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The artifacts affecting electroencephalographic (EEG) signals may undermine the correct interpretation of neural data that are used in a variety of applications spanning from diagnosis support systems to recreational brain-computer interfaces. Therefore, removing or - at least - reducing the noise content in respect to the actual brain activity data becomes of fundamental importance. However, manual removal of artifacts is not always applicable and appropriate, and sometimes the standard denoising techniques may encounter problems when dealing with noise frequency components overlapping with neural responses. In recent years, deep learning (DL) based denoising strategies have been developed to overcome these challenges and learn noise-related patterns to better discriminate actual EEG signals from artifact-related data. This study presents a novel DL-based EEG denoising model that leverages the prior knowledge on noise spectral features to adaptively compute optimal convolutional filters for multi-artifact noise removal. The proposed strategy is evaluated on a state-of-the-art benchmark dataset, namely EEGdenoiseNet, and achieves comparable to better performances in respect to other literature works considering both temporal and spectral metrics, providing a unique solution to remove muscle or ocular artifacts without needing a specific training on a particular artifact type.

Background: In the last decades, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic role of cancer therapies, making cardiovascular diseases (CVDs) among the most frequent, although rare, long-term sequalae. Leveraging innovative artificial intelligence (AI) tools and real-world data (RWD), we aimed to develop a causally interpretable model to identify young BC survivors at risk of developing CVDs. Methods: We designed and trained a Bayesian network (BN), an AI model, making use of expert knowledge and data from population-based (1036 patients) and clinical (339 patient) cohorts of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009–2019. The performance achieved by the BN model was validated against standard classification metrics, and two clinical applications were proposed. Results: The model showed a very good classification performance and a clear causal semantic. According to the predictions made by the model, focusing on the 25% of AYA BC survivors at higher risk of developing CVDs, we could identify 81% of the patients who would actually develop it. Moreover, a desktop-based app was implemented to calculate the individual patient’s risk. Conclusions: In this study, we developed the first causal model for predicting the CVD risk in AYA survivors of BC, also proposing an innovative AI approach that could be useful for all researchers dealing with RWD. The model could be pivotal for clinicians who aim to plan personalized follow-up strategies for AYA BC survivors.

This chapter begins discussing the methodical approaches to elucidate the diffusion of texts and proceeds examining the material, cultural and social factors which influenced their circulation. Among the material and economic factors are the cost and availability of writing materials, features such as script and format of books, the accessibility of libraries and different types of book manufacturing. Since a great part of medieval literature was transmitted orally, there is a complex relationship between oral and written transmission, occasionally changing between Latin and the vernacular languages. Knowledge of Latin was the prerequisite for having access to this literature and it varied greatly according to the period, the region, and the social group. Most Latin texts were written for an individual or a comparatively small group, but some of them reached a broad audience, depending on the social interaction of authors, their communities, and their dedicatees.

The increase in tobacco and alcohol prevalence among Nigerian teenagers necessitates the need to understand the factors influencing use. The aim of this systematic literature review was to synthesis evidence from studies on tobacco and alcohol use among Nigerian adolescents and young adults in order to determine factors influencing tobacco and alcohol use. Six databases MEDLINE, Embase, CINAHL, PsycINFO, PubMed, ScienceDirect, and Google Scholar were used to search for peer-reviewed articles reporting the prevalence and predictors of tobacco and alcohol use among adolescents/youths published between 2010 to 2022. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guided this review. From our search, twelve articles from the 6 geopolitical zones within Nigeria were included. The high prevalence of tobacco and alcohol use among Nigerian adolescents necessitates the need to understand the factors influencing use. A high prevalence rate was recorded among the male gender. Gender, age, and curiosity appear to place adolescents at extra risk for tobacco and alcohol use. Lack of parental monitoring, peer influence, low socio-economic status, low education level, stressful life events, advertisements, availability, and accessibility are factors identified to influence adolescents’ alcohol and tobacco use. Alcohol and tobacco use remains a major public health problem as it continues to contribute largely to the growing burden of diseases globally. Our review showed that adolescents’ alcohol and tobacco use behaviour is triggered by various factors on the personal, interpersonal, organisational, community, and policy levels, and these factors are understood to predict or protect against alcohol and tobacco use.

