Enhanced temporal resolution in femtosecond dynamic-grating experiments

Recording of gratings by interference of two pump pulses and diffraction of a third probe pulse is useful for investigating ultrafast material phenomena. We demonstrate, in theory and experiment, that the temporal resolution in such configurations does not degrade appreciably even for large angular separation between the pump pulses. Transient Kerr gratings are generated inside calcium fluoride (CaF2) crystals by two interfering femtosecond (pump) pulses at 388 nm and read out by a Bragg-matched probe pulse at 776 nm. The solution to the relevant coupled-mode equations is well corroborated by the experimental results, yielding a value of the Kerr coefficient of ~ 4.4×10^(–7) cm^2/GW for CaF2

Human Activity Classification with Online Growing Neural Gas

Panzner M, Beyer O, Cimiano P. Human Activity Classification with Online Growing Neural Gas. In: Workshop on New Challenges in Neural Computation (NC2). 2013: 106-113.In this paper we present an online approach to human ac- tivity classification based on Online Growing Neural Gas (OGNG). In contrast to state-of-the-art approaches that perform training in an offline fashion, our approach is online in the sense that it circumvents the need to store any training examples, processing the data on the fly and in one pass. The approach is thus particularly suitable in life-long learning settings where never-ending streams of data arise. We propose an archi- tecture that consists of two layers, allowing the storage of human actions in a more memory efficient structure. While the first layer (feature map) dynamically clusters Space-Time Interest Points (STIP) and serves as basis for the creation of histogram-based signatures of human actions, the second layer (class map) builds a classification model that relies on these human action signatures. We present experimental results on the KTH activity dataset showing that our approach has comparable per- formance to a Support Vector Machine (SVM) while performing online and avoiding to store examples explicitly

Life-long learning with Growing Conceptual Maps

Beyer O. Life-long learning with Growing Conceptual Maps. Bielefeld: Bielefeld University; 2013.A number of challenges arise when transferring the concept of life-long learning to technical systems. This thesis addresses the challenges of learning with continuous data streams, learning with weak supervision, learning with non-stationary data and learning with multi-view data. We introduce the Growing Conceptual Maps Framework as a solution to these challenges, allowing the incremental online learning of a classification model without the requirement of storing training data explicitly. The framework is based on topological feature maps, \emph{i.e.} Growing Neural Gas, and therefore allows a straight forward visualization of the trained model. With the rapidly increasing amount of data in many domains, such as news feeds, social media, or sensory networks \emph{etc.}, nowadays, assistive systems are required to process a theoretically infinite stream of data in order to help us in our daily tasks. While existing approaches coming from data mining mostly do not scale up to such large and complex tasks, new paradigms are required which allow the model to grow task-dependently and adapt to a changing environment, in order to learn in a life-long fashion. In this thesis we thus stepwise extend Growing Neural Gas with appropriate novel online labeling and prediction strategies, as well as novel neuron insertion strategies, according to meet these challenges. We evaluate the introduced approaches on benchmarking, artificial and real stream datasets, showing the benefit of our architecture and proving that the Growing Conceptual Maps Framework renders itself ideal for life-long learning by outperforming similar existing approach, and delivering comparable results to other well established classifiers such as a Support Vector Machine. As an application for our framework, we furthermore develop an online human activity classifier based on two Growing Conceptual Maps that can compete with state-of-the-art (offline) human activity classifiers. As a final contribution, we introduce a straight forward visualization schema for Growing Conceptual Maps that allows the user to track emerging categories and their relation according to the underlying map, and furthermore demonstrate its usability of identifying trends and events in stream data

Femtosecond holography in lithium niobate crystals

Spatial gratings are recorded holographically by two femtosecond pump pulses at 388 nm in lithium niobate (LiNbO3) crystals and read out by a Bragg-matched, temporally delayed probe pulse at 776 nm. We claim, to our knowledge, the first holographic pump-probe experiments with subpicosecond temporal resolution for LiNbO3. An instantaneous grating that is due mostly to the Kerr effect as well as a long-lasting grating that results mainly from the absorption caused by photoexcited carriers was observed. The Kerr coefficient of LiNbO3 for our experimental conditions, i.e., pumped and probed at different wavelengths, was approximately 1.0×10^-5 cm²/GW

