Information Theoretic Principles of Universal Discrete Denoising

Today, the internet makes tremendous amounts of data widely available. Often, the same information is behind multiple different available data sets. This lends growing importance to latent variable models that try to learn the hidden information from the available imperfect versions. For example, social media platforms can contain an abundance of pictures of the same person or object, yet all of which are taken from different perspectives. In a simplified scenario, one may consider pictures taken from the same perspective, which are distorted by noise. This latter application allows for a rigorous mathematical treatment, which is the content of this contribution. We apply a recently developed method of dependent component analysis to image denoising when multiple distorted copies of one and the same image are available, each being corrupted by a different and unknown noise process. In a simplified scenario, we assume that the distorted image is corrupted by noise that acts independently on each pixel. We answer completely the question of how to perform optimal denoising, when at least three distorted copies are available: First we define optimality of an algorithm in the presented scenario, and then we describe an aymptotically optimal universal discrete denoising algorithm (UDDA). In the case of binary data and binary symmetric noise, we develop a simplified variant of the algorithm, dubbed BUDDA, which we prove to attain universal denoising uniformly.Comment: 10 pages, 6 figure

Non-parametric regression for patch-based fluorescence microscopy image sequence denoising

We present a non-parametric regression method for denoising 3D image sequences acquired in fluorescence microscopy. The proposed method exploits 3D+time information to improve the signal-to-noise ratio of images corrupted by mixed Poisson-Gaussian noise. A variance stabilization transform is first applied to the image-data to introduce independence between the mean and variance. This pre-processing requires the knowledge of parameters related to the acquisition system, also estimated in our approach. In a second step, we propose an original statistical patch-based framework for noise reduction and preservation of space-time discontinuities. In our study, discontinuities are related to small moving spots with high velocity observed in fluorescence video-microscopy. The idea is to minimize an objective nonlocal energy functional involving spatio-temporal image patches. The minimizer has a simple form and is defined as the weighted average of input data taken in spatially-varying neighborhoods. The size of each neighborhood is optimized to improve the performance of the pointwise estimator. The performance of the algorithm which requires no motion estimation, is then demonstrated on both synthetic and real image sequences using qualitative and quantitative criteria

The Neural Mechanisms Supporting Structure and Inter-Brain Connectivity In Natural Conversation

Conversation is the height of human communication and social interaction, yet little is known about the neural mechanisms supporting it. To date, there have been no ecologically valid neuroimaging studies of conversation, and for good reason. Until recently, imaging techniques were hindered by artifact related to speech production. Now that we can circumvent this problem, I attempt to uncover the neural correlates of multiple aspects of conversation, including coordinating speaker change, the effect of conversation type (e.g. cooperative or argumentative) on inter-brain coupling, and the relationship between this coupling and social coherence. Pairs of individuals underwent simultaneous fMRI brain scans while they engaged in a series of unscripted conversations, for a total of 40 pairs (80 individuals). The first two studies in this dissertation lay a foundation by outlining brain regions supporting comprehension and production in both narrative and conversation - two aspects of discourse level communication. The subsequent studies focus on two unique features of conversation: alternating turns-at-talk and establishing inter-brain coherence through speech. The results show that at the moment of speaker change, both people are engaging attentional and mentalizing systems - which likely support orienting toward implicit cues signaling speaker change as well as anticipating the other person's intention to either begin or end his turn. Four networks were identified that are significantly predicted by a novel measure of social coherence; they include the posterior parietal cortex, medial prefrontal cortex, and right angular gyrus. Taken together, the findings reveal that natural conversation relies on multiple cognitive networks besides language to coordinate or enhance social interaction. &#8195

Artificial Intelligence - Intelligent Art? Human-Machine Interaction and Creative Practice

As algorithmic data processing increasingly pervades everyday life, it is also making its way into the worlds of art, literature and music. In doing so, it shifts notions of creativity and evokes non-anthropocentric perspectives on artistic practice. This volume brings together contributions from the fields of cultural studies, literary studies, musicology and sound studies as well as media studies, sociology of technology, and beyond, presenting a truly interdisciplinary, state-of-the-art picture of the transformation of creative practice brought about by various forms of AI

