Relational data is characterised by the rich structure it encodes in the dependencies between the individual entities of a given domain. Statistical Relational Learning (SRL) combines first-order logic and probability to learn and reason over relational domains by creating parametric probability distributions over relational structures. SRL models can succinctly represent the complex dependencies in relational data and admit learning and inference under uncertainty. However, these models are significantly limited when it comes to the tractability of learning and inference. This limitation emerges from the intractability of Weighted First Order Model Counting (WFOMC), as both learning and inference in SRL models can be reduced to instances of WFOMC. Hence, fragments of first-order logic that admit tractable WFOMC, widely known as domain-liftable, can significantly advance the practicality and efficiency of SRL models. Recent works have uncovered another limitation of SRL models, i.e., they lead to unintuitive behaviours when used across varying domain sizes, violating fundamental consistency conditions expected of sound probabilistic models. Such inconsistencies also mean that conventional machine learning techniques, like training with batched data, cannot be soundly used for SRL models. In this thesis, we contribute to both the tractability and consistency of probabilistic inference in SRL models. We first expand the class of domain-liftable fragments with counting quantifiers and cardinality constraints. Unlike the algorithmic approaches proposed in the literature, we present a uniform combinatorial approach, admitting analytical combinatorial formulas for WFOMC. Our approach motivates a new family of weight functions allowing us to express a larger class of probability distributions without losing domain-liftability. We further expand the class of domain-liftable fragments with constraints inexpressible in first-order logic, namely acyclicity and connectivity constraints. Finally, we present a complete characterization for a statistically consistent (a.k.a projective) models in the two-variable fragment of a widely used class of SRL models, namely Markov Logic Networks.

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We introduce a modal language L which is obtained from standard modal logic by adding the Boolean operators on accessibility relations, the identity relation, and the converse of relations. It is proved that L has the same expressive power as the two-variable fragment FO² of first-order logic, but speaks less succinctly about relational structures: if the number of relations is bounded, then L-satisfiability is EXPTIME-complete but FO² satisfiability is NEXPTIME-complete. We indicate that the relation between L and FO² provides a general framework for comparing modal and temporal languages with first-order languages.

We investigate ontology-based query answering (OBQA) in a setting where both the ontology and the query can refer to concrete values such as numbers and strings. In contrast to previous work on this topic, the built-in predicates used to compare values are not restricted to being unary. We introduce restrictions on these predicates and on the ontology language that allow us to reduce OBQA to query answering in databases using the so-called combined rewriting approach. Though at first sight our restrictions are different from the ones used in previous work, we show that our results strictly subsume some of the existing first-order rewritability results for unary predicates. / This is an extended version of a paper published in the proceedings of IJCAI 2017.

The Golog action programming language is a powerful means to express high-level behaviours in terms of programs over actions defined in a Situation Calculus theory. In particular for physical systems, verifying that the program satisfies certain desired temporal properties is often crucial, but undecidable in general, the latter being due to the language’s high expressiveness in terms of first-order quantification and program constructs. So far, approaches to achieve decidability involved restrictions where action effects either had to be contextfree (i.e. not depend on the current state), local (i.e. only affect objects mentioned in the action’s parameters), or at least bounded (i.e. only affect a finite number of objects). In this paper, we present a new, more general class of action theories (called acyclic) that allows for context-sensitive, non-local, unbounded effects, i.e. actions that may affect an unbounded number of possibly unnamed objects in a state-dependent fashion. We contribute to the further exploration of the boundary between decidability and undecidability for Golog, showing that for acyclic theories in the two-variable fragment of first-order logic, verification of CTL properties of programs over ground actions is decidable.

The verification problem for action logic programs with non-terminating behaviour is in general undecidable. In this paper, we consider a restricted setting in which the problem becomes decidable. On the one hand, we abstract from the actual execution sequences of a non-terminating program by considering infinite sequences of actions defined by a Büchi automaton. On the other hand, we assume that the logic underlying our action formalism is a decidable description logic rather than full first-order predicate logic.

Ontology-based data access (OBDA) generalizes query answering in relational databases. It allows to query a database by using the language of an ontology, abstracting from the actual relations of the database. For ontologies formulated in Description Logics of the DL-Lite family, OBDA can be realized by rewriting the query into a classical first-order query, e.g. an SQL query, by compiling the information of the ontology into the query. The query is then answered using classical database techniques. In this report, we consider a temporal version of OBDA. We propose a temporal query language that combines a linear temporal logic with queries over DL-Litecore-ontologies. This language is well-suited for expressing temporal properties of dynamical systems and is useful in context-aware applications that need to detect specific situations. Using a first-order rewriting approach, we transform our temporal queries into queries over a temporal database. We then present three approaches to answering the resulting queries, all having different advantages and drawbacks. / This revised version proves that the presented algorithm achieves a bounded history encoding.

