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    Dataset
    Simulation of Individual Polymer Chains and Polymer Solutions with Smoothed Dissipative Particle Dynamics
    (MDPI, 2016-02-06) Litvinov, Sergey; Xie, Qingguang; Hu, Xiangyu; Adams, Nikolaus; Ellero, Marco
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    Building a Collaborative Editorial Workbench for Legal Texts with Complex Structures
    (OpenEdition, 2020-05-20) Geißler, Nils; Schulz, Daniela
    This paper presents the work undertaken by the Capitularia project to integrate a collaborative editorial workbench into the open-source content management system (CMS) WordPress. It introduces the reasons for selecting WordPress as the project’s CMS, the workflows established (including a sophisticated XSL-scripting pipeline), as well as three plug-ins created to integrate certain functionalities. The Cap-X2WP plug-in facilitates XSL transformations of XML files to HTML directly within the WordPress framework. The Cap-PaGer plug-in is used to generate WordPress pages automatically based on the XML files located in specific folders on the server. Their publication status can be administered via a special interface added to the general WordPress dashboard at a moment’s notice. Whereas the aforementioned plug-ins facilitate the daily work of the staff members in the general management and enhancement of the project’s website, the Cap-Coll plug-in eases the specific editorial task of collating texts by including the CollateX algorithms in a WordPress plug-in. The report concludes with a brief perspective on the possibilities for further developments.
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    Content removal bias in web scraped data: A solution applied to real estate ads
    (De Gruyter, 2022-12-31) Marconi, Gabriele
    propose a solution to content removal bias in statistics from web scraped data. Content removal bias occurs when data is removed from the web before a scraper is able to collect it. The solution I propose is based on inverse probability weights, derived from the parameters of a survival function with complex forms of data censoring. I apply this solution to the calculation of the proportion of newly built dwellings with web scraped data on Luxembourg, and I run a counterfactual experiment and a Montecarlo simulation to confirm the findings. The results show that the extent of content removal bias is relatively small if the scraping occurs frequently compared with the online permanence of the data; and that it grows larger with less frequent scraping.
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    Dataset
    Volatile Memory Motifs: Minimal Spiking Neural Networks
    (arXiv.org, 2023-03-21) Schittler Neves, Fabio; Timme, Marc
    How spiking neuronal networks encode memories in their different time and spatial scales constitute a fundamental topic in neuroscience and neuro-inspired engineering. Much attention has been paid to large networks and long-term memory, for example in models of associative memory. Smaller circuit motifs may play an important complementary role on shorter time scales, where broader network effects may be of less relevance. Yet, compact computational models of spiking neural networks that exhibit short-term volatile memory and actively hold information until their energy source is switched off, seem not fully understood. Here we propose that small spiking neural circuit motifs may act as volatile memory components. A minimal motif consists of only two interconnected neurons -- one self-connected excitatory neuron and one inhibitory neuron -- and realizes a single-bit volatile memory. An excitatory, delayed self-connection promotes a bistable circuit in which a self-sustained periodic orbit generating spike trains co-exists with the quiescent state of no neuron spiking. Transient external inputs may straightforwardly induce switching between those states. Moreover, the inhibitory neuron may act as an autonomous turn-off switch. It integrates incoming excitatory pulses until a threshold is reached after which the inhibitory neuron emits a spike that then inhibits further spikes in the excitatory neuron, terminating the memory. Our results show how external bits of information (excitatory signal), can be actively held in memory for a pre-defined amount of time. We show that such memory operations are robust against parameter variations and exemplify how sequences of multidimensional input signals may control the dynamics of a many-bits memory circuit in a desired way.