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In this paper, we explore how ontological knowledge expressed via existential rules can be combined with possibilistic networks (i) to represent qualitative preferences along with domain knowledge, and (ii) to realize preference-based answering of conjunctive queries (CQs). We call these combinations ontological possibilistic networks (OP-nets). We define skyline and k-rank answers to CQs under preferences and provide complexity (including data tractability) results for deciding consistency and CQ skyline membership for OP-nets. We show that our formalism has a lower complexity than a similar existing formalism.

Answering queries posed over knowledge bases is a central problem in knowledge representation and database theory. In the database area, checking query containment is an important query optimization and schema integration technique. In knowledge representation it has been used for object classification, schema integration, service discovery, and more. In the presence of a knowledge base, the problem of query containment is strictly related to that of query answering; indeed, the two are reducible to each other; we focus on the latter, and our results immediately extend to the former.

We study the complexity of exchanging probabilistic data between ontology-based probabilistic databases. We consider the Datalog+/- family of languages as ontology and ontology mapping languages, and we assume different compact encodings of the probabilities of the probabilistic source databases via Boolean events. We provide an extensive complexity analysis of the problem of deciding the existence of a probabilistic (universal) solution for a given probabilistic source database relative to a (probabilistic) data exchange problem for the different languages considered.

We tackle a long-standing open research problem and prove the decidability of query answering under the stable model semantics for guarded existential rules, where rule bodies may contain negated atoms, and provide complexity results. The results extend to guarded Datalog+⁄− with negation, and thus provide a natural and decidable stable model semantics to description logics such as ELHI and DL-LiteR.

The tastes of a user can be represented in a natural way by using qualitative preferences. In this paper, we explore how ontological knowledge expressed via existential rules can be combined with CP-theories to (i) represent qualitative preferences along with domain knowledge, and (ii) perform preference-based answering of conjunctive queries (CQs). We call these combinations ontological CP-theories (OCP-theories). We define skyline and k-rank answers to CQs based on the user’s preferences encoded in an OCP-theory, and provide an algorithm for computing them. We also provide precise complexity (including data tractability) results for deciding consistency, dominance, and CQ skyline membership for OCP-theories.

We report on a recently introduced family of expressive extensions of Datalog, called Datalog±, which is a new framework for representing ontological axioms in form of integrity constraints, and for query answering under such constraints. Datalog± is derived from Datalog by allowing existentially quantified variables in rule heads, and by enforcing suitable properties in rule bodies, to ensure decidable and efficient query answering. We first present different languages in the Datalog ± family, providing tight complexity bounds for all cases but one (where we have a low complexity AC0 upper bound). We then show that such languages are general enough to capture the most common tractable ontology languages. In particular, we show that the DL-Lite family of description logics and F-Logic Lite are expressible in Datalog ±. We finally show how stratified negation can be added to Datalog± while keeping ontology querying tractable in the data complexity. Datalog± is a natural and very general framework that can be successfully employed in different contexts such as data integration and exchange. This survey mainly summarizes two recent papers. Copyright 2009 ACM.

Single crystals of GdCa4O(BO3)3 were examined to determine their thermal properties. Samples were grown by the Czochralski pulling technique. There were three types of samples: a pure crystal, the crystal doped with neodymium (4 at.% of Nd), and the third one doped with ytterbium (7 at.% of Yb). All samples were rectangular prisms with edges parallel to the axes of the optical indicatrix X, Y , Z (principal axes). The thermal diffusivity was determined by means of the thermal wave method with the optical detection of the temperature disturbance based on a mirage effect. Experimental results showed anisotropy of the thermal diffusivity. The thermal diffusivity along Y direction has the highest value while values obtained in X and Z axes are much lower. Dopants cause decrease in the thermal diffusivity for all investigated directions.

