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The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
Multi-attribute relation extraction (MARE): simplifying the application of relation extraction
(2021)
Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Heavy metal detection with semiconductor devices based on PLD-prepared chalcogenide glass thin films
(2007)
IASSE-2004 - 13th International Conference on Intelligent and Adaptive Systems and Software Engineering eds. W. Dosch, N. Debnath, pp. 245-250, ISCA, Cary, NC, 1-3 July 2004, Nice, France We introduce a UML-based model for conceptual design support in civil engineering. Therefore, we identify required extensions to standard UML. Class diagrams are used for elaborating building typespecific knowledge: Object diagrams, implicitly contained in the architect’s sketch, are validated against the defined knowledge. To enable the use of industrial, domain-specific tools, we provide an integrated conceptual design extension. The developed tool support is based on graph rewriting. With our approach architects are enabled to deal with semantic objects during early design phase, assisted by incremental consistency checks.
Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.
In the presented paper data collected from the field related to damage statistics of electrical and electronic apparatus in household are reported and investigated. These damages (total number approx. 74000 cases), registered by five German insurance companies in 2005 and 2006, were adviced by customers as caused by lightning overvoltages. With the use of stochastical methods it is possible, to reasses the collected data and to distinguish between cases, which are with high probability caused by lightning overvoltages, and those, which are not. If there was an indication for a direct strike, this case was excluded, so the focus was only on indirect lightning flashes, i.e. only flashes to ground near the structure and flashes to or nearby an incoming service line were investigated. The data from the field contain the location of damaged apparatus (residence of the policy holder) and the distances of the nearest cloud-to-ground stroke to the location of the damage registered by the German lightning location network BLIDS at the date of damage. The statistical data along with some complementary numerical simulations allow to verify the correspondence of the Standards rules used for IEC 62305-2 with the field data and to define some correction needs. The results could lead to a better understanding whether a damage reported to an insurance company is really caused by indirect lightning, or not.
Hydrophobic magnetic nanoparticles (NPs) consisting of undecanoate-capped magnetite (Fe3O4, average diameter ca. 5 nm) are used to control quantized electron transfer to surface-confined redox units and metal NPs. A two-phase system consisting of an aqueous electrolyte solution and a toluene phase that includes the suspended undecanoatecapped magnetic NPs is used to control the interfacial properties of the electrode surface. The attracted magnetic NPs form a hydrophobic layer on the electrode surface resulting in the change of the mechanisms of the surface-confined electrochemical processes. A quinone-monolayer modified Au electrode demonstrates an aqueous-type of the electrochemical process (2e-+2H+ redox mechanism) for the quinone units in the absence of the hydrophobic magnetic NPs, while the attraction of the magnetic NPs to the surface results in the stepwise single-electron transfer mechanism characteristic of a dry nonaqueous medium. Also, the attraction of the hydrophobic magnetic NPs to the Au electrode surface modified with Au NPs (ca. 1.4 nm) yields a microenvironment with a low dielectric constant that results in the single-electron quantum charging of the Au NPs.
The overall objective of this study is to develop a new external fixator, which closely maps the native kinematics of the elbow to decrease the joint force resulting in reduced rehabilitation time and pain. An experimental setup was designed to determine the native kinematics of the elbow during flexion of cadaveric arms. As a preliminary study, data from literature was used to modify a published biomechanical model for the calculation of the joint and muscle forces. They were compared to the original model and the effect of the kinematic refinement was evaluated. Furthermore, the obtained muscle forces were determined in order to apply them in the experimental setup. The joint forces in the modified model differed slightly from the forces in the original model. The muscle force curves changed particularly for small flexion angles but their magnitude for larger angles was consistent.
