TY - JOUR A1 - Evans, Myfanwy E. A1 - Hyde, Stephen T. T1 - Symmetric Tangling of Honeycomb Networks JF - Symmetry N2 - Symmetric, elegantly entangled structures are a curious mathematical construction that has found their way into the heart of the chemistry lab and the toolbox of constructive geometry. Of particular interest are those structures—knots, links and weavings—which are composed locally of simple twisted strands and are globally symmetric. This paper considers the symmetric tangling of multiple 2-periodic honeycomb networks. We do this using a constructive methodology borrowing elements of graph theory, low-dimensional topology and geometry. The result is a wide-ranging enumeration of symmetric tangled honeycomb networks, providing a foundation for their exploration in both the chemistry lab and the geometers toolbox. KW - tangles KW - knots KW - networks KW - periodic entanglement KW - molecular weaving KW - graphs Y1 - 2022 U6 - https://doi.org/10.3390/sym14091805 SN - 2073-8994 VL - 14 SP - 1 EP - 13 PB - MDPI CY - Basel, Schweiz ET - 9 ER - TY - JOUR A1 - Orland, Andreas A1 - Padubrin, Max T1 - Is there a gender hiring gap in academic economics? BT - evidence from a network analysis JF - Royal Society Open Science N2 - We collect a network dataset of tenured economics faculty in Austria, Germany and Switzerland. We rank the 100 institutions included with a minimum violation ranking. This ranking is positively and significantly correlated with the Times Higher Education ranking of economics institutions. According to the network ranking, individuals on average go down about 23 ranks from their doctoral institution to their employing institution. While the share of females in our dataset is only 15%, we do not observe a significant gender hiring gap (a difference in rank changes between male and female faculty). We conduct a robustness check with the Handelsblatt and the Times Higher Education ranking. According to these rankings, individuals on average go down only about two ranks. We do not observe a significant gender hiring gap using these two rankings (although the dataset underlying this analysis is small and these estimates are likely to be noisy). Finally, we discuss the limitations of the network ranking in our context. KW - gender KW - networks KW - academia Y1 - 2022 U6 - https://doi.org/10.1098/rsos.210717 SN - 2054-5703 VL - 9 IS - 2 PB - Royal Society CY - London ER - TY - JOUR A1 - Adolfs, Marjolijn A1 - Hoque, Mohammed Mainul A1 - Shprits, Yuri Y. T1 - Storm-time relative total electron content modelling using machine learning techniques JF - Remote sensing N2 - Accurately predicting total electron content (TEC) during geomagnetic storms is still a challenging task for ionospheric models. In this work, a neural-network (NN)-based model is proposed which predicts relative TEC with respect to the preceding 27-day median TEC, during storm time for the European region (with longitudes 30 degrees W-50 degrees E and latitudes 32.5 degrees N-70 degrees N). The 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 are used as inputs and the output of the network is the relative TEC. The relative TEC can be converted to the actual TEC knowing the median TEC. The median TEC is calculated at each grid point over the European region considering data from the last 27 days before the storm using global ionosphere maps (GIMs) from international GNSS service (IGS) sources. A storm event is defined when the storm time disturbance index Dst drops below 50 nanotesla. The model was trained with storm-time relative TEC data from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. Unseen storm data from 33 storm events during 2015 and 2020 were used to test the model. The UQRG GIMs were used because of their high temporal resolution (15 min) compared to other products from different analysis centers. The NN-based model predictions show the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and show a mixture of both phases during equinoxes. The model's performance was also compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet-time TEC model, both developed at the German Aerospace Agency (DLR). The storm model has a root mean squared error (RMSE) of 3.38 TEC units (TECU), which is an improvement by 1.87 TECU compared to the NTCM, where an RMSE of 5.25 TECU was found. This improvement corresponds to a performance increase by 35.6%. The storm-time model outperforms the quiet-time model by 1.34 TECU, which corresponds to a performance increase by 28.4% from 4.72 to 3.38 TECU. The quiet-time model was trained with Carrington averaged TEC and, therefore, is ideal to be used as an input instead of the GIM derived 27-day median. We found an improvement by 0.8 TECU which corresponds to a performance increase by 17% from 4.72 to 3.92 TECU for the storm-time model using the quiet-time-model predicted TEC as an input compared to solely using the quiet-time model. KW - ionosphere KW - relative total electron content KW - geomagnetic storms KW - neural KW - networks KW - NTCM KW - European storm-time model Y1 - 2022 U6 - https://doi.org/10.3390/rs14236155 SN - 2072-4292 VL - 14 IS - 23 PB - MDPI CY - Basel ER - TY - JOUR A1 - Ocampo-Espindola, Jorge Luis A1 - Omel'chenko, Oleh A1 - Kiss, Istvan Z. T1 - Non-monotonic transients to synchrony in Kuramoto networks and electrochemical oscillators JF - Journal of physics. Complexity N2 - We performed numerical simulations with the Kuramoto model and experiments with oscillatory nickel electrodissolution to explore the dynamical features of the transients from random initial conditions to a fully