<p>Single subject ERPs were averaged over 18 subjects and are shown for two stimulus condit... more <p>Single subject ERPs were averaged over 18 subjects and are shown for two stimulus conditions: Contour and Non-contour. This signal is generated by averaging all signals from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119489#pone.0119489.g003" target="_blank">Fig 3</a> for the two conditions separately. Plots in the left column represent the averaged signal (on top) followed by its event-related intrinsic modes (ERMs). Plots in the right column present related Hilbert spectra for each of the corresponding ERMs in the left column.</p
The corresponding four moments are () = 1.4, () = 1.0, skewness() = -0.95 and kurtosis() = 4.0 fo... more The corresponding four moments are () = 1.4, () = 1.0, skewness() = -0.95 and kurtosis() = 4.0 for (a) and () = -0.84, () = 1.0, skewness() = 0.49 and kurtosis() = 4.4 for (b).<b>Copyright information:</b>Taken from "Analyzing M-CSF dependent monocyte/macrophage differentiation: Expression modes and meta-modes derived from an independent component analysis"http://www.biomedcentral.com/1471-2105/9/100BMC Bioinformatics 2008;9():100-100.Published online 17 Feb 2008PMCID:PMC2277398.
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016
We introduce a new Blind Source Separation Approach called binNTF which operates on tensor-valued... more We introduce a new Blind Source Separation Approach called binNTF which operates on tensor-valued binary datasets. Assuming that several simultaneously acting sources or elementary causes are generating the observed data, the objective of our approach is to uncover the underlying sources as well as their individual contribution to each observation with a minimum number of assumptions in an unsupervised fashion. We motivate, develop and demonstrate our method in the context of binary wafer test data which evolve during microchip fabrication. In this application, we also have to deal with incomplete datasets which can occur due to the commonly used stop-on-first-fail testing procedure or result from the aggregation of several distinct tests into BIN categories.
Electroencephalographic (EEG) signals are used as a non-invasive clinical tool for the diagnosis ... more Electroencephalographic (EEG) signals are used as a non-invasive clinical tool for the diagnosis and treatment of brain deaseases. However, they are often disturbed by artifacts which limit the possibility to interpret the data. Independent Component Analysis (ICA) is able to recover n independent sources [s\vec](t) which are linearly mixed by an unknown mixing process [x\vec](t) = A [s\vec](t). Various ICA-algorithms have been successfully applied for discovering the artifacts in the EEG data and correcting the signals. We propose a geometric algorithm for the detection of artifacts in the EEG signals. The set of all probability distributions of the mixtures [x\vec]i(t) forms a hyperparallelepiped in the observation space. By taking n vectors [w\vec]i each one located at the edges of the cone that contains the mixing space, as column vectors of a matrix W, we obtain a matrix similar to the mixing matrix A. Considering n vectors [e\vec]1,...,[e\vec]n their angular proximity gn is gi...
The paper introduces a novel approach for the extraction of physically meaningful thermal compone... more The paper introduces a novel approach for the extraction of physically meaningful thermal component time series during the manufacturing of casting parts. We treat their extraction as Blind Source Separation (BSS) problem by exploiting process-related prior knowledge. Our proposed method arranges temperature time series into a data matrix, which is then decomposed by Non-negative Matrix Factorization (NMF). The latter is guided by a knowledge-based strategy, which initializes the NMF component matrix with time curves designed according to basic physical processes. The temperature time series encompass exclusively non-negative data. Hence NMF lends itself a natural choice as it does not impose mathematical constraints that lack any immediate physical interpretation. We show how to extract components linked to physical phenomena that typically occur during production and cannot be monitored directly. We apply our method to real world data, collected in a foundry during the series production of casting parts for the automobile industry and demonstrate its efficiency.
We investigated ensembles of artificial and real-world grey-scale images to find different invari... more We investigated ensembles of artificial and real-world grey-scale images to find different invariance properties: translation invariance, scale invariance and a new hierarchical invariance recently proposed by Ruderman [1]. We found that the assumption of ...
