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This Research-to-Practice paper examines the practical application of various forms of collaborative learning in MOOCs. Since 2012, about 60 MOOCs in the wider context of Information Technology and Computer Science have been conducted on our self-developed MOOC platform. The platform is also used by several customers, who either run their own platform instances or use our white label platform. We, as well as some of our partners, have experimented with different approaches in collaborative learning in these courses. Based on the results of early experiments, surveys amongst our participants, and requests by our business partners we have integrated several options to offer forms of collaborative learning to the system. The results of our experiments are directly fed back to the platform development, allowing to fine tune existing and to add new tools where necessary. In the paper at hand, we discuss the benefits and disadvantages of decisions in the design of a MOOC with regard to the various forms of collaborative learning. While the focus of the paper at hand is on forms of large group collaboration, two types of small group collaboration on our platforms are briefly introduced.
Our Conclusions
(2018)
High Mountain Asia provides water for more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow - the vast majority of which is not monitored by sparse weather networks. We leverage passive microwave data from the SSMI series of satellites (SSMI, SSMI/S, 1987-2016), reprocessed to 3.125 km resolution, to examine trends in the volume and spatial distribution of snow-water equivalent (SWE) in the Indus Basin. We find that the majority of the Indus has seen an increase in snow-water storage. There exists a strong elevation-trend relationship, where high-elevation zones have more positive SWE trends. Negative trends are confined to the Himalayan foreland and deeply-incised valleys which run into the Upper Indus. This implies a temperature-dependent cutoff below which precipitation increases are not translated into increased SWE. Earlier snowmelt or a higher percentage of liquid precipitation could both explain this cutoff.(1) Earlier work 2 found a negative snow-water storage trend for the entire Indus catchment over the time period 1987-2009 (-4 x 10(-3) mm/yr). In this study based on an additional seven years of data, the average trend reverses to 1.4 x 10(-3). This implies that the decade since the mid-2000s was likely wetter, and positively impacted long-term SWE trends. This conclusion is supported by an analysis of snowmelt onset and end dates which found that while long-term trends are negative, more recent (since 2005) trends are positive (moving later in the year).(3)
Capsella
(2018)
Participants of the 2017 European Space Weather Week in Ostend, Belgium, discussed the stakeholder requirements for space weather-related models. It was emphasized that stakeholders show an increased interest in space weather-related models. Participants of the meeting discussed particular prediction indicators that can provide first-order estimates of the impact of space weather on engineering systems.
In an effort to explain the formation of a narrow third radiation belt at ultra-relativistic energies detected during a solar storm in September 20121, Mann et al.2 present simulations from which they conclude it arises from a process of outward radial diffusion alone, without the need for additional loss processes from higher frequency waves. The comparison of observations with the model in Figs 2 and 3 of their Article clearly shows that even with strong radial diffusion rates, the model predicts a third belt near L* = 3 that is twice as wide as observed and approximately an order of magnitude more intense. We therefore disagree with their interpretation that “the agreement between the absolute fluxes from the model and those observed by REPT [the Relativistic Electron Proton Telescope] shown on Figs 2 and 3 is excellent.”
Previous studies3 have shown that outward radial diffusion plays a very important role in the dynamics of the outer belt and is capable of explaining rapid reductions in the electron flux. It has also been shown that it can produce remnant belts (Fig. 2 of a long-term simulation study4). However, radial diffusion alone cannot explain the formation of the narrow third belt at multi-MeV during September 2012. An additional loss mechanism is required.
Higher radial diffusion rates cannot improve the comparison of model presented by Mann et al. with observations. A further increase in the radial diffusion rates (reported in Fig. 4 of the Supplementary Information of ref. 2) results in the overestimation of the outer belt fluxes by up to three orders of magnitude at energy of 3.4 MeV.
Observations at 2 MeV, where belts show only a two-zone structure, were not presented by Mann et al. Moreover, simulations of electrons with energies below 2 MeV with the same diffusion rates and boundary conditions used by the authors would probably produce very strong depletions down to L = 3–3.5, where L is radial distance from the centre of the Earth to the given field line in the equatorial plane. Observations do not show a non-adiabatic loss below L ∼ 4.5 for 2 MeV. Such different dynamics between 2 MeV and above 4 MeV at around L = 3.5 are another indication that particles are scattered by electromagnetic ion cyclotron (EMIC) waves that affect only energies above a certain threshold.
Observations of the phase space density (PSD) provide additional evidence for the local loss of electrons. Around L* = 3.5–4 PSD shows significant decrease by an order of magnitude starting in the afternoon of 3 September (Fig. 1a), while PSD above L* = 4 is increasing. The minimum in PSD between L* = 3.5–4 continues to decrease until 4 September. This evolution demonstrates that the loss is not produced by outward diffusion. Radial diffusion cannot produce deepening minima, as it works to smooth gradients. Just as growing peaks in PSD show the presence of localized acceleration5, deepening minima show the presence of localized loss.
Figure 1: Time evolution of radiation profiles in electron PSD at relativistic and ultra-relativistic energies.
figure 1
a, Similar to Supplementary Fig. 3 of ref. 2, but using TS07D model10 and for μ = 2,500 MeV G−1, K = 0.05 RE G0.5 (where RE is the radius of the Earth). b, Similar to Supplementary Fig. 3 of ref. 2, but using TS07D model and for μ = 700 MeV G−1, corresponding to MeV energies in the heart of the belt. Minimum in PSD in the heart of the multi-MeV electron radiation belt between 3.5 and 4 RE deepening between the afternoon of 3 September and 5 September clearly show that the narrow remnant belt at multi-MeV below 3.5 RE is produced by the local loss.
Full size image
The minimum in the outer boundary is reached on the evening of 2 September. After that, the outer boundary moves up, while the minimum decreases by approximately an order of magnitude, clearly showing that this main decrease cannot be explained by outward diffusion, and requires additional loss processes. The analysis of profiles of PSD is a standard tool used, for example, in the study about electron acceleration5 and routinely used by the entire Van Allen Probes team. In the Supplementary Information, we show that this analysis is validated by using different magnetic field models. The Supplementary Information also shows that measurements are above background noise.
Deepening minima at multi-MeV during the times when the boundary flux increases are clearly seen in Fig. 1a. They show that there must be localized loss, as radial diffusion cannot produce a minimum that becomes lower with time. At lower energies of 1–2 MeV, which corresponds to lower values of the first adiabatic invariant μ (Fig. 1b), the profiles are monotonic between L* = 3–3.5, consistent with the absence of scattering by EMIC waves that affect only electrons above a certain energy threshold6,7,8,9.
In summary, the results of the modelling and observations presented by Mann et al. do not lend support to the claim of explaining the dynamics of the ultra-relativistic third Van Allen radiation belt in terms of an outward radial diffusion process alone. While the outward radial diffusion driven by the loss to the magnetopause2 is certainly operating during this storm, there is compelling observational and modelling2,6 evidence that shows that very efficient localized electron loss operates during this storm at multi-MeV energies, consistent with localized loss produced by EMIC waves.
The detection of all inclusion dependencies (INDs) in an unknown dataset is at the core of any data profiling effort. Apart from the discovery of foreign key relationships, INDs can help perform data integration, integrity checking, schema (re-)design, and query optimization. With the advent of Big Data, the demand increases for efficient INDs discovery algorithms that can scale with the input data size. To this end, we propose S-INDD++ as a scalable system for detecting unary INDs in large datasets. S-INDD++ applies a new stepwise partitioning technique that helps discard a large number of attributes in early phases of the detection by processing the first partitions of smaller sizes. S-INDD++ also extends the concept of the attribute clustering to decide which attributes to be discarded based on the clustering result of each partition. Moreover, in contrast to the state-of-the-art, S-INDD++ does not require the partition to fit into the main memory-which is a highly appreciable property in the face of the ever growing datasets. We conducted an exhaustive evaluation of S-INDD++ by applying it to large datasets with thousands attributes and more than 266 million tuples. The results show the high superiority of S-INDD++ over the state-of-the-art. S-INDD++ reduced up to 50 % of the runtime in comparison with BINDER, and up to 98 % in comparison with S-INDD.