synchronized (one-cluster) state. The numerical simulations revealed that certain networks (e.g., globally coupled or dense Erdos-Renyi random networks) showed relatively simple behavior with monotonic increase of the Kuramoto order parameter from the random initial condition to the fully synchronized state and that the transient times exhibited a unimodal distribution. However, some modular networks with bridge elements were identified which exhibited non-monotonic variation of the order parameter with local maximum and/or minimum. In these networks, the histogram of the transients times became bimodal and the mean transient time scaled well with inverse of the magnitude of the second largest eigenvalue of the network Laplacian matrix. The non-monotonic transients increase the relative standard deviations from about 0.3 to 0.5, i.e., the transient times became more diverse. The non-monotonic transients are related to generation of phase patterns where the modules are synchronized but approximately anti-phase to each other. The predictions of the numerical simulations were demonstrated in a population of coupled oscillatory electrochemical reactions in global, modular, and irregular tree networks. The findings clarify the role of network structure in generation of complex transients that can, for example, play a role in intermittent desynchronization of the circadian clock due to external cues or in deep brain stimulations where long transients are required after a desynchronization stimulus. KW - synchronization KW - networks KW - Kuramoto model KW - electrochemistry KW - chemical KW - oscillations Y1 - 2021 U6 - https://doi.org/10.1088/2632-072X/abe109 SN - 2632-072X VL - 2 IS - 1 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Belaid, Mohamed Karim A1 - Rabus, Maximilian A1 - Krestel, Ralf T1 - CrashNet BT - an encoder-decoder architecture to predict crash test outcomes JF - Data mining and knowledge discovery N2 - Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder-decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain. KW - Predictive models KW - Time series analysis KW - Supervised deep neural KW - networks KW - Car safety management Y1 - 2021 U6 - https://doi.org/10.1007/s10618-021-00761-9 SN - 1384-5810 SN - 1573-756X VL - 35 IS - 4 SP - 1688 EP - 1709 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Loster, Michael A1 - Koumarelas, Ioannis A1 - Naumann, Felix T1 - Knowledge transfer for entity resolution with siamese neural networks JF - ACM journal of data and information quality N2 - The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity-duplicates-into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise.
We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent. KW - Entity resolution KW - duplicate detection KW - transfer learning KW - neural KW - networks KW - metric learning KW - similarity learning KW - data quality Y1 - 2021 U6 - https://doi.org/10.1145/3410157 SN - 1936-1955 SN - 1936-1963 VL - 13 IS - 1 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Omelʹchenko, Oleh E. T1 - Nonstationary coherence-incoherence patterns in nonlocally coupled heterogeneous phase oscillators JF - Chaos : an interdisciplinary journal of nonlinear science N2 - We consider a large ring of nonlocally coupled phase oscillators and show that apart from stationary chimera states, this system also supports nonstationary coherence-incoherence patterns (CIPs). For identical oscillators, these CIPs behave as breathing chimera states and are found in a relatively small parameter region only. It turns out that the stability region of these states enlarges dramatically if a certain amount of spatially uniform heterogeneity (e.g., Lorentzian distribution of natural frequencies) is introduced in the system. In this case, nonstationary CIPs can be studied as stable quasiperiodic solutions of a corresponding mean-field equation, formally describing the infinite system limit. Carrying out direct numerical simulations of the mean-field equation, we find different types of nonstationary CIPs with pulsing and/or alternating chimera-like behavior. Moreover, we reveal a complex bifurcation scenario underlying the transformation of these CIPs into each other. These theoretical predictions are confirmed by numerical simulations of the original coupled oscillator system. KW - chimera states KW - synchronization KW - networks KW - Kuramoto KW - populations KW - dynamics KW - bumps KW - model Y1 - 2020 U6 - https://doi.org/10.1063/1.5145259 SN - 1054-1500 SN - 1089-7682 VL - 30 IS - 4 PB - American Institute of Physics CY - Melville ER - TY - JOUR A1 - Martykanova, Darina T1 - A Gateway to the World BT - Jewish and Armenian Engineers of Ottoman Background at the Ecole centrale des arts et manufactures (1853-1923) JF - Diasporas : circulations, migrations, histoire N2 - In the second half of the 19th century, the French École centrale des arts et manufactures became one of the engineering schools that enjoyed a worldwide reputation. There were many foreigners among its students. This article focuses on the graduates born in the Ottoman Empire, particularly on Jews and Armenians. It analyses their backgrounds, their common features and their professional careers, tracing their links with other centraliens. The patterns in the Ottoman centraliens’ professional trajectories help us picture a world full of opportunities where highly qualified men could cross borders and build careers with ease, but where, at the same time, origins, allegiances, contacts and credentials mattered greatly. N2 - Dans la seconde moitié du xixe siècle, l’École