In this paper we propose a new algorithm for the clustering of signals using incomplete independe... more In this paper we propose a new algorithm for the clustering of signals using incomplete independent component analysis (ICA). In the first step we apply the ICA to the dataset without dimension reduction, in the second step we reduce the dimension of the data to ...
Deuterium spin-lattice relaxation times (zH T I) are reported for the first time as a function of... more Deuterium spin-lattice relaxation times (zH T I) are reported for the first time as a function of temperature (180-300 K), pressure (p I 225 MPa), and composition (c I 5 m) in undercooled aqueous solutions of tetraalkylammonium ions (R4N+). A comparison with related investigations of undercooled alkali-metal halide solutions allows the competing influence of coulombic, hydrophobic, and H-bond interactions upon the dynamic structure of the random, transient H-bond network to be studied. Solvent dynamics are seen to be closely related to the glass-forming tendency of undercooled tetramethylammonium (Me4N+) and tetrapropylammonium (Pr4N+) bromide solutions and to the clathrate-forming tendency of undercooled tetrabutylammonium (Bu4N+) bromide solutions.
It is generally assumed that the analysis of intracranial pressure (ICP) waveforms could be used ... more It is generally assumed that the analysis of intracranial pressure (ICP) waveforms could be used for the detection of multiple cerebral pathophysiologies. A main obstacle for the analysis of ICP waveforms is given by the large variation of their generating signal, the arterial blood pressure (ABP). Using extended principal component analysis (PCA) we show that it is possible to distinguish between ICP waveforms generated by pathological ABP waveforms, e.g. in the case of a heart failure, without loosing information about the state of the cerebral compliance. We also create a dataset for ICP pulse classification that can be used to train models to distinguish between an intact and diminished cerebral compliance, as a function of the ICP-generating ABP pulse. As a baseline for classification we build a fully convolutional network (FCN) that reaches high performance with relatively few data.
Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of ... more Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of nonnegative data, there is no guarantee for this behavior. Several authors proposed NMF methods which enforce sparseness by constraining or penalizing the ' 1-norm of the factor matrices. On the other hand, little work has been done using a more natural sparseness measure, the ' 0-pseudo-norm. In this paper, we propose a framework for approximate NMF which constrains the ' 0-norm of the basis matrix, or the coefficient matrix, respectively. For this purpose, techniques for unconstrained NMF can be easily incorporated, such as multiplicative update rules, or the alternating nonnegative least-squares scheme. In experiments we demonstrate the benefits of our methods, which compare to, or outperform existing approaches. & 2011 Elsevier B.V. cannot be made via the ' 1-norm. Introducing the non-convex ' 0-norm as constraint function typically renders a problem NPhard, requiring exhaustive combinatoric search. However, Vavasis [16] has shown that NMF is NP-hard per se, 1 so we have to accept (most probably) that any algorithm for NMF is suboptimal. Hence, a heuristic method for NMF with ' 0-constraints might be just as appropriate and efficient as ' 1-sparse NMF. Little work is concerned with ' 0-sparse NMF. The K-SVD algorithm [17] aims to find an overcomplete dictionary for sparse Contents lists available at SciVerse ScienceDirect
This short survey reviews the recent literature on the relationship between the brain structure a... more This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding o...
Comprehending the interplay between spatial and temporal characteristics of neural dynamics can c... more Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ability to replicate neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently predominantly used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GN...
Process monitoring and optimization in an industrial setting is a complex topic, a large amount o... more Process monitoring and optimization in an industrial setting is a complex topic, a large amount of data is captured and its measurements are affected by many complex relations. There is, therefore, a growing necessity for methods to reduce its complexity and dimension such that it can be interpreted by humans. In this work we study the extraction of interpretable rules derived from components of a non-negative matrix factorization using real data.
Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool fo... more Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is not compatible to the analysis of group data in general. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA-based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to eliminate the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group
<p>Single subject ERPs were averaged over 18 subjects and are shown for two stimulus condit... more <p>Single subject ERPs were averaged over 18 subjects and are shown for two stimulus conditions: Contour and Non-contour. This signal is generated by averaging all signals from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119489#pone.0119489.g003" target="_blank">Fig 3</a> for the two conditions separately. Plots in the left column represent the averaged signal (on top) followed by its event-related intrinsic modes (ERMs). Plots in the right column present related Hilbert spectra for each of the corresponding ERMs in the left column.</p
The corresponding four moments are () = 1.4, () = 1.0, skewness() = -0.95 and kurtosis() = 4.0 fo... more The corresponding four moments are () = 1.4, () = 1.0, skewness() = -0.95 and kurtosis() = 4.0 for (a) and () = -0.84, () = 1.0, skewness() = 0.49 and kurtosis() = 4.4 for (b).<b>Copyright information:</b>Taken from "Analyzing M-CSF dependent monocyte/macrophage differentiation: Expression modes and meta-modes derived from an independent component analysis"http://www.biomedcentral.com/1471-2105/9/100BMC Bioinformatics 2008;9():100-100.Published online 17 Feb 2008PMCID:PMC2277398.
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016
We introduce a new Blind Source Separation Approach called binNTF which operates on tensor-valued... more We introduce a new Blind Source Separation Approach called binNTF which operates on tensor-valued binary datasets. Assuming that several simultaneously acting sources or elementary causes are generating the observed data, the objective of our approach is to uncover the underlying sources as well as their individual contribution to each observation with a minimum number of assumptions in an unsupervised fashion. We motivate, develop and demonstrate our method in the context of binary wafer test data which evolve during microchip fabrication. In this application, we also have to deal with incomplete datasets which can occur due to the commonly used stop-on-first-fail testing procedure or result from the aggregation of several distinct tests into BIN categories.
Electroencephalographic (EEG) signals are used as a non-invasive clinical tool for the diagnosis ... more Electroencephalographic (EEG) signals are used as a non-invasive clinical tool for the diagnosis and treatment of brain deaseases. However, they are often disturbed by artifacts which limit the possibility to interpret the data. Independent Component Analysis (ICA) is able to recover n independent sources [s\vec](t) which are linearly mixed by an unknown mixing process [x\vec](t) = A [s\vec](t). Various ICA-algorithms have been successfully applied for discovering the artifacts in the EEG data and correcting the signals. We propose a geometric algorithm for the detection of artifacts in the EEG signals. The set of all probability distributions of the mixtures [x\vec]i(t) forms a hyperparallelepiped in the observation space. By taking n vectors [w\vec]i each one located at the edges of the cone that contains the mixing space, as column vectors of a matrix W, we obtain a matrix similar to the mixing matrix A. Considering n vectors [e\vec]1,...,[e\vec]n their angular proximity gn is gi...
The paper introduces a novel approach for the extraction of physically meaningful thermal compone... more The paper introduces a novel approach for the extraction of physically meaningful thermal component time series during the manufacturing of casting parts. We treat their extraction as Blind Source Separation (BSS) problem by exploiting process-related prior knowledge. Our proposed method arranges temperature time series into a data matrix, which is then decomposed by Non-negative Matrix Factorization (NMF). The latter is guided by a knowledge-based strategy, which initializes the NMF component matrix with time curves designed according to basic physical processes. The temperature time series encompass exclusively non-negative data. Hence NMF lends itself a natural choice as it does not impose mathematical constraints that lack any immediate physical interpretation. We show how to extract components linked to physical phenomena that typically occur during production and cannot be monitored directly. We apply our method to real world data, collected in a foundry during the series production of casting parts for the automobile industry and demonstrate its efficiency.
We investigated ensembles of artificial and real-world grey-scale images to find different invari... more We investigated ensembles of artificial and real-world grey-scale images to find different invariance properties: translation invariance, scale invariance and a new hierarchical invariance recently proposed by Ruderman [1]. We found that the assumption of ...
