So called discrete models have been successfully used in engineering and computational systems biology. This thesis discusses algebraic methods for modeling and analysis of gene regulatory networks within the discrete modeling context. The first chapter gives a background for discrete models and put in context some of the main research problems that have been pursued in this field for the last fifty years. It also outlines the content of each subsequent chapter. The second chapter focuses on the problem of inferring dynamics from the structure (topology) of the network. It also discusses the characterization of the attractor structure of a network when a particular class of functions control the nodes of the network. Chapters~3 and 4 focus on the study of multi-state nested canalyzing functions as biologically inspired functions and the characterization of their dynamics. Chapter 5 focuses on stochastic methods, specifically on the development of a stochastic modeling framework for discrete models. Stochastic discrete modeling is an alternative approach from the well-known mathematical formalizations such as stochastic differential equations and Gillespie algorithm simulations. Within the discrete setting, a framework that incorporates propensity probabilities for activation and degradation is presented. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations. Finally, Chapter 6 discusses future research directions inspired by the work presented here. / Ph. D.

Semiconductor–superconductor hybrid systems are promising candidates for the realization of Majorana fermions and topological order, i.e. topologically protected degeneracies, in solid state devices. We show that the topological order is mirrored in the excitation spectra and can be observed in nonlinear Coulomb blockade transport through a ring-shaped nanowire. Especially, the excitation spectrum is almost independent of magnetic flux in the topologically trivial phase but acquires a characteristic h/e magnetic flux periodicity in the non-trivial phase. The transition between the trivial and nontrivial phase is reflected in the closing and reopening of an excitation gap. We show that the signatures of topological order are robust against details of the geometry, electrostatic disorder and the existence of additional subbands and only rely on the topology of the nanowire and the existence of a superconducting gap. Finally, we show that the coherence length in the non-trivial phase is much longer than in the trivial phase. This opens the possibility to coat the nanowire with superconducting nanograins and thereby significantly reduce the current due to cotunnelling of Cooper pairs and to enhance the Coulomb charging energy without destroying the superconducting gap.

<p dir="ltr">Despite their astonishing capacity to fit data, neural networks have difficulties extrapolating beyond training data distribution. When the out-of-distribution prediction task is formalized as a counterfactual query on a causal model, the reason for their extrapolation failure is clear: neural networks learn spurious correlations in the training data rather than features that are causally related to the target label. This thesis proposes to perform a causal search over a known family of causal models to learn robust (maximally invariant) predictors for single- and multiple-environment extrapolation tasks.</p><p dir="ltr">First, I formalize the out-of-distribution task as a counterfactual query over a structural causal model. For single-environment extrapolation, I argue that symmetries of the input data are valuable for training neural networks that can extrapolate. I introduce Asymmetry learning, a new learning paradigm that is guided by the hypothesis that all (known) symmetries are mandatory even without evidence in training, unless the learner deems it inconsistent with the training data. Asymmetry learning performs a causal model search to find the simplest causal model defining a causal connection between the target labels and the symmetry transformations that affect the label. My experiments on a variety of out-of-distribution tasks on images and sequences show that proposed methods extrapolate much better than the standard neural networks.</p><p dir="ltr">Then, I consider multiple-environment out-of-distribution tasks in dynamical system forecasting that arise due to shifts in initial conditions or parameters of the dynamical system. I identify key OOD challenges in the existing deep learning and physics-informed machine learning (PIML) methods for these tasks. To mitigate these drawbacks, I combine meta-learning and causal structure discovery over a family of given structural causal models to learn the underlying dynamical system. In three simulated forecasting tasks, I show that the proposed approach is 2x to 28x more robust than the baselines.</p>

The elderly population is growing at a rapid pace, and falls are a significant problem facing adults aged 65 and older in terms of both human suffering and economic losses. Falls are the leading cause of mortality among older adults, and non-fatal falls result in reduced function and poor quality of life for older adults. Although much is known about the mechanisms and contributing risk factors relevant to falls, falls still remain a significant problem associated with this age group. Therefore, new strategies and knowledge need to be introduced to understand and prevent falls. Studies show that early detection of impaired mobility is critical to the prevention of falls. In this study, the relationship between gait and postural parameters and falls among elderly participants using wearable inertial sensors was investigated. As such, the aim of this study is to investigate the critical gait and postural parameters contributing to falls, then further to classify fallers and non-fallers by utilizing gait and postural parameters and machine learning techniques, e.g. support vector machines (SVMs). Additionally, as the assessment of fall risk is linked to noisy environment, it is important to understand the capability of the SVM classifier to effectively address noisy data. Therefore, the robustness of the SVM classifier was also investigated in this study. In summary, the presented work addresses several challenges through research on the following three issues: 1) the significant differences in gait and pastoral parameters between fallers and non-fallers; 2) a machine learning based framework for classification of fallers and non-fallers by using only one IMU located at the sternum; and 3) robustness of SVM classifier to classify fallers and non-fallers in a noisy environment. The machine learning based framework developed in this dissertation contribute to advancing the state-of-art in fall risk assessment by 1) classifying fallers and non-fallers from a single IMU located at the sternum; 2) developing machine learning method for classification of fallers and non-fallers; and 3) investigating the robustness of SVM classifier in a noisy environment. / Ph. D.