The human arm consists of the humerus (upper arm), the medial ulna and the lateral radius (forearm). The joint between the humerus and the ulna is called humeroulnar joint and the joint between the humerus and the radius is called humeroradial joint. Lateral and medial collateral ligaments stabilize the elbow. Statistically, 2.5 out of 10,000 people suffer from radial head fractures [1]. In these fractures the cartilage is often affected. Caused by the injured cartilage, degenerative diseases like posttraumatic arthrosis may occur. The resulting pain and reduced range of motion have an impact on the patient’s quality of life. Until now, there has not been a treatment which allows typical loads in daily life activities and offers good long-term results. A new surgical approach was developed with the motivation to reduce the progress of the posttraumatic arthrosis. Here, the radius is shortened by 3 mm in the proximal part [2]. By this means, the load of the radius is intended to be reduced due to a load shift to the ulna. Since the radius is the most important stabilizer of the elbow it has to be confirmed that the stability is not affected. In the first test (Fig. 1 left), pressure distributions within the humeroulnar and humeroradial joints a native and a shortened radius were measured using resistive pressure sensors (I5076 and I5027, Tekscan, USA). The humerus was loaded axially in a tension testing machine (Z010, Zwick Roell, Germany) in 50 N steps up to 400 N. From the humerus the load is transmitted through both the radius and the ulna into the hand which is fixed on the ground. In the second test (Fig. 1 right), the joint stability was investigated using a digital image correlation system to measure the displacement of the ulna. Here, the humerus is fixed with a desired flexion angle and the unconstrained forearm lies on the ground. A rope connects the load actuator with a hook fixed in the ulna. A guide roller is used so that the rope pulls the ulna horizontally when a tensile load is applied. This creates a moment about the elbow joint with a maximum value of 7.5 Nm. Measurements were performed with varying flexion angles (0°, 30°, 60°, 90°, 120°). For both tests and each measurement, seven specimens were used. Student ́s t-test was employed to determine whether the mean values of the measurements in native specimen and operated specimens differ significantly.
Electromechanical model of hiPSC-derived ventricular cardiomyocytes cocultured with fibroblasts
(2018)
The CellDrum provides an experimental setup to study the mechanical effects of fibroblasts co-cultured with hiPSC-derived ventricular cardiomyocytes. Multi-scale computational models based on the Finite Element Method are developed. Coupled electrical cardiomyocyte-fibroblast models (cell level) are embedded into reaction-diffusion equations (tissue level) which compute the propagation of the action potential in the cardiac tissue. Electromechanical coupling is realised by an excitation-contraction model (cell level) and the active stress arising during contraction is added to the passive stress in the force balance, which determines the tissue displacement (tissue level). Tissue parameters in the model can be identified experimentally to the specific sample.
Biomechanical simulation of different prosthetic meshes for repairing uterine/vaginal vault prolapse
(2017)
The discovery of human induced pluripotent stem cells reprogrammed from somatic cells [1] and their ability to differentiate into cardiomyocytes (hiPSC-CMs) has provided a robust platform for drug screening [2]. Drug screenings are essential in the development of new components, particularly for evaluating the potential of drugs to induce life-threatening pro-arrhythmias. Between 1988 and 2009, 14 drugs have been removed from the market for this reason [3]. The microelectrode array (MEA) technique is a robust tool for drug screening as it detects the field potentials (FPs) for the entire cell culture. Furthermore, the propagation of the field potential can be examined on an electrode basis. To analyze MEA measurements in detail, we have developed an open-source tool.
Chemische Sensoren mit Bariumstrontiumtitanat als funktionelle Schicht zur Multiparameterdetektion
(2013)
Multi-parameter detection for supporting monitoring and control of biogas processes in agriculture
(2014)
Abstracts of the ACHEMA 2000 - International Meeting on Chemical Engineering, Environmental Protection and Biotechnology, May 22 - 27, 2000. Frankfurt am Main. Achema 2000 : special edition / Linde. [Ed.: Linde AG. Red.: Volker R. Leski]. - Wiesbaden : Linde AG, 2000. - 56 p. : Ill., . - pp: 79 - 81
Abstract of the authors: In many areas of computer science ontologies become more and more important. The use of ontologies for domain modeling often brings up the issue of ontology integration. The task of merging several ontologies, covering specific subdomains, into one united ontology has to be solved. Many approaches for ontology integration aim at automating the process of ontology alignment. However, a complete automation is not feasible, and user interaction is always required. Nevertheless, most ontology integration tools offer only very limited support for the interactive part of the integration process. In this paper, we present a novel approach for the interactive integration of ontologies. The result of the ontology integration is incrementally updated after each definition of a correspondence between ontology elements. The user is guided through the ontologies to be integrated. By restricting the possible user actions, the integrity of all defined correspondences is ensured by the tool we developed. We evaluated our tool by integrating different regulations concerning building design.
