70 research outputs found

    Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

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    Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

    Évaluation et modulation des fonctions exécutives en neuroergonomie - Continuums cognitifs et expérimentaux

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    Des études en neuroergonomie ont montré que le pilote d’avion pouvait commettre des erreurs en raison d’une incapacité transitoire à faire preuve de flexibilité mentale. Il apparait que certains facteurs, tels qu’une forte charge mentale ou une pression temporelle importante, un niveau de stress trop élevé, la survenue de conflits, ou une perte de conscience de la situation, peuvent altérer temporairement l’efficience des fonctions exécutives permettant cette flexibilité. Depuis mes travaux initiaux, dans lesquels je me suis intéressé aux conditions qui conduisent à une négligence auditive, j’ai souhaité développer une approche scientifique visant à quantifier et limiter les effets délétères de ces différents facteurs. Ceci a été fait à travers l’étude des fonctions exécutives chez l’être humain selon le continuum cognitif (du cerveau lésé au cerveau en parfait état de fonctionnement) et le continuum expérimental (de l’ordinateur au monde réel). L’approche fondamentale de l’étude des fonctions exécutives en neurosciences combinée à l’approche neuroergonomique graduelle avec des pilotes et des patients cérébro-lésés, a permis de mieux comprendre la manière dont ces fonctions sont mises en jeu et altérées. Cette connaissance à contribuer par la suite à la mise en place de solutions pour préserver leur efficacité en situation complexe. Après avoir rappelé mon parcours académique, je présente dans ce manuscrit une sélection de travaux répartis sur trois thématiques de recherche. La première concerne l’étude des fonctions exécutives impliquées dans l’attention et en particulier la façon dont la charge perceptive et la charge mentale peuvent altérer ces fonctions. La deuxième correspond à un aspect plus appliqué de ces travaux avec l’évaluation de l’état du pilote. Il a été question d’analyser cet état selon l’activité de pilotage elle-même ou à travers la gestion et la supervision d’un système en particulier. La troisième et dernière thématique concerne la recherche de marqueurs prédictifs de la performance cognitive et l’élaboration d’entraînements cognitifs pour limiter les troubles dysexécutifs, qu’ils soient d’origine contextuelle ou lésionnelle. Ces travaux ont contribué à une meilleure compréhension des troubles cognitifs transitoires ou chroniques, mais ils ont aussi soulevé des questions auxquelles je souhaite répondre aujourd’hui. Pour illustrer cette réflexion, je présente en dernière partie de ce document mon projet de recherche qui vise à développer une approche multifactorielle de l’efficience cognitive, éthique et en science ouverte

    Affinity-Based Reinforcement Learning : A New Paradigm for Agent Interpretability

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    The steady increase in complexity of reinforcement learning (RL) algorithms is accompanied by a corresponding increase in opacity that obfuscates insights into their devised strategies. Methods in explainable artificial intelligence seek to mitigate this opacity by either creating transparent algorithms or extracting explanations post hoc. A third category exists that allows the developer to affect what agents learn: constrained RL has been used in safety-critical applications and prohibits agents from visiting certain states; preference-based RL agents have been used in robotics applications and learn state-action preferences instead of traditional reward functions. We propose a new affinity-based RL paradigm in which agents learn strategies that are partially decoupled from reward functions. Unlike entropy regularisation, we regularise the objective function with a distinct action distribution that represents a desired behaviour; we encourage the agent to act according to a prior while learning to maximise rewards. The result is an inherently interpretable agent that solves problems with an intrinsic affinity for certain actions. We demonstrate the utility of our method in a financial application: we learn continuous time-variant compositions of prototypical policies, each interpretable by its action affinities, that are globally interpretable according to customers’ financial personalities. Our method combines advantages from both constrained RL and preferencebased RL: it retains the reward function but generalises the policy to match a defined behaviour, thus avoiding problems such as reward shaping and hacking. Unlike Boolean task composition, our method is a fuzzy superposition of different prototypical strategies to arrive at a more complex, yet interpretable, strategy.publishedVersio