Previous work has proposed consistent query answering as a way to resolve inconsistencies in ontologies. In these approaches to consistent query answering, however, only inconsistencies due to errors in the underlying database are considered. In this paper, we additionally assume that ontological axioms may be erroneous, and that some database atoms and ontological axioms may not be removed to resolve inconsistencies. This problem is especially well suited in debugging mappings between distributed ontologies. We define two different semantics, one where ontological axioms as a whole are ignored to resolve an inconsistency, and one where only some of their instances are ignored. We then give a precise picture of the complexity of consistent query answering under these two semantics when ontological axioms are encoded as different classes of existential rules. In the course of this, we also close two open complexity problems in standard consistent query answering under existential rules.

mCP-nets are an expressive and intuitive formalism based on CP-nets to reason about preferences of groups of agents. The dominance semantics of mCP-nets is based on the concept of voting, and different voting schemes give rise to different dominance semantics for the group. Unlike CP-nets, which received an extensive complexity analysis, mCP-nets, as reported multiple times in the literature, lack a precise study of the voting tasks' complexity. Prior to this work, only a complexity analysis of brute-force algorithms for these tasks was available, and this analysis only gave EXPTIME upper bounds for most of those problems. In this paper, we start to fill this gap by carrying out a precise computational complexity analysis of voting tasks on acyclic binary polynomially connected mCP-nets whose constituents are standard CP-nets. Interestingly, all these problems actually belong to various levels of the polynomial hierarchy, and some of them even belong to PTIME or LOGSPACE. Furthermore, for most of these problems, we provide completeness results, which show tight lower bounds for problems that (up to date) did not have any explicit non-obvious lower bound.

Machine learning can be used to identify relevant trajectory shape features for improved predictive risk modeling, which can help inform decisions for individualized patient management in intensive care during COVID-19 outbreaks. We present explainable random forests to dynamically predict next day mortality risk in COVID -19 positive and negative patients admitted to the Mount Sinai Health System between March 1st and June 8th, 2020 using patient time-series data of vitals, blood and other laboratory measurements from the previous 7 days. Three different models were assessed by using time series with: 1) most recent patient measurements, 2) summary statistics of trajectories (min/max/median/first/last/count), and 3) coefficients of fitted cubic splines to trajectories. AUROC and AUPRC with cross-validation were used to compare models. We found that the second and third models performed statistically significantly better than the first model. Model interpretations are provided at patient-specific level to inform resource allocation and patient care.

The renewal of interest in study praseodymium (Pr3+)-doped solid state materials is due to the emission spectrum of this ion extending from ultraviolet1 (UV) to near infrared.2 Laser action in several Pr3+ -doped crystals: LiYF4, YAlO3, as well as fibres has recently been reported.3 Especially the blue-green part of the Pr3+ emission spectrum is very interesting, since there is a great need for compact, integrated laser sources for many applications such as data storage, display technology, undersea communications, and optical processing.