Manufacturing industries are undergoing a major paradigm shift towards more autonomy. Automated planning and scheduling then becomes a necessity. The Planning and Execution Competition for Logistics Robots in Simulation held at ICAPS is based on this scenario and provides an interesting testbed. However, the posed problem is challenging as also demonstrated by the somewhat weak results in 2017. The domain requires temporal reasoning and dealing with uncertainty. We propose a novel planning system based on Answer Set Programming and the Clingo solver to tackle these problems and incentivize robot cooperation. Our results show a significant performance improvement, both, in terms of lowering computational requirements and better game metrics.
A hybrid design approach of the hierarchical physical implementation design flow is presented and demonstrated on a fault-tolerant low-power multiprocessor system. The proposed flow allows to implement selected submodules in parallel with contrary requirements such as identical placement and individual block implementation. The overall system contains four Leon2 cores and communicates via the Waterbear framework and supports Adaptive Voltage Scaling (AVS) functionality. Three of the processor core variants are derived from the first baseline reference core but implemented individually at block level based on their clock tree specification. The chip is prepared for space applications and designed with triple modular redundancy (TMR) for control parts. The low-power performance is enabled by contemporary power and clock management control. An ASIC is fabricated in a low-power 0.13 mu m BiCMOS technology process node.
DualPanto
(2018)
We present a new haptic device that enables blind users to continuously track the absolute position of moving objects in spatial virtual environments, as is the case in sports or shooter games. Users interact with DualPanto by operating the me handle with one hand and by holding on to the it handle with the other hand. Each handle is connected to a pantograph haptic input/output device. The key feature is that the two handles are spatially registered with respect to each other. When guiding their avatar through a virtual world using the me handle, spatial registration enables users to track moving objects by having the device guide the output hand. This allows blind players of a 1-on-1 soccer game to race for the ball or evade an opponent; it allows blind players of a shooter game to aim at an opponent and dodge shots. In our user study, blind participants reported very high enjoyment when using the device to play (6.5/7).
This introductory essay to the HSR Special Issue “Economists, Politics, and Society” argues for a strong field-theoretical programme inspired by Pierre Bourdieu to research economic life as an integral part of different social forms. Its main aim is threefold. First, we spell out the very distinct Durkheimian legacy in Bourdieu’s thinking and the way he applies it in researching economic phenomena. Without this background, much of what is actually part of how Bourdieu analysed economic aspects of social life would be overlooked or reduced to mere economic sociology. Second, we sketch the main theoretical concepts and heuristics used to analyse economic life from a field perspective. Third, we focus on practical methodological issues of field-analytical research into economic phenomena. We conclude with a short summary of the basic characteristics of this approach and discuss the main insights provided by the contributions to this special issue.
Impact of self-assessment of return to work on employable discharge from multi-component cardiac rehabilitation. Retrospective unicentric analysis of routine data from cardiac rehabilitation in patients below 65 years of age. Presentation in the "Cardiovascular rehabilitation revisited" high impact abstract session during ESC Congress 2018.
An IoT network may consist of hundreds heterogeneous devices. Some of them may be constrained in terms of memory, power, processing and network capacity. Manual network and service management of IoT devices are challenging. We propose a usage of an ontology for the IoT device descriptions enabling automatic network management as well as service discovery and aggregation. Our IoT architecture approach ensures interoperability using existing standards, i.e. MQTT protocol and SemanticWeb technologies. We herein introduce virtual IoT devices and their semantic framework deployed at the edge of network. As a result, virtual devices are enabled to aggregate capabilities of IoT devices, derive new services by inference, delegate requests/responses and generate events. Furthermore, they can collect and pre-process sensor data. These tasks on the edge computing overcome the shortcomings of the cloud usage regarding siloization, network bandwidth, latency and speed. We validate our proposition by implementing a virtual device on a Raspberry Pi.
One particular challenge in the Internet of Things is the management of many heterogeneous things. The things are typically constrained devices with limited memory, power, network and processing capacity. Configuring every device manually is a tedious task. We propose an interoperable way to configure an IoT network automatically using existing standards. The proposed NETCONF-MQTT bridge intermediates between the constrained devices (speaking MQTT) and the network management standard NETCONF. The NETCONF-MQTT bridge generates dynamically YANG data models from the semantic description of the device capabilities based on the oneM2M ontology. We evaluate the approach for two use cases, i.e. describing an actuator and a sensor scenario.
The electret state stability in nonpolar semicrystalline polymers is largely determined by the traps located at crystalline/ amorphous phase interfaces. Thus, the thermal history of such polymers should considerably influence their electret properties. In the present work, we investigate how recrystallization influences charge stability in low-density polyethylene corona electrets. It has been found that electret charge stability in quenched samples is higher than in slowly-crystallized ones. Phenomenologicaly, this can be explained by the increased number of deeper traps in samples with smaller crystallite size.
Precision fruticulture addresses site or tree-adapted crop management. In the present study, soil and tree status, as well as fruit quality at harvest were analysed in a commercial apple (Malus × domestica 'Gala Brookfield'/Pajam1) orchard in a temperate climate. Trees were irrigated in addition to precipitation. Three irrigation levels (0, 50 and 100%) were applied. Measurements included readings of apparent electrical conductivity of soil (ECa), stem water potential, canopy temperature obtained by infrared camera, and canopy volume estimated by LiDAR and RGB colour imaging. Laboratory analyses of 6 trees per treatment were done on fruit considering the pigment contents and quality parameters. Midday stem water potential (SWP), normalized crop water stress index (CWSI) calculated from thermal data, and fruit yield and quality at harvest were analysed. Spatial patterns of the variability of tree water status were estimated by CWSI imaging supported by SWP readings. CWSI ranged from 0.1 to 0.7 indicating high variability due to irrigation and precipitation. Canopy volume data were less variable. Soil ECa appeared homogeneous in the range of 0 to 4 mS m-1. Fruit harvested in a drought stress zone showed enhanced portion of pheophytin in the chlorophyll pool. Irrigation affected soluble solids content and, hence, the quality of fruit. Overall, results highlighted that spatial variation in orchards can be found even if marginal variability of soil properties can be assumed.
Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations.
This study examined the relationships between the three phenotypic domains of the triarchic model of psychopathy —boldness, meanness, disinhibition— and electrophysiological indices of inhibitory control (NoGo-N2/NoGo-P3). EEG data from a 256-channel dense array were recorded while participants (135 un-dergraduates assessed via the Triarchic Psychopathy Measure) performed a Go/NoGo task with three types of stimuli (60% frequent-Go, 20% infrequent-Go, 20% infrequent-NoGo). N2 was defined as the mean amplitude between 240 ms and 340 ms after stimuli onset over fronto-central sensors on correct trials; P300 was defined as the mean amplitude between 350 ms and 550 ms after stimuli onset over centro-parietal sensors on correct trials. Multiple regression analyses using gender-corrected triarchic scores as predictors revealed that only Disinhibition scores significantly predicted reduced NoGo-N2 amplitudes (3.5% explained variance, beta weight = .23, p < .05) and reduced P3 amplitudes for NoGo and infrequent-Go trials (3.1 and 3.2% explained variance, respectively, beta weights = -.21, ps < .05). Our results indicate that high disinhibition entails deviations in early conflict monitoring processes (reduced NoGo-N2), as well as in latter evaluative and updating processing stages of infrequent events (reduced NoGo-P3 and infrequent-Go-P3). The null contribution of meanness and boldness domains in these results suggests that N2 and P3 amplitudes in Go/NoGo tasks could be considered as neurobiological indices of the externalizing tendencies comprised in this personality disorder.
Point clouds provide high-resolution topographic data which is often classified into bare-earth, vegetation, and building points and then filtered and aggregated to gridded Digital Elevation Models (DEMs) or Digital Terrain Models (DTMs). Based on these equally-spaced grids flow-accumulation algorithms are applied to describe the hydrologic and geomorphologic mass transport on the surface. In this contribution, we propose a stochastic point-cloud filtering that, together with a spatial bootstrap sampling, allows for a flow accumulation directly on point clouds using Facet-Flow Networks (FFN). Additionally, this provides a framework for the quantification of uncertainties in point-cloud derived metrics such as Specific Catchment Area (SCA) even though the flow accumulation itself is deterministic.
Beacon in the Dark
(2018)
The large amount of heterogeneous data in these email corpora renders experts' investigations by hand infeasible. Auditors or journalists, e.g., who are looking for irregular or inappropriate content or suspicious patterns, are in desperate need for computer-aided exploration tools to support their investigations.
We present our Beacon system for the exploration of such corpora at different levels of detail. A distributed processing pipeline combines text mining methods and social network analysis to augment the already semi-structured nature of emails. The user interface ties into the resulting cleaned and enriched dataset. For the interface design we identify three objectives expert users have: gain an initial overview of the data to identify leads to investigate, understand the context of the information at hand, and have meaningful filters to iteratively focus onto a subset of emails. To this end we make use of interactive visualisations based on rearranged and aggregated extracted information to reveal salient patterns.
Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. This work enhances state-of-the-art neural style transfer techniques by a generalized user interface with interactive tools to facilitate a creative and localized editing process. Thereby, we first propose a problem characterization representing trade-offs between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, first user tests indicate different levels of satisfaction for the implemented techniques and interaction design.
Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines.
The influence of chemical composition and crystallisation conditions on the ferroelectric and paraelectric phases and the resulting morphology in Poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) (P(VDF-TrFE-CFE)) terpolymer films with 55.4/37.2/7.3 mol% or with 62.2/29.4/8.4 mol% of VDF/TrFE/CFE was studied. Poly(vinylidene fluoride trifluoroethylene) (P(VDF-TrFE)) with 75/25 mol% VDF/TrFE was employed as reference material. Fourier-Transform Infrared Spectroscopy (FTIR) was used to determine the fractions of the relevant terpolymer phases, and X-Ray Diffraction (XRD) was employed to assess the crystalline morphology. The FTIR results show an increase of the fraction of paraelectric phases after annealing. On the other hand, XRD results indicate a more stable paraelectric phase in the terpolymer with higher CFE content.
Business process simulation is an important means for quantitative analysis of a business process and to compare different process alternatives. With the Business Process Model and Notation (BPMN) being the state-of-the-art language for the graphical representation of business processes, many existing process simulators support already the simulation of BPMN diagrams. However, they do not provide well-defined interfaces to integrate new concepts in the simulation environment. In this work, we present the design and architecture of a proof-of-concept implementation of an open and extensible BPMN process simulator. It also supports the simulation of multiple BPMN processes at a time and relies on the building blocks of the well-founded discrete event simulation. The extensibility is assured by a plug-in concept. Its feasibility is demonstrated by extensions supporting new BPMN concepts, such as the simulation of business rule activities referencing decision models and batch activities.
Minimising Information Loss on Anonymised High Dimensional Data with Greedy In-Memory Processing
(2018)
Minimising information loss on anonymised high dimensional data is important for data utility. Syntactic data anonymisation algorithms address this issue by generating datasets that are neither use-case specific nor dependent on runtime specifications. This results in anonymised datasets that can be re-used in different scenarios which is performance efficient. However, syntactic data anonymisation algorithms incur high information loss on high dimensional data, making the data unusable for analytics. In this paper, we propose an optimised exact quasi-identifier identification scheme, based on the notion of k-anonymity, to generate anonymised high dimensional datasets efficiently, and with low information loss. The optimised exact quasi-identifier identification scheme works by identifying and eliminating maximal partial unique column combination (mpUCC) attributes that endanger anonymity. By using in-memory processing to handle the attribute selection procedure, we significantly reduce the processing time required. We evaluated the effectiveness of our proposed approach with an enriched dataset drawn from multiple real-world data sources, and augmented with synthetic values generated in close alignment with the real-world data distributions. Our results indicate that in-memory processing drops attribute selection time for the mpUCC candidates from 400s to 100s, while significantly reducing information loss. In addition, we achieve a time complexity speed-up of O(3(n/3)) approximate to O(1.4422(n)).
The overhead of moving data is the major limiting factor in todays hardware, especially in heterogeneous systems where data needs to be transferred frequently between host and accelerator memory. With the increasing availability of hardware-based compression facilities in modern computer architectures, this paper investigates the potential of hardware-accelerated I/O Link Compression as a promising approach to reduce data volumes and transfer time, thus improving the overall efficiency of accelerators in heterogeneous systems. Our considerations are focused on On-the-Fly compression in both Single-Node and Scale-Out deployments. Based on a theoretical analysis, this paper demonstrates the feasibility of hardware-accelerated On-the-Fly I/O Link Compression for many workloads in a Scale-Out scenario, and for some even in a Single-Node scenario. These findings are confirmed in a preliminary evaluation using software-and hardware-based implementations of the 842 compression algorithm.