Autonomous cyber-physical systems (CPSs) such as autonomous connected vehicles (ACVs), unmanned aerial vehicles (UAVs), critical infrastructure (CI), and the Internet of Things (IoT) will be essential to the functioning of our modern economies and societies. Therefore, maintaining the autonomy of CPSs as well as their stability, robustness, and security (SRS) in face of exogenous and disruptive events is a critical challenge. In particular, it is crucial for CPSs to be able to not only operate optimally in the vicinity of a normal state but to also be robust and secure so as to withstand potential failures, malfunctions, and intentional attacks. However, to evaluate and improve the SRS of CPSs one must overcome many technical challenges such as the unpredictable behavior of a CPS's cyber-physical environment, the vulnerability to various disruptive events, and the interdependency between CPSs. The primary goal of this dissertation is, thus, to develop novel foundational analytical tools, that weave together notions from machine learning, game theory, and control theory, in order to study, analyze, and optimize SRS of autonomous CPSs. Towards achieving this overarching goal, this dissertation led to several major contributions. First, a comprehensive control and learning framework was proposed to thwart cyber and physical attacks on ACV networks. This framework brings together new ideas from optimal control and reinforcement learning (RL) to derive a new optimal safe controller for ACVs in order to maximize the street traffic flow while minimizing the risk of accidents. Simulation results show that the proposed optimal safe controller outperforms the current state of the art controllers by maximizing the robustness of ACVs to physical attacks. Furthermore, using techniques from convex optimization and deep RL a joint trajectory and scheduling policy is proposed in UAV-assisted networks that aims at maintaining the freshness of ground node data at the UAV. The analytical and simulation results show that the proposed policy can outperform policies such discretized state RL and value-based methods in terms of maximizing the freshness of data. Second, in the IoT domain, a novel watermarking algorithm, based on long short term memory cells, is proposed for dynamic authentication of IoT signals. The proposed watermarking algorithm is coupled with a game-theoretic framework so as to enable efficient authentication in massive IoT systems. Simulation results show that using our approach, IoT messages can be transmitted from IoT devices with an almost 100% reliability. Next, a brainstorming generative adversarial network (BGAN) framework is proposed. It is shown that this framework can learn to generate real-looking data in a distributed fashion while preserving the privacy of agents (e.g. IoT devices, ACVs, etc). The analytical and simulation results show that the proposed BGAN architecture allows heterogeneous neural network designs for agents, works without reliance on a central controller, and has a lower communication over head compared to other state-of-the-art distributed architectures. Last, but not least, the SRS challenges of interdependent CI (ICI) are addressed. Novel game-theoretic frameworks are proposed that allow the ICI administrator to assign different protection levels on ICI components to maximizing the expected ICI security. The mixed-strategy Nash of the games are derived analytically. Simulation results coupled with theoretical analysis show that, using the proposed games, the administrator can maximize the security level in ICI components. In summary, this dissertation provided major contributions across the areas of CPSs, machine learning, game theory, and control theory with the goal of ensuring SRS across various domains such as autonomous vehicle networks, IoT systems, and ICIs. The proposed approaches provide the necessary fundamentals that can lay the foundations of SRS in CPSs and pave the way toward the practical deployment of autonomous CPSs and applications. / Doctor of Philosophy / In order to deliver innovative technological services to their residents, smart cities will rely on autonomous cyber-physical systems (CPSs) such as cars, drones, sensors, power grids, and other networks of digital devices. Maintaining stability, robustness, and security (SRS) of those smart city CPSs is essential for the functioning of our modern economies and societies. SRS can be defined as the ability of a CPS, such as an autonomous vehicular system, to operate without disruption in its quality of service. In order to guarantee SRS of CPSs one must overcome many technical challenges such as CPSs' vulnerability to various disruptive events such as natural disasters or cyber attacks, limited resources, scale, and interdependency. Such challenges must be considered for CPSs in order to design vehicles that are controlled autonomously and whose motion is robust against unpredictable events in their trajectory, to implement stable Internet of digital devices that work with a minimum communication delay, or to secure critical infrastructure to provide services such as electricity, gas, and water systems. The primary goal of this dissertation is, thus, to develop novel foundational analytical tools, that weave together notions from machine learning, game theory, and control theory, in order to study, analyze, and optimize SRS of autonomous CPSs which eventually will improve the quality of service provided by smart cities. To this end, various frameworks and effective algorithms are proposed in order to enhance the SRS of CPSs and pave the way toward the practical deployment of autonomous CPSs and applications. The results show that the developed solutions can enable a CPS to operate efficiently while maintaining its SRS. As such, the outcomes of this research can be used as a building block for the large deployment of smart city technologies that can be of immense benefit to tomorrow's societies.