In this paper we propose a new algorithm for the clustering of signals using incomplete independe... more In this paper we propose a new algorithm for the clustering of signals using incomplete independent component analysis (ICA). In the first step we apply the ICA to the dataset without dimension reduction, in the second step we reduce the dimension of the data to ...
Deuterium spin-lattice relaxation times (zH T I) are reported for the first time as a function of... more Deuterium spin-lattice relaxation times (zH T I) are reported for the first time as a function of temperature (180-300 K), pressure (p I 225 MPa), and composition (c I 5 m) in undercooled aqueous solutions of tetraalkylammonium ions (R4N+). A comparison with related investigations of undercooled alkali-metal halide solutions allows the competing influence of coulombic, hydrophobic, and H-bond interactions upon the dynamic structure of the random, transient H-bond network to be studied. Solvent dynamics are seen to be closely related to the glass-forming tendency of undercooled tetramethylammonium (Me4N+) and tetrapropylammonium (Pr4N+) bromide solutions and to the clathrate-forming tendency of undercooled tetrabutylammonium (Bu4N+) bromide solutions.
It is generally assumed that the analysis of intracranial pressure (ICP) waveforms could be used ... more It is generally assumed that the analysis of intracranial pressure (ICP) waveforms could be used for the detection of multiple cerebral pathophysiologies. A main obstacle for the analysis of ICP waveforms is given by the large variation of their generating signal, the arterial blood pressure (ABP). Using extended principal component analysis (PCA) we show that it is possible to distinguish between ICP waveforms generated by pathological ABP waveforms, e.g. in the case of a heart failure, without loosing information about the state of the cerebral compliance. We also create a dataset for ICP pulse classification that can be used to train models to distinguish between an intact and diminished cerebral compliance, as a function of the ICP-generating ABP pulse. As a baseline for classification we build a fully convolutional network (FCN) that reaches high performance with relatively few data.
Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of ... more Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of nonnegative data, there is no guarantee for this behavior. Several authors proposed NMF methods which enforce sparseness by constraining or penalizing the ' 1-norm of the factor matrices. On the other hand, little work has been done using a more natural sparseness measure, the ' 0-pseudo-norm. In this paper, we propose a framework for approximate NMF which constrains the ' 0-norm of the basis matrix, or the coefficient matrix, respectively. For this purpose, techniques for unconstrained NMF can be easily incorporated, such as multiplicative update rules, or the alternating nonnegative least-squares scheme. In experiments we demonstrate the benefits of our methods, which compare to, or outperform existing approaches. & 2011 Elsevier B.V. cannot be made via the ' 1-norm. Introducing the non-convex ' 0-norm as constraint function typically renders a problem NPhard, requiring exhaustive combinatoric search. However, Vavasis [16] has shown that NMF is NP-hard per se, 1 so we have to accept (most probably) that any algorithm for NMF is suboptimal. Hence, a heuristic method for NMF with ' 0-constraints might be just as appropriate and efficient as ' 1-sparse NMF. Little work is concerned with ' 0-sparse NMF. The K-SVD algorithm [17] aims to find an overcomplete dictionary for sparse Contents lists available at SciVerse ScienceDirect
This short survey reviews the recent literature on the relationship between the brain structure a... more This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding o...
Comprehending the interplay between spatial and temporal characteristics of neural dynamics can c... more Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ability to replicate neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently predominantly used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GN...
Process monitoring and optimization in an industrial setting is a complex topic, a large amount o... more Process monitoring and optimization in an industrial setting is a complex topic, a large amount of data is captured and its measurements are affected by many complex relations. There is, therefore, a growing necessity for methods to reduce its complexity and dimension such that it can be interpreted by humans. In this work we study the extraction of interpretable rules derived from components of a non-negative matrix factorization using real data.
Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool fo... more Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is not compatible to the analysis of group data in general. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA-based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to eliminate the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group
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