The increase in tobacco and alcohol prevalence among Nigerian teenagers necessitates the need to understand the factors influencing use. The aim of this systematic literature review was to synthesis evidence from studies on tobacco and alcohol use among Nigerian adolescents and young adults in order to determine factors influencing tobacco and alcohol use. Six databases MEDLINE, Embase, CINAHL, PsycINFO, PubMed, ScienceDirect, and Google Scholar were used to search for peer-reviewed articles reporting the prevalence and predictors of tobacco and alcohol use among adolescents/youths published between 2010 to 2022. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guided this review. From our search, twelve articles from the 6 geopolitical zones within Nigeria were included. The high prevalence of tobacco and alcohol use among Nigerian adolescents necessitates the need to understand the factors influencing use. A high prevalence rate was recorded among the male gender. Gender, age, and curiosity appear to place adolescents at extra risk for tobacco and alcohol use. Lack of parental monitoring, peer influence, low socio-economic status, low education level, stressful life events, advertisements, availability, and accessibility are factors identified to influence adolescents’ alcohol and tobacco use. Alcohol and tobacco use remains a major public health problem as it continues to contribute largely to the growing burden of diseases globally. Our review showed that adolescents’ alcohol and tobacco use behaviour is triggered by various factors on the personal, interpersonal, organisational, community, and policy levels, and these factors are understood to predict or protect against alcohol and tobacco use.

The increase in tobacco and alcohol prevalence among Nigerian teenagers necessitates the need to understand the factors influencing use. The aim of this systematic literature review was to synthesis evidence from studies on tobacco and alcohol use among Nigerian adolescents and young adults in order to determine factors influencing tobacco and alcohol use. Six databases MEDLINE, Embase, CINAHL, PsycINFO, PubMed, ScienceDirect, and Google Scholar were used to search for peer-reviewed articles reporting the prevalence and predictors of tobacco and alcohol use among adolescents/youths published between 2010 to 2022. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guided this review. From our search, twelve articles from the 6 geopolitical zones within Nigeria were included. The high prevalence of tobacco and alcohol use among Nigerian adolescents necessitates the need to understand the factors influencing use. A high prevalence rate was recorded among the male gender. Gender, age, and curiosity appear to place adolescents at extra risk for tobacco and alcohol use. Lack of parental monitoring, peer influence, low socio-economic status, low education level, stressful life events, advertisements, availability, and accessibility are factors identified to influence adolescents’ alcohol and tobacco use. Alcohol and tobacco use remains a major public health problem as it continues to contribute largely to the growing burden of diseases globally. Our review showed that adolescents’ alcohol and tobacco use behaviour is triggered by various factors on the personal, interpersonal, organisational, community, and policy levels, and these factors are understood to predict or protect against alcohol and tobacco use.

The increase in tobacco and alcohol prevalence among Nigerian teenagers necessitates the need to understand the factors influencing use. The aim of this systematic literature review was to synthesis evidence from studies on tobacco and alcohol use among Nigerian adolescents and young adults in order to determine factors influencing tobacco and alcohol use. Six databases MEDLINE, Embase, CINAHL, PsycINFO, PubMed, ScienceDirect, and Google Scholar were used to search for peer-reviewed articles reporting the prevalence and predictors of tobacco and alcohol use among adolescents/youths published between 2010 to 2022. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guided this review. From our search, twelve articles from the 6 geopolitical zones within Nigeria were included. The high prevalence of tobacco and alcohol use among Nigerian adolescents necessitates the need to understand the factors influencing use. A high prevalence rate was recorded among the male gender. Gender, age, and curiosity appear to place adolescents at extra risk for tobacco and alcohol use. Lack of parental monitoring, peer influence, low socio-economic status, low education level, stressful life events, advertisements, availability, and accessibility are factors identified to influence adolescents’ alcohol and tobacco use. Alcohol and tobacco use remains a major public health problem as it continues to contribute largely to the growing burden of diseases globally. Our review showed that adolescents’ alcohol and tobacco use behaviour is triggered by various factors on the personal, interpersonal, organisational, community, and policy levels, and these factors are understood to predict or protect against alcohol and tobacco use.

The increase in tobacco and alcohol prevalence among Nigerian teenagers necessitates the need to understand the factors influencing use. The aim of this systematic literature review was to synthesis evidence from studies on tobacco and alcohol use among Nigerian adolescents and young adults in order to determine factors influencing tobacco and alcohol use. Six databases MEDLINE, Embase, CINAHL, PsycINFO, PubMed, ScienceDirect, and Google Scholar were used to search for peer-reviewed articles reporting the prevalence and predictors of tobacco and alcohol use among adolescents/youths published between 2010 to 2022. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guided this review. From our search, twelve articles from the 6 geopolitical zones within Nigeria were included. The high prevalence of tobacco and alcohol use among Nigerian adolescents necessitates the need to understand the factors influencing use. A high prevalence rate was recorded among the male gender. Gender, age, and curiosity appear to place adolescents at extra risk for tobacco and alcohol use. Lack of parental monitoring, peer influence, low socio-economic status, low education level, stressful life events, advertisements, availability, and accessibility are factors identified to influence adolescents’ alcohol and tobacco use. Alcohol and tobacco use remains a major public health problem as it continues to contribute largely to the growing burden of diseases globally. Our review showed that adolescents’ alcohol and tobacco use behaviour is triggered by various factors on the personal, interpersonal, organisational, community, and policy levels, and these factors are understood to predict or protect against alcohol and tobacco use.