Surge-energy robustness is essential for power devices in many applications such as automotive powertrains and electricity grids. While Si and SiC MOSFETs can dissipate surge energy via avalanche, the GaN high-electron-mobility transistor (HEMT) has no avalanche capability and withstands surge energy by its overvoltage capability. However, a comprehensive study into the surge-energy robustness of the cascode GaN HEMT, a composite device made of a GaN HEMT and a Si metal-oxide-semiconductor field-effect-transistor (MOSFET), is still lacking. This work fills this gap by investigating the failure and degradation of 650-V-rated cascode GaN HEMTs in single-event and repetitive unclamped inductive switching (UIS) tests. The cascode was found to withstand surge energy by the overvoltage capability of the GaN HEMT, accompanied by an avalanche in the Si MOSFET. In single-event UIS tests, the cascode failed in the GaN HEMT at a peak overvoltage of 1.4~1.7 kV, which is statistically lower than the device's static breakdown voltage (1.8~2.2 kV). In repetitive UIS tests, the device failure boundary was found to be frequency-dependent. At 100 kHz, the failure boundary (~1.3 kV) was even lower than the single-event UIS boundary. After 1 million cycles of 1.25-kV UIS stresses, devices showed significant but recoverable parametric shifts. Physics-based device simulation and modeling were then performed to understand the circuit test results. The electron trapping in the buffer layer of the GaN HEMT can explain all the above failure and degradation behaviors in the GaN HEMT and the resulted change in its dynamic breakdown voltage. Moreover, the GaN buffer trapping is believed to be assisted by the Si MOSFET avalanche. An analytical model was also developed to extract the charges and losses produced in the Si avalanche in a UIS cycle. These results provide new insights into the surge-energy and overvoltage robustness of cascode GaN HEMTs. / M.S. / Power conversion technologies are now inseparable in industrial and commercial applications with widespread solar panels, laptops, data centers, and electric vehicles. Power devices are the critical components of power conversion systems. Since the introduction of Si power metal-oxide-semiconductor field-effect-transistor (MOSFET) in the mid-1970s, it has become the go-to device that enables efficient and reliable power conversion. After decades of practice on Si MOSFET, the device performance has reached the theoretical limit of the Si material. The recent introduction of wide-bandgap (WBG) power transistors, represented by silicon carbide (SiC) and gallium nitride (GaN) devices with superior figures of merits, opens the door for faster and more efficient power systems. To exploit the benefits of WBG devices, researchers need to evaluate the reliability and robustness of these devices comprehensively. The work presented here provides a study on the robustness of one mainstream GaN power transistor – the cascode GaN high-electron-mobility transistor (HEMT). This robustness test replicates the surge events in power electronics systems and exams their impact on power devices. Over the years, people have thoroughly investigated the surge-energy robustness of Si MOSFETs and concluded that Si MOSFETs are very robust against these surge events thanks to the avalanche mechanism. However, GaN HEMTs lack p-n junction structures between the two major electrodes, leading to the lack of avalanche ability. Instead, GaN HEMTs rely on the overvoltage capability to sustain the surge energy. For the first time, this work evaluates the surge-energy and overvoltage ruggedness of cascode GaN HEMTs, a major player in the GaN power device market. By analyzing the device failure mechanism and degradation behaviors, this research work provides insight into the weakness of these devices for both device designers and application engineers.

