Case-control studies with affected sibships

Related cases may be included in case-control association studies if correlations between related individuals due to identity-by-descent (IBD) sharing are taken into account. We derived a framework to test for association in a case-control design including affected sibships and unrelated controls. First, a corrected variance for the allele frequency difference between cases and controls was directly calculated or estimated in two ways on the basis of the fixation index FST and the inbreeding coefficient. Then the correlation-corrected association test including controls and affected sibs was carried out. We applied the three strategies to 20 candidate genes on the Genetic Analysis Workshop 15 rheumatoid arthritis data and to 9187 single-nucleotide polymorphisms of replicate one of the Genetic Analysis Workshop 15 simulated data with knowledge of the "answers". The three strategies used to correct for correlation give only minor differences in the variance estimates and yield an almost correct type I error rate for the association tests. Thus, all strategies considered to correct the variance performed quite well

Relaxation, Rattling and Decoupling: Dynamic Processes in Glassy Matter

Glass is a material of paramount importance. It is ubiquitous in everyday life and used in classical fields as packaging, household or architecture. In addition it can be applied in more recent fields like optical fibres in communication techniques, lenses in high-resolution microscopes or as bioactive implants in medicine. Despite its importance, the physics of glasses is only poorly understood and the transition from the liquid to the glassy state is still considered as one of the great unsolved problems in solid state physics. Dielectric spectroscopy is proven to be a very powerful tool for the investigation of the dynamic behaviour of glassy matter in a very broad frequency range. It reveals a whole zoo of dynamic processes: the structural alpha-relaxation, the excess wing and the slow beta-relaxation, the minimum regime and the boson peak. The present work provides information on all these processes in a series of different molecular glass-formers in the whole frequency range accessible. For the first time systematic studies on the behaviour of the fast beta-process and the boson peak are performed. These allow for testing several phenomenological models and theories of the dynamics of supercooled liquids.Glas ist ein Material von höchster Bedeutung. Sowohl im täglichen Leben als auch in anderen Bereichen, wie in der Verpackungsindustrie, im Haushalt oder in der Architektur ist es allgegenwärtig. Darüber hinaus wird es als optische Faser in der Nachrichtentechnik, als Linsen in hochauflösenden Mikroskopen oder etwa als Material für bioaktive Implantate eingesetzt. Trotz seiner Bedeutung ist die zugrundeliegende Physik der Gläser weitgehend unverstanden. Der Übergang von der Flüssigkeit zum Glas wird immer noch als eines der großen ungelösten Probleme in der Festkörperphysik betrachtet. Für die Untersuchung des dynamischen Verhaltens von glasbildenden Materialien hat sich die dielektrische Spektroskopie als eine leistungsstarke Messtechnik erwiesen, mit der ein sehr breiter Frequenzbereich zugänglich ist. Eine ganze Reihe an verschiedenen Prozessen kann mit ihr beobachtet werden: Die strukturelle alpha-Relaxation, der Excess Wing und die langsame beta-Relaxation sowie der schnelle beta-Prozess und der Bosonpeak. Die hier vorliegende Arbeit befasst sich mit all diesen Prozessen in einer Serie von verschiedenen molekularen Glasbildnern. Dabei wird der gesamte zugängliche Frequenzbereich abgedeckt. Zum ersten Mal wurden systematische Studien über das Verhalten des schnellen beta-Prozesses und des Bosonpeaks durchgeführt. Diese ermöglichen es, diverse phänomenologische Modelle und Theorien bezüglich der Glasdynamik zu testen

Robust adaptive MPC using control contraction metrics

We present a robust adaptive model predictive control (MPC) framework for nonlinear continuous-time systems with bounded parametric uncertainty and additive disturbance. We utilize general control contraction metrics (CCMs) to parameterize a homothetic tube around a nominal prediction that contains all uncertain trajectories. Furthermore, we incorporate model adaptation using set-membership estimation. As a result, the proposed MPC formulation is applicable to a large class of nonlinear systems, reduces conservatism during online operation, and guarantees robust constraint satisfaction and convergence to a neighborhood of the desired setpoint. One of the main technical contributions is the derivation of corresponding tube dynamics based on CCMs that account for the state and input dependent nature of the model mismatch. Furthermore, we online optimize over the nominal parameter, which enables general set-membership updates for the parametric uncertainty in the MPC. Benefits of the proposed homothetic tube MPC and online adaptation are demonstrated using a numerical example involving a planar quadrotor.Comment: This is the accepted version of the paper in Automatica, 202

Approximate non-linear model predictive control with safety-augmented neural networks

Model predictive control (MPC) achieves stability and constraint satisfaction for general nonlinear systems, but requires computationally expensive online optimization. This paper studies approximations of such MPC controllers via neural networks (NNs) to achieve fast online evaluation. We propose safety augmentation that yields deterministic guarantees for convergence and constraint satisfaction despite approximation inaccuracies. We approximate the entire input sequence of the MPC with NNs, which allows us to verify online if it is a feasible solution to the MPC problem. We replace the NN solution by a safe candidate based on standard MPC techniques whenever it is infeasible or has worse cost. Our method requires a single evaluation of the NN and forward integration of the input sequence online, which is fast to compute on resource-constrained systems. The proposed control framework is illustrated on three non-linear MPC benchmarks of different complexity, demonstrating computational speedups orders of magnitudes higher than online optimization. In the examples, we achieve deterministic safety through the safety-augmented NNs, where naive NN implementation fails

