Formal language theory has recently been successfully employed to unravel the power of transformer encoders. This setting is primarily applicable in Natural Language Processing (NLP), as a token embedding function (where a bounded number of tokens is admitted) is first applied before feeding the input to the transformer.

Francisco Mena 1, 2, Diego Arenas 2, Miro Miranda 1, 2, and Andreas Dengel 1, 2 1 University of Kaiserslautern-Landau (RPTU), Kaiserslautern, Germany 2 German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany Abstract --In recent years, the development of robust multi-source models has emerged in the Earth Observation (EO) field. These are models that leverage data from diverse sources to improve predictive accuracy when there is missing data. Despite these advancements, the factors influencing the varying effectiveness of such models remain poorly understood. In this study, we evaluate the predictive performance of six state-of-the-art multi-source models in predicting scenarios where either a single data source is missing or only a single source is available. Our analysis reveals that the efficacy of these models is intricately tied to the nature of the task, the complementarity among data sources, and the model design. Surprisingly, we observe instances where the removal of certain data sources leads to improved predictive performance, challenging the assumption that incorporating all available data is always beneficial.

Round particles and their properties are easy to describe mathematically. But the less round or spherical the shape, the harder it becomes to make predictions about their behavior. In his doctoral thesis at the Technical University of Kaiserslautern (TUK), Robert Hesse has trained a neural network to automatically determine the packing density and flowability of non-spherical particles. Few particles in nature or in industrial production are exactly round; instead, there are a multitude of variants and shape characteristics. This is exactly what makes it so complicated to describe non-spherical particles and optimize their handling based on the description.

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Opportunistic relay selection (ORS) has been recognized as a simple but efficient method for mobile nodes to achieve cooperative diversity in slow fading channels. With the proliferation of high-mobility applications and the adoption of higher frequency bands in 5G and beyond systems, the problem of outdated CSI will become more serious. Therefore, the design of a novel cooperative method that is applicable to not only slow fading but also fast fading is increasingly of importance. To this end, we develop and analyze a deep-learning-aided cooperative method coined predictive relay selection (PRS) in this article. It can remarkably improve the quality of CSI through fading channel prediction while retaining the simplicity of ORS by selecting a single opportunistic relay so as to avoid the complexity of multi-relay coordination and synchronization. Information-theoretic analysis and numerical results in terms of outage probability and channel capacity reveal that PRS achieves full diversity gain in slow fading wireless environments and substantially outperforms the existing schemes in fast fading channels. N wireless communications [1], diversity is an important and essential technique, which can effectively combat the effect of multi-path channel fading by means of transmitting redundant signals over independent channels and then combining multiple faded copies at the receiver. Spatial diversity is particularly attractive as it can be easily combined with other forms of diversity and achieve higher diversity order by simply installing more antennas. Because of the constraint on power supply, hardware size, and cost, it is difficult for mobile terminals in cellular systems or wireless nodes in ad hoc networks to exploit spatial diversity at sub-6GHz carrier frequencies. W. Jiang is with German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany, and is also with the University of Kaiserslautern, Germany, (e-mail: wei.jiang@dfki.de). H. D. Schotten is with German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany, and is also with the University of Kaiserslautern, Germany, (e-mail: schotten@eit.uni-kl.de). In such a cooperative network, when a node sends a signal, its neighboring nodes could act as relays to decode-and-forward (DF) or amplify-and-forward (AF) this signal. By combining multiple copied versions of the original signal at the destination, the network achieves cooperative diversity that is equivalent to spatial diversity gained from co-located multi-antenna systems [4].

Tim Althoff is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His research advances computational methods that leverage large-scale behavioral data to extract actionable insights about our lives, health and happiness through combining techniques from data science, social network analysis, and natural language processing. Tim holds Ph.D. and M.S. degrees from the Computer Science Department at Stanford University, where he worked with Jure Leskovec. Prior to his PhD, Tim obtained M.S. and B.S. degrees from the University of Kaiserslautern, Germany. He has received several fellowships and awards including the SAP Stanford Graduate Fellowship, Fulbright scholarship, German Academic Exchange Service scholarship, the German National Merit Foundation scholarship, a Best Paper Award by the International Medical Informatics Association, and the SIGKDD Dissertation Award 2019.

The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

NVIDIA Corporation (NASDAQ: NVDA) announced that it joining Andreas Dengel to get AI into more people's hands while advances are made in the technology. Dengel is a German computer scientist and university lecturer as well as site manager of the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, which was founded in 1988. NVIDIA has joined him and his roughly 1,000 colleagues as a shareholder in DFKI. "A study last week said many companies are collecting data, but they don't know what to do with it. We can help them join an increasingly data-driven economy," said Dengel.

German scientists are developing a smartwatch to correct bad posture. Engineers at TU Kaiserslautern and the German Research Centre for Artificial Intelligence are building a prototype of their vision, which would alert wearers to slouching. Using sensors worn under clothes and inside shoes, it would monitor angular velocity, which is the rate of change of angular position of a rotating body. It would then process this data against motion parameters, such as how much the spine moves and at what angle, then issue an alert if the posture was insufficiently upright. Using sensors worn under clothes and inside shoes, it would monitor angular velocity.

The TriRhenaTech alliance universities and their partners presented their competences in the field of artificial intelligence and their cross-border cooperations with the industry at the tri-national conference 'Artificial Intelligence : from Research to Application' on March 13th, 2019 in Offenburg. The TriRhenaTech alliance is a network of universities in the Upper Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.