Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis

Without non-linear basis functions many problems can not be solved by linear algorithms. This article proposes a method to automatically construct such basis functions with slow feature analysis (SFA). Non-linear optimization of this unsupervised learning method generates an orthogonal basis on the unknown latent space for a given time series. In contrast to methods like PCA, SFA is thus well suited for techniques that make direct use of the latent space. Real-world time series can be complex, and current SFA algorithms are either not powerful enough or tend to over-fit. We make use of the kernel trick in combination with sparsification to develop a kernelized SFA algorithm which provides a powerful function class for large data sets. Sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well. For small data sets, however, the kernel SFA approach leads to over-fitting and numerical instabilities. To enforce a stable solution, we introduce regularization to the SFA objective. We hypothesize that our algorithm generates a feature space that resembles a Fourier basis in the unknown space of latent variables underlying a given real-world time series. We evaluate this hypothesis at the example of a vowel classification task in comparison to sparse kernel PCA. Our results show excellent classification accuracy and demonstrate the superiority of kernel SFA over kernel PCA in encoding latent variables.     

Performance limits of electron holography

Transmission electron microscopy is wave optics. The object exit wave contains the full object information. However, in the usual intensity images, recorded either in real space or in Fourier space, the phases are missing. In many applications at medium and at high resolution, electron holography has shown its unique ability of solving the “missing phase problem” and utilizing the recovered phase for complete interpretation of the object structure. The question is “What are the performance limits?” with respect to field of view, lateral resolution and signal resolution. In this article, the performance limits are derived and discussed.     

Automatic data collection for enterprise architecture models

Enterprise Architecture (EA) is an approach used to provide decision support based on organization-wide models. The creation of such models is, however, cumbersome as multiple aspects of an organization need to be considered, making manual efforts time-consuming, and error prone. Thus, the EA approach would be significantly more promising if the data used when creating the models could be collected automatically—a topic not yet properly addressed by either academia or industry. This paper proposes network scanning for automatic data collection and uses an existing software tool for generating EA models (ArchiMate is employed as an example) based on the IT infrastructure of enterprises. While some manual effort is required to make the models fully useful to many practical scenarios (e.g., to detail the actual services provided by IT components), empirical results show that the methodology is accurate and (in its default state) require little effort to carry out.     

LXRα Regulates oxLDL-Induced Trained Immunity in Macrophages

Reprogramming of metabolic pathways in monocytes and macrophages can induce a proatherosclerotic inflammatory memory called trained innate immunity. Here, we have analyzed the role of the Liver X receptor (LXR), a crucial regulator of metabolism and inflammation, in oxidized low-density lipoprotein (oxLDL)-induced trained innate immunity. Human monocytes were incubated with LXR agonists, antagonists, and oxLDL for 24 h. After five days of resting time, cells were restimulated with the TLR-2 agonist Pam3cys. OxLDL priming induced the expression of LXRα but not LXRβ. Pharmacologic LXR activation was enhanced, while LXR inhibition prevented the oxLDL-induced inflammatory response. Furthermore, LXR inhibition blocked the metabolic changes necessary for epigenetic reprogramming associated with trained immunity. In fact, enrichment of activating histone marks at the IL-6 and TNFα promotor was reduced following LXR inhibition. Based on the differential expression of the LXR isoforms, we inhibited LXRα and LXRβ genes using siRNA in THP1 cells. As expected, siRNA-mediated knock-down of LXRα blocked the oxLDL-induced inflammatory response, while knock-down of LXRβ had no effect. We demonstrate a specific and novel role of the LXRα isoform in the regulation of oxLDL-induced trained immunity. Our data reveal important aspects of LXR signaling in innate immunity with relevance to atherosclerosis formation.     

Evaluation of non-linear strain paths using Generalized Forming Limit Concept and a modification of the Time Dependent Evaluation Method

The prediction of formability is one of the most important tasks in sheet metal forming process simulation. The common criterion for ductile fracture in industrial applications is the Forming Limit Diagram (FLD). This is only applicable for linear strain paths. However, in most industrial simulation cases non-linear strain paths occur. To resolve this problem, a phenomenological approach is introduced, the so-called Generalized Forming Limit Concept (GFLC). The GFLC enables prediction of localized necking on arbitrary non-linear strain paths. Another possibility is the use of the Time Dependent Evaluation Method (TDEM) within the simulation as a failure criteria. During the Numisheet Benchmark 1 (2014) a two-stage forming process was performed with three typical sheet materials (AA5182, DP600 and TRIP 780) and three different blank shapes. The task was to determinate the point in time and space of local instability. Therefore the strain path for the point of maximum local thinning is evaluated. To predict the start of local necking the Generalized Forming Limit Concept (GFLC), the Time Dependent Evaluation Method (TDEM) and the modified TDEM were applied. The results of the simulation are compared with the results of the Benchmark experiment.     

Using enterprise architecture analysis and interview data to estimate service response time

Insights into service response time is important for service-oriented architectures and service management. However, directly measuring the service response time is not always feasible or can be very costly. This paper extends an analytical modeling method which uses enterprise architecture modeling to support the analysis. The extensions consist of (i) a formalization using the Hybrid Probabilistic Relational Model formalism, (ii) an implementation in an analysis tool for enterprise architecture and (iii) a data collection approach using expert assessments collected via interviews and questionnaires. The accuracy and cost effectiveness of the method was tested empirically by comparing it with direct performance measurements of five services of a geographical information system at a Swedish utility company. The tests indicate that the proposed method can be a viable option for rapid service response time estimates when a moderate accuracy within 15% is sufficient.     

Connecting geodesics on smooth surfaces

In this paper, we present a novel method for computing multiple geodesic connections between two arbitrary points on a smooth surface. Our method is based on a homotopy approach that is able to capture the ambiguity of geodesic connections in the presence of positive Gaussian curvature that generates focal curves. Contrary to previous approaches, we exploit focal curves to gain theoretical insights on the number of connecting geodesics and a practical algorithm for collecting these. We consider our method as a contribution to the contemporary debate regarding the calculation of distances in general situations, applying continuous concepts of classical differential geometry which are not immediately transferable in purely discrete settings.