Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

A Formal View on Training of Weighted Tree Automata by Likelihood-Driven State Splitting and Merging

The use of computers and algorithms to deal with human language, in both spoken and written form, is summarized by the term natural language processing (nlp). Modeling language in a way that is suitable for computers plays an important role in nlp. One idea is to use formalisms from theoretical computer science for that purpose. For example, one can try to find an automaton to capture the valid written sentences of a language. Finding such an automaton by way of examples is called training. In this work, we also consider the structure of sentences by making use of trees. We use weighted tree automata (wta) in order to deal with such tree structures. Those devices assign weights to trees in order to, for example, distinguish between good and bad structures. The well-known expectation-maximization algorithm can be used to train the weights for a wta while the state behavior stays fixed. As a way to adapt the state behavior of a wta, state splitting, i.e. dividing a state into several new states, and state merging, i.e. replacing several states by a single new state, can be used. State splitting, state merging, and the expectation maximization algorithm already were combined into the state splitting and merging algorithm, which was successfully applied in practice. In our work, we formalized this approach in order to show properties of the algorithm. We also examined a new approach – the count-based state merging algorithm – which exclusively relies on state merging. When dealing with trees, another important tool is binarization. A binarization is a strategy to code arbitrary trees by binary trees. For each of three different binarizations we showed that wta together with the binarization are as powerful as weighted unranked tree automata (wuta). We also showed that this is still true if only probabilistic wta and probabilistic wuta are considered.:How to Read This Thesis 1. Introduction 1.1. The Contributions and the Structure of This Work 2. Preliminaries 2.1. Sets, Relations, Functions, Families, and Extrema 2.2. Algebraic Structures 2.3. Formal Languages 3. Language Formalisms 3.1. Context-Free Grammars (CFGs) 3.2. Context-Free Grammars with Latent Annotations (CFG-LAs) 3.3. Weighted Tree Automata (WTAs) 3.4. Equivalences of WCFG-LAs and WTAs 4. Training of WTAs 4.1. Probability Distributions 4.2. Maximum Likelihood Estimation 4.3. Probabilities and WTAs 4.4. The EM Algorithm for WTAs 4.5. Inside and Outside Weights 4.6. Adaption of the Estimation of Corazza and Satta [CS07] to WTAs 5. State Splitting and Merging 5.1. State Splitting and Merging for Weighted Tree Automata 5.1.1. Splitting Weights and Probabilities 5.1.2. Merging Probabilities 5.2. The State Splitting and Merging Algorithm 5.2.1. Finding a Good π-Distributor 5.2.2. Notes About the Berkeley Parser 5.3. Conclusion and Further Research 6. Count-Based State Merging 6.1. Preliminaries 6.2. The Likelihood of the Maximum Likelihood Estimate and Its Behavior While Merging 6.3. The Count-Based State Merging Algorithm 6.3.1. Further Adjustments for Practical Implementations 6.4. Implementation of Count-Based State Merging 6.5. Experiments with Artificial Automata and Corpora 6.5.1. The Artificial Automata 6.5.2. Results 6.6. Experiments with the Penn Treebank 6.7. Comparison to the Approach of Carrasco, Oncina, and Calera-Rubio [COC01] 6.8. Conclusion and Further Research 7. Binarization 7.1. Preliminaries 7.2. Relating WSTAs and WUTAs via Binarizations 7.2.1. Left-Branching Binarization 7.2.2. Right-Branching Binarization 7.2.3. Mixed Binarization 7.3. The Probabilistic Case 7.3.1. Additional Preliminaries About WSAs 7.3.2. Constructing an Out-Probabilistic WSA from a Converging WSA 7.3.3. Binarization and Probabilistic Tree Automata 7.4. Connection to the Training Methods in Previous Chapters 7.5. Conclusion and Further Research A. Proofs for Preliminaries B. Proofs for Training of WTAs C. Proofs for State Splitting and Merging D. Proofs for Count-Based State Merging Bibliography List of Algorithms List of Figures List of Tables Index Table of Variable Name

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum