Informatik

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Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zahari, Finn; Pérez, Eduardo; Mahadevaiah, Mamathamba Kalishettyhalli; Kohlstedt, Hermann; Wenger, Christian; Ziegler, Martin
Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells. © 2020, The Author(s).
Call to action for global access to and harmonization of quality information of individual earth science datasets
(Paris : CODATA, 2021) Peng, Ge; Downs, Robert R.; Lacagnina, Carlo; Ramapriyan, Hampapuram; Ivánová, Ivana; Moroni, David; Wei, Yaxing; Larnicol, Gilles; Wyborn, Lesley; Goldberg, Mitch; Schulz, Jörg; Bastrakova, Irina; Ganske, Anette; Bastin, Lucy; Khalsa, Siri Jodha S.; Wu, Mingfang; Shie, Chung-Lin; Ritchey, Nancy; Jones, Dave; Habermann, Ted; Lief, Christina; Maggio, Iolanda; Albani, Mirko; Stall, Shelley; Zhou, Lihang; Drévillon, Marie; Champion, Sarah; Hou, C. Sophie; Doblas-Reyes, Francisco; Lehnert, Kerstin; Robinson, Erin; Bugbee, Kaylin
Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.
Curating Scientific Information in Knowledge Infrastructures
(Paris : CODATA, 2018) Stocker, Markus; Paasonen, Pauli; Fiebig, Markus; Zaidan, Martha A.; Hardisty, Alex
Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information—meaningful secondary or derived data—about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation—as machine readable data and their machine readable meaning—is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Electron beam induced dehydrogenation of MgH2 studied by VEELS
(Cham : Springer International Publishing AG, 2016) Surrey, Alexander; Schultz, Ludwig; Rellinghaus, Bernd
Nanosized or nanoconfined hydrides are promising materials for solid-state hydrogen storage. Most of these hydrides, however, degrade fast during the structural characterization utilizing transmission electron microscopy (TEM) upon the irradiation with the imaging electron beam due to radiolysis. We use ball-milled MgH2 as a reference material for in-situ TEM experiments under low-dose conditions to study and quantitatively understand the electron beam-induced dehydrogenation. For this, valence electron energy loss spectroscopy (VEELS) measurements are conducted in a monochromated FEI Titan3 80–300 microscope. From observing the plasmonic absorptions it is found that MgH2 successively converts into Mg upon electron irradiation. The temporal evolution of the spectra is analyzed quantitatively to determine the thickness-dependent, characteristic electron doses for electron energies of both 80 and 300 keV. The measured electron doses can be quantitatively explained by the inelastic scattering of the incident high-energy electrons by the MgH2 plasmon. The obtained insights are also relevant for the TEM characterization of other hydrides.
Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets
(Paris : CODATA, 2022) Peng, Ge; Lacagnina, Carlo; Downs, Robert R.; Ganske, Anette; Ramapriyan, Hampapuram K.; Ivánová, Ivana; Wyborn, Lesley; Jones, Dave; Bastin, Lucy; Shie, Chung-lin; Moroni, David F.
Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity.
A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction
([London] : Nature Publishing Group UK, 2019) Schmack, Roman; Friedrich, Alexandra; Kondratenko, Evgenii V.; Polte, Jörg; Werwatz, Axel; Kraehnert, Ralph
Decades of catalysis research have created vast amounts of experimental data. Within these data, new insights into property-performance correlations are hidden. However, the incomplete nature and undefined structure of the data has so far prevented comprehensive knowledge extraction. We propose a meta-analysis method that identifies correlations between a catalyst’s physico-chemical properties and its performance in a particular reaction. The method unites literature data with textbook knowledge and statistical tools. Starting from a researcher’s chemical intuition, a hypothesis is formulated and tested against the data for statistical significance. Iterative hypothesis refinement yields simple, robust and interpretable chemical models. The derived insights can guide new fundamental research and the discovery of improved catalysts. We demonstrate and validate the method for the oxidative coupling of methane (OCM). The final model indicates that only well-performing catalysts provide under reaction conditions two independent functionalities, i.e. a thermodynamically stable carbonate and a thermally stable oxide support.
Organizing Scientific Knowledge from Engineering Sciences Using the Open Research Knowledge Graph: The Tailored Forming Process Chain Use Case
(Paris : CODATA, 2024) Karras, Oliver; Budde, Laura; Merkel, Paulina; Hermsdorf, Jörg; Stonis, Malte; Overmeyer, Ludger; Behrens, Bernd-Arno; Auer, Sören
Background: Engineering sciences are essential for addressing contemporary technical, environmental, and economic challenges. Despite its data-intensive and interdisciplinary nature, the organization of Findable, Accessible, Interoperable, and Reusable (FAIR) scientific knowledge and data in this research field remains understudied. Engineers need infrastructures with services that support them in organizing FAIR scientific knowledge and data for communication and (re-)use. Aim: We explore the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure by demonstrating how engineers can utilize the ORKG in innovative ways for communication and (re-)use. Method: For a use case from the Collaborative Research Center 1153 “Tailored Forming”, we collect, extract, and analyze scientific knowledge on 10 Tailored Forming Process Chains (TFPCs) from five publications in the ORKG. In particular, we semantically describe the TFPCs, i.a., regarding their steps, manufacturing methods, measurements, and results. The usefulness of the data extraction topics, their organization, and the relevance of the knowledge described is examined by an expert consultation with 21 experts. Results: Based on the described knowledge, we build and publish an ORKG comparison as a detailed overview for communication. Furthermore, we (re-)use the knowledge and answer eight competency questions asked by two domain experts. The validation shows a clear agreement of the 21 experts regarding the examined usefulness and relevance. Conclusions: Our use case shows that the ORKG as a ready-to-use infrastructure with services supports researchers, including engineers, in sustainably organizing FAIR scientific knowledge. The direct use of the ORKG by engineers is feasible, so the ORKG is a promising infrastructure for innovative ways of communicating and (re-)using FAIR scientific knowledge in engineering sciences, thus advancing this research field.