The cycling of carbon into and out of dust

Observational evidence seems to indicate that the depletion of interstellar carbon into dust shows rather wide variations and that carbon undergoes rather rapid recycling in the interstellar medium (ISM). Small hydrocarbon grains are processed in photo-dissociation regions by UV photons, by ion and electron collisions in interstellar shock waves and by cosmic rays. A significant fraction of hydrocarbon dust must therefore be re-formed by accretion in the dense, molecular ISM. A new dust model (Jones et al., Astron. Astrophys., 2013, 558, A62) shows that variations in the dust observables in the diffuse interstellar medium (nH = 1000 cm^3), can be explained by systematic and environmentally-driven changes in the small hydrocarbon grain population. Here we explore the consequences of gas-phase carbon accretion onto the surfaces of grains in the transition regions between the diffuse ISM and molecular clouds (e.g., Jones, Astron. Astrophys., 2013, 555, A39). We find that significant carbonaceous dust re-processing and/or mantle accretion can occur in the outer regions of molecular clouds and that this dust will have significantly different optical properties from the dust in the adjacent diffuse ISM. We conclude that the (re-)processing and cycling of carbon into and out of dust is perhaps the key to advancing our understanding of dust evolution in the ISM.Comment: 14 pages, 6 figure

Predictive safety filter using system level synthesis

Safety filters provide modular techniques to augment potentially unsafe control inputs (e.g. from learning-based controllers or humans) with safety guarantees in the form of constraint satisfaction. In this paper, we present an improved model predictive safety filter (MPSF) formulation, which incorporates system level synthesis techniques in the design. The resulting SL-MPSF scheme ensures safety for linear systems subject to bounded disturbances in an enlarged safe set. It requires less severe and frequent modifications of potentially unsafe control inputs compared to existing MPSF formulations to certify safety. In addition, we propose an explicit variant of the SL-MPSF formulation, which maintains scalability, and reduces the required online computational effort - the main drawback of the MPSF. The benefits of the proposed system level safety filter formulations compared to state-of-the-art MPSF formulations are demonstrated using a numerical example.Comment: https://gitlab.ethz.ch/ics/SLS_safety_filter

Homothetic tube model predictive control with multi-step predictors

We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard MPC formulations by integrating multi-step predictors, which provide reduced error bounds. These bounds, derived from multi-step predictors, are utilized in a homothetic tube formulation to mitigate conservatism. Lastly, a multi-rate formulation is adopted to handle the incompatibilities of multi-step predictors. We provide a theoretical analysis, guaranteeing robust recursive feasibility, constraint satisfaction, and (practical) stability of the desired setpoint. We use a simulation example to compare it to existing literature and demonstrate advantages in terms of conservatism and computational complexity

Active Learning-based Model Predictive Coverage Control

The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications. However, coverage applications face two major challenges: (1) dealing with nonlinear dynamics while respecting system and safety critical constraints, and (2) performing the task in an initially unknown environment. We solve the coverage problem by using a hierarchical framework, in which references are calculated at a central server and passed to the agents' local model predictive control (MPC) tracking schemes. Furthermore, to ensure that the environment is actively explored by the agents a probabilistic exploration-exploitation trade-off is deployed. In addition, we derive a control framework that avoids the hierarchical structure by integrating the reference optimization in the MPC formulation. Active learning is then performed drawing inspiration from Upper Confidence Bound (UCB) approaches. For all developed control architectures, we guarantee closed-loop constraint satisfaction and convergence to an optimal configuration. Furthermore, all methods are tested and compared on hardware using a miniature car platform.Comment: Extended version of accepted paper in IEEE Transactions on Automatic Control, 202

Robust Nonlinear Optimal Control via System Level Synthesis

This paper addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncertain nonlinear system is decomposed based on a first-order Taylor series expansion into a nominal system and an error (deviation) described as an uncertain linear time-varying system. This decomposition allows us to leverage System Level Synthesis to jointly optimize an affine error feedback, a nominal nonlinear trajectory, and, most importantly, a dynamic linearization error over-bound used to ensure robust constraint satisfaction for the nonlinear system. The proposed approach thereby results in less conservative planning compared with state-of-the-art techniques. We demonstrate the benefits of the proposed approach to control the rotational motion of a rigid body subject to state and input constraints.Comment: submitted to IEEE Transactions on Automatic Control (TAC

Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry

Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time, and accuracy. Volume reference was MRI. 28 healthy volunteers (24—50 a) were scanned with 2D and 3D ultrasound (and by MRI) by three physicians (MD 1, 2, 3) with different experience levels (6, 4, and 1 a). In the 2D scans, the thyroid lobe volumes were calculated with the ellipsoid formula. A convolutional deep neural network (CNN) automatically segmented the 3D thyroid lobes. 26, 6, and 6 random lobe scans were used for training, validation, and testing, respectively. On MRI (T1 VIBE sequence) the thyroid was manually segmented by an experienced MD. MRI thyroid volumes ranged from 2.8 to 16.7ml (mean 7.4, SD 3.05). The CNN was trained to obtain an average Dice score of 0.94. The interobserver variability comparing two MDs showed mean differences for 2D and 3D respectively of 0.58 to 0.52ml (MD1 vs. 2), −1.33 to −0.17ml (MD1 vs. 3) and −1.89 to −0.70ml (MD2 vs. 3). Paired samples t-tests showed significant differences for 2D (p = .140, p = .002 and p = .002) and none for 3D (p = .176, p = .722 and p = .057). Intraobsever variability was similar for 2D and 3D ultrasound. Comparison of ultrasound volumes and MRI volumes showed a significant difference for the 2D volumetry of all MDs (p = .002, p = .009, p <.001), and no significant difference for 3D ultrasound (p = .292, p = .686, p = 0.091). Acquisition time was significantly shorter for 3D ultrasound. Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times