Objective: We aimed to develop a machine learning model to infer OCEAN traits from text. Background: The psycholexical approach allows retrieving information about personality traits from human language. However, it has rarely been applied because of methodological and practical issues that current computational advancements could overcome. Method: Classical taxonomies and a large Yelp corpus were leveraged to learn an embedding for each personality trait. These embeddings were used to train a feedforward neural network for predicting trait values. Their generalization performances have been evaluated through two external validation studies involving experts (N = 11) and laypeople (N = 100) in a discrimination task about the best markers of each trait and polarity. Results: Intrinsic validation of the model yielded excellent results, with R2 values greater than 0.78. The validation studies showed a high proportion of matches between participants' choices and model predictions, confirming its efficacy in identifying new terms related to the OCEAN traits. The best performance was observed for agreeableness and extraversion, especially for their positive polarities. The model was less efficient in identifying the negative polarity of openness and conscientiousness. Conclusions: This innovative methodology can be considered a “psycholexical approach 2.0,” contributing to research in personality and its practical applications in many fields.

This paper deals with the problem of optimising bids and budgets of a set of digital advertising campaigns. We improve on the current state of the art by introducing support for multi-ad group marketing campaigns and developing a highly data efficient parametric contextual bandit. The bandit, which exploits domain knowledge to reduce the exploration space, is shown to be effective under the following settings; few clicks and/or small conversion rate, short horizon scenarios, rapidly changing markets and low budget. Furthermore, a bootstrapped Thompson sampling algorithm is adapted to fit the parametric bandit. Extensive numerical experiments, performed on synthetic and real-world data, show that, on average, the parametric bandit gains more conversions than state-of-the-art bandits. Gains in performance are particularly high when an optimisation algorithm is needed the most, i.e. with tight budget or many ad groups, though gains are present also in the case of a single-ad group.

This study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies.

We introduce a novel heuristic designed to address the supply chain inventory management problem in the context of a two-echelon divergent supply chain. The proposed heuristic advances the current state-of-the-art by combining deep reinforcement learning with multi-stage stochastic programming. In particular, deep reinforcement learning is employed to determine the number of batches to produce, while multi-stage stochastic programming is applied to make shipping decisions. To support further research, we release a publicly available software environment that simulates a wide range of two-echelon divergent supply chain settings, allowing the manipulation of various parameter values, including those associated with seasonal demands. We then present a comprehensive set of numerical experiments considering constraints on production and warehouse capacities under fixed and variable logistic costs. The results demonstrate that the proposed heuristic significantly and consistently outperforms pure deep reinforcement learning algorithms in minimizing total costs. Moreover, it overcomes several inherent limitations of multi-stage stochastic programming models, thus underscoring its potential advantages in addressing complex supply chain scenarios.

Causality is gaining more and more attention in the machine learning community and consequently also in recommender systems research. The limitations of learning offline from observed data are widely recognized, however, applying debiasing strategies like Inverse Propensity Weighting does not always solve the problem of making wrong estimates. This concept paper contributes a summary of debiasing strategies in recommender systems and the design of several toy examples demonstrating the limits of these commonly applied approaches. Therefore, we propose to map the causality frameworks of potential outcomes and structural causal models onto the recommender systems domain in order to foster future research and development. For instance, applying causal discovery strategies on offline data to learn the causal graph in order to compute counterfactuals or improve debiasing strategies.

The increase in tobacco and alcohol prevalence among Nigerian teenagers necessitates the need to understand the factors influencing use. The aim of this systematic literature review was to synthesis evidence from studies on tobacco and alcohol use among Nigerian adolescents and young adults in order to determine factors influencing tobacco and alcohol use. Six databases MEDLINE, Embase, CINAHL, PsycINFO, PubMed, ScienceDirect, and Google Scholar were used to search for peer-reviewed articles reporting the prevalence and predictors of tobacco and alcohol use among adolescents/youths published between 2010 and 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided this review. From our search, twelve articles from the 6 geopolitical zones within Nigeria were included. The high prevalence of tobacco and alcohol use among Nigerian adolescents necessitates the need to understand the factors influencing use. A high prevalence rate was recorded among the male gender. Gender, age, and curiosity appear to place adolescents at extra risk for tobacco and alcohol use. Lack of parental monitoring, peer influence, low socio-economic status, low education level, stressful life events, advertisements, availability, and accessibility are factors identified to influence adolescents’ alcohol and tobacco use. Alcohol and tobacco use remains a major public health issue as it continues to contribute largely to the growing occurence of diseases globally. Our review showed that adolescents’ alcohol and tobacco use behaviour is triggered by various factors on the personal, interpersonal, organisational, community, and policy levels, and these factors are understood to predict or protect against alcohol and tobacco use.

