Since the beginning of the digital revolution robots have become an increasingly important part of modern live. Until recently, this trend was primarily observable in industrial robots, however, with the success of the ...

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The landscape of machine learning applications is changing rapidly: large centralized datasets are replaced by high volume, high velocity data streams generated by a vast number of geographically distributed, loosely ...

Organic–inorganic hybrid interfaces play a pivotal role in determining the performance of next-generation optoelectronic devices. The structural organization, orientational ordering, and electronic coupling of molecular ...

Machine learning or multivariate statistical modeling is frequently applied in the chemical informatics domain to analyze the relationships between molecular structures and their target activities/properties. Structure-activity ...

In drug discovery, domain experts from different fields such as medicinal chemistry, biology, and computer science often collaborate to develop novel pharmaceutical agents. Computational models developed in this process ...

The analysis of trajectories from traffic data is an established and yet fast growing area of research in the related fields of Geo-analytics and Geographic Information Systems (GIS). It has a broad range of applications ...

This thesis is a first step to develop the necessary tools to automatically extract and parameterize sources from future HI surveys with ASKAP, WSRT/Apertif, and SKA.
The current approach to large-scale HI surveys, ...

In the contemporary digital era, the exponential increase in unstructured and semi-structured data has made information extraction a cornerstone of modern data-driven research and application. The ability to transform such ......

Recent years have shown a surge in the capability of deep learning models for various domains of interest and different types of data. Machine learning models are now capable of understanding, categorizing and generating ...