MuseReduce: A Generic Framework for Hierarchical Music Analysis

In comparison to computational linguistics, with its abundance of natural-language datasets, corpora of music analyses are rather fewer and generally smaller. This is partly due to difficulties inherent to the encoding of music analyses, whose multimodal representations—typically a combination of music notation, graphic notation, and natural language—are designed for communication between human musician-analysts, not for automated large-scale data analysis. Analyses based on hierarchical models of tonal structure, such as Heinrich Schenker’s, present additional notational and encoding challenges, since they establish relations between non- adjacent tones, and typically interpret successions of tones as expressions of abstract chordal sonorities, which may not be literally present in the music score. Building on a published XML format by Rizo and Marsden (2019), which stores analyses alongside symbolically encoded scores, this paper presents a generic graph model for reasoning about music analyses, as well as a graphical web application for creating and encoding music analyses in the aforementioned XML format. Several examples are given showing how various techniques of music analysis, primarily but not necessarily hierarchical, might be unambiguously represented through this model

A Shift In Artistic Practices through Artificial Intelligence

The explosion of content generated by Artificial Intelligence models has initiated a cultural shift in arts, music, and media, where roles are changing, values are shifting, and conventions are challenged. The readily available, vast dataset of the internet has created an environment for AI models to be trained on any content on the web. With AI models shared openly, and used by many, globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring into music, arts, and new media?Comment: Submitted to Leonardo Journa

Self-assembly of mechanoplasmonic bacterial cellulose-metal nanoparticle composites

Nanocomposites of metal nanoparticles (NPs) and bacterial nanocellulose (BC) enable fabrication of soft and biocompatible materials for optical, catalytic, electronic, and biomedical applications. Current BC-NP nanocomposites are typically prepared by in situ synthesis of the NPs or electrostatic adsorption of surface functionalized NPs, which limits possibilities to control and tune NP size, shape, concentration, and surface chemistry and influences the properties and performance of the materials. Here a self-assembly strategy is described for fabrication of complex and well-defined BC-NP composites using colloidal gold and silver NPs of different sizes, shapes, and concentrations. The self-assembly process results in nanocomposites with distinct biophysical and optical properties. In addition to antibacterial materials and materials with excellent senor performance, materials with unique mechanoplasmonic properties are developed. The homogenous incorporation of plasmonic gold NPs in the BC enables extensive modulation of the optical properties by mechanical stimuli. Compression gives rise to near-field coupling between adsorbed NPs, resulting in tunable spectral variations and enhanced broadband absorption that amplify both nonlinear optical and thermoplasmonic effects and enables novel biosensing strategies

Prototyping the Tree Automata Workbench Marbles

In [Dre09], Drewes outlines Marbles, a programming framework for working in a generic and systematic way, not only on trees, as several frameworks already exist for this purpose, but on tree recognisers, transducers, generators and other formal devices as well. This thesis presents a prototype of a proposed implementation of this framework, demontrating its functionality by using it as a base for implementing a well-known algorithm on tree transducers

A bottom-up automaton for tree adjoining languages

Current tree parsing algorithms for nonregular tree languages all have superlinear running times, possibly limiting their practical applicability. We present a bottom-up tree automaton that captures exactly the tree-adjoining languages in the non-deterministic case. The determinstic case captures a strict superset of the regular tree languages, while preserving running times linear in the size of the tree

Investigating different graph representations of semantics

Combinatory Categorial Grammar is a generic approach to the mechanical understanding of language, where movement is minimised in favour of using combinators such as B (composition) and T (type lifting) to clearly define in which ways various constituents can refer to each other. Taking the tree languages induced by the syntactic derivations and connecting the various leaves linked through the semantics, one ends up with a class of graph languages. The present work aims to point out promising avenues of research in order to investigate this class, specifically in terms of similarities with other graph-based semantic representations, such as Abstract Meaning Representations (AMR), and furthermore what graph generating or recognising formalism would be most suitable to define the class characteristics