High-throughput RNA sequencing (RNAseq) produces large data sets containing expression levels of thousands of genes. The analysis of RNAseq data leads to a better understanding of gene functions and interactions, which eventually helps to study diseases like cancer and develop effective treatments. Large-scale RNAseq expression studies on cancer comprise samples from multiple cancer types and aim to identify their distinct molecular characteristics. Analyzing samples from different cancer types implies analyzing samples from different tissue origin. Such multi-tissue RNAseq data sets require a meaningful analysis that accounts for the inherent tissue-related bias: The identified characteristics must not originate from the differences in tissue types, but from the actual differences in cancer types. However, current analysis procedures do not incorporate that aspect. As a result, we propose to integrate a tissue-awareness into the analysis of multi-tissue RNAseq data. We introduce an extension for gene selection that provides a tissue-wise context for every gene and can be flexibly combined with any existing gene selection approach. We suggest to expand conventional evaluation by additional metrics that are sensitive to the tissue-related bias. Evaluations show that especially low complexity gene selection approaches profit from introducing tissue-awareness.
In the course of patient treatments, psychotherapists aim to meet the challenges of being both a trusted, knowledgeable conversation partner and a diligent documentalist. We are developing the digital whiteboard system Tele-Board MED (TBM), which allows the therapist to take digital notes during the session together with the patient. This study investigates what therapists are experiencing when they document with TBM in patient sessions for the first time and whether this documentation saves them time when writing official clinical documents. As the core of this study, we conducted four anamnesis session dialogues with behavior psychotherapists and volunteers acting in the role of patients. Following a mixed-method approach, the data collection and analysis involved self-reported emotion samples, user experience curves and questionnaires. We found that even in the very first patient session with TBM, therapists come to feel comfortable, develop a positive feeling and can concentrate on the patient. Regarding administrative documentation tasks, we found with the TBM report generation feature the therapists save 60% of the time they normally spend on writing case reports to the health insurance.
An Information System Supporting the Eliciting of Expert Knowledge for Successful IT Projects
(2018)
In order to guarantee the success of an IT project, it is necessary for a company to possess expert knowledge. The difficulty arises when experts no longer work for the company and it then becomes necessary to use their knowledge, in order to realise an IT project. In this paper, the ExKnowIT information system which supports the eliciting of expert knowledge for successful IT projects, is presented and consists of the following modules: (1) the identification of experts for successful IT projects, (2) the eliciting of expert knowledge on completed IT projects, (3) the expert knowledge base on completed IT projects, (4) the Group Method for Data Handling (GMDH) algorithm, (5) new knowledge in support of decisions regarding the selection of a manager for a new IT project. The added value of our system is that these three approaches, namely, the elicitation of expert knowledge, the success of an IT project and the discovery of new knowledge, gleaned from the expert knowledge base, otherwise known as the decision model, complement each other.
We describe how inversion symmetry separation of electronic state manifolds in resonant inelastic soft X-ray scattering (RIXS) can be applied to probe excited-state dynamics with compelling selectivity. In a case study of Fe L3-edge RIXS in the ferricyanide complex Fe(CN)63−, we demonstrate with multi-configurational restricted active space spectrum simulations how the information content of RIXS spectral fingerprints can be used to unambiguously separate species of different electronic configurations, spin multiplicities, and structures, with possible involvement in the decay dynamics of photo-excited ligand-to-metal charge-transfer. Specifically, we propose that this could be applied to confirm or reject the presence of a hitherto elusive transient Quartet species. Thus, RIXS offers a particular possibility to settle a recent controversy regarding the decay pathway, and we expect the technique to be similarly applicable in other model systems of photo-induced dynamics.
Published results on LDPE/MgO nanocomposites (3wt%) show that they promise to be good electrical-insulation materials. In this work, the nanocomposites are examined as a potential (ferro-)electret material as well. Isothermal surface-potential decay measurements show that charged LDPE/MgO films still exhibit significant surface potentials after heating for 4 hours at 80 degrees C, which suggests good capabilities of LDPE/MgO nanocomposites to hold electric charges of both polarities. Open-tubular-channel ferroelectrets prepared from LDPE/MgO nanocomposite films show significant piezoelectricity with d(33) coefficients of about 20 pC/N after charging and are stable up to temperatures of at least 80 degrees C. Thus LDPE/MgO nanocomposites may become available as a new ferroelectret material. To increase their d(33) coefficients, it is desirable to optimize the charging conditions and the ferroelectret structure.
