Aspect-Controlled Neural Argument Generation

We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that our generation model is able to generate high-quality, aspect-specific arguments. Moreover, these arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments. We publish all datasets and code to fine-tune the language model

Self and other in black and white: slaves' letters and the epistolary cultures of American slavery c.1730-1865

Understanding American slavery, which for me means at least trying to comprehend how African Americans made it survivable and European Americans made it conscionable, is no easy task, and if I have achieved anything with the foregoing discussion I hope it has been to present a history of the epistolary cultures of slavery which complicates rather than simplifies this story. For to the history of American slavery the slave letters are just that, a complication. They complicate our view of the relationships between bondspeople and of the ways in which masters and slaves related to one another. So too do they complicate our understanding of how their authors saw and constructed themselves and defined and thought about others. Furthermore, they also raise significant questions about the nature of the slave community and the linkages and disjunctures that existed between the cultures African and European Americans constructed in the shadow of slavery, and thus present useful complications to our thinking about the formation of these cultures and the ways in which each sought to appropriate and subvert the cultural practices of the other. In this regard, they also raise complications regarding the transformations of slavery, for while we may view the epistolary and archival cultures that are apparent in the nineteenth century as products of paternalism and the sentimentality that lay at the heart of this project, the intimations of continuity in terms of enslaved people's self-perceptions that are afforded by a comparison of antebellum letters with those that were written in the late colonial and early republican eras suggest that the transformation that slaveholders worked on themselves was perhaps rather less significant for the victims of their slaveholding.Perhaps most importantly, however, they complicate the idea of resistance, a concept that has proved of central importance to studies of slavery and yet which often seems to be used either as a coded reference to a particular concept of masculinity, as Baptist argues, or else in a rather nebulous way in order to give meaning to almost every aspect of slaves' behaviour which did not conform to the o wishes of their masters. But if everything from the slaves' economy to their medicinal practice, from playing dumb to committing infanticide is to be categorized as a form of resistance, it is important to consider whether those that committed these acts were actively engaging in forms of resistance, which is to say in Bhabha's terms, self-consciously situating their actions "within the rules of recognition of dominating discourses," in order to critique and counter "deferential relations of power," or whether they were merely, as Stampp would have it, "unconscious reflections of the o character that slavery had given." As should be quite apparent, my own interpretation suggests the former and not the latter position, and while the slave letters may often be mute on the specifics of such actions, what they do reveal is that the way in which slaves conducted themselves in their epistolary dealings with slaveowners and their disciplinary agents were intimately informed by sophisticated understandings of the workings of power.This is not to suggest, however, that my imaginary archive of slaves' letters is somehow to be treated as the Rosetta stone for understanding slaves' behaviour. It is not. For one thing it is inherently limited, not only because of the preponderance of letters written by slaves who occupied particularly ambivalent positions within the power structures constructed by their masters, but also because of how much it omits. Furthermore, as I have been at pains to stress throughout, it is much more of a record of the power to archive than of the power to correspond, and while this makes a reading of the historical archives from which it has been constructed a profitable way to analyse the archivists who created them, it nonetheless means that as a set of sources the value we attach to the letters must be measured against the reasons for their survival. Nor am I suggesting that this archive should be used to the exclusion of others, and as my own evidentiary choices will have demonstrated, the meanings of the slaves' letters are best analysed by reading them alongside other forms of testimony, whether this comes from ex-slaves, masters or other observers.But even with such limitations in mind, the letters nonetheless afford us an opportunity to see at close quarters what were surely highly significant negotiations over identity for those slaves and masters that they involved, and they have the advantage over many other sources by being the texts of such negotiations as opposed to texts written about them. As such I think it is legitimate to suggest that what we can learn from them may well be representative of similar negotiations which took place beyond the bounds of the epistolary cultures that have been the subject of this thesis. Indeed, I would suggest that the "contextual, contested and contingent" identities that bondspeople and owners constructed for themselves and each other in these negotiations constituted a most important aspect of the competition between domination and resistance, and thus, as I suggested in the introduction, are a useful way to open up conversations about other aspects of resistance and other ways in which African Americans sought to make their enslavement bearable whilst their masters sought to make it excusable.But of course it might be argued that in their literacy, or at least their letter writing, these slaves were transformed, for the textual transcription of identity and the opportunities this allows for its refinement, revision and correction perhaps makes the writing laboratory and indeed the archival laboratory that is its counterpart, such specific and exceptional conceptual spaces as to be completely unrepresentative of the venues in which other slaves had to construct or perform identity.But since so much of human interaction, even amongst the most highly literate of people, is in fact spoken and not written and consists in gesture and action and not in scripture and inscription, one is of course tempted merely to dismiss this putative problem by repeating C. Vann Woodward's oft-quoted defence of slave testimony in all of its subjectivity, contingency and bias: "as if the same objection did not exist to the testimony of the slaveowners." But in fact, Miller is actually raising a rather more profound point, namely the question of whether it fundamentally alters an individual to conceive of language textually rather than orally, and if we are to utilise the texts generated by bondspeople as a measure of the way in which enslaved African Americans, both literate and non-literate, constructed the world around them and constructed themselves within that world, then this is a crucial issue.Without doubt, many philosophers of language regard the transition from orality to textuality as a fundamental paradigm shift since written language is amenable to "microanalysis, annotation, revision, rearrangement and interpretation" by both readers and writers in a way that oral dialogue, which only exists in an historical present, can never be. By extension, therefore, the interior life of the self - which is assumed to be at least partially signified in linguistic terms - is also transformed when it is expressed in a written text to be analysed, reviewed and re¬ written/re-read. Moreover, it can certainly be argued that slaves' own perceptions of the transformatory effects of literacy provide the cue for applying such an analysis to slave writings. Douglass refers to his acquisition of literacy as "the path from slavery to freedom," a pivotal event that allowed him to redefine and reconstitute himself as a subject rather than an object.I think that this concept of a fundamental distinction between the textual and the oral is artificial, and as Paul Ricoeur argues, one may construct an oral hermeneutic model which shares many features with textual hermeneutics in that by memorisation oral discourse may be "fixed in such a way that memory appears as the support of an inscription similar to that provided by external marks."9 This is not to suggest that textualising one's self (and others) does not have potentially transformatory, revelatory effects, but rather that to imagine that this is only true of scriptural textualisation is a mistake. For while written texts do indeed have the potential to allow their authors to reflect on their self-constructions in a unique way, but there are many other mirrors in which to style and restyle one's perception of both self and other and thus such textual construction and reconstruction should not be seen as exceptional or atypical, but in fact merely as a manifestation of a very normal, very human process. In predominantly oral cultures, however, it is frequently unrecoverable and thus it is the fact that the slave letters afford us an opportunity to examine this everyday sociocultural process that is exceptional

