Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality

A recent line of work in NLP focuses on the (dis)ability of models to generalise compositionally for artificial languages. However, when considering natural language tasks, the data involved is not strictly, or locally, compositional. Quantifying the compositionality of data is a challenging task, which has been investigated primarily for short utterances. We use recursive neural models (Tree-LSTMs) with bottlenecks that limit the transfer of information between nodes. We illustrate that comparing data's representations in models with and without the bottleneck can be used to produce a compositionality metric. The procedure is applied to the evaluation of arithmetic expressions using synthetic data, and sentiment classification using natural language data. We demonstrate that compression through a bottleneck impacts non-compositional examples disproportionately and then use the bottleneck compositionality metric (BCM) to distinguish compositional from non-compositional samples, yielding a compositionality ranking over a dataset.</p

Modelling Word Associations with Word Embeddings for a Guesser Agent in the Taboo City Challenge Competition

In the Taboo City Challenge, artificial agents should guess the names of cities from simple textual hints and are evaluated with games played by humans. Thus, playing the games successfully requires mimicking associations that humans have with geographical locations. In this paper, an architecture is proposed that calculates the associative similarity between a city and a hint from a semantic vector space. The semantic vector space is created using the Skip-gram hierarchical softmax model, from a tailored corpus about travel destinations. We investigate the effect of varying training parameters and introduce a targeted corpus annotation method that significantly improves performance. The results on a dataset of 149 games indicate that the proposed architecture can guess the target city with up to 22.45% accuracy — a substantial improvement over the 4.11% accuracy achieved by the baseline architecture

Artificial cells with viscoadaptive behavior based on hydrogel-loaded giant unilamellar vesicles

Viscoadaptation is an essential process in natural cells, where supramolecular interactions between cytosolic components drive adaptation of the cellular mechanical features to regulate metabolic function. This important relationship between mechanical properties and function has until now been underexplored in artificial cell research. Here, we have created an artificial cell platform that exploits internal supramolecular interactions to display viscoadaptive behavior. As supramolecular material to mimic the cytosolic component of these artificial cells, we employed a pH-switchable hydrogelator based on poly(ethylene glycol) coupled to ureido-pyrimidinone units. The hydrogelator was membranized in its sol state in giant unilamellar lipid vesicles to include a cell-membrane mimetic component. The resulting hydrogelator-loaded giant unilamellar vesicles (designated as HL-GUVs) displayed reversible pH-switchable sol-gel behavior through multiple cycles. Furthermore, incorporation of the regulatory enzyme urease enabled us to increase the cytosolic pH upon conversion of its substrate urea. The system was able to switch between a high viscosity (at neutral pH) and a low viscosity (at basic pH) state upon addition of substrate. Finally, viscoadaptation was achieved via the incorporation of a second enzyme of which the activity was governed by the viscosity of the artificial cell. This work represents a new approach to install functional self-regulation in artificial cells, and opens new possibilities for the creation of complex artificial cells that mimic the structural and functional interplay found in biological systems.</p

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Triple-marker cardiac MRI detects sequential tissue changes of healing myocardium after a hydrogel-based therapy

Regenerative therapies based on injectable biomaterials, hold an unparalleled potential for treating myocardial ischemia. Yet, noninvasive evaluation of their efficacy has been lagging behind. Here, we report the development and longitudinal application of multiparametric cardiac magnetic resonance imaging (MRI) to evaluate a hydrogel-based cardiac regenerative therapy. A pH-switchable hydrogel was loaded with slow releasing insulin growth factor 1 and vascular endothelial growth factor, followed by intramyocardial injection in a mouse model of ischemia reperfusion injury. Longitudinal cardiac MRI assessed three hallmarks of cardiac regeneration: angiogenesis, resolution of fibrosis and (re)muscularization after infarction. The multiparametric approach contained dynamic contrast enhanced MRI that measured improved vessel features by assessing fractional blood volume and permeability*surface area product, T1-mapping that displayed reduced fibrosis, and tagging MRI that showed improved regional myocardial strain in hydrogel treated infarcts. Finally, standard volumetric MRI demonstrated improved left ventricular functioning in hydrogel treated mice followed over time. Histology confirmed MR-based vessel features and fibrotic measurements. Our novel triple-marker strategy enabled detection of ameliorated regeneration in hydrogel treated hearts highlighting the translational potential of these longitudinal MRI approaches