A shunting yard is used to store trains between arrival and departure. A conflict arises in a shunting yard when one train obstructs another from leaving. Resolving a conflict is done by re-allocating the trains obstructing the departing train to other tracks in the shunting yard. However, these re-allocations complicate the problem at hand and incur high costs for train operators. Therefore, it is desirable to avoid conflicts whenever possible. The aim of this paper is to find an effective manner to deal with conflicts in a train shunting yard in an existing planner system. We propose the split into a portfolio planner, which first tries to find a solution without any re-allocations, and if that does not yield a solution will look for a solution with re-allocations. For both planners, a model is defined. Furthermore, the paper explores techniques to increase the speed of the first planner, namely heuristic search, a set partitioning approach, and constraint programming. An implementation of the latter approach has exhibited excellent performance across problems of all sizes.

This paper explored the usage of the Planning Domain Definition Language in the Train Unit Shunting Problem or TUSP, an NP-hard problem that occurs in train storage. This paper focuses on the evaluation and improvement of existing planners to solve TUSP with multiple shunting yard layouts, as well as an appropriate domain with it. Starting with the implementation of multiple shunting yards in the domain, by connecting train tracks in problem instances. Followed by the evaluation of the planners, where planners are evaluated on speed, plan cost and solvability. Among the evaluated planners, the Team4 planner from the International Planning Competition 2018 demonstrates exceptional performance by successfully solving all problem instances and having the highest speed overall. Finally, the Team4 planner was optimised, specifically by improving the domain for Last In First Out, LIFO, tracks, since it encountered difficulties when solving problems that contained these tracks. The tracks were given new predicates and actions, resulting in much higher speeds for problems that consisted of LIFO tracks. Results suggest that a suitable planner has been improved for solving TUSP with multiple shunting yard layouts and that planners are indeed capable of handling the complexities that come along with multiple shunting yard layouts.

It is possible to improve the performance of planners by modifying the PDDL domain of a problem. The goal of this research is to implement this to the domain of the Train Unit Shunting Problem (TUSP). The research question we attempt to answer is: To what extent can we improve planner performance by optimizing the PDDL domain of TUSP? The main contributions of this research are: a formalization of the TUSP and its constraints in PDDL terminology, a comprehensive evaluation of the performance of planners on the PDDL domain of TUSP, provide general approaches and techniques that can be used to optimize a PDDL domain, and provide insights into the relationship between the performance of planners and PDDL domain properties. To answer the research question, we measure the performance of the planners in terms of execution time, plan quality, and problem solvability. We modified the domain by combining actions such that the number of computations is decreased and by introducing action costs. With these modifications, we found a decrease in planner execution time and an increase in plan quality. We found no difference in problem solvability. Therefore, we can conclude that we can indeed improve the performance of planners by implementing these modifications to the domain of TUSP.

In this paper, we discuss our novel approach to support mixed-direction shunting in the planning domain for tree-like shunting yards. We explain how we used numeric fluents to improve an existing PDDL domain to support such actions. We elaborate on the underlying conceptual model of our domain and argue how does it accurately represent shunting problems. Furthermore, we describe the experiment that we performed with the MetricFF planner to evaluate our domain and discuss the results of the experiment. Finally, we argue why the MetricFF planning system is not suitable to efficiently support our domain in real-life scenarios.

As the sharing of machine learning (ML) models has increased in popularity, more so-called model zoos are created. These repositories facilitate the sharing of models and their metadata, and other people to find and re-use an existing model. However, the metadata provided for models is insufficient, with little focus on practical aspects of a model such as performance and limitations. This leaves model zoos to provide little functinality beyond the sharing of existing models, and potential users unable to find an optimal model for their needs. In this thesis we focus on the reporting of model performance, and aim to answer two questions: (1) What are the limitations of model performance reporting found in current model zoos, and (2) How can we provide rich, comparable performance metadata for these models. As a result, we have created a framework which can be used to create a benchmarking system for ML models. Our framework enables the automatic evaluation of models from any source, and through its design encourages the collection of rich and comparable performance metadata for those models. To demonstrate the effectiveness of our framework, we have created a benchmarking system and evaluated 1215 models from existing model zoos, along with a new interface to view this new metadata, and have enabled new use-cases such as the performance comparison of ML models.

