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This presentation explores how n-grams can be used to automatically classify and learn about music. An overall discussion is provided of various ways in which n-grams can be adapted for use with digital scores, and of how musically meaningful features can be extracted from them. The jSymbolic 3.0 alpha prototype feature extractor is then used in three sets of music classification experiments investigating how n-gram features perform relative to and combined with other types of features extracted from symbolic music files., Funded by the FRQSC and SSHRC.

We propose and conceptualize an approach for literary network analysis that does not rely on co-appearance, but rather what can be called semantic context: characters are connected if they share a semantic neighborhood, or similar meaning-based contexts, even if they are distant in the text sequence.

We explore the hypothetical distinction between texts of priestly (P) and non-priestly (nonP) origin in Genesis and Exodus with an independent computational and statistical methodology. We introduce a novel textual stylometry pipeline, where we intertwine descriptive and inferential statistics for distinction optimization, quantification of uncertainty, and feature importance analysis.

During the pandemic, the idea that digital adoption would revolutionise audience engagement became widespread in the museum sector. This paper explores whether this is true, utilising tools and debates from the digital humanities to analyse the online content and audiences of a representative sample of 315 museums in the UK.

We present a modeling of document dates such that it can allow scholars to express uncertainty when annotating data, act as a differentiable loss function for training models and allow for unbiased interpretable performance evaluation under uncertainty.

Widespread in the Middle Ages, anonymity is the hallmark of heraldic literature. Only rarely indeed do heralds claim ownership, and, when they do, they generally use a pseudonym.This paper intends to answer the question of whether or not two pieces of topical poetry can be ascribed to Nicaise Ladam, herald of arms of the Burgundian-Habsburgs' House.

This contribution presents the difficulties encountered and methods to overcome them when using ready-to-use tools for the elaboration of a processing chain going from OCR to NER and then to the cartographic representation of spaces mentioned in literary texts.

In early modern Europe information circulated in the form of regular manuscript newsletters. However, most of the once existing news sheets did not survive. This study will present novel methods (random sampling with replacement, aka bootstrapping, and Bayesian statistics) to harness this lost corpus and to analyze its gender dimensions.

This project draws on the Index of DH Conferences compiled by Scott Weingart, Matthew Lincoln, and Nickoal Eichmann-Kalwara that covers approximately 60 years of DH conferences, 7300 presentations, 8650 different authors, 1850 institutions, and 8 countries (Lincoln et al., 2021). The objective was to assess how the burden of conference travel— represented by a measure of distance traversed—differs based on race, gender, work type (keynote, paper, workshop, and poster), and previous authorship.

Digitized heritage texts are important for computational tasks. Regrettably, OCR software used for digitizing texts is not flawless. Ngram models are a promising method to detect and correct OCR errors, but may be corpora specific. Our findings will indicate if ngram models can be used interchangeably between different corpera.

This paper investigates the relationship between emotions and literary periods, as well as other factors in German poetry between 1850-1920 through formal modeling and Bayesian analysis.

About representing characters in text. We present an analysis of pronouns, nouns and names in Norwegian novels. The purpose is to arrive at a discourse representation of texts, in terms of characters and their relationships and properties. We use the Norwegian national library and its digital resources for this purpose.

We consider the problem of estimating a signal from measurements obtained via a generalized linear model. We focus on estimators based on approximate message passing (AMP), a family of iterative algorithms with many appealing features: the performance of AMP in the high-dimensional limit can be succinctly characterized under suitable model assumptions; AMP can also be tailored to the empirical distribution of the signal entries, and for a wide class of estimation problems, AMP is conjectured to be optimal among all polynomial-time algorithms. However, a major issue of AMP is that in many models (such as phase retrieval), it requires an initialization correlated with the ground-truth signal and independent from the measurement matrix. Assuming that such an initialization is available is typically not realistic. In this paper, we solve this problem by proposing an AMP algorithm initialized with a spectral estimator. With such an initialization, the standard AMP analysis fails since the spectral estimator depends in a complicated way on the design matrix. Our main contribution is a rigorous characterization of the performance of AMP with spectral initialization in the high-dimensional limit. The key technical idea is to define and analyze a two-phase artificial AMP algorithm that first produces the spectral estimator, and then closely approximates the iterates of the true AMP. We also provide numerical results that demonstrate the validity of the proposed approach., Comment: 38 pages, 5 figures, AISTATS 2021