Publisher Correction: Rapid and low-cost insect detection for analysing species trapped on yellow sticky traps
(London : Nature Publishing Group, 2021) Böckmann, Elias; Pfaff, Alexander; Schirrmann, Michael; Pflanz, Michael
Correction to: Scientific Reports https://doi.org/10.1038/s41598-021-89930-w, published online 17 May 2021
Rapid and low-cost insect detection for analysing species trapped on yellow sticky traps
(London : Nature Publishing Group, 2021) Böckmann, Elias; Pfaff, Alexander; Schirrmann, Michael; Pflanz, Michael
While insect monitoring is a prerequisite for precise decision-making regarding integrated pest management (IPM), it is time- and cost-intensive. Low-cost, time-saving and easy-to-operate tools for automated monitoring will therefore play a key role in increased acceptance and application of IPM in practice. In this study, we tested the differentiation of two whitefly species and their natural enemies trapped on yellow sticky traps (YSTs) via image processing approaches under practical conditions. Using the bag of visual words (BoVW) algorithm, accurate differentiation between both natural enemies and the Trialeurodes vaporariorum and Bemisia tabaci species was possible, whereas the procedure for B. tabaci could not be used to differentiate this species from T. vaporariorum. The decay of species was considered using fresh and aged catches of all the species on the YSTs, and different pooling scenarios were applied to enhance model performance. The best performance was reached when fresh and aged individuals were used together and the whitefly species were pooled into one category for model training. With an independent dataset consisting of photos from the YSTs that were placed in greenhouses and consequently with a naturally occurring species mixture as the background, a differentiation rate of more than 85% was reached for natural enemies and whiteflies.
Redox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing
(Windsor ; Beijing : English China Online Journals, ECOJ, 2022) Wang, Yu-Hao; Gong, Tian-Cheng; Ding, Ya-Xin; Li, Yang; Wang, Wei; Chen, Zi-Ang; Du, Nan; Covi, Erika; Farronato, Matteo; Ielmini, Daniele; Zhang, Xu-Meng; Luo, Qing
The spiking neural network (SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore’s Law, the traditional complementary metal-oxide-semiconductor (CMOS) technology is gradually fading and is unable to meet the growing needs of neuromorphic computing. Besides, the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices. Memristors with volatile threshold switching (TS) behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems. Herein, the state-of-the-art about the fundamental knowledge of SNNs is reviewed. Moreover, we review the implementation of TS memristor-based neurons and their systems, and point out the challenges that should be further considered from devices to circuits in the system demonstrations. We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.
Terminal restriction fragment length polymorphism is an “old school” reliable technique for swift microbial community screening in anaerobic digestion
([London] : Macmillan Publishers Limited, part of Springer Nature, 2018-11-14) De Vrieze, Jo; Ijaz, Umer Z.; Saunders, Aaron M.; Theuerl, Susanne
The microbial community in anaerobic digestion has been analysed through microbial fingerprinting techniques, such as terminal restriction fragment length polymorphism (TRFLP), for decades. In the last decade, high-throughput 16S rRNA gene amplicon sequencing has replaced these techniques, but the time-consuming and complex nature of high-throughput techniques is a potential bottleneck for full-scale anaerobic digestion application, when monitoring community dynamics. Here, the bacterial and archaeal TRFLP profiles were compared with 16S rRNA gene amplicon profiles (Illumina platform) of 25 full-scale anaerobic digestion plants. The α-diversity analysis revealed a higher richness based on Illumina data, compared with the TRFLP data. This coincided with a clear difference in community organisation, Pareto distribution, and co-occurrence network statistics, i.e., betweenness centrality and normalised degree. The β-diversity analysis showed a similar clustering profile for the Illumina, bacterial TRFLP and archaeal TRFLP data, based on different distance measures and independent of phylogenetic identification, with pH and temperature as the two key operational parameters determining microbial community composition. The combined knowledge of temporal dynamics and projected clustering in the β-diversity profile, based on the TRFLP data, distinctly showed that TRFLP is a reliable technique for swift microbial community dynamics screening in full-scale anaerobic digestion plants. © 2018, The Author(s).

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