Quantum networks allow quantum processors to communicate over large distances. These networks often require simultaneously existing multiple entangled pairs of quantum bits (entangled links) as a fundamental resource for communication. Link generation is a sequential and probabilistic process, and successfully generated links are stored in a quantum memory. Links in memory are subject to noise that causes their quality to decay and become unusable. This paper uses reinforcement learning (RL) to investigate dynamic tuning of the entanglement generation protocol to minimise the time to generate multiple links. By comparing a fixed number of actions to a continuous action space, we analyse the importance of finer-grained tunings of the protocol. This is tested in simulated near-term and medium-term network abstractions. The results show that protocol tuning significantly reduces the mean time to generate entangled links, with finer tuning providing greater benefits up to a point. Furthermore, a heuristic is derived from the RL policies which matches and exceeds their performance. Future work can explore more advanced reinforcement learning algorithms to find better policies, as well as using different noise models to make more generally applicable policies.

Entangled links can be seen as a connection between two parties that persists remotely, but whose quality (fidelity) decreases over time. Many quantum applications rely on having a certain number of simultaneously active entangled links for their execution. Every application has a characteristic threshold fidelity - the minimum fidelity a link should have for it to be useful. There are several link generation protocols; a protocol i generates with probability P_i a link with initial fidelity F_i. Until now, research has focused on generating all links with the same protocol. Therefore, the aim of this research is to use multiple generation protocols in order to minimize the expected time needed to reach the desired number of links, and to analyze how this time changes with regard to the threshold fidelity of the target protocol. We present how to reach the minimum possible expected time by modelling the problem as a Markov decision process and determining the optimal sequence of protocols to be used. Then, we observe that our approach generally performs several orders of magnitude better than previous research in which all links were generated with the same protocol.

The advent of pretrained probabilistic time series foundation models has significantly advanced the field of time series forecasting. Despite these models’ growing popularity, the application of watermarking techniques to them remains underexplored. This paper addresses this research gap by benchmarking several widely used watermarking methods to time series models and by introducing a novel watermarking technique named HTW (Heads Tails Watermark). Unlike traditional probabilistic watermarking approaches, HTW uses a pseudo-random function to directly embed a signal into the numeric structure of the series, thereby greatly enhancing its robustness against potential attacks. Comprehensive experiments and evaluations reveal that on average, HTW retains 98.4% prediction accuracy, significantly outperforming other conventional LLM watermarks. Furthermore, HTW demonstrates robust performance with an average z-score of 5.28 across various datasets and attack scenarios for a series length of 48. These findings establish HTW as a superior alternative for securing pretrained probabilistic time series foundation models

The advent of pretrained probabilistic time series foundation models has significantly advanced the field of time series forecasting. Despite these models’ growing popularity, the application of watermarking techniques to them remains underexplored. This paper addresses this research gap by benchmarking several widely used watermarking methods to time series models and by introducing a novel watermarking technique named HTW (Heads Tails Watermark). Unlike traditional probabilistic watermarking approaches, HTW uses a pseudo-random function to directly embed a signal into the numeric structure of the series, thereby greatly enhancing its robustness against potential attacks. Comprehensive experiments and evaluations reveal that on average, HTW retains 98.4% prediction accuracy, significantly outperforming other conventional LLM watermarks. Furthermore, HTW demonstrates robust performance with an average z-score of 5.28 across various datasets and attack scenarios for a series length of 48. These findings establish HTW as a superior alternative for securing pretrained probabilistic time series foundation models

Optimizing SQL query execution through effective cost models is a critical challenge in database management systems (DBMS). This thesis introduces a modular benchmarking system for cost models, with a pluggable architecture for both cost models and execution engines, enabling comprehensive benchmarking across various scenarios. Accompanied by a detailed methodology for the empirical measurement of cost model performance across different execution engines, a standardized approach is established, ensuring consistent and reproducible benchmarks. Furthermore, as a showcase of the developed system's capabilities, an analysis of key features influencing join-order optimization performance in both CPU and GPU systems is presented. This analysis demonstrates the system's utility in developing more effective cost models and optimizers. These contributions pave the way for future research in DBMS optimization, providing a research platform for the accelerated development of new cost models.