The last few years have seen considerable mathematical progress concerning the asymptotic structure of gravitational radiation in dynamical, astrophysical spacetimes. In this paper, we distil some of the key ideas from recent works and assemble them in a new way in order to make them more accessible to the wider general relativity community. We also announce some new physical findings in this process. First, we introduce the conserved $f(r)$-modified Newman--Penrose charges on asymptotically flat spacetimes, and we show that these charges provide a dictionary that relates asymptotics of massless, general spin fields in different regions: Asymptotic behaviour near $i^+$ ("late-time tails") can be read off from asymptotic behaviour towards $\mathcal I^+$, and, similarly, asymptotic behaviour towards $\mathcal I^+$ can be read off from asymptotic behaviour near $i^-$ or $\mathcal I^-$. Using this dictionary, we then explain how: (I) the quadrupole approximation for a system of $N$ infalling masses from $i^-$ causes the "peeling property towards $\mathcal I^+$" to be violated, and (II) this failure of peeling results in deviations from the usual predictions for tails in the late-time behaviour of gravitational radiation: Instead of the Price's law rate $r\Psi^{[4]}|_{\mathcal I^+}\sim u^{-6}$ as~$u\to\infty$, we predict that $r\Psi^{[4]}|_{\mathcal I^+}\sim u^{-4}$, with the coefficient of this latter decay rate being a multiple of the monopole and quadrupole moments of the matter distribution in the infinite past., Comment: 24 pages, 8 figures. Comments welcome!

We study linear inverse problems under the premise that the forward operator is not at hand but given indirectly through some input-output training pairs. We demonstrate that regularization by projection and variational regularization can be formulated by using the training data only and without making use of the forward operator. We study convergence and stability of the regularized solutions in view of Seidman (1980 J. Optim. Theory Appl. 30 535), who showed that regularization by projection is not convergent in general, by giving some insight on the generality of Seidman’s nonconvergence example. Moreover, we show, analytically and numerically, that regularization by projection is indeed capable of learning linear operators, such as the Radon transform.

We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems. An encoder-decoder type network defines a regularizer consisting of a penalty term that enforces regularity in the encoder domain, augmented by a penalty that penalizes the distance to the signal manifold. We present a rigorous convergence analysis including stability estimates and convergence rates. For that purpose, we prove the coercivity of the regularizer used without requiring explicit coercivity assumptions for the networks involved. We propose a possible realization together with a network architecture and a modular training strategy. Applications to sparse-view and low-dose CT show that aNETT achieves results comparable to state-of-the-art deep-learning-based reconstruction methods. Unlike learned iterative methods, aNETT does not require repeated application of the forward and adjoint models during training, which enables the use of aNETT for inverse problems with numerically expensive forward models. Furthermore, we show that aNETT trained on coarsely sampled data can leverage an increased sampling rate without the need for retraining.

Funder: Cambridge Commonwealth, European and International Trust; doi: https://doi.org/10.13039/501100003343, Funder: Natural Sciences and Engineering Research Council of Canada (NSERC), We develop a resource theory of symmetric distinguishability, the fundamental objects of which are elementary quantum information sources, i.e., sources that emit one of two possible quantum states with given prior probabilities. Such a source can be represented by a classical-quantum state of a composite system $XA$, corresponding to an ensemble of two quantum states, with $X$ being classical and $A$ being quantum. We study the resource theory for two different classes of free operations: $(i)$ ${\rm{CPTP}}_A$, which consists of quantum channels acting only on $A$, and $(ii)$ conditional doubly stochastic (CDS) maps acting on $XA$. We introduce the notion of symmetric distinguishability of an elementary source and prove that it is a monotone under both these classes of free operations. We study the tasks of distillation and dilution of symmetric distinguishability, both in the one-shot and asymptotic regimes. We prove that in the asymptotic regime, the optimal rate of converting one elementary source to another is equal to the ratio of their quantum Chernoff divergences, under both these classes of free operations. This imparts a new operational interpretation to the quantum Chernoff divergence. We also obtain interesting operational interpretations of the Thompson metric, in the context of the dilution of symmetric distinguishability.

