Bart L.R. De Moor

KU Leuven | ku leuven · Department of Electrical Engineering (ESAT)

Mass spectrometry imaging (MSI) is a promising technique to assess the spatial distribution of molecules in a tissue sample. Non-linear dimensionality reduction methods such as Uniform Manifold Approximation and Projection (UMAP) can be very valuable for the visualization of the massive datasets produced by MSI. These visualizations can offer us go...

Objectives: The role of intrauterine haematoma on pregnancy outcomes remains uncertain. Some studies report an association with miscarriage, whilst others refute this. The impact on long-term outcomes is not known. We aimed to assess if intrauterine haematomas detected using ultrasonography in the first trimester are associated with adverse pregna...

We show that globally optimal least-squares identification of autoregressive moving-average (ARMA) models is an eigenvalue problem (EP). The first order optimality conditions of this identification problem constitute a system of multivariate polynomial equations, in which most variables appear linearly. This system is basically a multiparameter eig...

In this work Uniform Manifold Approximation and Projection (UMAP) is applied for non-linear dimensionality reduction and visualisation of Mass Spectrometry Imaging (MSI) data. We evaluate the performance of the UMAP algorithm on MSI datasets acquired in mouse pancreas and human lymphoma samples and compare it to those of principal component analysi...

Objective To assess prospectively the association between pelvic pain, vaginal bleeding, and nausea and vomiting occurring in the first trimester of pregnancy and the incidence of later adverse pregnancy outcomes. Methods This was a prospective observational cohort study of consecutive women with confirmed intrauterine singleton pregnancy between...

Results Conclusions To characterize the cardiac transcriptional signature of the metabolic cardiomyopathy To identify the pathways and transcription factors: • induced by ACE-I in the healthy heart • induced by ACE-I in the metabolic syndrome heart • of HPC-induced cardioprotection in the healthy heart • of impaired cardioprotection in the MetS hea...

This paper extends the concept of scalar cepstrum coefficients from single-input single-output linear time invariant dynamical systems to multiple-input multiple-output models, making use of the Smith-McMillan form of the transfer function. These coefficients are interpreted in terms of poles and transmission zeros of the underlying dynamical syste...

Quantifying similarity between data objects is an important part of modern data science. Deciding what similarity measure to use is very application dependent. In this paper, we combine insights from systems theory and machine learning, and investigate the weighted cepstral distance, which was previously defined for signals coming from ARMA models....

Abstract Background Blood glucose control in the intensive care unit (ICU) has the potential to save lives. However, maintaining blood glucose concentrations within a chosen target range is difficult in clinical practice and holds risk of potentially harmful hypoglycemia. Clinically validated computer algorithms to guide insulin dosing by nurses ha...

This paper presents the results of using the Least-Squares Support Vector Machines (LS-SVMs) framework for estimating CO2 levels at the Holst Center building in the Netherlands. Within the IoT framework, a Wireless Sensor Network (WSN) consisting of seven sensors is currently deployed at the third floor of the building. Each sensor node provides me...

Objectives: To describe the sonographic features of endometrial cancer in relation to stage, grade, and histological type using the International Endometrial Tumor Analysis (IETA) terminology. Methods: Prospective multicenter study on 1714 women with endometrial cancer undergoing a standardized transvaginal grayscale and Doppler ultrasound exami...

Multidimensional systems are becoming increasingly important as they provide a promising tool for estimation, simulation and control, while going beyond the traditional setting of one-dimensional systems. The analysis of multidimensional systems is linked to multivariate polynomials, and is therefore more difficult than the well-known analysis of o...

Objectives: To estimate intra- and inter-rater agreement and reliability with regard to describing ultrasound images of the endometrium using the International Endometrial Tumor Analysis (IETA) terminology. Methods: Four expert and four non-expert raters assessed video clips of transvaginal ultrasound examinations of the endometrium from 99 wome...

Starting from a dataset with input/output time series generated by multiple deterministic linear dynamical systems, this letter tackles the problem of automatically clustering these time series. We propose an extension to the so-called Martin cepstral distance, that allows to efficiently cluster these time series, and apply it to simulated electric...

Multiplex immunoassays range from small-scaled multiplex sandwich ELISAs in a planar or bead-based format to the more expanded antibody arrays employing direct sample labeling. The plethora of data generated from these arrays could be of great interest to understand a complex disorder such as endometriosis. Multiplex immunoassay analysis may provid...