Formality Style Transfer Within and Across Languages with Limited Supervision

While much natural language processing work focuses on analyzing language content, language style also conveys important information about the situational context and purpose of communication. When editing an article, professional editors take into account the target audience to select appropriate word choice and grammar. Similarly, professional translators translate documents for a specific audience and often ask what is the expected tone of the content when taking a translation job. Computational models of natural language should consider both their meaning and style. Controlling style is an emerging research area in text rewriting and is under-investigated in machine translation. In this dissertation, we present a new perspective which closely connects formality transfer and machine translation: we aim to control style in language generation with a focus on rewriting English or translating French to English with a desired formality. These are challenging tasks because annotated examples of style transfer are only available in limited quantities. We first address this problem by inducing a lexical formality model based on word embeddings and a small number of representative formal and informal words. This enables us to assign sentential formality scores and rerank translation hypotheses whose formality scores are closer to user-provided formality level. To capture broader formality changes, we then turn to neural sequence to sequence models. Joint modeling of formality transfer and machine translation enables formality control in machine translation without dedicated training examples. Along the way, we also improve low-resource neural machine translation

Discourse-Level Language Understanding with Deep Learning

Designing computational models that can understand language at a human level is a foundational goal in the field of natural language processing (NLP). Given a sentence, machines are capable of translating it into many different languages, generating a corresponding syntactic parse tree, marking words that refer to people or places, and much more. These tasks are solved by statistical machine learning algorithms, which leverage patterns in large datasets to build predictive models. Many recent advances in NLP are due to deep learning models (parameterized as neural networks), which bypass user-specified features in favor of building representations of language directly from the text. Despite many deep learning-fueled advances at the word and sentence level, however, computers still struggle to understand high-level discourse structure in language, or the way in which authors combine and order different units of text (e.g., sentences, paragraphs, chapters) to express a coherent message or narrative. Part of the reason is data-related, as there are no existing datasets for many contextual language-based problems, and some tasks are too complex to be framed as supervised learning problems; for the latter type, we must either resort to unsupervised learning or devise training objectives that simulate the supervised setting. Another reason is architectural: neural networks designed for sentence-level tasks require additional functionality, interpretability, and efficiency to operate at the discourse level. In this thesis, I design deep learning architectures for three NLP tasks that require integrating information across high-level linguistic context: question answering, fictional relationship understanding, and comic book narrative modeling. While these tasks are very different from each other on the surface, I show that similar neural network modules can be used in each case to form contextual representations

Machine Learning Algorithm for the Scansion of Old Saxon Poetry

Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

It really does depend: an exploration into the dichotomous positions held across the psycho-motoric concomitants to high-level performance

As the practice of performance psychology has evolved, so too has the underpinning knowledge within this field. Throughout this evolution, however, a number of theoretical stances or positions have emerged which often sit in stark contrast to one another, therefore creating divides or disagreements amongst the practitioners attempting to optimise translational impact. Accordingly, this thesis aimed to explore these contrasting positions, presented as paired dichotomies, and better understand which side of the dichotomy was more representative of high-level performance and/or practice. Of note, these dichotomies were divided into absolutist (whereby the positions or contentions made were seen as the explanations) versus nuanced (in which a number of possible explanations exist to explain performance) positions. As an applied practitioner and academic, this thesis employed a pragmatic philosophy which meant that a number of real world scenarios that I, and my peers, often encounter were explored in order to better understand the dichotomies. These were examined through three empirical studies and one desk-based study, exploring a variety of sports. Following a literature based desktop study, the veracity of the belief in ‘natural talent’ was explored through a literature and media analysis in Motorsport. Next, EEG measures were taken during a Golf-putting task in which participants used two different visual aiming styles. In the second empirical chapter, the role of cognition and understanding in decision making by elite Rugby Union players was explored. Finally, to consider a sport which has not experienced as much, if any, formal coaching, I sought to understand the practice habits and learning tools of Skateboarding performers. Taken together, the results of this research indicate the following: i) from a learning perspective, performers are not born with a natural talent, but instead develop their skills and a number of effortful learning behaviours through both deliberate cognitive processes as well as an ongoing interaction with their environment; ii) from a learning, performance and refinement perspective, performers still require a combination of cognition and explicit knowledge as well as an ongoing interaction with the environment, notably, practitioners are able to switch between appropriate levels of focus as required; and iii) exclusively from a performance perspective, very little execution is fully automatic and instead, scalable cognition is required for high-level performance. In short, practitioner should take an ‘it depends’ approach to their research and practice