DL
D. Lihotský
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Byzantine fault-tolerant (BFT) consensus underpins modern state machine replication and blockchain systems. Recent consensus protocols reduce the classical 3f+1 replica requirement to 2f+1 by leveraging trusted execution environments, but view synchronization in this setting has received little systematic attention and typically relies on exponential timeout backoff, which has unbounded worst-case latency. On the other hand, in the 3f+1 setting, recent view synchronization work has focused on minimizing asymptotic message complexity, with limited consideration of the latency cost this imposes. This work addresses both gaps. We first adapt the message-optimal LP22 and Lumiere synchronizers to the 2f+1 setting. We then design two latency-optimized synchronizers tailored to the 2f+1 setting: a broadcast-based protocol and a leader-based protocol, which we call Babette. We implemented all four algorithms on top of the OneShot consensus protocol and evaluated them on the DAS-5 cluster across network sizes up to n=51 and fault scenarios ranging from no failures to the maximum tolerated f. With f failures, Babette achieves around 5.5× higher throughput and correspondingly lower latency than Lumiere across all tested network sizes, demonstrating that asymptotic message complexity-optimality does not immediately translate to performance improvement when timeout constants dominate end-to-end performance.
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Byzantine fault-tolerant (BFT) consensus underpins modern state machine replication and blockchain systems. Recent consensus protocols reduce the classical 3f+1 replica requirement to 2f+1 by leveraging trusted execution environments, but view synchronization in this setting has received little systematic attention and typically relies on exponential timeout backoff, which has unbounded worst-case latency. On the other hand, in the 3f+1 setting, recent view synchronization work has focused on minimizing asymptotic message complexity, with limited consideration of the latency cost this imposes. This work addresses both gaps. We first adapt the message-optimal LP22 and Lumiere synchronizers to the 2f+1 setting. We then design two latency-optimized synchronizers tailored to the 2f+1 setting: a broadcast-based protocol and a leader-based protocol, which we call Babette. We implemented all four algorithms on top of the OneShot consensus protocol and evaluated them on the DAS-5 cluster across network sizes up to n=51 and fault scenarios ranging from no failures to the maximum tolerated f. With f failures, Babette achieves around 5.5× higher throughput and correspondingly lower latency than Lumiere across all tested network sizes, demonstrating that asymptotic message complexity-optimality does not immediately translate to performance improvement when timeout constants dominate end-to-end performance.
Procedural music generation with Hierarchical Wave Function Collapse
Visualizing HWFC-generated music and "locking in" parts of the output for later reiteration
Procedurally generating a coherent and emotionally resonant piece of music can be very challenging. The Wave Function Collapse (WFC) algorithm is very effective when it comes to generating randomized patterns and maps that resemble an input sample. A version of this algorithm using a hierarchy of sections, chords and melody was used to create a model capable of generating music. In this paper, we extend the capabilities of this model to improve its utility and help composers more effectively utilize this music generation method to create engaging pieces. Our model offers improvements over previous methods by allowing composers to retain desirable elements of the music output while regenerating others, thus streamlining the iterative nature of music composition. We consider and compare different music visualization techniques and explore various user interface (UI) interaction methods to facilitate the effective selection of elements from the output. We designed and implemented this model with the conclusion that it significantly enhances the user experience and allows for creating a much more sound and complete piece of music compared to the original.
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Procedurally generating a coherent and emotionally resonant piece of music can be very challenging. The Wave Function Collapse (WFC) algorithm is very effective when it comes to generating randomized patterns and maps that resemble an input sample. A version of this algorithm using a hierarchy of sections, chords and melody was used to create a model capable of generating music. In this paper, we extend the capabilities of this model to improve its utility and help composers more effectively utilize this music generation method to create engaging pieces. Our model offers improvements over previous methods by allowing composers to retain desirable elements of the music output while regenerating others, thus streamlining the iterative nature of music composition. We consider and compare different music visualization techniques and explore various user interface (UI) interaction methods to facilitate the effective selection of elements from the output. We designed and implemented this model with the conclusion that it significantly enhances the user experience and allows for creating a much more sound and complete piece of music compared to the original.