Anaerob nedbrytning vid låg temperatur är ett energieffektivt system, men få studier har fastställt kinetiken för anaerob nedbrytning av matavfall vid dessa temperaturer. I denna studie bestämdes kinetiken för en anaerob rötkammare för matavfall med mesofil inokulum vid 20°C. Effekten av acklimatiseringstiden avslöjades. Därefter användes biokol och spårämnen för att utvärdera deras inverkan vid 20°C. Det visade sig att tillsats av spårämnen ökade kinetiken upp till 114% av kontrollreaktorn. Denna studie visade att olika biokol har varierande effekt på kinetiken (här negativ och neutral effekt). De kinetiska värdena utvärderades med en första ordningens modell och användes för att uppskatta en virtuell fabrik. Med ett inflöde på 27 000 ton bioavfall/år och 80 % av den maximala metanpotentialen ansågs en psykrofil i ett steg vara orealistiskt (hydraulisk retentionstid (HRT): 9,6 månader med den experimentella kinetiken). Lovande resultat kan dock uppnås med en lämplig kombination av utetemperatur och substrattemperatur. / Low temperature anaerobic digestion is an energy efficient system, however few studies have determined the kinetics of the anaerobic digestion of food waste at those temperatures. This study determined the kinetics of a food waste anaerobic digestor with mesophilic inoculum at 20°C. The impact of acclimatation time was revealed. Then biochar and trace elements were employed to evaluate their impact at 20°C. It was shown that addition of the trace elements boosted the kinetic up to 114% of the control reactor. This study proved that different biochars have variable effect on the kinetics (here negative and neutral effect). The kinetic values were evaluated with a first order model and used to estimate a virtual factory. With 27 000 tons of biowaste /year influx and 80% expression of the maximum methane potential, a single stage psychrophilic was deemed unrealizable (Hydraulic Retention Time (HRT): 9.6 month with the experimental kinetics). However, promising results can be obtained with an adequate exterior temperature & substrate temperature couple.

