Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines

Unsupervised syntactic chunking with acoustic cues: Computational models for prosodic bootstrapping

Learning to group words into phrases without supervision is a hard task for NLP systems, but infants routinely accomplish it. We hypothesize that infants use acoustic cues to prosody, which NLP systems typically ignore. To evaluate the utility of prosodic information for phrase discovery, we present an HMM-based unsupervised chunker that learns from only transcribed words and raw acoustic correlates to prosody. Unlike previous work on unsupervised parsing and chunking, we use neither gold standard part-of-speech tags nor punctuation in the input. Evaluated on the Switchboard corpus, our model outperforms several baselines that exploit either lexical or prosodic information alone, and, despite producing a flat structure, performs competitively with a state-of-the-art unsupervised lexicalized parser, with a substantial advantage in precision. Our results support the hypothesis that acoustic-prosodic cues provide useful evidence about syntactic phrases for language-learning infants.10 page(s

Domain-general Stroop Performance and Hemispheric Asymmetries: A Resting-state EEG Study

The ability to suppress irrelevant information while executing a task or interference resistance is a function of pFC that is critical for successful goal-directed human behavior. In the study of interference resistance and, more generally, executive functions, two key questions are still open: Does pFC contribute to cognitive control abilities through lateralized but domain-general mechanisms or through hemispheric specialization of domain-specific processes? And what are the underlying causes of interindividual differences in executive control performance? To shed light on these issues, here we employed an interindividual difference approach to investigate whether participants' hemispheric asymmetry in resting-state electrophysiological brain dynamics may reflect their variability in domain-general interference resistance. We recorded participants' resting-state electroencephalographic activity and performed spectral power analyses on the estimated cortical source activity. To measure participants' lateralized brain dynamics at rest, we computed the right-left hemispheric asymmetry score for the \u3b2/\u3b1 power ratio. To measure their domain-general interference resistance ability, verbal and spatial Stroop tasks were used. Robust correlations followed by intersection analyses showed that participants with stronger resting-state-related left-lateralized activity in different pFC regions, namely the mid-posterior superior frontal gyrus, middle and posterior middle frontal gyrus, and inferior frontal junction, were more able to inhibit irrelevant information in both domains. The present results confirm and extend previous findings showing that neurophysiological difference factors may explain interindividual differences in executive functioning. They also provide support for the hypothesis of a left pFC hemispheric specialization for domain-independent phasic cognitive control processes mediating Stroop performance