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training...

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training...

In this paper, we investigate and analyze in detail the structure and properties of a simultaneous decomposition for fifteen matrices: , , , , and ( ). We show that from this simultaneous decomposition we can derive some necessary and sufficient conditions for the existence of a solution to the system of two-sided coupled generalized Sylvester matr...

Supplementary materials.

Objectives: Recurrence of hepatocellular carcinoma can arise from the primary tumor ("early recurrence") or de novo from tumor formation in a cirrhotic environment ("late recurrence"). We aimed to develop one simple gene expression score applicable in both the tumor and the surrounding liver that can predict the recurrence risk. Methods: We dete...

MSC: 15A03 15A21 15A23 15A24 Keywords: Generalized Sylvester matrix equations Matrix decompositions Solvability General solution Rank In this paper, we investigate and analyze in detail the structure and properties of a simultaneous decomposition for fifteen matrices: A i ∈ C pi×ti , B i ∈ C si×qi , C i ∈ C pi×ti+1 , D i ∈ C si+1×qi , and E i ∈ C p...

This paper presents the results of using a data assimilation technique known as Optimal Interpolation (OI) for improving the PM10 estimates of the air quality model AURORA. Ground-based measurements provided by IRCEL (the Belgian Interregional Environment Agency) have been used in the data assimilation process. AURORA has been set up to cover a dom...

Context: Several observational studies and meta-analyses have reported increased mortality of patients taking sulfonylurea and insulin. The impact of patient profiles and concomitant therapies often remains unclear. Objective: To quantify survival of patients after starting glucose-lowering agents (GLAs) and compare it to control subjects, match...

We propose FS-Scala, a flexible and modular Scala based implementation of the Fixed Size Least Squares Support Vector Machine (FS-LSSVM) for large data sets. The framework consists of a set of modules for (gradient and gradient free) optimization , model representation, kernel functions and evaluation of FS-LSSVM models. A kernel based Fixed-Size L...

Recent developments in the field of gene sequencing technology greatly accelerated discovery of mutations that cause various genetic disorders. At the same time, a typical sequencing experiment generates a large number of candidate mutations, hence detecting single or few causative variants is still a formidable problem. Many computational methods...

This paper presents the results of using a data assimilation technique known as Optimal Interpolation (OI) for improving the PM10 estimates of the air quality model AURORA. Ground-based measurements provided by IRCEL (the Belgian Interregional Environment Agency) have been used in the data assimilation process. AURORA has been set up to cover a dom...

We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task with label noise in the negative set. We use an ensemble of SVM models trained on bootstrap subsamples of the training data for increased robustness against label noise....

Assessing the performance of a learned model is a crucial part of machine learning. Most evaluation metrics can only be computed with labeled data. Unfortunately, in many domains we have many more unlabeled than labeled examples. Furthermore, in some domains only positive and unlabeled examples are available, in which case most standard metrics can...

Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related to drug purchases and medical p...

Assessing the performance of a learned model is a crucial part of machine learning. However, in some domains only positive and unlabeled examples are available, which prohibits the use of most standard evaluation metrics. We propose an approach to estimate any metric based on contingency tables, including ROC and PR curves, using only positive and...

The optimal filtering problem for linear systems with unknown inputs is addressed. Based on recursive least-squares estimation, information formulas for joint input and state estimation are derived. By establishing duality relations to the Kalman filter equations, covariance and square-root forms of the formulas follow almost instantaneously.

In Model Predictive Control stability can be gua-ranteed by the use of an invariant terminal set. In this paper a numerical method is described concerning the computation of a low-complexity polytopic-invariant set for linear and nonlinear continuous time-systems subject to state, input and rate constraints. The method determines an (sub)optimal li...

The subspace identification method for time-invariant linear systems is extended to the class of multi-mode or hybrid systems. Under an excitation criterion, the parameters in all modes are simultaneously estimated, and combined with a detection scheme for the mode switching. In turn, parameters of the mode switching mechanism, whether deterministi...

Total least squares (TLS) technique is introduced to dynamically identify intersection traffic flow patterns when both of the observations for entering and exiting vehicles have random measurement errors. An algorithm of dynamic TLS estimations of intersection traffic flow patterns is proposed. Simulation experiments show that it can improve estima...

RaPID (Robust Advanced PID Control) is a new and original design methodology for optimal PID controllers. It consists of a process modeling step with linear identification, followed by an optimal PID control design step based on engineering specifications. The design methodology is applied to a chemical continuously stirred tank reactor.

Data from biomedical domains often have an inherit hierarchical structure. As this structure is usually implicit, its existence can be overlooked by practitioners interested in constructing and evaluating predictive models from such data. Ignoring these constructs leads to potentially problematic and the routinely unrecognized bias in the models an...

We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based on given data. Most common learning algorithms feature a set of hyperparameters that must be determined before...

This paper is a consensus statement on terms, definitions and measurements to describe and report the sonographic features of the myometrium using grayscale sonography, color/power Doppler, and three-dimensional ultrasound imaging. These terms and definitions may be relevant both for the clinician when reporting ultrasound examinations in daily pra...

Background Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few...

Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm and evolutionary optimization. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in all machine learning environme...

In this article both the left and right null space of the Macaulay matrix are described. The left null space is shown to be linked with the occurrence of syzygies in its row space. It is also demonstrated how the dimension of the left null space is described by a piecewise function of polynomials. We present two algorithms that determine these poly...

This paper describes the design and implementa-tion of a model predictive controller (MPC) for a pilot scale binary distillation column containing a mixture of methanol and isopropanol. In a first step experimental data are collected and linear black box models are identified. Secondly, the MPC is configured based on given requirements and restrict...

This paper describes the identification of a binary distillation column with Least-Squares Support Vector Machines (LS-SVM). It is our intention to investigate whether a kernel based model, particularly an LS-SVM, can be used for the simulation of the top and bottom temperature of a binary distillation column. Furthermore, we compare the latter mod...

Background: Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integrat...

In this article we present a fast recursive orthogonalization scheme for two important subspaces of the Macaulay matrix: its row space and null space. It requires a graded monomial ordering and exploits the resulting structure of the Macaulay matrix induced by this graded ordering. The resulting orthogonal basis for the row space will retain a simi...

In this work a new model for online clustering named Incremental Kernel Spectral Clustering (IKSC) is presented. It is based on Kernel Spectral Clustering (KSC), a model designed in the Least Squares Support Vector Machines (LS-SVMs) framework, with primal-dual setting. The IKSC model is developed to quickly adapt itself to a changing environment,...

We present Optunity, a Python library which bundles various strategies to solve hyperparameter tuning problems. The library provides general purpose algorithms, ranging from undirected search methods to adaptive methods based on refinement strategies, heuristics and evolutionary computing. Optunity aspires to become a Swiss army knife to solve tuni...

Background: Effective and safe glycemic control in critically ill patients requires accurate glucose sensors and adequate insulin dosage calculators. The LOGIC-Insulin calculator for glycemic control has recently been validated in the LOGIC-1 randomized controlled trial. In this study, we aimed to determine the allowable error for intermittent and...

Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many h...

Endometriosis is a benign gynecological disease defined by the ectopic presence of endometrium and associated with pelvic pain and infertility. The etiology and pathogenesis remain unclear. The gold standard of diagnosing endometriosis is laparoscopy followed by histological confirmation, associated with an 8-year delay in the diagnosis of endometr...

Throughout the years, the computing power of industrial controllers has steadily increased. Together with the development of efficient quadratic program (QP) solvers, this raises the question whether these devices can host an online model predictive controller (MPC). The applicability of online MPC is investigated using a programmable automation co...

We propose a method, maximum likelihood estimation of generalized eigenvalue decomposition (MLGEVD) that employs a well known technique relying on the generalization of singular value decomposition (SVD). The main aim of the work is to show the tight equivalence between MLGEVD and generalized ridge regression. This relationship reveals an important...

We present EnsembleSVM, a free software package containing efficient routines to perform en- semble classification with support vector machine (SVM) base models. The EnsembleSVM software is implemented in C++11 and is licensed under the GNU LGPL. The software is freely available at http://esat.kuleuven.be/stadius/ensemblesvm/. Ensembles of SVM mod...

Background DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify...

Recent developments in the field of-omics technologies brought great potential for conducting biomedical research in very efficient manner, but also raised a plethora of new computational challenges to be addressed. Extremely high dimensionality accompanied with poor signal-to-noise ratio and small sample size of data resulting from high-throughput...

We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner products between support vectors and test instances. The approximation is applicable to all kernel methods featuring sums of kernel evaluations and makes no assumptions regardin...