In an attempt to understand the soliton resolution conjecture, we consider the Sine-Gordon equation on a spherically symmetric wormhole spacetime. We show that within each topological sector (indexed by a positive integer degree $n$) there exists a unique linearly stable soliton, which we call the $n$-kink. We give numerical evidence that the $n$-kink is a global attractor in the evolution of any smooth, finite energy solutions of degree $n$. When the radius of the wormhole throat $a$ is large enough, the convergence to the $n$-kink is shown to be governed by internal modes that slowly decay due to the resonant transfer of energy to radiation. We compute the exact asymptotics of this relaxation process for the $1$-kink using the Soffer-Weinstein weakly nonlinear perturbation theory., Comment: 19 pages, 10 figures, final version

Using various techniques from dynamical systems theory, we rigorously study an experimentally validated model by [Barkley et al 2015 Nature 526 550–3], which describes the rise of turbulent pipe flow via a PDE system of reduced complexity. The fast evolution of turbulence is governed by reaction-diffusion dynamics coupled to the centerline velocity, which evolves with advection of Burgers’ type and a slow relaminarization term. Applying to this model a spatial dynamics ansatz and geometric singular perturbation theory, we prove the existence of a heteroclinic loop between a turbulent and a laminar steady state and establish a cascade of bifurcations of various traveling waves mediating the transition to turbulence. The most complicated behaviour can be found in an intermediate Reynolds number regime, where the traveling waves exhibit arbitrarily long periodic-like dynamics indicating the onset of chaos. Our analysis provides a systematic mathematical approach to identifying the transition to spatio–temporal turbulent structures that may also be applicable to other models arising in fluid dynamics.

Strain engineering is used to obtain desirable materials properties in a range of modern technologies. Direct nanoscale measurement of the three-dimensional strain tensor field within these materials has however been limited by a lack of suitable experimental techniques and data analysis tools. Scanning electron diffraction has emerged as a powerful tool for obtaining two-dimensional maps of strain components perpendicular to the incident electron beam direction. Extension of this method to recover the full three-dimensional strain tensor field has been restricted though by the absence of a formal framework for tensor tomography using such data. Here, we show that it is possible to reconstruct the full non-symmetric strain tensor field as the solution to an ill-posed tensor tomography inverse problem. We then demonstrate the properties of this tomography problem both analytically and computationally, highlighting why incorporating precession to perform scanning precession electron diffraction may be important. We establish a general framework for non-symmetric tensor tomography and demonstrate computationally its applicability for achieving strain tomography with scanning precession electron diffraction data.

Funder: University of Cambridge Centre for Doctoral Training, We consider the nearest-neighbour tight binding model of the honeycomb lattice in magnetic fields and discover surprizing new analytical results that fully explain fractal spectra and experimentally observed asymmetries in the density of states of molecular graphene. We describe a fractal Cantor spectrum for irrational magnetic flux through a honeycomb, and establish the existence of zero energy Dirac cones for each rational flux with fully explicit estimates on the cone angle. Our results give a substantially more refined description of subtleties in the de Haas–van Alphen and quantum Hall effects, and provide the first quantitative bounds on transport coefficients for the tight-binding model under disorder.

In this work we introduce the r-Hunter-Saxton equation, a generalisation of the Hunter-Saxton equation arising as extremals of an action principle posed in L_r. We characterise solutions to the Cauchy problem, quantifying the blow-up time and studying various symmetry reductions. We construct piecewise linear functions and show that they are weak solutions to the r-Hunter-Saxton equation., Revised after referee comments

Funder: John Simon Guggenheim Memorial Foundation; doi: https://doi.org/10.13039/100005851, In this work we study the global solvability of the primitive equations for the atmosphere coupled to moisture dynamics with phase changes for warm clouds, where water is present in the form of water vapor and in the liquid state as cloud water and rain water. This moisture model contains closures for the phase changes condensation and evaporation, as well as the processes of auto conversion of cloud water into rainwater and the collection of cloud water by the falling rain droplets. It has been used by Klein and Majda in [17] and corresponds to a basic form of the bulk microphysics closure in the spirit of Kessler [16] and Grabowski and Smolarkiewicz [12]. The moisture balances are strongly coupled to the thermodynamic equation via the latent heat associated to the phase changes. In [14] we assumed the velocity field to be given and proved rigorously the global existence and uniqueness of uniformly bounded solutions of the moisture balances coupled to the thermodynamic equation. In this paper we present the solvability of a full moist atmospheric flow model, where the moisture model is coupled to the primitive equations of atmospherical dynamics governing the velocity field. For the derivation of a priori estimates for the velocity field we thereby use the ideas of Cao and Titi [6], who succeeded in proving the global solvability of the primitive equations.