Expressive movement generation with machine learning

Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing – developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement’s walking direction, and the mover’s movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Representation learning in finance

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing. Financial analysts’ earnings forecast is one of the most critical inputs for security valuation and investment decisions. However, it is challenging to fully utilize this type of data due to the missing values. This work proposes one matrix-based algorithm, “Coupled Matrix Factorization,” and one tensor-based algorithm, “Nonlinear Tensor Coupling and Completion Framework,” to impute missing values in analysts’ earnings forecasts and then use the imputed data to predict firms’ future earnings. Experimental analysis shows that missing value imputation and representation learning by coupled matrix/tensor factorization from the observed entries improve the accuracy of firm earnings prediction. The results confirm that representing financial time-series in their natural third-order tensor form improves the latent representation of the data. It learns high-quality embedding by overcoming information loss of flattening data in spatial or temporal dimensions. Traditional asset pricing models focus on linear relationships among asset pricing factors and often ignore nonlinear interaction among firms and factors. This dissertation formulates novel methods to identify nonlinear asset pricing factors and develops asset pricing models that capture global and local properties of data. First, this work proposes an artificial neural network “auto enco der” based model to capture the latent asset pricing factors from the global representation of an equity index. It also shows that autoencoder effectively identifies communal and non-communal assets in an index to facilitate portfolio optimization. Second, the global representation is augmented by propagating information from local communities, where the network determines the strength of this information propagation. Based on the Laplacian spectrum of the equity market network, a network factor “Z-score” is proposed to facilitate pertinent information propagation and capture dynamic changes in network structures. Finally, a “Dynamic Graph Learning Framework for Asset Pricing” is proposed to combine both global and local representations of data into one end-to-end asset pricing model. Using graph attention mechanism and information diffusion function, the proposed model learns new connections for implicit networks and refines connections of explicit networks. Experimental analysis shows that the proposed model incorporates information from negative and positive connections, captures the network evolution of the equity market over time, and outperforms other state-of-the-art asset pricing and predictive machine learning models in stock return prediction. In a broader context, this is a pioneering work in FinTech, particularly in understanding complex financial market structures and developing explainable artificial intelligence models for finance applications. This work effectively demonstrates the application of machine learning to model financial networks, capture nonlinear interactions on data, and provide investors with powerful data-driven techniques for informed decision-making

Semantic Memory for Food and Brain Correlates

Semantic memory stores knowledge about different types of objects: plants, animals, vehicles, utensils, conspecifics and food, among the others. Our ability to quickly recognize and categorize an object when we encounter it depends upon having experienced that object before and on semantic knowledge integrity. Semantic memory is one the most resilient cognitive abilities, it is less prone to interference than episodic memory and more declines slowly. The interest in how semantic memory is organized traces way back, however a great impulse was provided by the first systematic neuropsychological observations of patients with category specific recognition deficits. However, this debate is far from being resolved. In my dissertation, I will show how the study of food as a semantic category is extremely suitable to shed light on the organization of semantic knowledge. The thesis is organized as follow. In Chapter 1, I will first define semantic memory, focusing on its characteristics, such as its relationship with experience, its resilience to cognitive decline and its neural correlates, and on how it has been studied by neuropsychologists. In addition, I will review the studies on the food category, focusing on some intrinsic dimensions such as the level of transformation. Chapter 2 includes Study 1, in which I have investigated the organization of semantic memory by using food (natural and transformed) and non-food (living and on-living things) in a group of patients suffering from temporal lobe atrophy (Alzheimer\u2019s disease, PPA and FTD) and healthy controls, using Voxel Based Morphometry and DTI. Results have shown that food breaks down in natural and transformed, and that this parsing mirrors that of living and non-living things, thus strongly supporting the Sensory-functional model of semantic knowledge. Chapter 3 contains Study 2, in which I have explored the relationship between semantic memory and experience. I collected information about life-long eating habits as a proxy of long-term experience with specific foods as well as information about semantic memory of food in participants of different ages (36 \u2013 108 years old). Results support the hypothesis that semantic memory is modulated by experience. In Chapter 4, the focus of Study 3 is on episodic memory. Here I investigated whether the difference between semantic memory for natural and transformed food highlighted in Study 2 extends also to episodic memory, and whether the animacy effect - a facilitation to remember living exemplars - holds for food as well. Specifically, I administered a recognition memory task to the same participants of Study 2, to a group of young participants and to patients with Alzheimer\u2019s disease, PPA and FTD. I found that young adults had better recognition memory for transformed foods compared to natural foods. This difference disappeared in centenarians, consistently with Study 2, and in patients. The natural/transformed distinction appears susceptible to decay only in the presence of a high degree of episodic memory impairment. Finally, with Study 4, described in Chapter 5, my aim was more translational, that is, to test whether a deficit in semantic memory for food could lead to specific eating disorders. This study empirically establishes the behavioural and neural correlates of abnormal changes in eating habits in dementia and their relationship with semantic memory. In this thesis, I have shown that natural and transformed food do have different neural correlates, and that they are differently represented in semantic memory. By drawing together evidence from my studies and from studies of others I was allowed to propose a comprehensive model of semantic knowledge. Additionally, in my thesis I showed how food can be employed to study the organization of semantic knowledge, the way in which semantic knowledge is shaped by learning and experience, and its effect on behaviour

On Explainable Deep Learning for Macroeconomic Forecasting and Finance

Deep Learning (DL) has gained momentum in recent years due to its incredible generalisation performance achieved across many learning tasks. Nevertheless, practitioners and academics have sometime been reluctant to apply these models because perceived as black boxes. This is particularly problematic in Economics and Finance. The objective of this thesis is to develop interpretable DL models and explainable DL tools with a focus on macroeconomic and financial applications. In doing so we highlight connections between such models and the standard economic ones. The first part of this work introduces a new class of interpretable models called Deep Dynamic Factor Models. The study merges the DL literature on autoencoders with that of the Econometrics on Dynamic Factor Models. Empirical validations of the approach are carried out both on synthetic and on real-time macroeconomic data. Part two of the work analyses feature attribution methods and Shapley values among explainability tools that are used to additively decompose model predictions. One of their limitations is highlighted, given that it is necessary to define a baseline that represents the missingness of a feature. A solution to the problem is proposed and compared against the ones currently in use both on simulated data and in the financial context of credit card default. We show that the proposed baseline is the only one that accounts for the specific use of the model. The final part of the work discusses the use of DL techniques for dynamic asset allocation. Using US market data, a comparison in recursive out-of-sample among different machine learning, economic-financial and hybrid models, including the one introduced in the first part of the work, is performed. Finally, a nonlinear factor-based portfolio performance attribution via the use of Shapley values and the baseline proposed in part two of the work is presented

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace