Salting constant measurements have been made on 1-naphthol, 2-naphthol, and 1-NO-2-naphthol using a distribution method at 298 K. In general, the order of the salting effect is similar to that for other acidic nonelectrolytes. However, a net salting-in is observed in the pair 1-NO-2-naphthol – NaF, and such an effect is interpreted by postulating hydrogen bonding. A comparison of results with those found for benzene, naphthalene, phenol, etc …, reveals that salt effects for organic nonelectrolytes depend upon the solute size for a given salt.Experimental ks are compared with those calculated using various salting theories. The agreement is not quantitative.

We address the problem of automatically attributing quotations to speakers, which has great relevance in text mining and media monitoring applications. While current systems report high accuracies for this task, they either work at mentionlevel (getting credit for detecting uninformative mentions such as pronouns), or assume the coreferent mentions have been detected beforehand; the inaccuracies in this preprocessing step may lead to error propagation. In this paper, we introduce a joint model for entity-level quotation attribution and coreference resolution, exploiting correlations between the two tasks. We design an evaluation metric for attribution that captures all speakers' mentions. We present results showing that both tasks benefit from being treated jointly.

This paper describes our participation in the message polarity classification task of SemEval 2014. We focused on exploiting unlabeled data to improve accuracy, combining features leveraging word representations with other, more common features, based on word tokens or lexicons. We analyse the contribution of the different features, concluding that unlabeled data yields significant improvements.

086ChemInform Abstract (increase in acidity of (I)-(III) with the degree of aromaticity).

Bank transaction fraud results in over $13B annual losses for banks, merchants, and card holders worldwide. Much of this fraud starts with a Point-of-Compromise (a data breach or a "skimming" operation) where credit and debit card digital information is stolen, resold, and later used to perform fraud. We introduce this problem and present an automatic Pointsof-Compromise (POC) detection procedure. BreachRadar is a distributed alternating algorithm that assigns a probability of being compromised to the different possible locations. We implement this method using Apache Spark and show its linear scalability in the number of machines and transactions. BreachRadar is applied to two datasets with billions of real transaction records and fraud labels where we provide multiple examples of real Points-of-Compromise we are able to detect. We further show the effectiveness of our method when injecting Points-of-Compromise in one of these datasets, simultaneously achieving over 90% precision and recall when only 10% of the cards have been victims of fraud.