We investigate models of the mitogenactivated protein kinases (MAPK) network, with the aim of determining where in parameter space there exist multiple positive steady states. We build on recent progress which combines various symbolic computation methods for mixed systems of equalities and inequalities. We demonstrate that those techniques benefit tremendously from a newly implemented graph theoretical symbolic preprocessing method. We compare computation times and quality of results of numerical continuation methods with our symbolic approach before and after the application of our preprocessing.

We consider the problem of determining multiple steady states for positive real values in models of biological networks. Investigating the potential for these in models of the mitogen-activated protein kinases (MAPK) network has consumed considerable effort using special insights into the structure of corresponding models. Here we apply combinations of symbolic computation methods for mixed equality/inequality systems, specifically virtual substitution, lazy real triangularization and cylindrical algebraic decomposition. We determine multistationarity of an 11-dimensional MAPK network when numeric values are known for all but potentially one parameter. More precisely, our considered model has 11 equations in 11 variables and 19 parameters, 3 of which are of interest for symbolic treatment, and furthermore positivity conditions on all variables and parameters.

MOTIVATION: Integration of metabolic networks with '-omics' data has been a subject of recent research in order to better understand the behaviour of such networks with respect to differences between biological and clinical phenotypes. Under the conditions of steady state of the reaction network and the non-negativity of fluxes, metabolic networks can be algebraically decomposed into a set of sub-pathways often referred to as extreme currents (ECs). Our objective is to find the statistical association of such sub-pathways with given clinical outcomes, resulting in a particular instance of a self-contained gene set analysis method. In this direction, we propose a method based on sparse group lasso (SGL) to identify phenotype associated ECs based on gene expression data. SGL selects a sparse set of feature groups and also introduces sparsity within each group. Features in our model are clusters of ECs, and feature groups are defined based on correlations among these features. RESULTS: We apply our method to metabolic networks from KEGG database and study the association of network features to prostate cancer (where the outcome is tumor and normal, respectively) as well as glioblastoma multiforme (where the outcome is survival time). In addition, simulations show the superior performance of our method compared to global test, which is an existing self-contained gene set analysis method. AVAILABILITY AND IMPLEMENTATION: R code (compatible with version 3.2.5) is available from http://www.abi.bit.uni-bonn.de/index.php?id=17. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence. We use neighborhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance. The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. In addition, we introduce a feature bundling preprocessing technique to make the segmentation more robust to noise, as well as a notion of motion symmetry for more refined primitive detection. We test our method on several sensor modalities, including markered and markerless motion capture as well as on electromyograph and accelerometer recordings. The results highlight our system's capabilities for both segmentation and for analysis of the finer structures of motion data, all in a completely unsupervised manner.

One major challenge for 3D pose estimation from a single RGB image is the acquisition of sufficient training data. In particular, collecting large amounts of training data that contain unconstrained images and are annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of images with annotated 2D poses and the second source consists of accurate 3D motion capture data. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval. In our experiments, we show that our approach achieves state-of-the-art results and is even competitive when the skeleton structure of the two sources differ substantially.

We investigate the application of exponential integrators to stiff elastodynamic problems governed by second-order differential equations. Classical explicit numerical integration schemes have the shortcoming that the stepsizes are limited by the highest frequency that occurs within the solution spectrum of the governing equations, while implicit methods suffer from an inevitable and mostly uncontrollable artificial viscosity that often leads to a nonphysical behavior. In order to overcome these specific detriments, we devise an appropriate class of exponential integrators that solve the stiff part of the governing equations of motion by employing a closed-form solution. As a consequence, we are able to handle up to three orders of magnitude larger time-steps as with conventional implicit integrators and at the same time achieve a tremendous increase in the overall long-term stability due to a strict energy conservation. The advantageous behavior of our approach is demonstrated on a broad spectrum of complex deformable models like fibers, textiles, and solids, including collision response, friction, and damping.

Generating plausible hairstyles is a very challenging problem. Despite recent efforts no definite solution was presented so far. Many of the current limitations are related to the optical complexity of hair. In this paper we present a technique for hair reconstruction based on thermal imaging. By using this technique several issues of conventional image-based techniques, such as shadowing and anisotropy in reflectance, can be avoided. Moreover, hair-skin segmentation becomes a trivial problem, and no special care about lighting has to be taken, as the hair is "lit from inside" with the head as light source. The capture process is fast and requires a single hand-held device only. The potential of the proposed method is demonstrated by several challenging examples.

