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This paper presents the ParlaMint corpora containing transcriptions of the sessions of the 17 European national parliaments with half a billion words. The corpora are uniformly encoded, contain rich meta-data about 11 thousand speakers, and are linguistically annotated following the Universal Dependencies formalism and with named entities. Samples of the corpora and conversion scripts are available from the project’s GitHub repository, and the complete corpora are openly available via the CLARIN.SI repository for download, as well as through the NoSketch Engine and KonText concordancers and the Parlameter interface for on-line exploration and analysis.

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The paper examines the citation network of the via incidentale rulings of the Italian Constitutional Court ("ICC"), vis-à-vis the web of scholarly opinions, comments, and annotations, devoted to such cases. The aim is to deepen the notion of legal relevance. On the one hand, a remarkable number of cases that are considerably discussed by experts, are neither hubs nor authorities in the ICC citation network. On the other hand, cases that are relevant in the ICC citation network are scarcely debated, or even ignored, by scholars. This twofold outcome suggests that we should combine research on the citation network of the courts with the web of scholarly opinions, to obtain a more detailed picture of which decisions and verdicts have to be reckoned as relevant in a given legal system.

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The paper addresses the controller falsification problem in feedback-control systems where the plant dynamics are uncertain and possibly time-varying. The adopted approach considers a statistic (or residual) in the form of a ratio of closed-loop variables. The test hinges upon a small-gain stability lemma which ensures that, under divergence, the statistic certainly exceeds a precomputable bound. Use of multiple models appropriately distributed over the uncertainty region of the plant dynamics allows one to generate a multi-statistic such that the denser the model distribution the larger the value taken on by the mentioned bounds. This allows one to arbitrarily widen the gap between the level that the statistic is guaranteed to stay below at stability and the one that will exceed at divergence, thus removing limitations on the applicability of the test based on a single model.

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