Taking its point of departure in the recent developments in the field of digital humanities and the increasing automatisation of scholarly workflows, this study explores the implications of digital approaches to textual traditions for the broader field of textual scholarship. It argues that the relative simplicity of creating computergenerated stemmas allows us to view the stemma codicum as a research tool rather than the final product of our scholarly investigation. Using the Old Norse saga of Hrómundur as a case study, this article demonstrates that stemmas can serve as a starting point for exploring textual traditions further. In doing so, they enable us to address research questions that otherwise remain unanswered. The article is accompanied by datasets used to generate stemmas for the Hrómundar saga tradition as well as two custom Python scripts. The scripts are designed to convert XML-based textual data, encoded according to the TEI Guidelines, into the input format used for the analysis in the PHYLIP package to generate unrooted trees of relationships between texts.

The recently proposed Quantum Neuron Born Machine (QNBM) has demonstrated quality initial performance as the first quantum generative machine learning (ML) model proposed with non-linear activations. However, previous investigations have been limited in scope with regards to the model's learnability and simulatability. In this work, we make a considerable leap forward by providing an extensive deep dive into the QNBM's potential as a generative model. We first demonstrate that the QNBM's network representation makes it non-trivial to be classically efficiently simulated. Following this result, we showcase the model's ability to learn (express and train on) a wider set of probability distributions, and benchmark the performance against a classical Restricted Boltzmann Machine (RBM). The QNBM is able to outperform this classical model on all distributions, even for the most optimally trained RBM among our simulations. Specifically, the QNBM outperforms the RBM with an improvement factor of 75.3x, 6.4x, and 3.5x for the discrete Gaussian, cardinality-constrained, and Bars and Stripes distributions respectively. Lastly, we conduct an initial investigation into the model's generalization capabilities and use a KL test to show that the model is able to approximate the ground truth probability distribution more closely than the training distribution when given access to a limited amount of data. Overall, we put forth a stronger case in support of using the QNBM for larger-scale generative tasks.

With hours of extra time on their hands because of social distancing and quarantine, new players to Nintendo's "Animal Crossing: New Horizons" like Ash Wolf, also known on Twitter as Ninji, have been drawn to the slow, laid-back life simulator that allows them to build idyllic islands, decorate their homes, visit friends and more. "People are using this as a sort of escape," Wolf said. "I joked when I first got the game that it was literally the only thing giving me structure in my life." But this influx of new users produced an unexpected evolution, recalibrating the game's serene speed to a fast-paced hustle one player compared to Wall Street. Animal Crossing isn't designed for such gameplay -- in fact, it purposefully slows players down by design. Yet the game's community became obsessed with optimization, in the process exploiting features meant to encourage day-by-day progress. Now, they've become a dominant part of the audience, finding loopholes or strategies to get rich fast.

A procedure for unfolding the true distribution from experimental data is presented. Machine learning methods are applied for simultaneous identification of an apparatus function and solving of an inverse problem. A priori information about the true distribution from theory or previous experiments is used for Monte-Carlo simulation of the training sample. The training sample can be used to calculate a transformation from the true distribution to the measured one. This transformation provides a robust solution for an unfolding problem with minimal biases and statistical errors for the set of distributions used to create the training sample. The dimensionality of the solved problem can be arbitrary. A numerical example is presented to illustrate and validate the procedure.

Imbalanced data sets containing much more background than signal instances are very common in particle physics, and will also be characteristic for the upcoming analyses of LHC data. Following up the work presented at ACAT 2008, we use the multivariate technique presented there (a rule growing algorithm with the meta-methods bagging and instance weighting) on much more imbalanced data sets, especially a selection of D0 decays without the use of particle identification. It turns out that the quality of the result strongly depends on the number of background instances used for training. We discuss methods to exploit this in order to improve the results significantly, and how to handle and reduce the size of large training sets without loss of result quality in general. We will also comment on how to take into account statistical fluctuation in receiver operation characteristic curves (ROC) for comparing classifier methods.