Ordningsbevarande grafgrammatiker

The field of semantic modelling concerns formal models for semantics, that is, formal structures for the computational and algorithmic processing of meaning. This thesis concerns formal graph languages motivated by this field. In particular, we investigate two formalisms: Order-Preserving DAG Grammars (OPDG) and Order-Preserving Hyperedge Replacement Grammars (OPHG), where OPHG generalise OPDG. Graph parsing is the practise of, given a graph grammar and a graph, to determine if, and in which way, the grammar could have generated the graph. If the grammar is considered fixed, it is the non-uniform graph parsing problem, while if the grammars is considered part of the input, it is named the uniform graph parsing problem. Most graph grammars have parsing problems known to be NP-complete, or even exponential, even in the non-uniform case. We show both OPDG and OPHG to have polynomial uniform parsing problems, under certain assumptions. We also show these parsing algorithms to be suitable, not just for determining membership in graph languages, but for computing weights of graphs in graph series. Additionally, OPDG is shown to have several properties common to regular languages, such as MSO definability and MAT learnability. We moreover show a direct corresponcence between OPDG and the regular tree grammars. Finally, we present some limited practical experiments showing that real-world semantic graphs appear to mostly conform to the requirements set by OPDG, after minimal, reversible processing

Complexity and expressiveness for formal structures in Natural Language Processing

The formalized and algorithmic study of human language within the field of Natural Language Processing (NLP) has motivated much theoretical work in the related field of formal languages, in particular the subfields of grammar and automata theory. Motivated and informed by NLP, the papers in this thesis explore the connections between expressibility – that is, the ability for a formal system to define complex sets of objects – and algorithmic complexity – that is, the varying amount of effort required to analyse and utilise such systems. Our research studies formal systems working not just on strings, but on more complex structures such as trees and graphs, in particular syntax trees and semantic graphs. The field of mildly context-sensitive languages concerns attempts to find a useful class of formal languages between the context-free and context-sensitive. We study formalisms defining two candidates for this class; tree-adjoining languages and the languages defined by linear context-free rewriting systems. For the former, we specifically investigate the tree languages, and define a subclass and tree automaton with linear parsing complexity. For the latter, we use the framework of parameterized complexity theory to investigate more deeply the related parsing problems, as well as the connections between various formalisms defining the class. The field of semantic modelling aims towards formally and accurately modelling not only the syntax of natural language statements, but also the meaning. In particular, recent work in semantic graphs motivates our study of graph grammars and graph parsing. To the best of our knowledge, the formalism presented in Paper III of this thesis is the first graph grammar where the uniform parsing problem has polynomial parsing complexity, even for input graphs of unbounded node degree

Ordningsbevarande grafgrammatiker

The field of semantic modelling concerns formal models for semantics, that is, formal structures for the computational and algorithmic processing of meaning. This thesis concerns formal graph languages motivated by this field. In particular, we investigate two formalisms: Order-Preserving DAG Grammars (OPDG) and Order-Preserving Hyperedge Replacement Grammars (OPHG), where OPHG generalise OPDG. Graph parsing is the practise of, given a graph grammar and a graph, to determine if, and in which way, the grammar could have generated the graph. If the grammar is considered fixed, it is the non-uniform graph parsing problem, while if the grammars is considered part of the input, it is named the uniform graph parsing problem. Most graph grammars have parsing problems known to be NP-complete, or even exponential, even in the non-uniform case. We show both OPDG and OPHG to have polynomial uniform parsing problems, under certain assumptions. We also show these parsing algorithms to be suitable, not just for determining membership in graph languages, but for computing weights of graphs in graph series. Additionally, OPDG is shown to have several properties common to regular languages, such as MSO definability and MAT learnability. We moreover show a direct corresponcence between OPDG and the regular tree grammars. Finally, we present some limited practical experiments showing that real-world semantic graphs appear to mostly conform to the requirements set by OPDG, after minimal, reversible processing

Ett enat men delat Tyskland : En artikelserie om hur DDR påverkar människor i östra Tyskland mer än två decennier efter Berlinmurens fall.

I Tyskland är det skillnad på väst och öst. I väst tjänar människorna mer pengar och i öst har de svårare att få arbeten. Det finns också en tydlig skillnad på människors sätt att se tillbaka på Berlinmuren och det forna landet DDR. I väst är gemene man oerhört negativt inställd till DDR - i öst är det väldigt annorlunda. Där ser många positivt på den diktatur som förknippas med Stasi, gränsvakter och tomma mathyllor. Under våren har jag bott i den östtyska staden Leipzig. Här har jag talat med forskare, politiker och vanliga människor för att få kunskap om hur människor i östra Tyskland ser på DDR och sin identitet i det enade Tyskland - och förklaringar på varför de ser så positivt på den forna diktaturen DDR. Arbetet fokuserar på dels människor som växt upp i DDR och som var med när Berlinmuren föll. Men även på unga människor i östra Tyskland som aldrig levt i DDR - som trots det påverkas av det forna landet