We investigate whether and how deep reinforcement learning (DRL) can be exploited for managing inventory systems with a specific reference to perishable pharmaceutical products. A real-world case study is formulated as a Markov decision process, where states, actions, and rewards are defined. We then developed a DRL agent based on the Proximal Policy Optimization algorithm and compared its performance with a human decision-maker with several years of experience. Our findings reveal that the DRL agent outperforms the human policy by 11%, optimizing storage space and leading to growing profitability. Such incremental improvements can translate into substantial value for pharmaceutical companies operating in complex scenarios, and patients also stand to benefit. Finally, the study highlights the strategic advantage of integrating DRL into inventory management business operations, particularly for its ability to estimate uncertainty and manage corresponding supply chain risks.

Recommender systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors when making decisions, and the recommender must account for these to be effective. In this work, we use a causal graph to investigate the influence of different factors on the user's decision to click or not on the recommended accommodations. To learn the causal graph, we combine data provided by a meta-search booking platform for online hotel searches with prior knowledge made available by domain experts. The analysis confirms that the learnt causal model correctly models the well-known effect of the ranking position and price on user decision-making. Furthermore, we discover some interactions between the considered factors. For example, the country of the user market influences the user's decisions for different values of the price.

Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is important to understand which states of the system trigger them. For this purpose, we propose a modeling framework based on continuous-time Bayesian networks (CTBNs) to analyze cascading behavior in complex systems. This framework allows us to describe how events propagate through the system and to identify likely sentry states, that is, system states that may lead to imminent cascading behavior. Moreover, CTBNs have a simple graphical representation and provide interpretable outputs, both of which are important when communicating with domain experts. We also develop new methods for knowledge extraction from CTBNs and we apply the proposed methodology to a data set of alarms in a large industrial system.

Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements. Under this setting, we tackle the challenge of distinguishing neurological activity from noise-related sources. We develop a novel EEG denoising model that operates in the frequency domain, leveraging prior knowledge about noise spectral features to adaptively compute optimal convolutional filters for noise separation. The model is trained to learn an empirical relationship connecting the spectral characteristics of noise and noisy signal to a non-linear transformation which allows signal denoising. Performance evaluation on the EEGdenoiseNet dataset shows that the proposed model achieves optimal results according to both temporal and spectral metrics. The model is found to remove physiological artifacts from input EEG data, thus achieving effective EEG denoising. Indeed, the model performance either matches or outperforms that achieved by benchmark models, proving to effectively remove both muscle and ocular artifacts without the need to perform any training on the particular type of artifact.

Learning the structure of continuous-time Bayesian networks directly from data has traditionally been performed using score-based structure learning algorithms. Only recently has a constraint-based method been proposed, proving to be more suitable under specific settings, as in modelling systems with variables having more than two states. As a result, studying diverse structure learning algorithms is essential to learn the most appropriate models according to data characteristics and task-related priorities, such as learning speed or accuracy. This article proposes alternative algorithms for learning multidimensional continuous-time Bayesian network classifiers, introducing, for the first time, constraint-based and hybrid algorithms for these models. Nevertheless, these contributions also apply to the simpler one-dimensional classification problem for which only score-based solutions exist in the literature. More specifically, the aforementioned constraint-based structure learning algorithm is first adapted to the supervised classification setting. Then, a novel algorithm of this kind, specifically tailored for the multidimensional classification problem, is presented to improve the learning times for the induction of multidimensional classifiers. Finally, a hybrid algorithm is introduced, attempting to combine the strengths of the score- and constraint-based approaches. Experiments with synthetic and real-world data are performed not only to validate the capabilities of the proposed algorithms but also to conduct a comparative study of the available competitors.

Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or ”significance” assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook’s distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook’s distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.

Recommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by several factors in their decision-making. We analysed a historical dataset from a meta-search booking platform with the aim of exploring how these factors influence user choices in the context of online hotel search and booking. Specifically, we focused our study on the influence of (i) ranking position, (ii) number of reviews, (iii) average ratings and (iv) price when analysing users’ click behaviour. Our results confirmed conventional wisdom that position and price were the “two elephants in the room” heavily influencing user decision-making. Thus, they need to be taken into account when, for instance, trying to learn user preferences from clickstream data. Using the results coming from this analysis, we performed an online A/B test on this meta-search booking platform comparing the current policy with a price-based re-rank policy. Our online experiments suggested that, although in offline experiments items with lower prices tend to have a higher Click-Through Rate, in an online context a price-based re-rank was only capable to improve the Click-Through Rate metric for the first positions of the recommended lists.

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods.

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.

Learning the structure of continuous-time Bayesian networks directly from data has traditionally been performed using score-based structure learning algorithms. Only recently has a constraint-based method been proposed, proving to be more suitable under specific settings, as in modelling systems with variables having more than two states. As a result, studying diverse structure learning algorithms is essential to learn the most appropriate models according to data characteristics and task-related priorities, such as learning speed or accuracy. This article proposes several alternatives of such algorithms for learning multidimensional continuous-time Bayesian network classifiers, introducing for the first time constraint-based and hybrid algorithms for these models. Nevertheless, these contributions also apply to the simpler one-dimensional classification problem for which only score-based solutions exist in the literature. More specifically, the aforementioned constraint-based structure learning algorithm is first adapted to the supervised classification setting. Then, a novel algorithm of this kind, specifically tailored for the multidimensional classification problem, is presented to improve the learning times for the induction of multidimensional classifiers. Finally, a hybrid algorithm is introduced, attempting to combine the strengths of the score- and constraint-based approaches. Experiments with synthetic data are performed not only to validate the capabilities of the proposed algorithms but also to conduct a comparative study of the available structure learning algorithms.

Recommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors in their decision making. Hence, we aim at exploring how these factors influence user choices in the context of online hotel search and booking. Specifically, we focused our study on (i) ranking position, (ii) price, (iii) rating, and (iv) number of reviews when analyzing users’ click behavior. The results showed that there were “two elephants in the room”: position and price, that heavily influenced the user decision-making and need to be taken into account when, for instance, trying to learn user preferences from offline data in order to bootstrap a recommender system.

Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical risk assessment. However, in this setting we are limited to observational data with quality issues, missing values, small sample size and high dimensionality: we cannot reliably learn such models from limited observational data with these sources of bias. Instead, we choose to learn a causal Bayesian network to mitigate the issues above and to leverage the prior knowledge on endometrial cancer available from clinicians and physicians. We introduce a causal discovery algorithm for causal Bayesian networks based on bootstrap resampling, as opposed to the single imputation used in related works. Moreover, we include a context variable to evaluate whether selection bias results in learning spurious associations. Finally, we discuss the strengths and limitations of our findings in light of the presence of missing data that may be missing-not-at-random, which is common in real-world clinical settings.

Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.

This article presents the development of an expert system to support the diagnosis of post-harvest diseases of stored apples. We propose a picture-based and conversational interaction with users, where sampled images depicting symptoms of apples with known diseases are presented to users to elicit their feedback on perceived similarities in order to determine the most likely diagnosis of a diseased target apple. This article makes, besides the description of the industrial application scenario, multiple contributions circled around three rounds of user studies: (i) an usability and effectiveness assessment of the approach, where three user interface configurations are put to a test and the effectiveness of different types of user feedback mechanisms is assessed; (ii) contextual multi-armed bandit approaches for dynamic selection of displayed images with symptoms of diseased apples, that clearly outperform random and greedy sampling baseline strategies; (iii) a comparison of two different strategies for determining the context representation of a contextual multi-armed bandit approach, namely based on PCA of image features and a gamified large-scale user study. We therefore provide design insights for the development of such diagnosis applications on diseases that manifest themselves through visual symptoms in general and, hence, the findings can be also valid for domains other than post-harvest fruit diseases.

Radboud University, 13 februari 2023, Promotores : Battaglia, F.P., Tiesinga, P.H.E. Co-promotores : Genzel, L.K.E., Stella, F., Contains fulltext : 289475.pdf (Publisher’s version ) (Open Access)

More than 70 years after its introduction, the framework of resource density-dependent consumption rates, also known as predator-prey functional responses, remains a core concept in population and food web ecology. Initially, three types of responses were defined: linear (type I), hyperbolic (type II), and sigmoid (type III). Due to its potential to stabilize consumer-resource population dynamics, the sigmoid type III functional response immediately became a “holy grail” in population ecology. However, experimentally proving that type III functional responses exist, whether in controlled laboratory systems or in nature, was challenging. While theoretical and practical advances make identifying type III responses easier today, decades of research have brought only a limited number of studies that provide empirical evidence for type III response curves. Here, we review this evidence from laboratory- and field-based studies published during the last two decades. We found 107 studies that reported type III responses, but these studies ranged across various taxa, interaction types, and ecosystems. To put these studies into context, we also discuss the various biological mechanisms that may lead to the emergence of type III responses. We summarize how three different and mutually independent intricacies bedevil the empirical documentation of type III responses: (1) challenges in statistical modeling of functional responses, (2) inadequate resource density ranges and spacing, and (3) biologically meaningful and realistic design of experimental arenas. Finally, we provide guidelines on how the field should move forward based on these considerations.

More than 70 years after its introduction, the framework of resource density-dependent consumption rates, also known as predator-prey functional responses, remains a core concept in population and food web ecology. Initially, three types of responses were defined: linear (type I), hyperbolic (type II), and sigmoid (type III). Due to its potential to stabilize consumer-resource population dynamics, the sigmoid type III functional response immediately became a “holy grail” in population ecology. However, experimentally proving that type III functional responses exist, whether in controlled laboratory systems or in nature, was challenging. While theoretical and practical advances make identifying type III responses easier today, decades of research have brought only a limited number of studies that provide empirical evidence for type III response curves. Here, we review this evidence from laboratory- and field-based studies published during the last two decades. We found 107 studies that reported type III responses, but these studies ranged across various taxa, interaction types, and ecosystems. To put these studies into context, we also discuss the various biological mechanisms that may lead to the emergence of type III responses. We summarize how three different and mutually independent intricacies bedevil the empirical documentation of type III responses: (1) challenges in statistical modeling of functional responses, (2) inadequate resource density ranges and spacing, and (3) biologically meaningful and realistic design of experimental arenas. Finally, we provide guidelines on how the field should move forward based on these considerations.

More than 70 years after its introduction, the framework of resource density-dependent consumption rates, also known as predator-prey functional responses, remains a core concept in population and food web ecology. Initially, three types of responses were defined: linear (type I), hyperbolic (type II), and sigmoid (type III). Due to its potential to stabilize consumer-resource population dynamics, the sigmoid type III functional response immediately became a “holy grail” in population ecology. However, experimentally proving that type III functional responses exist, whether in controlled laboratory systems or in nature, was challenging. While theoretical and practical advances make identifying type III responses easier today, decades of research have brought only a limited number of studies that provide empirical evidence for type III response curves. Here, we review this evidence from laboratory- and field-based studies published during the last two decades. We found 107 studies that reported type III responses, but these studies ranged across various taxa, interaction types, and ecosystems. To put these studies into context, we also discuss the various biological mechanisms that may lead to the emergence of type III responses. We summarize how three different and mutually independent intricacies bedevil the empirical documentation of type III responses: (1) challenges in statistical modeling of functional responses, (2) inadequate resource density ranges and spacing, and (3) biologically meaningful and realistic design of experimental arenas. Finally, we provide guidelines on how the field should move forward based on these considerations.

More than 70 years after its introduction, the framework of resource density-dependent consumption rates, also known as predator-prey functional responses, remains a core concept in population and food web ecology. Initially, three types of responses were defined: linear (type I), hyperbolic (type II), and sigmoid (type III). Due to its potential to stabilize consumer-resource population dynamics, the sigmoid type III functional response immediately became a “holy grail” in population ecology. However, experimentally proving that type III functional responses exist, whether in controlled laboratory systems or in nature, was challenging. While theoretical and practical advances make identifying type III responses easier today, decades of research have brought only a limited number of studies that provide empirical evidence for type III response curves. Here, we review this evidence from laboratory- and field-based studies published during the last two decades. We found 107 studies that reported type III responses, but these studies ranged across various taxa, interaction types, and ecosystems. To put these studies into context, we also discuss the various biological mechanisms that may lead to the emergence of type III responses. We summarize how three different and mutually independent intricacies bedevil the empirical documentation of type III responses: (1) challenges in statistical modeling of functional responses, (2) inadequate resource density ranges and spacing, and (3) biologically meaningful and realistic design of experimental arenas. Finally, we provide guidelines on how the field should move forward based on these considerations.

More than 70 years after its introduction, the framework of resource density-dependent consumption rates, also known as predator-prey functional responses, remains a core concept in population and food web ecology. Initially, three types of responses were defined: linear (type I), hyperbolic (type II), and sigmoid (type III). Due to its potential to stabilize consumer-resource population dynamics, the sigmoid type III functional response immediately became a “holy grail” in population ecology. However, experimentally proving that type III functional responses exist, whether in controlled laboratory systems or in nature, was challenging. While theoretical and practical advances make identifying type III responses easier today, decades of research have brought only a limited number of studies that provide empirical evidence for type III response curves. Here, we review this evidence from laboratory- and field-based studies published during the last two decades. We found 107 studies that reported type III responses, but these studies ranged across various taxa, interaction types, and ecosystems. To put these studies into context, we also discuss the various biological mechanisms that may lead to the emergence of type III responses. We summarize how three different and mutually independent intricacies bedevil the empirical documentation of type III responses: (1) challenges in statistical modeling of functional responses, (2) inadequate resource density ranges and spacing, and (3) biologically meaningful and realistic design of experimental arenas. Finally, we provide guidelines on how the field should move forward based on these considerations.

Survival analysis is a crucial tool in healthcare, allowing us to understand and predict time-to-event occurrences using statistical and machine-learning techniques. As deep learning gains traction in this domain, a specific challenge emerges: neural network-based survival models often produce discrete-time outputs, with the number of discretization points being much fewer than the unique time points in the dataset, leading to potentially inaccurate survival functions. To this end, our study explores post-processing techniques for survival functions. Specifically, interpolation and smoothing can act as effective regularization, enhancing performance metrics integrated over time, such as the Integrated Brier Score and the Cumulative Area-Under-the-Curve. We employed various regularization techniques on diverse real-world healthcare datasets to validate this claim. Empirical results suggest a significant performance improvement when using these post-processing techniques, underscoring their potential as a robust enhancement for neural network-based survival models. These findings suggest that integrating the strengths of neural networks with the non-discrete nature of survival tasks can yield more accurate and reliable survival predictions in clinical scenarios.

We show that large language models (LLMs), such as ChatGPT, can guide the robot design process, on both the conceptual and technical level, and we propose new human–AI co-design strategies and their societal implications., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.

We approached the causal discovery task in the recommender system domain to learn a causal graph by combining observational data provided by a meta-search booking platform for online hotel search with prior knowledge made available by domain experts. The results show that it is possible to learn a causal graph coherent with previous findings in the recommender systems literature about the relations between different factors. Furthermore, we also discovered new insights that could help in the recommendation process.

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.

Elephants and other animals heavily rely on the sense of smell to operate. Soft robots would also benefit from an artificial sense of smell, which could be helpful in typical soft robotic tasks such as search and rescue, pipe inspection, and all the tasks involving unstructured environments. This work proposes an artificial nose on a soft robotic arm that ensures separate smell concentration readings. We propose designing the nose to generate a one-to-one matching between the sensors' inputs and the actuators. This design choice allows us to implement a simple control strategy tailored to reach a dynamically varying smell in the environment, which we validate on a two-segment tendon-driven soft robotic arm equipped with the proposed artificial nose. We also propose and validate in simulation a control strategy for reaching tasks in the case of a stationary smell, Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

The success of soft robots in displaying emergent behaviors is tightly linked to the compliant interaction with the environment. However, to exploit such phenomena, proprioceptive sensing methods which do not hinder their softness are needed. In this work we propose a new sensing approach for soft underwater slender structures based on embedded pressure sensors and use a learning-based pipeline to link the sensor readings to the shape of the soft structure. Using two different modeling techniques, we compare the pose reconstruction accuracy and identify the optimal approach. Using the proprioceptive sensing capabilities we show how this information can be used to assess the swimming performance over a number of metrics, namely swimming thrust, tip deflection, and the traveling wave index. We conclude by demonstrating the robustness of the embedded sensor on a free swimming soft robotic squid swimming at a maximum velocity of 9.5 cm/s, with the absolute tip deflection being predicted within an error less than 9% without the aid of external sensors., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Soft robot are celebrated for their propensity to enable compliant and complex robot-environment interactions. Soft robotic manipulators, or slender continuum structure robots have the potential to exploit these interactions to enable new exploration and manipulation capabilities and safe human-robot interactions. However, the interactions, or perturbations by external forces cause the soft structure to deform in an infinite degree of freedom (DOF) space. To control such system, reduced order models are needed; typically models consider piecewise sections of constant curvature although external forces often deform the structure out of the constant curvature hypothesis. In this work we perform an analysis of the trade-off between computational treatability and modelling accuracy. We then propose a new kinematic model, the Piecewise Affine Curvature (PAC) which we validate theoretically and experimentally showing that this higher-order model better captures the configuration of a soft continuum body robot when perturbed by the external forces. In comparison to the current state of the art Piecewise Constant Curvature (PCC) model we demonstrate up to 30% reduction in error for the end position of a soft continuum body robot., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Post-harvest diseases of apple can cause considerable economic losses. Thus, we developed DSSApple, an interactive web-based decision support system, that helps users to diagnose post-harvest diseases of domesticated apple based on observed macroscopic symptoms on fruit. Specifically, DSSApple is designed as a two-stream hybrid diagnostic tool, that can be effectively used by both expert and non-expert users to diagnose diseased instances of apple. The image-based stream allows the user to interact simply by selecting pictures, representing the variety of symptoms of diseases at different stages of the infection and on different cultivars. Instead, the expert-based stream of the system incrementally collects user feedback about the target disease by asking questions related to the macroscopic characteristics of the observed symptoms on a target apple. The expert-based reasoning mechanism of DSSApple is developed by leveraging the framework of Bayesian Networks (BNs). We detail the process of building this knowledge base with the support of a domain expert. We further exploit the BN to process incomplete or conflicting user feedback within the inference mechanism as well as to provide human-understandable explanations on the suggested diagnoses. The proposed hybrid approach has been thoroughly evaluated in two studies, involving simulated (by photos) as well as real infected apples. Thus, the proposed hybrid version of DSSApple is able to outperform both the single streams and the user intuition in terms of diagnostic accuracy.

