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An Iterative Projection method for Synchronization of Invertible Matrices Over Graphs Thunberg, Johan ; Colombo, Nicolo ; Yue, Zuogong et al in 22nd International Symposium on Mathematical Theory of Networks and Systems (2016) This paper addresses synchronization of invertible matrices over graphs. The matrices represent pairwise transformations between n euclidean coordinate systems. Synchronization means that composite ... [more ▼] This paper addresses synchronization of invertible matrices over graphs. The matrices represent pairwise transformations between n euclidean coordinate systems. Synchronization means that composite transformations over loops are equal to the identity. Given a set of measured matrices that are not synchronized, the synchronization problem amounts to fining new synchronized matrices close to the former. Under the assumption that the measurement noise is zero mean Gaussian with known covariance, we introduce an iterative method based on linear subspace projection. The method is free of step size determination and tuning and numerical simulations show significant improvement of the solution compared to a recently proposed direct method as well as the Gauss-Newton method. [less ▲] Detailed reference viewed: 132 (10 UL)Linear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation Bernard, Florian ; ; Hertel, Frank et al in Linear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation (2016) Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly ... [more ▼] Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. In this paper, a method to obtain deformation factors with local support is presented. The benefits of such models include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. For that, based on a well-grounded theoretical motivation, we formulate a matrix factorisation problem employing sparsity and graph-based regularisation terms. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation ability and sparse shape reconstruction, whereas for human body shapes our method gives more realistic deformations. [less ▲] Detailed reference viewed: 135 (14 UL)Expression of the Parkinson’s Disease-Associated Gene Alpha-Synuclein is Regulated by the Neuronal Cell Fate Determinant TRIM32 Pavlou, Maria Angeliki ; Colombo, Nicolo ; et al in Molecular Neurobiology (2016) Detailed reference viewed: 237 (13 UL)Identifying Biochemical Reaction Networks From Heterogeneous Datasets Goncalves, Jorge ; ; et al in IEEE Conference on Decision and Control, Osaka, Japan, December 2015 (2015, December) Detailed reference viewed: 126 (3 UL)Assessing the effect of unknown widespread perturbations in complex systems using the mu-gap Goncalves, Jorge ; ; in EEE Conference on Decision and Control, Osaka, Japan, December 2015 (2015, December) Detailed reference viewed: 49 (0 UL)Shape-aware 3D Interpolation using Statistical Shape Models Bernard, Florian ; Salamanca Mino, Luis ; Thunberg, Johan et al in Symposium on Statistical Shape Models and Applications, Delemont, Switzerland, October 2015 (2015, October) Detailed reference viewed: 150 (34 UL)Critical transitions in chronic disease: transferring concepts from ecology to systems medicine Trefois, Christophe ; Antony, Paul ; Goncalves, Jorge et al in Current Opinion in Biotechnology (2015), 34 Ecosystems and biological systems are known to be inherently complex and to exhibit nonlinear dynamics. Diseases such as microbiome dysregulation or depression can be seen as complex systems as well and ... [more ▼] Ecosystems and biological systems are known to be inherently complex and to exhibit nonlinear dynamics. Diseases such as microbiome dysregulation or depression can be seen as complex systems as well and were shown to exhibit patterns of nonlinearity in their response to perturbations. These nonlinearities can be revealed by a sudden shift in system states, for instance from health to disease. The identification and characterization of early warning signals which could predict upcoming critical transitions is of primordial interest as prevention of disease onset is a major aim in health care. In this review, we focus on recent evidence for critical transitions in diseases and discuss the potential of such studies for therapeutic applications. [less ▲] Detailed reference viewed: 390 (53 UL)On minimal realisations of dynamical structure functions ; ; Goncalves, Jorge in Automatica (2015), 55 Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships between measured nodes in ... [more ▼] Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships between measured nodes in networks. From the dynamical structure functions, a) we show that the actual number of hidden states can be larger than the number of hidden states estimated from the corresponding transfer function; b) we can obtain partial information about the true state-space equation, which cannot in general be obtained from the transfer function. Based on these properties, this paper proposes algorithms to find minimal realisations for a given dynamical structure function. This helps to estimate the minimal number of hidden states, to better understand the complexity of the network, and to identify potential targets for new measurements. [less ▲] Detailed reference viewed: 181 (21 UL)Online Fault Diagnosis for Nonlinear Power Systems Pan, Wei ; ; et al in Automatica (2015), 55 In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission ... [more ▼] In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission lines. Transmission line protection is an important issue in power system engineering because a large portion of power system faults is occurring in transmission lines. This paper presents a novel technique to detect, isolate and identify the faults on transmissions using only a small number of observations. We formulate the problem of fault diagnosis of nonlinear power network into a compressive sensing framework and derive an optimisationbased formulation of the fault identification problem. An iterative reweighted `1-minimisation algorithm is finally derived to solve the detection problem efficiently. Under the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles of nonlinear power networks. [less ▲] Detailed reference viewed: 138 (14 UL)A solution for Multi-Alignment by Transformation Synchronisation Bernard, Florian ; Thunberg, Johan ; et al in The proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015) The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered ... [more ▼] The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail. Based on the observation that the underlying noise-free transformations lie in the null space of a matrix that can directly be obtained from pairwise alignments, this paper presents a novel method for the synchronisation of pairwise transformations such that they are globally consistent. Simulations demonstrate that for a high amount of noise, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results. [less ▲] Detailed reference viewed: 203 (38 UL)Dynamical Structure Function and Granger Causality: Similarities and Differences Yue, Zuogong ; Thunberg, Johan ; et al in 54th IEEE Conference on Decision and Control, Osaka, Japan, December 15-18, 2015 (2015) Detailed reference viewed: 141 (14 UL)Transitively Consistent and Unbiased Multi-Image Registration Using Numerically Stable Transformation Synchronisation Bernard, Florian ; Thunberg, Johan ; Salamanca Mino, Luis et al in MIDAS Journal (2015) Abstract. Transitive consistency of pairwise transformations is a desir- able property of groupwise image registration procedures. The transfor- mation synchronisation method [4] is able to retrieve ... [more ▼] Abstract. Transitive consistency of pairwise transformations is a desir- able property of groupwise image registration procedures. The transfor- mation synchronisation method [4] is able to retrieve transitively con- sistent pairwise transformations from pairwise transformations that are initially not transitively consistent. In the present paper, we present a numerically stable implementation of the transformation synchronisa- tion method for a ne transformations, which can deal with very large translations, such as those occurring in medical images where the coor- dinate origins may be far away from each other. By using this method in conjunction with any pairwise (a ne) image registration algorithm, a transitively consistent and unbiased groupwise image registration can be achieved. Experiments involving the average template generation from 3D brain images demonstrate that the method is more robust with re- spect to outliers and achieves higher registration accuracy compared to reference-based registration. [less ▲] Detailed reference viewed: 145 (21 UL)Modelling the natural history of Huntingtons disease progression ; ; et al in BMJ Open (2014) Background: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington’s disease (HD) as an example, we ... [more ▼] Background: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington’s disease (HD) as an example, we describe a novel approach to model HD and show that the progression of a neurological disorder can be predicted for individual patients. Methods : Starting with an initial cohort of 343 patients with HD that we have followed since 1995, we used data from 68 patients that satisfied our filtering criteria to model disease progression, based on the Unified Huntington’s Disease Rating Scale (UHDRS), a measure that is routinely used in HD clinics worldwide. Results : Our model was validated by: (A) extrapolating our equation to model the age of disease onset, (B) testing it on a second patient data set by loosening our filtering criteria, (C) cross-validating with a repeated random subsampling approach and (D) holdout validating with the latest clinical assessment data from the same cohort of patients. With UHDRS scores from the past four clinical visits (over a minimum span of 2 years), our model predicts disease progression of individual patients over the next 2 years with an accuracy of 89–91%. We have also provided evidence that patients with similar