Infrastructures are the most fundamental facilities and systems serving the society. Due to the existence of infrastructures in economic, social, and environmental contexts, all lifecycle phases of such fundamental facilities should maximize utility for the designers, occupants, and the society. With respect to the nature of the decision problem, two main types of uncertainties may exist: 1) the aleatory uncertainty associated with the nature of the built environment (i.e., the economic, social, and environmental impacts of infrastructures must be described as probabilistic); and 2) the epistemic uncertainty associated with the lack of knowledge of decision maker utilities. Although a number of decision analysis models exist that consider the uncertainty associated with the nature of the built environment, they do not provide a systematic framework for including aleatory and epistemic uncertainties, and decision maker utilities in the decision analysis process. In order to address the identified knowledge gap, a three-phase modular decision analysis methodology is proposed. Module one uses a formal preference assessment methodology (i.e., utility function/indifference curve) for assessing decision maker utility functions with respect to a range of alternative design configurations. Module two utilizes the First Order Reliability Method (FORM) in a systems reliability approach for assessing the reliability of alternative infrastructure design configurations with respect to the probabilistic decision criteria and decision maker defined utility functions (indifference curves), and provides a meaningful feedback loop for improving the reliability of the alternative design configurations. Module three provides a systematic framework to incorporate both aleatory and epistemic uncertainties in the decision analysis methodology (i.e., uncertain utility functions and group decision making). The multi-criteria, probabilistic decision analysis framework is tested on a nine-story office building in a seismic zone with the probabilistic decision criteria of: building damage and business interruption costs, casualty costs, and CO2 emission costs. Twelve alternative design configurations and four decision maker utility functions under aleatory and epistemic uncertainties are utilized. The results of the decision analysis methodology revealed that the high-performing design configurations with an initial cost of up to $3.2M (in a cost range between $1.7M and $3.2M), a building damage and business interruption cost as low as $303K (in a cost range between $303K and $6.2M), a casualty cost as low as $43K (in a cost range between $43K and $1.2M), and a CO2 emission as low as $146K (in a cost range between $133K to $150K) can be identified by having a higher probability (i.e., up to 80%) of meeting the decision makers' preferences. The modular, holistic, decision analysis framework allows decision makers to make more informed performance-based design decisions—and allows designers to better incorporate the preferences of the decision makers—during the early design process. / PHD / Infrastructures, including buildings, roads, and bridges, are the most fundamental facilities and systems serving the society. Because infrastructures exist in economic, social, and environmental contexts, the design, construction, operations, and maintenance phases of such fundamental facilities should maximize value and usability for the designers, occupants, and the society. Identifying infrastructure configurations that maximize value and usability is challenged by two sources of uncertainty: 1) the nature of the built environment is variable (i.e., whether or not a natural hazard will occur during the infrastructure lifetime, or how costs might change over time); and 2) there is lack of knowledge of decision maker preferences and values (e.g., design cost versus social impact tradeoffs). Although a number of decision analysis models exist that consider the uncertainty associated with the nature of the built environment (e.g., natural hazard events), they do not provide a systematic framework for including the uncertainties associated with the decision analysis process (e.g., lack of knowledge about decision maker preferences), and decision maker requirements in the decision analysis process. In order to address the identified knowledge gap, a three-phase modular decision analysis methodology is proposed. Module one uses a formal preference assessment methodology for assessing decision maker values with respect to a range of alternative design configurations. Module two utilizes an algorithm for assessing the reliability of alternative infrastructure design configurations with respect to the probabilistic decision criteria and decision maker requirements, and provides a meaningful feedback loop for understanding the decision analysis results (i.e., improving the value and usability of the alternative design configurations). Module three provides a systematic framework to incorporate both the random uncertainty associated with the built environment and the knowledge uncertainty associated with lack of knowledge of decision maker preferences, and tests the reliability of the decision analysis results under random and knowledge uncertainties (i.e., uncertain decision maker preferences and group decision making). The holistic decision analysis framework is tested on a nine-story office building in a seismic zone with the probabilistic decision criteria of: building damage and business interruption costs, casualty costs, and CO2 emission costs. Twelve alternative design configurations, four decision makers, and random and knowledge sources of uncertainty are considered in the decision analysis methodology. Results indicate that the modular, holistic, decision analysis framework allows decision makers to make more informed design decisions—and allows designers to better incorporate the preferences of the decision makers—during the early design process.

Ce travail se place dans le contexte des recherches sur les mémoires quantiques pour la lumière. L’information quantique est stockée dans un état de superposition atomique, dont la durée de vie détermine le temps maximum de stockage.On s’intéresse particulièrement aux matériaux capables de capturer la lumière par excitation résonnante d’une raie d’absorption, puis de conserver l’information quantique dans un état de superposition du fondamental électronique.Dans Tm3+:YAG, l’information est enregistrée dans un état de spin nucléaire. Cependant le champ magnétique qui lève la dégénérescence nucléaire entraîne les différents spins à des vitesses de précession différentes, ce qui tend à détruire l’aimantation initiale, porteuse de l’information.Une étude quantique du cristal est réalisée lors du premier chapitre de ce manuscrit. Les trois chapitres suivants traitent des différents mécanismes conduisant au déphasage des spins nucléaires. On y trouvera différente analyses théoriques qui seront confirmées par un ensemble de résultats expérimentaux, ainsi qu’une description détaillée du dispositif expérimental. Enfin le dernier chapitre, prospectif, exploite les outils développés au cours de la thèse pour préserver les cohérences optiques. Il présente quelques résultats expérimentaux prometteurs sur l’allongement du temps de vie de ces cohérences optiques. / This work takes place in the context of research about quantum memories for light. The quantum information is stored in an atomic superposition state whose lifetime sets the maximum storage time. We are particularly interested in materials which are able to hold the light by resonant excitation of an absorption line, preserving the quantum information in a superposition state of the electronicfundamental.n Tm3+:YAG the information is stored in a nuclear spin state. However, the magnetic field which lifts the nuclear degeneracy generates different precession speeds of the spins. This destroys theinitial magnetization carrier of the information.In the first chapter of this thesis, a quantum analysis of the crystal is done. The following three chapters are devoted to different mechanisms to control the nuclear spins dephasing. There it ispossible to find different theoretical analysis which will be confirmed by a series of experimental measurements, including an extended description of the set-up. Finally, the last chapter presentsthe different techniques used to preserve the optical coherence. Promising experimental measurements are presented to extend the life time of the optical coherences.