Beim Ausbau nachhaltiger, regenerativer Energieversorgung hat die Umwandlung von organischer Biomasse in Biogas ein großes Potential. Der zugrundeliegende, komplexe biologische Prozess wird noch immer unzureichend verstanden und bedarf systematischer Untersuchungen der Prozessparameter, um einen hohen Ertrag bei guter Gasqualität zu ermöglichen. Die Fragestellungen zur Entschlüsselung des Prozesses sind sowohl verfahrenstechnischer als auch mikrobiologischer Natur. Aus mikrobiologischer Sicht ist die Kenntnis der tatsächlich beteiligten prozesstragenden Mikroorganismen von erheblicher Bedeutung, aus verfahrenstechnischer Sicht die Kenntnis der physikalischen und chemischen Faktoren, welche die mikrobiologischen Prozesse und kontrollieren. Im Zusammenspiel aller dieser Parameter wird die Biogasbildung befördert oder behindert, bis zum Abbruch des Prozesses.
Eine mögliche Kontrollmethode ist die Messung der metabolischen Aktivität prozesstragender Organismen.
Diese soll, beruhend auf fundierten Prozessdaten, gewonnen durch eine Parallelanlage, mit einem lichtadressierbaren potentiometrischen Sensor-System (LAPS) realisiert werden. Dieser Sensor ist in der Lage, pH-Wert-änderungen zu detektieren, die durch den Stoffwechsel der auf dem Chip immobilisierten Organismen hervorgerufen werden, um eine Online-Überwachung von Biogasanlagen zu ermöglichen.
An increasing number of applications target their executions on specific hardware like general purpose Graphics Processing Units. Some Cloud Computing providers offer this specific hardware so that organizations can rent such resources. However, outsourcing the whole application to the Cloud causes avoidable costs if only some parts of the application benefit from the specific expensive hardware. A partial execution of applications in the Cloud is a tradeoff between costs and efficiency. This paper addresses the demand for a consistent framework that allows for a mixture of on- and off-premise calculations by migrating only specific parts to a Cloud. It uses the concept of workflows to present how individual workflow tasks can be migrated to the Cloud whereas the remaining tasks are executed on-premise.
Inference on the basis of high-dimensional and functional data are two topics which are discussed frequently in the current statistical literature. A possibility to include both topics in a single approach is working on a very general space for the underlying observations, such as a separable Hilbert space. We propose a general method for consistently hypothesis testing on the basis of random variables with values in separable Hilbert spaces. We avoid concerns with the curse of dimensionality due to a projection idea. We apply well-known test statistics from nonparametric inference to the projected data and integrate over all projections from a specific set and with respect to suitable probability measures. In contrast to classical methods, which are applicable for real-valued random variables or random vectors of dimensions lower than the sample size, the tests can be applied to random vectors of dimensions larger than the sample size or even to functional and high-dimensional data. In general, resampling procedures such as bootstrap or permutation are suitable to determine critical values. The idea can be extended to the case of incomplete observations. Moreover, we develop an efficient algorithm for implementing the method. Examples are given for testing goodness-of-fit in a one-sample situation in [1] or for testing marginal homogeneity on the basis of a paired sample in [2]. Here, the test statistics in use can be seen as generalizations of the well-known Cramérvon-Mises test statistics in the one-sample and two-samples case. The treatment of other testing problems is possible as well. By using the theory of U-statistics, for instance, asymptotic null distributions of the test statistics are obtained as the sample size tends to infinity. Standard continuity assumptions ensure the asymptotic exactness of the tests under the null hypothesis and that the tests detect any alternative in the limit. Simulation studies demonstrate size and power of the tests in the finite sample case, confirm the theoretical findings, and are used for the comparison with concurring procedures. A possible application of the general approach is inference for stock market returns, also in high data frequencies. In the field of empirical finance, statistical inference of stock market prices usually takes place on the basis of related log-returns as data. In the classical models for stock prices, i.e., the exponential Lévy model, Black-Scholes model, and Merton model, properties such as independence and stationarity of the increments ensure an independent and identically structure of the data. Specific trends during certain periods of the stock price processes can cause complications in this regard. In fact, our approach can compensate those effects by the treatment of the log-returns as random vectors or even as functional data.
GaAs-based Gunn diodes with graded AlGaAs hot electron injector heterostructures have been developed under the special needs in automotive applications. The fabrication of the Gunn diode chips was based on total substrate removal and processing of integrated Au heat sinks. Especially, the thermal and RF behavior of the diodes have been analyzed by DC, impedance and S-parameter measurements. The electrical investigations have revealed the functionality of the hot electron injector. An optimized layer structure could fulfill the requirements in adaptive cruise control (ACC) systems at 77 GHz with typical output power between 50 and 90 mW.
Effectiveness of the edge-based smoothed finite element method applied to soft biological tissues
(2012)