Experimental Comparison of two Active Vibration Control Approaches: Velocity Feedback and Negative Capacitance Shunt Damping

This paper outlines a direct, experimental comparison between two established active vibration control techniques. Active vibration control methods, many of which rely upon piezoelectric patches as actuators and/or sensors, have been widely studied, showing many advantages over passive techniques. However, few direct comparisons between different active vibration control methods have been made to determine the performance benefit of one method over another. For the comparison here, the first control method, velocity feedback, is implemented using four accelerometers that act as sensors along with an analog control circuit which drives a piezoelectric actuator. The second method, negative capacitance shunt damping, consists of a basic analog circuit which utilizes a single piezoelectric patch as both a sensor and actuator. Both of these control methods are implemented individually using the same piezoelectric actuator attached to a clamped Plexiglas window. To assess the performance of each control method, the spatially averaged velocity of the window is compared to an uncontrolled response

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1's proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of the methods. Therefore, we propose a new F1-based metric yielding a changed system ranking. Next, we compare the features and architectures used, which leads to a novel feature-rich stacked LSTM model that performs on par with the best systems, but is superior in predicting minority classes. To understand the methods' ability to generalize, we derive a new dataset and perform both in-domain and cross-domain experiments. Our qualitative and quantitative study helps interpreting the original FNC-1 scores and understand which features help improving performance and why. Our new dataset and all source code used during the reproduction study are publicly available for future research

Graph-based Analysis of Dynamic Systems

The analysis of dynamic systems provides insights into their time-dependent characteristics. This enables us to monitor, evaluate, and improve systems from various areas. They are often represented as graphs that model the system's components and their relations. The analysis of the resulting dynamic graphs yields great insights into the system's underlying structure, its characteristics, as well as properties of single components. The interpretation of these results can help us understand how a system works and how parameters influence its performance. This knowledge supports the design of new systems and the improvement of existing ones. The main issue in this scenario is the performance of analyzing the dynamic graph to obtain relevant properties. While various approaches have been developed to analyze dynamic graphs, it is not always clear which one performs best for the analysis of a specific graph. The runtime also depends on many other factors, including the size and topology of the graph, the frequency of changes, and the data structures used to represent the graph in memory. While the benefits and drawbacks of many data structures are well-known, their runtime is hard to predict when used for the representation of dynamic graphs. Hence, tools are required to benchmark and compare different algorithms for the computation of graph properties and data structures for the representation of dynamic graphs in memory. Based on deeper insights into their performance, new algorithms can be developed and efficient data structures can be selected. In this thesis, we present four contributions to tackle these problems: A benchmarking framework for dynamic graph analysis, novel algorithms for the efficient analysis of dynamic graphs, an approach for the parallelization of dynamic graph analysis, and a novel paradigm to select and adapt graph data structures. In addition, we present three use cases from the areas of social, computer, and biological networks to illustrate the great insights provided by their graph-based analysis. We present a new benchmarking framework for the analysis of dynamic graphs, the Dynamic Network Analyzer (DNA). It provides tools to benchmark and compare different algorithms for the analysis of dynamic graphs as well as the data structures used to represent them in memory. DNA supports the development of new algorithms and the automatic verification of their results. Its visualization component provides different ways to represent dynamic graphs and the results of their analysis. We introduce three new stream-based algorithms for the analysis of dynamic graphs. We evaluate their performance on synthetic as well as real-world dynamic graphs and compare their runtimes to snapshot-based algorithms. Our results show great performance gains for all three algorithms. The new stream-based algorithm StreaM_k, which counts the frequencies of k-vertex motifs, achieves speedups up to 19,043 x for synthetic and 2882 x for real-world datasets. We present a novel approach for the distributed processing of dynamic graphs, called parallel Dynamic Graph Analysis (pDNA). To analyze a dynamic graph, the work is distributed by a partitioner that creates subgraphs and assigns them to workers. They compute the properties of their respective subgraph using standard algorithms. Their results are used by the collator component to merge them to the properties of the original graph. We evaluate the performance of pDNA for the computation of five graph properties on two real-world dynamic graphs with up to 32 workers. Our approach achieves great speedups, especially for the analysis of complex graph measures. We introduce two novel approaches for the selection of efficient graph data structures. The compile-time approach estimates the workload of an analysis after an initial profiling phase and recommends efficient data structures based on benchmarking results. It achieves speedups of up to 5.4 x over baseline data structure configurations for the analysis of real-word dynamic graphs. The run-time approach monitors the workload during analysis and exchanges the graph representation if it finds a configuration that promises to be more efficient for the current workload. Compared to baseline configurations, it achieves speedups up to 7.3 x for the analysis of a synthetic workload. Our contributions provide novel approaches for the efficient analysis of dynamic graphs and tools to further investigate the trade-offs between different factors that influence the performance.:1 Introduction 2 Notation and Terminology 3 Related Work 4 DNA - Dynamic Network Analyzer 5 Algorithms 6 Parallel Dynamic Network Analysis 7 Selection of Efficient Graph Data Structures 8 Use Cases 9 Conclusion A DNA - Dynamic Network Analyzer B Algorithms C Selection of Efficient Graph Data Structures D Parallel Dynamic Network Analysis E Graph-based Intrusion Detection System F Molecular Dynamic

Does Amazon Exercise Its Market Power? Evidence from Toys“R”Us

Since its founding, Amazon has established a reputation for being consumer friendly by consistently offering lower prices than its market position would seem to allow. However, recent antitrust concerns about dominant online platforms have revived questions about whether Amazon’s growing market share threatens consumer welfare. Given its reputation, regulators have proposed a new focus on conduct unrelated to prices. We ask whether such a move is premature. Using the sudden and unanticipated US exit of Toys“R”Us as a natural experiment, we find that Amazon’s toy prices on its US site increased by almost 5 percent in the wake of the exit relative to similar products and to toys on its Canadian site. Thus, despite Amazon’s long-standing reputation, it may exploit increases in market power in traditional ways as competing retailers cease operating

