Improving Variational Encoder-Decoders in Dialogue Generation

Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding the KL-vanishing problem and inconsistent training objective. In this paper, we separate the training step into two phases: The first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learns to generalize latent representations by reconstructing the encoded embedding. In this case, latent variables are sampled by transforming Gaussian noise through multi-layer perceptrons and are trained with a separate VED model, which has the potential of realizing a much more flexible distribution. We compare our model with current popular models and the experiment demonstrates substantial improvement in both metric-based and human evaluations.Comment: Accepted by AAAI201

NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems. Though highly efficient in learning the backbone of human-computer communications, they suffer from the problem of strongly favoring short generic responses. In this paper, we argue that a good response should smoothly connect both the preceding dialogue history and the following conversations. We strengthen this connection through mutual information maximization. To sidestep the non-differentiability of discrete natural language tokens, we introduce an auxiliary continuous code space and map such code space to a learnable prior distribution for generation purpose. Experiments on two dialogue datasets validate the effectiveness of our model, where the generated responses are closely related to the dialogue context and lead to more interactive conversations.Comment: Accepted by EMNLP201

Optimal block size determination for application of a transform for file compression

File compression often involves the use of a transform. Prior to applying a transform, the information in the file is converted to the frequency domain to make it easier to compress via the transform. Application of the chosen transform can be performed at different sizes of input data blocks. The rate-distortion (RD) cost, and correspondingly, the speed of the compression process depends on the block size chosen for application of the transform. The techniques described in this disclosure use a trained machine learning model to predict optimal block size for the application of a given transform used to compress a file. The techniques can automatically determine if it is optimal to apply the transform to the entire input block or split the block into smaller units to which the transform is subsequently applied

Perspectives of Ethical Identity in Ng\u27s Steer toward Rock and Jen\u27s Mona in the Promised Land

In her article Perspectives of Ethical Identity in Ng\u27s Steer toward Rock and Jen\u27s Mona in the Promised Land Hui Su examines Fae Myenne Ng\u27s and Gish Jen\u27s novels. In the novels, the protagonists make different decisions: in Steer Toward Rock Jack after displacement in China adopts US-American identity and in Mona in the Promised Land Mona, a second generation Chinese American, selects Jewish identity. Owing to their different situations, the two protagonists reflect challenges of identity building in the case of the Other in US-American culture and society. Su argues that Ng and Jen, although varying in their perspectives, suggest enlightening views in their configurations of identity building in order to re-examine US-American literature and culture

Laser-induced fluorescence detection in high-throughput screening of heterogeneous catalysts and single cells analysis

The purpose of this research was to develop laser-induced fluorescence detection in high-throughput screening of heterogeneous catalysts and single cells analysis.;First, a high-throughput in situ screening approach for solid heterogeneous catalysts by laser-induced fluorescence imaging is introduced. We demonstrated LIFI has good detection performance and the spatial and temporal resolution needed for high-throughput screening of heterogeneous catalysts. We further applied the LIFI screening system for combinatorial discovery of heterogeneous catalysts and reaction condition optimization for naphthalene oxidation, as well as combinatorial study of catalytic performance of zeolites in acylation of aromatics.;In the second part of this dissertation, we used laser-induced native fluorescence coupled with capillary electrophoresis (LINF-CE) and microscope imaging to study the single cell degranulation. On the basis of good temporal correlation with events observed through an optical microscope, we have identified individual peaks in the fluorescence electropherograms as serotonin released from the granular core on contact with the surrounding fluid