    Stock Market Investment Using Machine Learning

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    Genetic Algorithm-Support Vector Regression (GA-SVR) and Random Forest Regression (RFR) were constructed to forecast stock returns in this research. 15 financial indicators were selected through fuzzy clustering from 42 financial indicators, then combined with 8 technical indicators as input space, the 10-day stocks return was used as labels. The results show that GA-SVR and RFR can make compelling forecasting and pass the robustness test. GA-SVR and RFR exhibit different processing preferences for features with different importance. Furthermore, by testing stock markets in China, Hong Kong (China) and the United States, the model shows different effectiveness

    Statistical Modelling

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    The book collects the proceedings of the 19th International Workshop on Statistical Modelling held in Florence on July 2004. Statistical modelling is an important cornerstone in many scientific disciplines, and the workshop has provided a rich environment for cross-fertilization of ideas from different disciplines. It consists in four invited lectures, 48 contributed papers and 47 posters. The contributions are arranged in sessions: Statistical Modelling; Statistical Modelling in Genomics; Semi-parametric Regression Models; Generalized Linear Mixed Models; Correlated Data Modelling; Missing Data, Measurement of Error and Survival Analysis; Spatial Data Modelling and Time Series and Econometrics

    Remote sensing of phytoplankton biomass in oligotrophic and mesotrophic lakes: addressing estimation uncertainty through machine learning

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    Phytoplankton constitute the bottom of the aquatic food web, produce half of Earth’s oxygen and are part of the global carbon cycle. A measure of aquatic phytoplankton biomass therefore functions as a biological indicator of water status and quality. The abundance of phytoplankton in most lakes on Earth is low because they are weakly nourished (i.e., oligotrophic). It is practically infeasible to measure the millions of oligotrophic lakes on Earth through field sampling. Fortunately, phytoplankton universally contain the optically active pigment chlorophyll-a, which can be detected by optical sensors. Earth-orbiting satellite missions carry optical sensors that provide unparalleled high spatial coverage and temporal revisit frequency of lakes. However, when compared to waters with high nutrient loading (i.e., eutrophic), the remote sensing estimation of phytoplankton biomass in oligotrophic lakes is prone to high estimation uncertainties. Accurate retrieval of phytoplankton biomass is severely constrained by imperfect atmospheric correction, complicated inherent optical property (IOP) compositions, and limited model applicability. In order to address and reduce the current estimation uncertainties in phytoplankton remote sensing of low - moderate biomass lakes, machine learning is used in this thesis. In the first chapter the chlorophyll-a concentration (chla) estimation uncertainty from 13 chla algorithms is characterised. The uncertainty characterisation follows a two-step procedure: 1. estimation of chla from a representative dataset of field measurements and quantification of estimation uncertainty, 2. characterisation of chla estimation uncertainty. The results of this study show that estimation uncertainty across the dataset used in this chapter is high, whereby chla is both systematically under- and overestimated by the tested algorithms. Further, the characterisation reveals algorithm-specific causes of estimation uncertainty. The uncertainty sources for each of the tested algorithms are discussed and recommendations provided to improve the estimation capabilities. In the second chapter a novel machine learning algorithm for chla estimation is developed by combining Bayesian theory with Neural Networks (NNs). The resulting Bayesian Neural Networks (BNNs) are designed for the Ocean and Land Cover Instrument (OLCI) and MultiSpectral Imager (MSI) sensors aboard the Sentinel-3 and Sentinel-2 satellites, respectively. Unlike established chla algorithms, the BNNs provide a per-pixel uncertainty associated with estimated chla. Compared to reference chla algorithms, gains in chla estimation accuracy > 15% are achieved. Moreover, the quality of the provided BNN chla uncertainty is analysed. For most observations (> 75%) the BNN uncertainty estimate covers the reference in situ chla value, but the uncertainty calibration is not constantly accurate across several assessment strategies. The BNNs are applied to OLCI and MSI products to generate chla and uncertainty estimates in lakes from Africa, Canada, Europe and New Zealand. The BNN uncertainty estimate is furthermore used to deal with uncertainty introduced by prior atmospheric correction algorithms, adjacency affects and complex optical property compositions. The third chapter focuses on the estimation of lake biomass in terms of trophic status (TS). TS is conventionally estimated through chla. However, the remote sensing of chla, as shown in the two previous chapters, can be prone to high uncertainty. Therefore, in this chapter an algorithm for the direct classification of TS is designed. Instead of using a single algorithm for TS estimation, multiple individual algorithms are ensembled through stacking, whose estimates are evaluated by a higher-level meta-learner. The results of this ensemble scheme are compared to conventional switching of reference chla algorithms through optical water types (OWTs). The results show that estimation of TS is increased through direct classification rather than indirect estimation through chla. The designed meta-learning algorithm outperforms OWT switching of chla algorithms by 5-12%. Highest TS estimation accuracy is achieved for high biomass waters, whereas for low biomass waters extremely turbid waters produced high TS estimation uncertainty. Combining an ensemble of algorithms through a meta-learner represents a solution for the problem of algorithm selection across the large variation of global lake constituent concentrations and optical properties