Hypoxia. Hypoxia in the pathogenesis of systemic sclerosis

Autoimmunity, microangiopathy and tissue fibrosis are hallmarks of systemic sclerosis (SSc). Vascular alterations and reduced capillary density decrease blood flow and impair tissue oxygenation in SSc. Oxygen supply is further reduced by accumulation of extracellular matrix (ECM), which increases diffusion distances from blood vessels to cells. Therefore, severe hypoxia is a characteristic feature of SSc and might contribute directly to the progression of the disease. Hypoxia stimulates the production of ECM proteins by SSc fibroblasts in a transforming growth factor-β-dependent manner. The induction of ECM proteins by hypoxia is mediated via hypoxia-inducible factor-1α-dependent and -independent pathways. Hypoxia may also aggravate vascular disease in SSc by perturbing vascular endothelial growth factor (VEGF) receptor signalling. Hypoxia is a potent inducer of VEGF and may cause chronic VEGF over-expression in SSc. Uncontrolled over-expression of VEGF has been shown to have deleterious effects on angiogenesis because it leads to the formation of chaotic vessels with decreased blood flow. Altogether, hypoxia might play a central role in pathogenesis of SSc by augmenting vascular disease and tissue fibrosis

Nonlinear Dynamics of Magnetic Islands Imbedded in Small-Scale Turbulence

International audienceThe nonlinear dynamics of magnetic tearing islands imbedded in a pressure gradient driven turbulence is investigated numerically in a reduced magnetohydrodynamic model. The study reveals regimes where the linear and nonlinear phases of the tearing instability are controlled by the properties of the pressure gradient. In these regimes, the interplay between the pressure and the magnetic flux determines the dynamics of the saturated state. A secondary instability can occur and strongly modify the magnetic island dynamics by triggering a poloidal rotation. It is shown that the complex nonlinear interaction between the islands and turbulence is nonlocal and involves small scales

Collaborative and Robot-Based Plug & Produce for Rapid Reconfiguration of Modular Production Systems

Wojtynek M, Oestreich H, Beyer O, Wrede S. Collaborative and Robot-Based Plug & Produce for Rapid Reconfiguration of Modular Production Systems. In: Proceedings of the 2017 IEEE/SICE International Symposium on System Integration. Piscataway, NJ: IEEE; 2017.The manufacturing of individualized products down to batch size 1 poses ongoing challenges for the design and integration of future production systems. Today’s production lines with a high degree of automation achieve high efficiency, but usually come with high costs for adaptation to product variants. In order to combine full automation with high flexibility, we propose a concept for the dynamic composition of automation components in a modular production system that facilitates the rapid adaptation of collaborative and robot-supported manufacturing processes. To achieve this, we integrate self-descriptive automation components at runtime into the control architecture of the production system using a Plug-and-Produce approach. While the location and orientation of automation components in the modular production system are derived from physical human-robot interaction, the adaptation and verification of the robot behavior is made possible through a simulation-based planning subsystem. Once this dynamic reconfiguration process by the machine setter is finished, the adapted production process is executed in a fully automated way with high efficiency. A case study carried out in an industrial collaboration project on flexible assembly demonstrates the benefits of the presented approach

Peptide Signatures for Prognostic Markers of Pancreatic Cancer by MALDI Mass Spectrometry Imaging

Simple Summary: Pancreatic cancer remains one of the most lethal tumor entities worldwide given its overall 5-year survival after diagnosis of 9%. Thus, further understanding of molecular changes to improve individual prognostic assessment as well as diagnostic and therapeutic advancement is crucial. The aim of this study was to investigate the feasibility of Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to identify specific peptide signatures linked to established prognostic parameters of pancreatic cancer. In a patient cohort of 18 patients with exocrine pancreatic cancer after tumor resection, MALDI imaging analysis additional to histopathological assessment was performed. Applying this method to tissue sections of the tumors, we were able to identify discriminative peptide signatures corresponding to nine proteins for the prognostic histopathological features lymphatic vessel invasion, lymph node metastasis and angioinvasion. This demonstrates the technical feasibility of MALDI-MSI to identify peptide signatures with prognostic value through the workflows used in this study. Abstract: Despite the overall poor prognosis of pancreatic cancer there is heterogeneity in clinical courses of tumors not assessed by conventional risk stratification. This yields the need of additional markers for proper assessment of prognosis and multimodal clinical management. We provide a proof of concept study evaluating the feasibility of Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to identify specific peptide signatures linked to prognostic parameters of pancreatic cancer. On 18 patients with exocrine pancreatic cancer after tumor resection, MALDI imaging analysis was performed additional to histopathological assessment. Principal component analysis (PCA) was used to explore discrimination of peptide signatures of prognostic histopathological features and receiver operator characteristic (ROC) to identify which specific m/z values are the most discriminative between the prognostic subgroups of patients. Out of 557 aligned m/z values discriminate peptide signatures for the prognostic histopathological features lymphatic vessel invasion (pL, 16 m/z values, eight proteins), nodal metastasis (pN, two m/z values, one protein) and angioinvasion (pV, 4 m/z values, two proteins) were identified. These results yield proof of concept that MALDI-MSI of pancreatic cancer tissue is feasible to identify peptide signatures of prognostic relevance and can augment risk assessment