Evaluating the Hardness of SAT Instances Using Evolutionary Optimization Algorithms

Propositional satisfiability (SAT) solvers are deemed to be among the most efficient reasoners, which have been successfully used in a wide range of practical applications. As this contrasts the well-known NP-completeness of SAT, a number of attempts have been made in the recent past to assess the hardness of propositional formulas in conjunctive normal form (CNF). The present paper proposes a CNF formula hardness measure which is close in conceptual meaning to the one based on Backdoor set notion: in both cases some subset B of variables in a CNF formula is used to define the hardness of the formula w.r.t. this set. In contrast to the backdoor measure, the new measure does not demand the polynomial decidability of CNF formulas obtained when substituting assignments of variables from B to the original formula. To estimate this measure the paper suggests an adaptive (?,?)-approximation probabilistic algorithm. The problem of looking for the subset of variables which provides the minimal hardness value is reduced to optimization of a pseudo-Boolean black-box function. We apply evolutionary algorithms to this problem and demonstrate applicability of proposed notions and techniques to tests from several families of unsatisfiable CNF formulas

Martian dust storm impact on atmospheric H₂O and D/H observed by ExoMars Trace Gas Orbiter

Global dust storms on Mars are rare but can affect the Martian atmosphere for several months. They can cause changes in atmospheric dynamics and inflation of the atmosphere, primarily owing to solar heating of the dust. In turn, changes in atmospheric dynamics can affect the distribution of atmospheric water vapour, with potential implications for the atmospheric photochemistry and climate on Mars. Recent observations of the water vapour abundance in the Martian atmosphere during dust storm conditions revealed a high-altitude increase in atmospheric water vapour that was more pronounced at high northern latitudes, as well as a decrease in the water column at low latitudes. Here we present concurrent, high-resolution measurements of dust, water and semiheavy water (HDO) at the onset of a global dust storm, obtained by the NOMAD and ACS instruments onboard the ExoMars Trace Gas Orbiter. We report the vertical distribution of the HDO/H O ratio (D/H) from the planetary boundary layer up to an altitude of 80 kilometres. Our findings suggest that before the onset of the dust storm, HDO abundances were reduced to levels below detectability at altitudes above 40 kilometres. This decrease in HDO coincided with the presence of water-ice clouds. During the storm, an increase in the abundance of H2O and HDO was observed at altitudes between 40 and 80 kilometres. We propose that these increased abundances may be the result of warmer temperatures during the dust storm causing stronger atmospheric circulation and preventing ice cloud formation, which may confine water vapour to lower altitudes through gravitational fall and subsequent sublimation of ice crystals. The observed changes in H2O and HDO abundance occurred within a few days during the development of the dust storm, suggesting a fast impact of dust storms on the Martian atmosphere

No detection of methane on Mars from early ExoMars Trace Gas Orbiter observations

The detection of methane on Mars has been interpreted as indicating that geochemical or biotic activities could persist on Mars today. A number of different measurements of methane show evidence of transient, locally elevated methane concentrations and seasonal variations in background methane concentrations. These measurements, however, are difficult to reconcile with our current understanding of the chemistry and physics of the Martian atmosphere, which-given methane's lifetime of several centuries-predicts an even, well mixed distribution of methane. Here we report highly sensitive measurements of the atmosphere of Mars in an attempt to detect methane, using the ACS and NOMAD instruments onboard the ESA-Roscosmos ExoMars Trace Gas Orbiter from April to August 2018. We did not detect any methane over a range of latitudes in both hemispheres, obtaining an upper limit for methane of about 0.05 parts per billion by volume, which is 10 to 100 times lower than previously reported positive detections. We suggest that reconciliation between the present findings and the background methane concentrations found in the Gale crater would require an unknown process that can rapidly remove or sequester methane from the lower atmosphere before it spreads globally

Fault Tolerant Assembly Line As Multi-Agent System

Assembly line consists of robots with internal programs which are placed along a step conveyor. If any robot is disabled, a total assembly is stopped. To ensure the efficiency of assembly line, a neighboring robot takes a program of disabled robot. Fault tolerant assembly line is presented as multi-agent system where agents (robots) change their programs after a fault of any agent. A joint behavior of agents in multi-agent system was simulated by Petri nets. Places of faults and repair time were introduced randomly during simulation experiments. As a result, the losses of productivity were determined

Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks

The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make it possible to generate artificial spectral information, for example, to solve the image colorization problem. In this research, we aim to investigate whether this approach can produce not only visually similar images but also an artificial spectral band that can improve the performance of computer vision algorithms for solving remote sensing tasks. We study the use of a generative adversarial network (GAN) approach in the task of the NIR band generation using only RGB channels of high-resolution satellite imagery. We evaluate the impact of a generated channel on the model performance to solve the forest segmentation task. Our results show an increase in model accuracy when using generated NIR compared to the baseline model, which uses only RGB (0.947 and 0.914 F1-scores, respectively). The presented study shows the advantages of generating the extra band such as the opportunity to reduce the required amount of labeled data

Augmentation-Based Methodology for Enhancement of Trees Map Detalization on a Large Scale

Remote sensing tasks play a very important role in the domain of sensing and measuring, and can be very specific. Advances in computer vision techniques allow for the extraction of various information from remote sensing satellite imagery. This information is crucial in making quantitative and qualitative assessments for monitoring of forest clearing in protected areas for power lines, as well as for environmental analysis, in particular for making assessments of carbon footprint, which is a highly relevant task. Solving these problems requires precise segmentation of the forest mask. Although forest mask extraction from satellite data has been considered previously, no open-access applications are able to provide the high-detailed forest mask. Detailed forest masks are usually obtained using unmanned aerial vehicles (UAV) that set particular limitations such as cost and inapplicability for vast territories. In this study, we propose a novel neural network-based approach for high-detailed forest mask creation. We implement an object-based augmentation technique for a minimum amount of labeled high-detailed data. Using this augmented data we fine-tune the models, which are trained on a large forest dataset with less precise labeled masks. The provided algorithm is tested for multiple territories in Russia. The F1-score, for small details (such as individual trees) was improved to 0.929 compared to the baseline score of 0.856. The developed model is available in an SAAS platform. The developed model allows a detailed and precise forest mask to be easily created, which then be used for solving various applied problems

Augmentation-Based Methodology for Enhancement of Trees Map Detalization on a Large Scale

Remote sensing tasks play a very important role in the domain of sensing and measuring, and can be very specific. Advances in computer vision techniques allow for the extraction of various information from remote sensing satellite imagery. This information is crucial in making quantitative and qualitative assessments for monitoring of forest clearing in protected areas for power lines, as well as for environmental analysis, in particular for making assessments of carbon footprint, which is a highly relevant task. Solving these problems requires precise segmentation of the forest mask. Although forest mask extraction from satellite data has been considered previously, no open-access applications are able to provide the high-detailed forest mask. Detailed forest masks are usually obtained using unmanned aerial vehicles (UAV) that set particular limitations such as cost and inapplicability for vast territories. In this study, we propose a novel neural network-based approach for high-detailed forest mask creation. We implement an object-based augmentation technique for a minimum amount of labeled high-detailed data. Using this augmented data we fine-tune the models, which are trained on a large forest dataset with less precise labeled masks. The provided algorithm is tested for multiple territories in Russia. The F1-score, for small details (such as individual trees) was improved to 0.929 compared to the baseline score of 0.856. The developed model is available in an SAAS platform. The developed model allows a detailed and precise forest mask to be easily created, which then be used for solving various applied problems