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Hierarchical active inference for cognitive architectures
Get PDFThis thesis explores the methods based on the free energy principle and active inference for modelling cognition. Active inference is an emerging framework for designing intelligent agents where psychological processes are cast in terms of Bayesian inference. Here, I appeal to it to test the design of a set of cognitive architectures, via simulation. These architectures are defined in terms of generative models where an agent executes a task under the assumption that all cognitive processes aspire to the same objective: the minimization of variational free energy. Chapter 1 introduces the free energy principle and its assumptions about self-organizing systems. Chapter 2 describes how from the mechanics of self-organization can emerge a minimal form of cognition able to achieve autopoiesis. In chapter 3 I present the method of how I formalize generative models for action and perception. The architectures proposed allow providing a more biologically plausible account of more complex cognitive processing that entails deep temporal features. I then present three simulation studies that aim to show different aspects of cognition, their associated behavior and the underlying neural dynamics. In chapter 4, the first study proposes an architecture that represents the visuomotor system for the encoding of actions during action observation, understanding and imitation. In chapter 5, the generative model is extended and is lesioned to simulate brain damage and neuropsychological patterns observed in apraxic patients. In chapter 6, the third study proposes an architecture for cognitive control and the modulation of attention for action selection. At last, I argue how active inference can provide a formal account of information processing in the brain and how the adaptive capabilities of the simulated agents are a mere consequence of the architecture of the generative models. Cognitive processing, then, becomes an emergent property of the minimization of variational free energy
How to explain: from theory to practice
Get PDFToday we live in an age where the internet and artificial intelligence allow us to search for information through impressive amounts of data, opening up revolutionary new ways to make sense of reality and understand our world. However, it is still an area of improvement to exploit the full potential of large amounts of explainable information by distilling it automatically in an intuitive and user-centred explanation. For instance, different people (or artificial agents) may search for and request different types of information in a different order, so it is unlikely that a short explanation can suffice for all needs in the most generic case. Moreover, dumping a large portion of explainable information in a one-size-fits-all representation may also be sub-optimal, as the needed information may be scarce and dispersed across hundreds of pages. The aim of this work is to investigate how to automatically generate (user-centred) explanations from heterogeneous and large collections of data, with a focus on the concept of explanation in a broad sense, as a critical artefact for intelligence, regardless of whether it is human or robotic. Our approach builds on and extends Achinstein’s philosophical theory of explanations, where explaining is an illocutionary (i.e., broad but relevant) act of usefully answering questions. Specifically, we provide the theoretical foundations of Explanatory Artificial Intelligence (YAI), formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. We present empirical results in support of our theory, showcasing the implementation of YAI tools and strategies for assessing explainability. To justify and evaluate the proposed theories and models, we considered case studies at the intersection of artificial intelligence and law, particularly European legislation. Our tools helped produce better explanations of software documentation and legal texts for humans and complex regulations for reinforcement learning agents
Ethics in the flesh: formalizing moral values in embodied cognition
Get PDFValues are beliefs or principles that are deemed significant or desirable within a specific society or culture, serving as the fundamental underpinnings for ethical and socio-behavioral norms. The objective of this research is to explore the domain encompassing moral, cultural, and individual values. To achieve this, we employ an ontological approach to formally represent the semantic relations within the value domain. The theoretical framework employed adopts Fillmore’s frame semantics, treating values as semantic frames. A value situation is thus characterized by the co-occurrence of specific semantic roles fulfilled within a given event or circumstance. Given the intricate semantics of values as abstract entities with high social capital, our investigation extends to two interconnected domains. The first domain is embodied cognition, specifically image schemas, which are cognitive patterns derived from sensorimotor experiences that shape our conceptualization of entities in the world. The second domain pertains to emotions, which are inherently intertwined with the realm of values. Consequently, our approach endeavors to formalize the semantics of values within an embodied cognition framework, recognizing values as emotional-laden semantic frames. The primary ontologies proposed in this work are: (i) ValueNet, an ontology network dedicated to the domain of values; (ii) ISAAC, the Image Schema Abstraction And Cognition ontology; and (iii) EmoNet, an ontology for theories of emotions. The knowledge formalization adheres to established modeling practices, including the reuse of semantic web resources such as WordNet, VerbNet, FrameNet, DBpedia, and alignment to foundational ontologies like DOLCE, as well as the utilization of Ontology Design Patterns. These ontological resources are operationalized through the development of a fully explainable frame-based detector capable of identifying values, emotions, and image schemas generating knowledge graphs from from natural language, leveraging the semantic dependencies of a sentence, and allowing non trivial higher layer knowledge inferences
Big data analysis systems in IoE environments for managing privacy and data protection: pseudonymity, de-anonymization and the right to be forgotten
Get PDFThe thesis represents the conclusive outcome of the European Joint Doctorate programmein Law, Science & Technology funded by the European Commission with the instrument Marie Skłodowska-Curie Innovative Training Networks actions inside of the H2020, grantagreement n. 814177.
The tension between data protection and privacy from one side, and the need of granting further uses of processed personal datails is investigated, drawing the lines of the technological development of the de-anonymization/re-identification risk with an explorative survey. After acknowledging its span, it is questioned whether a certain degree of anonymity can still be granted focusing on a double perspective: an objective and a subjective perspective. The objective perspective focuses on the data processing models per se, while the subjective perspective investigates whether the distribution of roles and responsibilities among stakeholders can ensure data anonymity
Multidisciplinary approach to horse welfare in different management systems
Get PDFFrom its domestication until nowadays, the horse has assumed multiple roles in human society. Over time, this condition and the lack of specific regulation have led to the development of different kinds of management systems for this species. This Ph.D. research project aims to investigate horses' welfare in different management practices and housing systems, considering a multidisciplinary approach, taking into account biological function, naturalness, and affective dimension. The results are presented in five articles that evidence risk factors that can mine horse welfare, and examine tools and parameters that can be employed for its assessment. Our research shows the importance of considering the evolutionary history and the species-specific and behavioural needs of horses in their management and housing. Sociality, free movement, diet composition and foraging routine, and the workload that these animals undergo are important factors that should be taken into account. Furthermore, this research has evidenced the importance of employing different parameters (e.g., behaviour, endocrinological parameters, and immune activity) in welfare assessment and proposes the use of horsehair DHEA (dehydroepiandrosterone) as a possible useful additional non-invasive measure for the investigation of long-term stress conditions. Finally, our results underline the importance of considering the affective dimension in welfare research. Recently, Judgement Bias Tests (JBT), which are based on the influence of affective states on the decision-making process, have been widely employed in animal welfare research. However, our studies show that the use of spatial JBT in horses can have some limitations. Still today several management systems do not fulfill species-specific needs of horses, thus the implementation of specific regulations could ameliorate horse welfare. A multidisciplinary approach to welfare assessment is fundamental, but it should be always remembered the individual and its own characteristics, which can influence not only physiological, immunological, and behavioural responses but also emotional and cognitive dimensions
New perspectives in statistical mechanics and high-dimensional inference
Get PDFThe main purpose of this thesis is to go beyond two usual assumptions that accompany theoretical analysis in spin-glasses and inference: the i.i.d. (independently and identically distributed) hypothesis on the noise elements and the finite rank regime. The first one appears since the early birth of spin-glasses. The second one instead concerns the inference viewpoint. Disordered systems and Bayesian inference have a well-established relation, evidenced by their continuous cross-fertilization. The thesis makes use of techniques coming both from the rigorous mathematical machinery of spin-glasses, such as the interpolation scheme, and from Statistical Physics, such as the replica method. The first chapter contains an introduction to the Sherrington-Kirkpatrick and spiked Wigner models. The first is a mean field spin-glass where the couplings are i.i.d. Gaussian random variables. The second instead amounts to establish the information theoretical limits in the reconstruction of a fixed low rank matrix, the “spike”, blurred by additive Gaussian noise. In chapters 2 and 3 the i.i.d. hypothesis on the noise is broken by assuming a noise with inhomogeneous variance profile. In spin-glasses this leads to multi-species models. The inferential counterpart is called spatial coupling. All the previous models are usually studied in the Bayes-optimal setting, where everything is known about the generating process of the data. In chapter 4 instead we study the spiked Wigner model where the prior on the signal to reconstruct is ignored. In chapter 5 we analyze the statistical limits of a spiked Wigner model where the noise is no longer Gaussian, but drawn from a random matrix ensemble, which makes its elements dependent. The thesis ends with chapter 6, where the challenging problem of high-rank probabilistic matrix factorization is tackled. Here we introduce a new procedure called "decimation" and we show that it is theoretically to perform matrix factorization through it
Mathematical methods to analyze and interpret calcium signals of astrocytes
Get PDFAstrocytes are the most numerous glial cell type in the mammalian brain and permeate the entire CNS interacting with neurons, vasculature, and other glial cells. Astrocytes display intracellular calcium signals that encode information about local synaptic function, distributed network activity, and high-level cognitive functions.
Several studies have investigated the calcium dynamics of astrocytes in sensory areas and have shown that these cells can encode sensory stimuli. Nevertheless, only recently the neuro-scientific community has focused its attention on the role and functions of astrocytes in associative areas such as the hippocampus. In our first study, we used the information theory formalism to show that astrocytes in the CA1 area of the hippocampus recorded with 2-photon fluorescence microscopy during spatial navigation encode spatial information that is complementary and synergistic to information encoded by nearby "place cell" neurons.
In our second study, we investigated various computational aspects of applying the information theory formalism to astrocytic calcium data. For this reason, we generated realistic simulations of calcium signals in astrocytes to determine optimal hyperparameters and procedures of information measures and applied them to real astrocytic calcium imaging data.
Calcium signals of astrocytes are characterized by complex spatiotemporal dynamics occurring in subcellular parcels of the astrocytic domain which makes studying these cells in 2-photon calcium imaging recordings difficult. However, current analytical tools which identify the astrocytic subcellular regions are time consuming and extensively rely on user-defined parameters. Here, we present Rapid Astrocytic calcium Spatio-Temporal Analysis (RASTA), a novel machine learning algorithm for spatiotemporal semantic segmentation of 2-photon calcium imaging recordings of astrocytes which operates without human intervention. We found that RASTA provided fast and accurate identification of astrocytic cell somata, processes, and cellular domains, extracting calcium signals from identified regions of interest across individual cells and populations of hundreds of astrocytes recorded in awake mice
Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses
Get PDFWith the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community.
The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner.
Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services
Relationship between the viral load, the host immune response, the microbiome and the clinical course of acute bronchiolitis in infants (RMB2018)
Get PDFIl principale motivo di ospedalizzazione del lattante è la bronchiolite acuta, causata in primis dal Virus Respiratorio Sinciziale (VRS). In questo ambito appaiono controversi i risultati in letteratura riguardo: carica del VRS, risposta immunitaria ed infiammatoria dell’ospite, microbiota del paziente (durante l’infezione e per successivo sviluppo di wheezing e asma).
In questo studio di coorte prospettico monocentrico vengono arruolati lattanti peraltro sani ricoverati per primo episodio di bronchiolite acuta, da VRS o da altro agente, per valutare primariamente la relazione tra decorso clinico e carica del VRS, secondariamente l’associazione con specifiche composizioni e modifiche nel tempo del microbiota nasofaringeo ed intestinale durante la fase acuta e nel lungo termine in relazione a sviluppo di wheezing ricorrente.
Nello studio sono stati arruolati finora 89 pazienti, di cui 68 con bronchiolite da VRS (76.4%), con analisi della carica virale su 41 lattanti (60.3%) e del microbiota su 20 (29.4%).
L’analisi della carica del VRS non ha riscontrato associazione tra outcome di severità clinica quali necessità e durata di ossigenoterapia, nonché durata di ricovero. La presenza di trend di associazione tra carica virale all’ingresso e picco del VRS-RNA con necessità di ossigenoterapia ad alto flusso (HFNC) e l’associazione significativa di clearance di VRS con HFNC (p = 0.03) suggeriscono che la carica virale potrebbe influenzare la severità della bronchiolite da VRS.
Le analisi del microbiota evidenziano numerosi genera mai descritti finora in letteratura a nostra conoscenza (es. Alloiococcus e Leptotrichia su aspirato, nonché tutti i dati emersi dallo studio della saliva, prima volta in letteratura) ed un generale aumento della diversity a 6 mesi dalla dimissione.
Occorrerà completare l’analisi della carica del VRS con quella del sistema infiammatorio dell’ospite, nonché studiare il microbiota sui campioni del follow-up a lungo termine per la verifica di eventuali associazioni con sviluppo di wheezing e asma.The leading cause of hospitalization in infancy consists of acute bronchiolitis, firstly caused by Respiratory Syncytial Virus (RSV). About infection’s severity, different results are present in literature regarding the RSV load, the host inflammatory and immune response, the patient’s microbiome during acute bronchiolitis, and related to long-term outcomes such as wheezing and asthma development.
This prospective, monocentric cohort study enrolls previously healthy infants hospitalized for their first acute bronchiolitis caused by RSV or other pathogens, firstly evaluating the relationship between the clinical course and the RSV load, then analyzing the association between bronchiolitis and specific nasopharyngeal and fecal microbiome’s genera composition and evolution concerning wheezing’s development.
Up to now, 89 infants were enrolled, 68 of them with RSV bronchiolitis (76.4%), with RSV load and microbiome analysis in 41/68 cases (60.3%) and in 20/68 patients (29.4) respectively.
No significant association emerged between RSV load and outcome measures of severity as need and duration of oxygen therapy or length of stay in the hospital. A trend of association between RSV load on admission, but also the peak of RSV-RNA and the needing for high flow oxygen therapy (HFNC), and a significant relation between RSV clearance and HFNC therapy (p = 0.03) suggested that bronchiolitis clinical course and severity could be directly influenced by RSV load.
Microbiome analyses reported a lot of bacterial genera that have not yet been described in the literature to date (e.g. Alloiococcus and Leptotrichia genera in the nasopharyngeal microbiome, and salivary data, described in our study for the first time in literature). Moreover, a general increase in diversity 6 months after discharge was observed.
Future steps will consist in analyzing the inflammatory host system influence with the cytokines’ evaluation and in extending the microbiome’s study to long-term follow-up time points and outcomes such as wheezing and asthma development
Empirical ontology design patterns
Get PDFKnowledge graphs (KGs) and ontologies have been widely adopted for modelling numerous domains.
However, understanding the content of an ontology/KG is far from straightforward: existing methods partially address this issue.
This thesis is based on the assumption that identifying the Ontology Design Patterns (ODPs) in an ontology or a KG contributes to address this problem.
Most times, the reused ODPs are not explicitly annotated, or their reuse is unintentional.
Therefore, there is a challenge to automatically identify ODPs in existing ontologies and KGs, which is the main focus of this research work.
This thesis analyses the role of ODPs in ontology engineering, through experiences in actual ontology projects, placing this analysis in the context of existing ontology reuse approaches.
Moreover, this thesis introduces a novel method for extracting empirical ODPs (EODPs) from ontologies, and a novel method for extracting EODPs from knowledge graphs, whose schemas are implicit.
The first method groups the extracted EODPs in clusters: conceptual components. Each conceptual component represents a modelling problem, e.g. representing collections.
As EODPs are fragments possibly extracted from different ontologies, some of them will fall in the same cluster, meaning that they are implemented solutions to the same modelling problem.
EODPs and conceptual components enable the empirical observation and comparison of modelling solutions to common modelling problems in different ontologies.
The second method extracts EODPs from a KG as sets of probabilistic axioms/constraints involving the ontological entities instantiated.
These EODPs may support KG inspection and comparison, providing insights on how certain entities are described in a KG.
An additional contribution of this thesis is an ontology for annotating ODPs in ontologies and KGs