower/higher than the SPI values derived from data of the considered time period a recent downward/upward shift of precipitation has occurred. Coherently, a common pattern of drought aggravation from the initial until the more recent period was not detected. However, in southern locations, lower precipitation thresholds of the SPI drought categories were generally found in the more recent period, particularly for more severe drought categories, whereas in the northern locations Porto and Montalegre, an increase was detected. The impacts of the reference period on the computed SPI drought severity and frequency are shown, bringing to discussion the need for updating ´normal´ conditions when long term precipitation records are available and precipitation changes are observed.