Real-world autonomous systems are often controlled using conventional model-based control methods. But if accurate models of a system are not available, these methods may be unsuitable. For many safety-critical systems, such as robotic systems, a model of the system and a control strategy may be learned using data. When applying learning to safety-critical systems, guaranteeing safety during learning ...

Text classification is one of the widely used phenomena in different natural language processing tasks. State-of-the-art text classifiers use the vector space model for extracting features. Recent progress in deep models, recurrent neural networks those preserve the positional relationship among words achieve a higher accuracy. To push text classification accuracy even higher, multi-dimensional ...

High-density lipoproteins (HDL) are a heterogenous group of plasma molecules with a large variety in composition. There is a wide specter in lipid content and the number of different proteins that has been associated with HDL is approaching 100. Given this heterogeneity and the fact that the total amount of HDL is inversely related to the risk of coronary heart disease (CHD), there has been ...

Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually ...

On-chip devices for absorption spectroscopy and Raman spectroscopy have been developing rapidly in the last few years, triggered by the growing availability of compact and affordable tunable lasers, detectors, and on-chip spectrometers. Material processing that is compatible with mass production has been proven to be capable of long low-loss waveguides of sophisticated designs, which are indispensable ...

When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on modeling the physical phenomena occurring on the Earth’s surface. In this article, we introduce a flexible ...

Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and ...

Ground-based inverse synthetic aperture radar is a tool that can provide insights into the early history and formative processes of planetary bodies in the inner solar system. This information is gathered by measuring the scattering matrix of the target body, providing composite information about the physical structure and chemical makeup of its surface and subsurface down to the penetration ...

Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. ...

The determination of the temperature dependence of enzyme catalysis has traditionally been a labourious undertaking. We have developed a new approach to the classical Arrhenius parameter estimation by fitting the change in velocity under a gradual change in temperature. The evaluation with a simulated dataset shows that the approach is valid. The approach is demonstrated as a useful tool by ...