Post-harvest diseases are one of the main causes of economical losses in the apple fruit production sector. Therefore, this paper presents an application of a knowledge-based expert system to diagnose post-harvest diseases of apple. Specifically, we detail the process of domain knowledge elicitation for constructing a Bayesian network reasoning system. We describe the developed expert system, dubbed BN-DSSApple, and the diagnostic mechanism given the evidence provided by the user, as well as a likelihood evidence method, learned from the estimated consensus of users’ and expert’s interactions, to effectively transfer the performance of the model to different cohorts of users. Finally, we detail a novel technique for explaining the provided diagnosis, thus increasing the trust in the system. We evaluate BN-DSSApple with three different types of user studies, involving real diseased apples, where the ground truth of the target instances was established by microbiological and DNA analysis. The experiments demonstrate the performance differences in the knowledge-based reasoning mechanism due to heterogeneous users interacting with the system under various conditions and the capability of the likelihood-based method to improve the diagnostic performance in different environments.

A key challenge in computer vision and deep learning is the definition of robust strategies for the detection of adversarial examples. In this work, we propose the adoption of ensemble approaches to leverage the effectiveness of multiple detectors in exploiting distinct properties of the input data. To this end, the ENsemble Adversarial Detector (ENAD) framework integrates scoring functions from state-of-the-art detectors based on Mahalanobis distance, Local Intrinsic Dimensionality, and One-Class Support Vector Machines, which process the hidden features of deep neural networks. ENAD is designed to ensure high standardization and reproducibility to the computational workflow. Extensive tests on benchmark datasets, models and adversarial attacks show that ENAD outperforms all competing methods in the large majority of settings. The improvement over the state-of-the-art and the intrinsic generality of the framework, which allows one to easily extend ENAD to include any set of detectors and integration strategies, set the foundations for the new area of ensemble adversarial detection.

Fully exploiting soft robots' capabilities requires devising strategies that can accurately control their movements with the limited amount of control sources available. This task is challenging for reasons including the hard-to-model dynamics, the system's underactuation, and the need of using a prominent feedforward control action to preserve the soft and safe robot behavior. To tackle this challenge, this letter proposes a purely feedforward iterative learning control algorithm that refines the torque action by leveraging both the knowledge of the model and data obtained from past experience. After presenting a 3D polynomial description of soft robots, we study their intrinsic properties, e.g., input-to-state stability, and we prove the convergence of the controller coping with locally Lipschitz nonlinearities. Finally, we validate the proposed approach through simulations and experiments involving multiple systems, trajectories, and in the case of external disturbances and model mismatches., Learning & Autonomous Control

More than 70 years after its introduction, the framework of resource density-dependent consumption rates, also known as predator-prey functional responses, remains a core concept in population and food web ecology. Initially, three types of responses were defined: linear (type I), hyperbolic (type II), and sigmoid (type III). Due to its potential to stabilize consumer-resource population dynamics, the sigmoid type III functional response immediately became a “holy grail” in population ecology. However, experimentally proving that type III functional responses exist, whether in controlled laboratory systems or in nature, was challenging. While theoretical and practical advances make identifying type III responses easier today, decades of research have brought only a limited number of studies that provide empirical evidence for type III response curves. Here, we review this evidence from laboratory- and field-based studies published during the last two decades. We found 107 studies that reported type III responses, but these studies ranged across various taxa, interaction types, and ecosystems. To put these studies into context, we also discuss the various biological mechanisms that may lead to the emergence of type III responses. We summarize how three different and mutually independent intricacies bedevil the empirical documentation of type III responses: (1) challenges in statistical modeling of functional responses, (2) inadequate resource density ranges and spacing, and (3) biologically meaningful and realistic design of experimental arenas. Finally, we provide guidelines on how the field should move forward based on these considerations.