centrale des arts et manufactures française devint une école d’ingénieurs jouissant d’une réputation internationale ; les étudiants étrangers y furent nombreux. Cet article porte sur les diplômés nés dans l’Empire ottoman, en particuliers les étudiants juifs et arméniens ; il s’attache à leur parcours, à leurs caractéristiques et à leurs carrières professionnelles et restitue leurs liens avec les autres centraliens. L’étude de leurs trajectoires professionnelles permet d’appréhender un monde riche en opportunités, où des hommes hautement qualifiés pouvaient aisément traverser les frontières et construire une carrière, mais où, dans le même temps, les origines, les réseaux d’allégeance, les relations et les diplômes jouent un rôle de premier plan. KW - Engineers KW - Jews KW - Armenians KW - Ottomans KW - Ecole centrale des arts et manufactures KW - networks KW - Ottoman Empire KW - France KW - transnational Y1 - 2017 U6 - https://doi.org/10.4000/diasporas.718 SN - 1637-5823 SN - 2431-1472 VL - 29 SP - 33 EP - 51 PB - Presses Universitaires du Midi CY - Toulouse ER - TY - JOUR A1 - Breuer, David A1 - Nowak, Jacqueline A1 - Ivakov, Alexander A1 - Somssich, Marc A1 - Persson, Staffan A1 - Nikoloski, Zoran T1 - System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells JF - Proceedings of the National Academy of Sciences of the United States of America N2 - The actin cytoskeleton is an essential intracellular filamentous structure that underpins cellular transport and cytoplasmic streaming in plant cells. However, the system-level properties of actin-based cellular trafficking remain tenuous, largely due to the inability to quantify key features of the actin cytoskeleton. Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport. We show that the actin cytoskeleton in both growing and elongated hypocotyl cells has structural properties facilitating efficient transport. Our findings suggest that the erratic movement of Golgi is a stable cellular phenomenon that might optimize distribution efficiency of cell material. Moreover, we demonstrate that Golgi transport in hypocotyl cells can be accurately predicted from the actin network topology alone. Thus, our framework provides quantitative evidence for system-wide coordination of cellular transport in plant cells and can be readily applied to investigate cytoskeletal organization and transport in other organisms. KW - actin KW - cytoskeleton KW - Golgi KW - image processing KW - networks Y1 - 2017 U6 - https://doi.org/10.1073/pnas.1706711114 SN - 0027-8424 VL - 114 SP - E5741 EP - E5749 PB - National Acad. of Sciences CY - Washington ER - TY - JOUR A1 - Ellis, Jason Brent A1 - Abreu-Ellis, Carla Reis T1 - Student Perspectives of Social Networking use in Higher Education JF - KEYCIT 2014 - Key Competencies in Informatics and ICT N2 - Social networks are currently at the forefront of tools that lend to Personal Learning Environments (PLEs). This study aimed to observe how students perceived PLEs, what they believed were the integral components of social presence when using Facebook as part of a PLE, and to describe student’s preferences for types of interactions when using Facebook as part of their PLE. This study used mixed methods to analyze the perceptions of graduate and undergraduate students on the use of social networks, more specifically Facebook as a learning tool. Fifty surveys were returned representing a 65 % response rate. Survey questions included both closed and open-ended questions. Findings suggested that even though students rated themselves relatively well in having requisite technology skills, and 94 % of students used Facebook primarily for social use, they were hesitant to migrate these skills to academic use because of concerns of privacy, believing that other platforms could fulfil the same purpose, and by not seeing the validity to use Facebook in establishing social presence. What lies at odds with these beliefs is that when asked to identify strategies in Facebook that enabled social presence to occur in academic work, the majority of students identified strategies in five categories that lead to social presence establishment on Facebook during their coursework. KW - Social KW - networks KW - higher KW - education KW - personal KW - learning KW - environments KW - Facebook Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-82632 SN - 1868-0844 SN - 2191-1940 IS - 7 SP - 117 EP - 131 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Julich-Gruner, Konstanze K. A1 - Löwenberg, Candy A1 - Neffe, Axel T. A1 - Behl, Marc A1 - Lendlein, Andreas T1 - Recent trends in the chemistry of shape-memory polymers JF - Macromolecular chemistry and physics N2 - Shape-memory polymers (SMPs) are stimuli-sensitive materials capable of performing complex movements on demand, which makes them interesting candidates for various applications, for example, in biomedicine or aerospace. This trend article highlights current approaches in the chemistry of SMPs, such as tailored segment chemistry to integrate additional functions and novel synthetic routes toward permanent and temporary netpoints. Multiphase polymer networks and multimaterial systems illustrate that SMPs can be constructed as a modular system of different building blocks and netpoints. Future developments are aiming at multifunctional and multistimuli-sensitive SMPs. KW - multifunctional polymers KW - networks KW - shape-memory polymers KW - stimuli-sensitive polymers KW - triple-shape effect Y1 - 2013 U6 - https://doi.org/10.1002/macp.201200607 SN - 1022-1352 VL - 214 IS - 5 SP - 527 EP - 536 PB - Wiley-VCH CY - Weinheim ER -