Optimizing SQL query execution through effective cost models is a critical challenge in database management systems (DBMS). This thesis introduces a modular benchmarking system for cost models, with a pluggable architecture for both cost models and execution engines, enabling comprehensive benchmarking across various scenarios. Accompanied by a detailed methodology for the empirical measurement of cost model performance across different execution engines, a standardized approach is established, ensuring consistent and reproducible benchmarks. Furthermore, as a showcase of the developed system's capabilities, an analysis of key features influencing join-order optimization performance in both CPU and GPU systems is presented. This analysis demonstrates the system's utility in developing more effective cost models and optimizers. These contributions pave the way for future research in DBMS optimization, providing a research platform for the accelerated development of new cost models.

When writing functional code that composes multiple recursive functions that operate on a datastrcuture, we often incur a lot of computational overhead allocating memory, only to later read, use, and discard this information. This can be alleviated using fusion, a technique that combines these multiple recursive datastructure traversals into one. This thesis explores shortcut fusion using (Co)Church encodings based on the work of Harper (2011), focusing on two questions: What is needed to reliably achieve fusion in Haskell, and the correctness of these transformations through a formalization in Agda. The first contribution replicates and extends Harper's (Co)Church encodings in Haskell, uncovering optimizer weaknesses and providing practical insights for achieving fusion within Haskell. The second contribution formalizes these encodings in Agda, leveraging parametricity and the category theory described by Harper. The formalization proves the equivalence of these encoded functions to the unencoded ones, showing that the encodings are in fact isomorphisms, as long as parametricity (Wadler, 1989) is assumed. These findings highlight the effectiveness and correctness of shortcut fusion techniques and show the promise of shortcut fusion: Reduce the computational overhead associated with functional programming while retaining its nice, compositional properties.

When writing functional code that composes multiple recursive functions that operate on a datastrcuture, we often incur a lot of computational overhead allocating memory, only to later read, use, and discard this information. This can be alleviated using fusion, a technique that combines these multiple recursive datastructure traversals into one. This thesis explores shortcut fusion using (Co)Church encodings based on the work of Harper (2011), focusing on two questions: What is needed to reliably achieve fusion in Haskell, and the correctness of these transformations through a formalization in Agda. The first contribution replicates and extends Harper's (Co)Church encodings in Haskell, uncovering optimizer weaknesses and providing practical insights for achieving fusion within Haskell. The second contribution formalizes these encodings in Agda, leveraging parametricity and the category theory described by Harper. The formalization proves the equivalence of these encoded functions to the unencoded ones, showing that the encodings are in fact isomorphisms, as long as parametricity (Wadler, 1989) is assumed. These findings highlight the effectiveness and correctness of shortcut fusion techniques and show the promise of shortcut fusion: Reduce the computational overhead associated with functional programming while retaining its nice, compositional properties.

A quantum network allows us to connect quantum information processors to achieve capabilities that are not possible using classical computation. Quantum network protocols typically require several entangled states available simultaneously. Previously, an entanglement generation process was analysed where, at each time step, we generate an entangled state with success probability p. Here, we consider adaptive entangled state generation with more flexibility. At each time step, our process chooses a protocol (pi, Fi) from a discrete number of entanglement generation protocols. An entangled state is generated successfully with probability pi, and its fidelity Fi defines how close the entangled state is to an ideal Bell state. The new state is subject to depolarising noise in the quantum memory. Because of the memory noise, states are discarded after a certain number of time steps ti when they are no longer useful to our application. We model our process as a Markov decision process and derive a policy π to generate n entangled states with minimal expected time Eπ[τ]. We analyse the offered improvement of the optimal policy of our adaptive entanglement generation process over the previously studied static process. We conclude that this improvement becomes more significant as the required number of links in memory increases.