The reconstruction of hairstyles is one of the most challenging tasks in the field of hair modeling.

The development of methods and tools for the generation of visually appealing motion sequences using prerecorded motion capture data has become an important research area in computer animation. In particular, data-driven approaches have been used for reconstructing high-dimensional motion sequences from low-dimensional control signals. In this article, we contribute to this strand of research by introducing a novel framework for generating full-body animations controlled by only four 3D accelerometers that are attached to the extremities of a human actor. Our approach relies on a knowledge base that consists of a large number of motion clips obtained from marker-based motion capturing. Based on the sparse accelerometer input a cross-domain retrieval procedure is applied to build up a lazy neighborhood graph in an online fashion. This graph structure points to suitable motion fragments in the knowledge base, which are then used in the reconstruction step. Supported by a kd-tree index structure, our procedure scales to even large datasets consisting of millions of frames. Our combined approach allows for reconstructing visually plausible continuous motion streams, even in the presence of moderate tempo variations which may not be directly reflected by the given knowledge base.

In this work a practical approach to photometric acquisition of hair color is presented. Based on a single input photograph of a simple setup we are able to extract physically plausible optical properties of hair and to render virtual hair closely matching the original. Our approach does not require any costly special hardware but a standard consumer camera only.

When rendering light colored hair, multiple fiber scattering is essential for the right perception of the overall hair color. In this context, we present a novel technique to efficiently approximate multiple fiber scattering for a full head of human hair or a similar fiber based geometry. In contrast to previous ad-hoc approaches, our method relies on the physically accurate concept of the Bidirectional Scattering Distribution Functions and gives physically plausible results with no need for parameter tweaking. We show that complex scattering effects can be approximated very well by using aggressive simplifications based on this theoretical model. When compared to unbiased Monte-Carlo path tracing, our approximations preserve photo-realism in most settings but with rendering times at least two-orders of magnitude lower. Time and space complexity are much lower compared to photon mapping-based techniques and we can even achieve realistic results in real-time on a standard PC with consumer graphics hardware.

Photo realistic visualization of a huge number of individual filaments like in the case of hair, fur, or knitwear is a challenging task: Explicit rendering approaches for simulating radiance transfer at a filament get totally impracticable with respect to rendering performance and it is also not obvious how to derive efficient scattering functions for different levels of (geometric) abstraction or how to deal with very complex scattering mechanisms. We present a novel uniform formalism for light scattering from filaments in terms of radiance, which we call the Bidirectional Fiber Scattering Distribution Function (BFSDF). We show that previous specialized approaches, which have been developed in the context of hair rendering, can be seen as instances of the BFSDF. Similar to the role of the BSSRDF for surface scattering functions, the BFSDF can be seen as a general approach for light scattering from filaments, which is suitable for deriving approximations in a canonic and systematic way. For the frequent cases of distant light sources and observers, we deduce an efficient far field approximation (Bidirectional Curve Scattering Distribution Function, BCSDF). We show that on the basis of the BFSDF, parameters for common rendering techniques can be estimated in a non-ad-hoc, but physically-based way.

Sonification of human movement offers a wide range of new kinds of information for supporting motor learning in sports and rehabilitation. Even though motor learning is dominated visually, auditory perception offers unique subtle temporal resolution as well as enormous integrative capacity - both are important features on perception of human movement patterns. But how to address the auditory system adequately? A sonification based on kinematic movement data can mediate structural features of movement via the auditory system, like polyrhythms of movement etc. And sonification of dynamic movement data makes muscle forces audible approximately. Here, a flexible framework for the sonification of human movement data is presented, capable of processing standard kinematic motion capture data as well as derived quantities such as force data. Force data are computed by inverse dynamics algorithms and can be used as input parameters for real time sonification. Simultaneous visualization is provided using OpenGL.

In the field of computer algebra, joining separate computer algebra systems with individual strengths into problem solving environments is an important recent research direction. In this paper we describe how we wrapped a specialized computer algebra system, which uses shared-memory parallelism to speed up Gröbner basis computations, into a Web component that can be used by other systems. By this component also a new algorithmic development in Gröbner basis computations – the use of the Fractal Gröbner Walk – was made available to various general purpose computer algebra systems in the form of a single optimized implementation. Previously, all of the important algorithmic improvements to an algorithm had to be reimplemented in the general purpose systems. Our implementation allows access to the Gröbner basis computations via ‘intelligent’ graphical user interfaces and via scripting from existing general purpose algebra systems. © 1998 John Wiley & Sons, Ltd.