    Recent Advances in Social Data and Artificial Intelligence 2019

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    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

    Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

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    In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems

    Cryptocurrency trading as a Markov Decision Process

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    A gestão de portefólio é um problema em que, em vez de olhar para ativos únicos, o objetivo é olhar para um portefólio ou um conjunto de ativos como um todo. O objetivo é ter o melhor portefólio, a cada momento, enquanto tenta maximizar os lucros no final de uma sessão de trading. Esta tese aborda esta problemática, empregando algoritmos de Deep Reinforcement Learning, num ambiente que simula uma sessão de trading. É também apresentada a implementação desta metodologia proposta, aplicada a 11 criptomoedas e cinco algoritmos DRL. Foram avaliados três tipos de condições de mercado: tendência de alta, tendência de baixa e lateralização. Cada condição de mercado em cada algoritmo foi avaliada, usando três funções de recompensa diferentes, no ambiente de negociação, e todos os diferentes cenários foram testados contra as estratégias de gestão de portefólio clássicas, como seguir o vencedor, seguir o perdedor e portefólios igualmente distribuídos. Assim, esta estratégia foi o benchmark mais performativo e os modelos que produziram os melhores resultados tiveram uma abordagem semelhante, diversificar e segurar. Deep Deterministic Policy Gradient apresentou-se como o algoritmo mais estável, junto com seu algoritmo de extensão, Twin Delayed Deep Deterministic Policy Gradient. Proximal Policy Optimization foi o único algoritmo que não conseguiu produzir resultados decentes ao comparar com as estratégias de benchmark e outros algoritmos de Deep Reinforcement Learning.The problem with portfolio management is that, instead of looking at single assets, the goal is to look at a portfolio or a set of assets as a whole. The objective is to have the best portfolio at each given time while trying to maximize profits at the end of a trading session. This thesis addresses this issue by employing the Deep Reinforcement Learning algorithms in a cryptocurrency trading environment which simulates a trading session. It is also presented the implementation of this proposed methodology applied to 11 cryptocurrencies and five Deep Reinforcement Learning algorithms. Three types of market conditions were evaluated namely, up trending or bullish, down trending or bearish, and lateralization or sideways. Each market condition in each algorithm was evaluated using three different reward functions in the trading environment and all different scenarios were back tested against old school portfolio management strategies such as following-the-winner, following-the-loser, and equally weighted portfolios. The results seem to indicate that an equally-weighted portfolio is an hard to beat strategy in all market conditions. This strategy was the most performative benchmark and the models that produced the best results had a similar approach, diversify and hold. Deep Deterministic Policy Gradient presented itself to be the most stable algorithm along with its extension algorithm, Twin Delayed Deep Deterministic Policy Gradient. Proximal Policy Optimization was the only algorithm that could not produce decent results when comparing with the benchmark strategies and other Deep Reinforcement Learning algorithms
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