The predictions of classical theory for the establishment of a network by free-radical polymerization have been tested under conditions for which those predictions should be simple, i.e., short pregel regimes in which the composition and chain length distribution of the primary chains should be nearly invariant and for a nearly equally reactive system (methyl methacrylate/ethylene glycol dimethacrylate). At the moderate to high concentrations of cross-linker used, the classical theory is inapplicable, each of its predictions being disobeyed. Gelation occurs 1-2 orders of magnitude later than predicted, the gel point is relatively insensitive to the amount of cross-linker, the dependence on the primary chain length is weaker than the predicted inverse dependence, and the divergence of the weight-average molecular weight is more consistent with the percolation picture. All of these point to a violation of the mean-field picture, probably through cyclization and (size-dependent) reduction in the reactivity of the pendants.

Supervised machine learning algorithms require training data whose generation for complex relation extraction tasks tends to be difficult. Being optimized for relation extraction at sentence level, many annotation tools lack in facilitating the annotation of relational structures that are widely spread across the text. This leads to nonintuitive and cumbersome visualizations, making the annotation process unnecessarily time-consuming. We propose SANTO, an easy-to-use, domain-adaptive annotation tool specialized for complex slot filling tasks which may involve problems of cardinality and referential grounding. The webbased architecture enables fast and clearly structured annotation for multiple users in parallel. Relational structures are formulated as templates following the conceptualization of an underlying ontology. Further, import and export procedures of standard formats enable interoperability with external sources and tools.

This paper presents the slurk software, a lightweight interaction server for setting up dialog data collections and running experiments. slurk enables a multitude of settings including text-based, speech and video interaction between two or more humans or humans and bots, and a multimodal display area for presenting shared or private interactive context. The software is implemented in Python with an HTML and JAVASCRIPT frontend that can easily be adapted to individual needs. It also provides a setup for pairing participants on common crowdworking platforms such as Amazon Mechanical Turk and some example bot scripts for common interaction scenarios.