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Recent advancements in specialized large-scale architectures for training image and language have profoundly impacted the field of computer vision and natural language processing (NLP). Language models, such as the recent ChatGPT and GPT4 have demonstrated exceptional capabilities in processing, translating, and generating human languages. These breakthroughs have also been reflected in protein research, leading to the rapid development of numerous new methods in a short time, with unprecedented performance. Language models, in particular, have seen widespread use in protein research, as they have been utilized to embed proteins, generate novel ones, and predict tertiary structures. In this book chapter, we provide an overview of the use of protein generative models, reviewing 1) language models for the design of novel artificial proteins, 2) works that use non-Transformer architectures, and 3) applications in directed evolution approaches., Comment: 16 pages, 4 figures, 2 tables

The OMNeT++ simulator is well-suited for the simulation of randomized user behavior in communication networks. However, there are scenarios, where such a random model is unsuited to evaluate a communication system, and this paper attempts to highlight such a case. Using this example of ad-hoc communication between aircraft mid-flight, a tutorial-style description is attempted that shall show how the OMNeT++ simulator can be used when a wealth of real-world trace data is available. In particular, it is described how mobility trace files can be directly used within OMNeT++, and how to link the generation of data messages to this mobility data. This is explained via an example simulation that evaluates a communication network in which an aircraft notifies the ground control when it enters or leaves a specific geographic region. Additionally, a novel trace-based application has been developed to achieve this link between mobility and message generation. Furthermore, a new TDMA-based medium access protocol for decentralized communication networks is presented, which is oracle-based and thus allows a TDMA-like behavior of medium access without causing any overhead; it can be useful when upper-layer protocols should be evaluated under the assumption of TDMA-like behavior, but isolated from the effects of a full-fledged TDMA protocol. Finally, physical layer behavior is often either overly simplistic or overly computationally expensive. For the latter case, when a detailed channel model is available but its evaluation requires prohibitive computational effort, then averaging its behavior into trace data can find a middle ground between efficient evaluation and realistic representation. Hence, a novel trace-based radio model has been developed that makes use of an SNR to PER mapping. In the spirit of open science, all implementations have been made available under open licenses., Comment: Published in: M. Marek, G. Nardini, V. Vesely (Eds.), Proceedings of the 8th OMNeT++ Community Summit, Virtual Summit, September 8-10, 2021