The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of “vocabulary gap” and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores.
When training bimanual skills, such as playing piano, people sometimes practice each hand separately and at a later stage combine the movements of the two hands. This poses the critical question of whether motor skills can be acquired by separately practicing each subcomponent or should be trained as a whole. In the present study, we addressed this question by training human subjects for 4 days in a unimanual or bimanual version of the discrete sequence production task. Both groups were then tested on trained and untrained sequences on both unimanual and bimanual versions of the task. Surprisingly, we found no evidence of transfer from trained unimanual to bimanual or from trained bimanual to unimanual sequences. In half the participants, we also investigated whether cuing the sequences on the left and right hand with unique letters would change transfer. With these cues, untrained sequences that shared some components with the trained sequences were performed more quickly than sequences that did not. However, the amount of this transfer was limited to ∼10% of the overall sequence-specific learning gains. These results suggest that unimanual and bimanual sequences are learned in separate representations. Making participants aware of the interrelationship between sequences can induce some transferrable component, although the main component of the skill remains unique to unimanual or bimanual execution. Studies in reaching movement demonstrated that approximately half of motor learning can transfer across unimanual and bimanual contexts, suggesting that neural representations for unimanual and bimanual movements are fairly overlapping at the level of elementary movement. In this study, we show that little or no transfer occurred across unimanual and bimanual sequential finger movements. This result suggests that bimanual sequences are represented at a level of the motor hierarchy that integrates movements of both hands.
Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training and between tasks knowledge transfer. The applications that rely heavily on continuous prediction on emotional valance, e.g., to monitor prisoners' emotional stability in jail, can be directly benefited from our framework. Abstract:To lower the single-label dependency on affective facial analysis, it urges the fruition of multi-label affective learning. The impediment to practical implementation of existing multi-label algorithms pertains to scarcity of scalable multi-label training datasets. To resolve this, an inductive transfer learning based framework, i.e., Uncertainty Flow, is put forward in this research to allow knowledge transfer from a single labelled emotion recognition task to a multi-label affective recognition task. I.e., the model uncertainty-which can be quantified in Uncertainty Flow-is distilled from a single-label learning task. The distilled model uncertainty ensures the later efficient zero-shot multi-label affective learning. On the theoretical perspective, within our proposed Uncertainty Flow framework, the feasibility of applying weakly informative priors, e.g., uniform and Cauchy prior, is fully explored in this research. More importantly, based on the derived weight uncertainty, three sets of prediction related uncertainty indexes, i.e., soft-max uncertainty, pure uncertainty and uncertainty plus are proposed to produce reliable and accurate multi-label predictions. Validated on our manual annotated evaluation dataset, i.e., the multi-label annotated FER2013, our proposed Uncertainty Flow in multi-label facial expression analysis exhibited superiority to conventional multi-label learning algorithms and multi-label compatible neural networks. The success of our proposed Uncertainty Flow provides a glimpse of future in continuous, uncertain, and multi-label affective computing.
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