baseline clinical profiles can exhibit very different trajectories of disease progression. Conclusions : This new model therefore has important implications for HD research, most obviously in the development of potential disease-modifying therapies. We believe that a similar approach can also be adapted to model disease progression in other chronic neurological disorders. [less ▲] Detailed reference viewed: 158 (11 UL)H2-Based Network Volatility Measures Goncalves, Jorge ; ; et al in American Control Conference (2014, June) Detailed reference viewed: 59 (0 UL)Finite-time road grade computation for a vehicle platoon ; ; et al in IEEE (2014) Given a platoon of vehicles traveling uphill, this paper considers the finite-time road grade computation problem. We propose a decentralized algorithm for an arbitrarily chosen vehicle to compute the ... [more ▼] Given a platoon of vehicles traveling uphill, this paper considers the finite-time road grade computation problem. We propose a decentralized algorithm for an arbitrarily chosen vehicle to compute the road grade in a finite number of time-steps by using only its own successive velocity measurements. Simulations then illustrate the theoretical results. These new results can be applied to real-world vehicle platooning problems to reduce fuel consumption and carbon dioxide emissions. [less ▲] Detailed reference viewed: 426 (6 UL)Network Reconstruction from Intrinsic Noise ; ; Goncalves, Jorge in The proceedings of the American Control Conference (2014) This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest ... [more ▼] This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest variables only from observations of these variables. We consider linear, time-invariant systems of minimal order and with one noise source per measured state. If the transfer matrix from the inputs to manifest states is known to be minimum phase, this problem is shown to have a unique solution irrespective of the network topology. This is equivalent to there being only one spectral factor (up to a choice of signs of the inputs) of the output spectral density that satisfies these assumptions. Hence for this significant class of systems, the network reconstruction problem is well posed. [less ▲] Detailed reference viewed: 62 (1 UL)H2 Norm Based Network Volatility Measures ; ; Goncalves, Jorge et al in The proceedings of the American Control Conference (2014) Motivated by applications in biology and economics, we propose new volatility measures based on the H2 system norm for linear networks stimulated by independent or correlated noise. We identify critical ... [more ▼] Motivated by applications in biology and economics, we propose new volatility measures based on the H2 system norm for linear networks stimulated by independent or correlated noise. We identify critical links in a network, where relatively small improvements can lead to large reductions in network volatility measures. We also examine volatility measures of individual nodes and their dependence on the topological position in the network. Finally, we investigate the dependence of the volatility on different network interconnections, weights of the edges and other network properties. Hence in an intuitive and efficient way, we can identify critical links, nodes and interconnections in network which can shed light in the network design to make it more robust. [less ▲] Detailed reference viewed: 117 (5 UL)Biexcitability and Bursting Mechanisms in Neural and Genetic Circuits ; Goncalves, Jorge in The proceedings of the 19th World Congress The International Federation of Automatic Control (2014) This paper compares mechanisms for generating repetitive spikes (bursts) in neural and transcriptional circuits. Neurons generate bursts followed by refractory periods controlled by ion channels in the ... [more ▼] This paper compares mechanisms for generating repetitive spikes (bursts) in neural and transcriptional circuits. Neurons generate bursts followed by refractory periods controlled by ion channels in the membrane. In contrast, in gene transcription the bursts occur during a short time period followed by silent periods regulated by sis-regulatory elements. The role of excitability in producing different patterns of bursts is discussed by comparing the topology of a neural model with natural and synthetic transcriptional genetic circuits. In particular, a special bi-excitable architecture which embeds two excitable states are compared in these systems. [less ▲] Detailed reference viewed: 96 (1 UL)Understanding and Predicting Biological Networks Using Linear System Identi cation ; ; et al in Kulkarni, V.; Stan, G.; Raman, K. (Eds.) A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations (2014) Detailed reference viewed: 196 (11 UL)Analysis of synchronizing biochemical networks via incremental dissipativity ; Goncalves, Jorge ; in Kulkarni, V.; Stan, G.; Raman, K. (Eds.) A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular Systems (2014) Detailed reference viewed: 90 (3 UL) |
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