Machine Learning Nucleation Collective Variables with Graph Neural Networks

The efficient calculation of nucleation collective variables (CVs) is one of the main limitations to the application of enhanced sampling methods to the investigation of nucleation processes in realistic environments. Here we discuss the development of a graph-based model for the approximation of nucleation CVs that enables orders-of-magnitude gains in computational efficiency in the on-the-fly evaluation of nucleation CVs. By performing simulations on a nucleating colloidal system mimicking a multistep nucleation process from solution, we assess the model's efficiency in both postprocessing and on-the-fly biasing of nucleation trajectories with pulling, umbrella sampling, and metadynamics simulations. Moreover, we probe and discuss the transferability of graph-based models of nucleation CVs across systems using the model of a CV based on sixth-order Steinhardt parameters trained on a colloidal system to drive the nucleation of crystalline copper from its melt. Our approach is general and potentially transferable to more complex systems as well as to different CVs

Encoding of phonology in a recurrent neural model of grounded speech

We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the MFCC features extracted from the speech signal, and the activations of the layers of the model. Via experiments with phoneme decoding and phoneme discrimination we show that phoneme representations are most salient in the lower layers of the model, where low-level signals are processed at a fine-grained level, although a large amount of phonological information is retain at the top recurrent layer. We further find out that the attention mechanism following the top recurrent layer significantly attenuates encoding of phonology and makes the utterance embeddings much more invariant to synonymy. Moreover, a hierarchical clustering of phoneme representations learned by the network shows an organizational structure of phonemes similar to those proposed in linguistics.Comment: Accepted at CoNLL 201

Deviating Angular Feature for Image Recognition System Using the Improved Neural Network Classifier.

The ability to recognize images makes it possible to abstractly conceptualize the world. Many in the field of machine learning have attempted to invent an image recognition system with the recognition capabilities of a human. This dissertation presents a method of modifications to existent image recognition systems, which greatly improves the efficiency of existing data imaging methods. This modification, the Deviating Angular Feature (DAF), has two obvious applications: (1) the recognition of handwritten numerals; and (2) the automatic identification of aircraft. Modifications of feature extraction and classification processes of current image recognition systems can leads to the systemic enhancement of data imaging. This research proposes a customized blend of image curvature extraction algorithms and the neural network classifiers trained by the Epoch Gradual Increase in Accuracy (EGIA) training algorithm. Using the DAF, the recognition of handwritten numerals and the automatic identification of aircraft have been improved. According to the preliminary results, the recognition system achieved an accuracy rate of 98.7% when applied to handwritten numeral recognition. When applied to automatic aircraft identification, the system achieved a 100% rate of recognition. The novel design of the prototype is quite flexible; thus, the system is easy to maintain, modify, and distribute