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

A Conditional Variational Framework for Dialog Generation

Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.Comment: Accepted by ACL201

Joint Problems in Learning Multiple Dynamical Systems

Clustering of time series is a well-studied problem, with applications ranging from quantitative, personalized models of metabolism obtained from metabolite concentrations to state discrimination in quantum information theory. We consider a variant, where given a set of trajectories and a number of parts, we jointly partition the set of trajectories and learn linear dynamical system (LDS) models for each part, so as to minimize the maximum error across all the models. We present globally convergent methods and EM heuristics, accompanied by promising computational results

Thin film aluminum nitride surface acoustic wave resonators for quantum acoustodynamics

The quantum excitations of macroscopic surface acoustic waves (SAWs) have been tailored to control, communicate and transduce stationary and flying quantum states. However, the limited lifetime of this hybrid quantum systems remains critical obstacles to extend their applications in quantum information processing. Here we present the potentials of thin film aluminum nitride to on-chip integrate phonons with superconducting qubits over previous bulk piezoelectric substrates. We have reported high-quality thin film GHz-SAW resonators with the highest internal quality factor Qi of 5 e4 at the single-phonon level. The internal loss of SAW resonators are systematically investigated with tuning the parameters of sample layout, power and temperature. Our results manifest that SAWs on piezoelectric films are readily integrable with standard fabrication of Josephson junction quantum circuits, and offer excellent acoustic platforms for the high-coherence quantum acoustodynamics architectures.Comment: 10 pages, 4 figure

Meta-analysis of structural and functional alterations of brain in patients with attention-deficit/hyperactivity disorder

BackgroundA large and growing body of neuroimaging research has concentrated on patients with attention-deficit/hyperactivity disorder (ADHD), but with inconsistent conclusions. This article was intended to investigate the common and certain neural alterations in the structure and function of the brain in patients with ADHD and further explore the differences in brain alterations between adults and children with ADHD.MethodsWe conducted an extensive literature search of whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies associated with ADHD. Two separate meta-analyses with the seed-based d mapping software package for functional neural activation and gray matter volume (GMV) were carried out, followed by a joint analysis and a subgroup analysis.ResultsThis analysis included 29 VBM studies and 36 fMRI studies. Structurally, VBM analysis showed that the largest GMV diminutions in patients with ADHD were in several frontal-parietal brain regions, the limbic system, and the corpus callosum. Functionally, fMRI analysis discovered significant hypoactivation in several frontal-temporal brain regions, the right postcentral gyrus, the left insula, and the corpus callosum.ConclusionThis study showed that abnormal alterations in the structure and function of the left superior frontal gyrus and the corpus callosum may be the key brain regions involved in the pathogenesis of ADHD in patients and may be employed as an imaging metric for patients with ADHD pending future research. In addition, this meta-analysis discovered neuroanatomical or functional abnormalities in other brain regions in patients with ADHD as well as findings that can be utilized to guide future research