Background: Risk models are essential for care planning and disease prevention. The unsatisfactory performance of the established clinical models has raised broad awareness and concerns. An accurate, explainable, and reliable risk model is highly beneficial but remains a challenge. Objective: This thesis aims to develop deep learning models that can make more accurate risk predictions with the provision of uncertainty estimation and the ability to provide medical explanations using a large and representative electronic health records (EHR) dataset. Methods: We investigated three directions in this thesis: risk prediction, explainability, and uncertainty estimation. For risk prediction, we investigated deep learning tools that can incorporate the minimal processed EHR for modelling and comprehensively compared them with the established machine learning and clinical models. Additionally, the post-hoc explanations were applied to deep learning models for medical information retrieval, and we specifically looked into explanations in risk association and counterfactual reasoning. Uncertainty estimation was qualitatively investigated using probabilistic modelling techniques. Our analyses relied on Clinical Practice Research Datalink, which contains anonymised EHR collected from primary care, secondary care, and death registration and is representative of the UK population. Results: We introduced a deep learning model, named BEHRT, that can incorporate minimal processed EHR for risk prediction. Without expert engagement, it learned meaningful representations that can automatically cluster highly correlated diseases. Compared to the established machine learning and clinical models that relied on expert- selected predictors, our proposed deep learning model showed superior performance on a wide range of risk prediction tasks and highlighted the necessity of recalibration when applying a risk model to a population with severe prior distribution shifts, and the importance of regular model updating to preserve the model’s discrimination performance under temporal data shifts. Additionally, we showed that the deep learning model explanation is an excellent tool for discovering risk factors. By explaining the deep learning model, we not only identified factors that were highly consistent with the established evidence but also those that have not been considered in expert-driven studies. Furthermore, the deep learning model also captured the interplay between risk and treated risk and the differential association of medications across different years, which would be difficult if the temporal context was not included in the modelling. Besides the explanations in terms of association, we introduced a framework that can achieve accurate risk prediction, while enabling counterfactual reasoning under hypothetical interventions. This offers counterfactual explanations that could inform clinicians for selection of those who will benefit the most. We demonstrated the benefit of the proposed framework using two exemplary case studies. Furthermore, transforming a deterministic deep learning model to probabilistic can make predictions with an uncertainty range. We showed that such information has many potential implications in practice, such as quantifying the confidence of a decision, indicating data insufficiency, distinguishing the correct and incorrect predictions, and indicating risk associations. Conclusions: Deep learning models led to substantially improved performance for risk prediction. The ability of uncertainty estimation can quantify the confidence of risk prediction to further inform clinical decision-making. Deep learning model explanation can generate hypotheses to guide medical research and provide counterfactual analysis to assist clinical decision-making. This encouraging evidence supports the great potential of incorporating deep learning methods into electronic health records to inform a wide range of health applications such as care planning, disease prevention, and medical study design.

The divide between continuous and discrete data is a fundamental one in computer science and mathematics, as well as related areas such as cognitive science. Historically, most of computing has operated in the discrete domain, but connectionism offers an alternative set of techniques for representing data with continuous vectors, an alterative which has come to the fore with the advent of deep learning over the past decade. This thesis explores techniques for converting continuous, high-dimensional data, of the sort processed so successfully by deep learning, to discrete compact representations, of the sort used by traditional computing. Each of the five main chapters introduces a novel technique that contributes towards this goal, but is also able to be read as a stand-alone piece of research. These techniques fall under deep learning and clustering, and, in keeping with representation learning in general, are mostly, though not entirely, in the unsupervised setting. Some chapters focus on deep learning or clustering separately as a means to form discrete representations of continuous data. Others explore how to combine both deep learning and clustering in a single end-to-end learning system. Such a combination itself involves the interface between continuous and discrete, as deep learning operates on the former, and clustering on the latter. Being able to bridge the gap between the worlds of continuous and discrete also aligns with the original goal of AI to model human intelligence, as an important part of human cognition is the movement between the worlds of continuous and discrete. Our sensory input is largely continuous, but we represent it with a natural language and reasoning apparatus that is largely discrete. A machine that one day thinks and acts as a human will have to learn to do the same.

Neural networks trained with backpropagation achieved impressive results in the last decade. However, training such models requires sequential backward updates and non-local computations, making it challenging to parallelize at scale, implement in novel hardware, and is unlike how learning works in the brain. Neuroscience-inspired learning algorithms, such as predictive coding, have the potential to overcome these limitations and advance beyond current deep learning technologies. This potential, however, has only recently gained the attention of the community. As a consequence, the properties of these algorithms are still underexplored. In this thesis, I aim at filling this gap by exploring three interesting properties of predictive coding: First, there exists a variation of predictive coding that is equivalent to backpropagation in supervised learning, second, predictive coding is able to perform powerful associative memories, and third, it is able to train neural networks with graphs of any topology. The first result implies that predictive coding networks can be as accurate as standard ones when used to perform supervised learning tasks. The last two, that they are able to perform tasks with a robustness and flexibility that is lacking in standard deep learning models. I then conclude by discussing future directions of research, such as neural architecture search, novel hardware implementations, and implications in neuroscience. All in all, the results presented in this thesis are coherent with recent trends in the literature, which show that neuroscience-inspired learning methods may have interesting machine learning properties, and that they should be considered as as a valid alternative to backpropagation.