no / This paper identifies the need for thorough experimental based study for Software-over-the-air (SOTA) in an automotive context. The paper outlines the challenges and context for automotive SOTA with an extensive literature review. It then details the early stages of the experimental studies, which aim to identify the key control and noise factors that affect performance of the SOTA in an automotive environment. This contribution establishes a framework for uncertainty modelling of SOTA as a system which highlights the needs to develop solutions requiring big data gathering and analysis as next research opportunities to the scientific community. / Jaguar Land-Rover

Yes / Deepfakes constitute fake content -generally in the form of video clips and other media formats such as images or audio- created using deep learning algorithms. With the rapid development of artificial intelligence (AI) technologies, the deepfake content is becoming more sophisticated, with the developed detection techniques proving to be less effective. So far, most of the detection techniques in the literature are based on AI algorithms and can be considered as passive. This paper presents a proof-of-concept deepfake detection system that detects fake news video clips generated using voice impersonation. In the proposed scheme, digital watermarks are embedded in the audio track of a video using a hybrid speech watermarking technique. This is an active approach for deepfake detection. A standalone software application can perform the detection of robust and fragile watermarks. Simulations are performed to evaluate the embedded watermark's robustness against common signal processing and video integrity attacks. As far as we know, this is one of the first few attempts to use digital watermarking for fake content detection. / EIG CONCERT-Japan call to the project entitled “Detection of fake newS on SocIal MedIa pLAtfoRms” (DISSIMILAR) through grants PCI2020-120689-2 (Ministry of Science and Innovation, Spain) and JPMJSC20C3 (JST SICORP, Japan). In addition, the work of the first two authors was partly funded by the Spanish Government through RTI2018-095094-B-C22 “CONSENT”

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.

With a higher power rating and broader application, Gallium nitride (GaN) is a promising next-generation power switch. The current four GaN HEMTs in paralleled phase leg that can block 400 V and conduct 200 A current is very beneficial, thus making the protection method on a GaN phase leg an urgent topic. This thesis starts with an overview of shortcircuit robustness among silicon (Si), silicon carbide (SiC) and GaN devices. An approximately safe operation area (SOA) for a GaN power switch will also be determined. The various common shortcircuit protection methods are mentioned. Additionally, current research on a GaN semiconductor is summarized. Among all of the protection methods, desaturation detection is selected and analyzed through simulation and then implemented in a parallel enhancement-mode high-electron-mobility transistor (E-HEMT) GaN phase leg. With this desaturation detection feature, the GaN E-HEMT can be turned off as quickly as 200 ns, and in the worst case, 500 ns, during a shortcircuit test. The phase leg survived a series of shortcircuit tests with shortcircuit protection. For the proposed protection scheme, the best-case reaction time (200 ns) is similar to others in the literature, while the shortcircuit peak current and peak energy are higher. The worst-case performance of this design is limited by both the gate driver and the device shortcircuit robustness. Due to the fast switching speed of the GaN HEMT, the false turn-on phenomenon caused by the Miller effect can be a problem. A shoot through may occur with one switch false turn on. The Miller clamp is added to the phase leg to improve its reliability. After the hardware was implemented, the Miller clamp was tested and verified through a double pulse test (DPT). Compared to the phase leg without the Miller clamp, the gate is better protected from gate voltage overshoot and undershoot. The switching loss is reduced by 20 percent by using a new gate driver IC with higher current driving capability. The degradation effect of GaN power switches in different shortcircuit pulses was also studied. The device passes through the shortcircuit tests, but any degradation effect that may change its parameters and influence its normal operation characteristic need to be addressed. Several GaN devices were selected and characterized after several shortcircuit tests to observe any degradation effect caused by the shortcircuit. The degradation test results reveal a "recovery effect" of the GaN HEMT used in this project. The parameter variations on threshold voltage and on-resistance recover to the original state, several hours after the shortcircuit test. The test results match with the conclusion drawn in degradation test conducts by other research groups that the parameter variation during shortcircuit test is negligible. Also, repetitively fast shortcircuit tests on the GaN HEMT show that the shortcircuit protection limit for this device under 400 V bus should be limited to 300 ns. / Master of Science

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.

No / Triple-negative breast cancers (TNBC) are clinically heterogeneous, an aggressive form of breast cancer with poor diagnosis and highly therapeutic resistant. It is urgently needed for identifying novel biomarkers with increased sensitivity and specificity for early detection and personalised therapeutic intervention. Microarray profiling offered significant advances in molecular classification but sample scarcity and cohort heterogeneity remains challenging areas. Here, we investigated diagnostics signatures derived from human triple-negative tissue. We applied REMARK criteria for the selection of relevant studies and compared the signatures gene lists directly as well as assessed their classification performance in predicting diagnosis using leave-one-out cross-validation. The cross-validation results shows excellent classification accuracy ratios using all data sets. A subset signature (17-gene) extracted from the convergence of eligible signatures have also achieved excellent classification accuracy of 89.37% across all data sets. We also applied gene ontology functional enrichment analysis to extract potentially biological process, pathways and network involved in TNBC disease progression. Through functional analysis, we recognized that these independent signatures have displayed commonalities in functional pathways of cell signaling, which play important role in the development and progression of TNBC. We have also identified five unique TNBC pathways genes (SYNCRIP, NFIB, RGS4, UGCG, LOX and NNMT), which could be important for therapeutic interventions as indicated by their close association with known drivers of TNBC and previously published experimental studies. / Yorkshire Cancer Research for the Supplementary ort of CWS (BPP049 and B209PG)