Unmanned aerial systems have been widely used for variety of civilian applications over the past few years. Some of these applications require accurate guidance and control. Consequently, Unmanned Aerial Vehicle (UAV) guidance and control attracted many researchers in both control theory and aerospace engineering. Flying wings, as a particular type of UAV, are considered to have one of the most efficient aerodynamic structures. It is however difficult to design robust controller for such systems. This is due to the fact that flying wings are highly sensitive to control inputs. The focus of this thesis is on modeling and control design for a UAV system. The platform understudy is a flying wing developed by SmartPlanes Co. located in Skellefteå, Sweden. This UAV is particularly used for topological mapping and aerial photography. The novel approach suggested in this thesis is to use two controllers in sequence. More precisely, Linear Quadratic Regulator (LQR) is suggested to provide robust stability, and Proportional, Integral, Derivative (PID) controller is suggested to provide reference signal regulation. The idea behind this approach is that with LQR in the loop, the system becomes more stable and less sensitive to control signals. Thus, PID controller has an easier task to do, and is only used to provide the required transient response. The closed-loop system containing the developed controller and a UAV non-linear dynamic model was simulated in Simulink. Simulated controller was then tested for stability and robustness with respect to some parametric uncertainty. Obtained results revealed that the LQR successfully managed to provide robust stability, and PID provided reference signal regulation.

To enhance the protection against cyberattacks, significant research is directed towards anomaly-based host intrusion detection systems (HIDS), which particularly appear suited for detecting zero-day attacks. This thesis addresses two problems in HIDS training sets that are often neglected in other publications: unclean and incomplete data. First, using the Leipzig Intrusion Detection - Data Set (LID-DS), a methodology to measure HIDS robustness against contaminated training data is presented. Furthermore, three baseline HIDS approaches (STIDE, SCG, and SOM) are evaluated, and robustness improvements are proposed for them. The results indicate that the baselines are not robust if test and training data share identical attacks. However, the suggested modifications, particularly the removal of anomalous threads from the training set, can enhance robustness significantly. For the problem of incomplete training data, the thesis leverages machine learning models to predict a training set’s suitability, quantified by either data drift measures or the STIDE performance. The thesis then presents rules, extracted from the best models, for assessing the suitability of new training data. Given the practical significance of both issues, for contaminated training data emphasized by the results, further research is essential. This involves examining the robustness of other HIDS algorithms, refining the proposed robustness improvements, and validating the suitability rules on other datasets, preferably real-world data.

Selection strategies to increase productive traits of farm animals have been effective and highly specialized breeds and strains were obtained. At the same time, the effort made to obtain extremely high producing animals was accompanied by undesired effects, such as a reduced ability to sustain production, reproduction and health; especially under constrained conditions. The perception that selection was degrading robustness, lead to selection strategies aiming to improve the ability of animals to perform in a wider range of environmental constrain. However, at the present moment, the physiological mechanisms allowing farm animals to perform well in a wide range of environments, while others succumb, have not been described. The present thesis intended to address this question by describing the evolution of traits related to fitness, survival and to the adaptability to environmental constraints. Two maternal rabbit lines differing in their ability to face the environmental constraints, i.e. a `specialist¿ and a `generalist¿ maternal rabbit line were available. Additionally, two generations (20 generations apart) of the specialized line were simultaneously available. During the first two consecutive reproductive cycles, female rabbits were simultaneously subjected to three environmental conditions differing in the intensity and in the physiological constrain imposed. Digestive capacity, the acquisition of resources and the partitioning of resources into different function (i.e. litter size, milk yield, growth, body reserves, etc.) was also assessed. Results showed a greater acquisition capacity of `generalist¿ females in constrained conditions with respect to `specialist¿ females. Moreover, the greater acquisition capacity was not accompanied by a reduction in the digestive efficiency, allowing the `generalist¿ females a relative greater acquisition of digestible energy. The maintenance of reproductive performance by having a greater acquisition capacity, together with the avoidance of making an intensive use of body reserves were both related to the capacity of `generalist¿ females to sustain reproduction in a wide range of environmental conditions. Twenty generations of selection exclusively for reproduction (specialized line), was not accompanied by a higher acquisition capacity, but by a change in the relative priority between the litter being nursed (actual) and the litter being gestate (future litter). In this sense, females from the actual generation of selection for litter size at weaning had a greater milk yield in the first week of lactation (period of great importance to kits survival), reducing it by the end of lactation. The present thesis also evidenced the importance of the environment where the animals are being selected in the evolution of the interplay between competing functions. / Savietto, D. (2014). Environmental and genetic factors driving robustness in reproductive rabbit does [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37198 / Premios Extraordinarios de tesis doctorales

