Social phobia is one of the most frequent mental disorders and is accessible to two forms of scientifically validated treatments: anti-depressant drugs and cognitive behavior therapies (CBT). In this last case, graded exposure to feared social situations is one of the fundamental therapeutic ingredients. Virtual reality technologies are an interesting alternative to the standard exposure in social phobia, especially since studies have shown its usefulness for the fear of public speaking. This paper reports a preliminary study in which a virtual reality therapy (VRT), based on exposure to virtual environments, was used to treat social phobia. The sample consisted of 36 participants diagnosed with social phobia assigned to either VRT or a group-CBT (control condition). The virtual environments used in the treatment recreate four situations dealing with social anxiety: performance, intimacy, scrutiny, and assertiveness. With the help of the therapist, the patient learns adapted cognitions and behaviors in order to reduce anxiety in the corresponding real situations. Both treatments lasted 12 weeks, and sessions were delivered according to a treatment manual. Results showed statistically and clinically significant improvement in both conditions. The effect-sizes comparing the efficacy of VRT to the control traditional group-CBT revealed that the differences between the two treatments are trivial. 76

This paper describes our contribution to the semantic role labeling task (SRL-only) of the CoNLL-2009 shared task in the closed challenge (Hajič et al., 2009). Our system consists of a pipeline of independent, local classifiers that identify the predicate sense, the arguments of the predicates, and the argument labels. Using these local models, we carried out a beam search to generate a pool of candidates. We then reranked the candidates using a joint learning approach that combines the local models and proposition features. To address the multilingual nature of the data, we implemented a feature selection procedure that systematically explored the feature space, yielding significant gains over a standard set of features. Our system achieved the second best semantic score overall with an average labeled semantic F1 of 80.31. It obtained the best F1 score on the Chinese and German data and the second best one on English.

This paper presents our contribution in the closed track of the 2008 CoNLL Shared Task (Surdeanu et al., 2008). To tackle the problem of joint syntactic-semantic analysis, the system relies on a syntactic and a semantic subcomponent. The syntactic model is a bottom-up projective parser using pseudo-projective transformations, and the semantic model uses global inference mechanisms on top of a pipeline of classifiers. The complete syntactic-semantic output is selected from a candidate pool generated by the subsystems. The system achieved the top score in the closed challenge: a labeled syntactic accuracy of 89.32%, a labeled semantic F1 of 81.65, and a labeled macro F1 of 85.49.

We present a PropBank semantic role labeling system for English that is integrated with a dependency parser. To tackle the problem of joint syntactic-semantic analysis, the system relies on a syntactic and a semantic subcomponent. The syntactic model is a projective parser using pseudo-projective transformations, and the semantic model uses global inference mechanisms on top of a pipeline of classifiers. The complete syntactic-semantic output is selected from a candidate pool generated by the subsystems. We evaluate the system on the CoNLL-2005 test sets using segment-based and dependency-based metrics. Using the segment-based CoNLL-2005 metric, our system achieves a near state-of-the-art F1 figure of 77.97 on the WSJ+Brown test set, or 78.84 if punctuation is treated consistently. Using a dependency-based metric, the F1 figure of our system is 84.29 on the test set from CoNLL-2008. Our system is the first dependency-based semantic role labeler for PropBank that rivals constituent-based systems in terms of performance.

The primary objective of this study is to compare the accuracy of two risk models, International Heart Transplantation Survival Algorithm (IHTSA), developed using deep learning technique, and Index for Mortality Prediction After Cardiac Transplantation (IMPACT), to predict survival after heart transplantation. Data from adult heart transplanted patients between January 1997 to December 2011 were collected from the UNOS registry. The study included 27,860 heart transplantations, corresponding to 27,705 patients. The study cohorts were divided into patients transplanted before 2009 (derivation cohort) and from 2009 (test cohort). The receiver operating characteristic (ROC) values, for the validation cohort, computed for one-year mortality, were 0.654 (95% CI: 0.629–0.679) for IHTSA and 0.608 (0.583–0.634) for the IMPACT model. The discrimination reached a C-index for long-term survival of 0.627 (0.608–0.646) for IHTSA, compared with 0.584 (0.564–0.605) for the IMPACT model. These figures correspond to an error reduction of 12% for ROC and 10% for C-index by using deep learning technique. The predicted one-year mortality rates for were 12% and 22% for IHTSA and IMPACT, respectively, versus an actual mortality rate of 10%. The IHTSA model showed superior discriminatory power to predict one-year mortality and survival over time after heart transplantation compared to the IMPACT model.