An efficient Design Space Exploration (DSE) is imperative for the design of modern, highly complex embedded systems in order to steer the development towards optimal design points. The early evaluation of design decisions at system-level abstraction layer helps to find promising regions for subsequent development steps in lower abstraction levels by diminishing the complexity of the search problem. In recent works, symbolic techniques, especially Answer Set Programming (ASP) modulo Theories (ASPmT), have been shown to find feasible solutions of highly complex system-level synthesis problems with non-linear constraints very efficiently. In this paper, we present a novel approach to a holistic system-level DSE based on ASPmT. To this end, we include additional background theories that concurrently guarantee compliance with hard constraints and perform the simultaneous optimization of several design objectives. We implement and compare our approach with a state-of-the-art preference handling framework for ASP. Experimental results indicate that our proposed method produces better solutions with respect to both diversity and convergence to the true Pareto front.
Utilizing quad-trees for efficient design space exploration with partial assignment evaluation
(2018)
Recently, it has been shown that constraint-based symbolic solving techniques offer an efficient way for deciding binding and routing options in order to obtain a feasible system level implementation. In combination with various background theories, a feasibility analysis of the resulting system may already be performed on partial solutions. That is, infeasible subsets of mapping and routing options can be pruned early in the decision process, which fastens the solving accordingly. However, allowing a proper design space exploration including multi-objective optimization also requires an efficient structure for storing and managing non-dominated solutions. In this work, we propose and study the usage of the Quad-Tree data structure in the context of partial assignment evaluation during system synthesis. Out experiments show that unnecessary dominance checks can be avoided, which indicates a preference of Quad-Trees over a commonly used list-based implementation for large combinatorial optimization problems.
Introduction
(2018)
Microstructure Characterisation of Advanced Materials via 2D and 3D X-Ray Refraction Techniques
(2018)
3D imaging techniques have an enormous potential to understand the microstructure, its evolution, and its link to mechanical, thermal, and transport properties. In this conference paper we report the use of a powerful, yet not so wide-spread, set of X-ray techniques based on refraction effects. X-ray refraction allows determining internal specific surface (surface per unit volume) in a non-destructive fashion, position and orientation sensitive, and with a nanometric detectability. We demonstrate showcases of ceramics and composite materials, where microstructural parameters could be achieved in a way unrivalled even by high-resolution techniques such as electron microscopy or computed tomography. We present in situ analysis of the damage evolution in an Al/Al2O3 metal matrix composite during tensile load and the identification of void formation (different kinds of defects, particularly unsintered powder hidden in pores, and small inhomogeneity’s like cracks) in Ti64 parts produced by selective laser melting using synchrotron X-ray refraction radiography and tomography.
Low back pain (LBP) is a leading cause of activity limitation. Objective assessment of the spinal motion plays a key role in diagnosis and treatment of LBP. We propose a method that facilitates clinical assessment of lower back motions by means of a wireless inertial sensor network. The sensor units are attached to the right and left side of the lumbar region, the pelvis and the thighs, respectively. Since magnetometers are known to be unreliable in indoor environments, we use only 3D accelerometer and 3D gyroscope readings. Compensation of integration drift in the horizontal plane is achieved by estimating the gyroscope biases from automatically detected initial rest phases. For the estimation of sensor orientations, both a smoothing algorithm and a filtering algorithm are presented. From these orientations, we determine three-dimensional joint angles between the thighs and the pelvis and between the pelvis and the lumbar region. We compare the orientations and joint angles to measurements of an optical motion tracking system that tracks each skin-mounted sensor by means of reflective markers. Eight subjects perform a neutral initial pose, then flexion/extension, lateral flexion, and rotation of the trunk. The root mean square deviation between inertial and optical angles is about one degree for angles in the frontal and sagittal plane and about two degrees for angles in the transverse plane (both values averaged over all trials). We choose five features that characterize the initial pose and the three motions. Interindividual differences of all features are found to be clearly larger than the observed measurement deviations. These results indicate that the proposed inertial sensor-based method is a promising tool for lower back motion assessment.