Tesis por compendio / Despite the continuous evolution in computers and information technology, real-world combinatorial optimization problems are NP-problems, in particular in the domain of planning and scheduling. Thus, although exact techniques from the Operations Research (OR) field, such as Linear Programming, could be applied to solve optimization problems, they are difficult to apply in real-world scenarios since they usually require too much computational time, i.e: an optimized solution is required at an affordable computational time. Furthermore, decision makers often face different and typically opposing goals, then resulting multi-objective optimization problems. Therefore, approximate techniques from the Artificial Intelligence (AI) field are commonly used to solve the real world problems. The AI techniques provide richer and more flexible representations of real-world (Gomes 2000), and they are widely used to solve these type of problems. AI heuristic techniques do not guarantee the optimal solution, but they provide near-optimal solutions in a reasonable time. These techniques are divided into two broad classes of algorithms: constructive and local search methods (Aarts and Lenstra 2003). They can guide their search processes by means of heuristics or metaheuristics depending on how they escape from local optima (Blum and Roli 2003). Regarding multi-objective optimization problems, the use of AI techniques becomes paramount due to their complexity (Coello Coello 2006). Nowadays, the point of view for planning and scheduling tasks has changed. Due to the fact that real world is uncertain, imprecise and non-deterministic, there might be unknown information, breakdowns, incidences or changes, which become the initial plans or schedules invalid. Thus, there is a new trend to cope these aspects in the optimization techniques, and to seek robust solutions (schedules) (Lambrechts, Demeulemeester, and Herroelen 2008). In this way, these optimization problems become harder since a new objective function (robustness measure) must be taken into account during the solution search. Therefore, the robustness concept is being studied and a general robustness measure has been developed for any scheduling problem (such as Job Shop Problem, Open Shop Problem, Railway Scheduling or Vehicle Routing Problem). To this end, in this thesis, some techniques have been developed to improve the search of optimized and robust solutions in planning and scheduling problems. These techniques offer assistance to decision makers to help in planning and scheduling tasks, determine the consequences of changes, provide support in the resolution of incidents, provide alternative plans, etc. As a case study to evaluate the behaviour of the techniques developed, this thesis focuses on problems related to container terminals. Container terminals generally serve as a transshipment zone between ships and land vehicles (trains or trucks). In (Henesey 2006a), it is shown how this transshipment market has grown rapidly. Container terminals are open systems with three distinguishable areas: the berth area, the storage yard, and the terminal receipt and delivery gate area. Each one presents different planning and scheduling problems to be optimized (Stahlbock and Voß 2008). For example, berth allocation, quay crane assignment, stowage planning, and quay crane scheduling must be managed in the berthing area; the container stacking problem, yard crane scheduling, and horizontal transport operations must be carried out in the yard area; and the hinterland operations must be solved in the landside area. Furthermore, dynamism is also present in container terminals. The tasks of the container terminals take place in an environment susceptible of breakdowns or incidences. For instance, a Quay Crane engine stopped working and needs to be revised, delaying this task one or two hours. Thereby, the robustness concept can be included in the scheduling techniques to take into consideration some incidences and return a set of robust schedules. In this thesis, we have developed a new domain-dependent planner to obtain more effi- cient solutions in the generic problem of reshuffles of containers. Planning heuristics and optimization criteria developed have been evaluated on realistic problems and they are applicable to the general problem of reshuffling in blocks world scenarios. Additionally, we have developed a scheduling model, using constructive metaheuristic techniques on a complex problem that combines sequences of scenarios with different types of resources (Berth Allocation, Quay Crane Assignment, and Container Stacking problems). These problems are usually solved separately and their integration allows more optimized solutions. Moreover, in order to address the impact and changes that arise in dynamic real-world environments, a robustness model has been developed for scheduling tasks. This model has been applied to metaheuristic schemes, which are based on genetic algorithms. The extension of such schemes, incorporating the robustness model developed, allows us to evaluate and obtain more robust solutions. This approach, combined with the classical optimality criterion in scheduling problems, allows us to obtain, in an efficient in way, optimized solution able to withstand a greater degree of incidents that occur in dynamic scenarios. Thus, a proactive approach is applied to the problem that arises with the presence of incidences and changes that occur in typical scheduling problems of a dynamic real world. / Rodríguez Molins, M. (2015). Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48545 / Compendio