415 research outputs found
Tracking of enriched dialog states for flexible conversational information access
Dialog state tracking (DST) is a crucial component in a task-oriented dialog
system for conversational information access. A common practice in current
dialog systems is to define the dialog state by a set of slot-value pairs. Such
representation of dialog states and the slot-filling based DST have been widely
employed, but suffer from three drawbacks. (1) The dialog state can contain
only a single value for a slot, and (2) can contain only users' affirmative
preference over the values for a slot. (3) Current task-based dialog systems
mainly focus on the searching task, while the enquiring task is also very
common in practice. The above observations motivate us to enrich current
representation of dialog states and collect a brand new dialog dataset about
movies, based upon which we build a new DST, called enriched DST (EDST), for
flexible accessing movie information. The EDST supports the searching task, the
enquiring task and their mixed task. We show that the new EDST method not only
achieves good results on Iqiyi dataset, but also outperforms other
state-of-the-art DST methods on the traditional dialog datasets, WOZ2.0 and
DSTC2.Comment: 5 pages, 2 figures, accepted by ICASSP201
Hydrogenation and Hydro-Carbonation and Etching of Single-Walled Carbon Nanotubes
We present a systematic experimental investigation of the reactions between
hydrogen plasma and single-walled carbon nanotubes (SWNTs) at various
temperatures. Microscopy, infrared (IR) and Raman spectroscopy and electrical
transport measurements are carried out to investigate the properties of SWNTs
after hydrogenation. Structural deformations, drastically reduced electrical
conductance and increased semiconducting nature of SWNTs upon sidewall
hydrogenation are observed. These changes are reversible upon thermal annealing
at 500C via dehydrogenation. Harsh plasma or high temperature reactions lead to
etching of nanotube likely via hydro-carbonation. Smaller SWNTs are markedly
less stable against hydro-carbonation than larger tubes. The results are
fundamental and may have implications to basic and practical applications
including hydrogen storage, sensing, band-gap engineering for novel electronics
and new methods of manipulation, functionalization and etching of nanotubes.Comment: 3 pages, 4 figure
Domain Adaptation and Image Classification via Deep Conditional Adaptation Network
Unsupervised domain adaptation aims to generalize the supervised model
trained on a source domain to an unlabeled target domain. Marginal distribution
alignment of feature spaces is widely used to reduce the domain discrepancy
between the source and target domains. However, it assumes that the source and
target domains share the same label distribution, which limits their
application scope. In this paper, we consider a more general application
scenario where the label distributions of the source and target domains are not
the same. In this scenario, marginal distribution alignment-based methods will
be vulnerable to negative transfer. To address this issue, we propose a novel
unsupervised domain adaptation method, Deep Conditional Adaptation Network
(DCAN), based on conditional distribution alignment of feature spaces. To be
specific, we reduce the domain discrepancy by minimizing the Conditional
Maximum Mean Discrepancy between the conditional distributions of deep features
on the source and target domains, and extract the discriminant information from
target domain by maximizing the mutual information between samples and the
prediction labels. In addition, DCAN can be used to address a special scenario,
Partial unsupervised domain adaptation, where the target domain category is a
subset of the source domain category. Experiments on both unsupervised domain
adaptation and Partial unsupervised domain adaptation show that DCAN achieves
superior classification performance over state-of-the-art methods. In
particular, DCAN achieves great improvement in the tasks with large difference
in label distributions (6.1\% on SVHN to MNIST, 5.4\% in UDA tasks on
Office-Home and 4.5\% in Partial UDA tasks on Office-Home)
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