Graph-based Analysis of Dynamic Systems

The analysis of dynamic systems provides insights into their time-dependent characteristics. This enables us to monitor, evaluate, and improve systems from various areas. They are often represented as graphs that model the system's components and their relations. The analysis of the resulting dynamic graphs yields great insights into the system's underlying structure, its characteristics, as well as properties of single components. The interpretation of these results can help us understand how a system works and how parameters influence its performance. This knowledge supports the design of new systems and the improvement of existing ones. The main issue in this scenario is the performance of analyzing the dynamic graph to obtain relevant properties. While various approaches have been developed to analyze dynamic graphs, it is not always clear which one performs best for the analysis of a specific graph. The runtime also depends on many other factors, including the size and topology of the graph, the frequency of changes, and the data structures used to represent the graph in memory. While the benefits and drawbacks of many data structures are well-known, their runtime is hard to predict when used for the representation of dynamic graphs. Hence, tools are required to benchmark and compare different algorithms for the computation of graph properties and data structures for the representation of dynamic graphs in memory. Based on deeper insights into their performance, new algorithms can be developed and efficient data structures can be selected. In this thesis, we present four contributions to tackle these problems: A benchmarking framework for dynamic graph analysis, novel algorithms for the efficient analysis of dynamic graphs, an approach for the parallelization of dynamic graph analysis, and a novel paradigm to select and adapt graph data structures. In addition, we present three use cases from the areas of social, computer, and biological networks to illustrate the great insights provided by their graph-based analysis. We present a new benchmarking framework for the analysis of dynamic graphs, the Dynamic Network Analyzer (DNA). It provides tools to benchmark and compare different algorithms for the analysis of dynamic graphs as well as the data structures used to represent them in memory. DNA supports the development of new algorithms and the automatic verification of their results. Its visualization component provides different ways to represent dynamic graphs and the results of their analysis. We introduce three new stream-based algorithms for the analysis of dynamic graphs. We evaluate their performance on synthetic as well as real-world dynamic graphs and compare their runtimes to snapshot-based algorithms. Our results show great performance gains for all three algorithms. The new stream-based algorithm StreaM_k, which counts the frequencies of k-vertex motifs, achieves speedups up to 19,043 x for synthetic and 2882 x for real-world datasets. We present a novel approach for the distributed processing of dynamic graphs, called parallel Dynamic Graph Analysis (pDNA). To analyze a dynamic graph, the work is distributed by a partitioner that creates subgraphs and assigns them to workers. They compute the properties of their respective subgraph using standard algorithms. Their results are used by the collator component to merge them to the properties of the original graph. We evaluate the performance of pDNA for the computation of five graph properties on two real-world dynamic graphs with up to 32 workers. Our approach achieves great speedups, especially for the analysis of complex graph measures. We introduce two novel approaches for the selection of efficient graph data structures. The compile-time approach estimates the workload of an analysis after an initial profiling phase and recommends efficient data structures based on benchmarking results. It achieves speedups of up to 5.4 x over baseline data structure configurations for the analysis of real-word dynamic graphs. The run-time approach monitors the workload during analysis and exchanges the graph representation if it finds a configuration that promises to be more efficient for the current workload. Compared to baseline configurations, it achieves speedups up to 7.3 x for the analysis of a synthetic workload. Our contributions provide novel approaches for the efficient analysis of dynamic graphs and tools to further investigate the trade-offs between different factors that influence the performance.:1 Introduction 2 Notation and Terminology 3 Related Work 4 DNA - Dynamic Network Analyzer 5 Algorithms 6 Parallel Dynamic Network Analysis 7 Selection of Efficient Graph Data Structures 8 Use Cases 9 Conclusion A DNA - Dynamic Network Analyzer B Algorithms C Selection of Efficient Graph Data Structures D Parallel Dynamic Network Analysis E Graph-based Intrusion Detection System F Molecular Dynamic

Numerical Study of Transmission Loss Through a Slow Gas Layer Adjacent to a Plate

This paper describes a systematic numerical investigation of the sound transmission loss through a multilayer system consisting of a bagged gas and lightweight panel. The goal of the study is to better understand the effect of the gas on transmission loss and determine whether a gas with a slow speed of sound is beneficial for noise control applications. As part of the study, the density and speed of sound of the gas are varied independently to assess the impact of each on transmission loss. Results show that near grazing incidence the plane wave transmission loss through the multilayer system is more sensitive to the speed of sound than the density of the gas. In addition, it was found that a slow wave speed in the bagged gas provides more low-frequency transmission loss benefit than a fast wave speed. At low angles of incidence, close to the plate normal, the benefit is due to the reduction of the characteristic impedance of the gas. At high angles of incidence, the benefit is attributed to the fact that the incident waves at the air/gas interface are bent towards the surface normal. Since transmission loss is angle dependent, refraction in the slow gas layer results in a significant improvement in the transmission loss at high angles of incidence

Focusing Knowledge-based Graph Argument Mining via Topic Modeling

Decision-making usually takes five steps: identifying the problem, collecting data, extracting evidence, identifying pro and con arguments, and making decisions. Focusing on extracting evidence, this paper presents a hybrid model that combines latent Dirichlet allocation and word embeddings to obtain external knowledge from structured and unstructured data. We study the task of sentence-level argument mining, as arguments mostly require some degree of world knowledge to be identified and understood. Given a topic and a sentence, the goal is to classify whether a sentence represents an argument in regard to the topic. We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata, building a graph based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence. Also, we build a second graph based on topic-specific articles found via Google to tackle the general incompleteness of structured knowledge bases. Combining these graphs, we obtain a graph-based model which, as our evaluation shows, successfully capitalizes on both structured and unstructured data