384 research outputs found
Structural and Dynamical Properties of Metallic Glassy Films
In this chapter, a series of molecular dynamics simulations have been carried out to explore structural and dynamical features of monatomic liquid metallic films during rapid cooling. Results show a semiâordered inhomogeneous morphology containing crystalâlike and disordered regions. The icosahedron contributes to nucleation through the synergy with other shortârange ordered structures and participates in crystal growth via assimilation, but the pinning effect should be overcome. The secondâpeak splitting in pair correlation functions is found as the result of a statistical average of crystalâlike and disordered structural regions, not just the amorphous structure. The splitting can be viewed as a prototype of crystalâlike peaks exhibiting distorted and vestigial features. Besides, we use the parameter P(a, Ď, ν) for predicting both local structural order and motion propensity. The fraction of crystalline clusters follows a negative powerâlaw scaling with the cooling rate increasing, which is the inverse of P(a, Ď, ν)
Context-TAP: Tracking Any Point Demands Spatial Context Features
We tackle the problem of Tracking Any Point (TAP) in videos, which
specifically aims at estimating persistent long-term trajectories of query
points in videos. Previous methods attempted to estimate these trajectories
independently to incorporate longer image sequences, therefore, ignoring the
potential benefits of incorporating spatial context features. We argue that
independent video point tracking also demands spatial context features. To this
end, we propose a novel framework Context-TAP, which effectively improves point
trajectory accuracy by aggregating spatial context features in videos.
Context-TAP contains two main modules: 1) a SOurse Feature Enhancement (SOFE)
module, and 2) a TArget Feature Aggregation (TAFA) module. Context-TAP
significantly improves PIPs all-sided, reducing 11.4% Average Trajectory Error
of Occluded Points (ATE-Occ) on CroHD and increasing 11.8% Average Percentage
of Correct Keypoint (A-PCK) on TAP-Vid-Kinectics. Demos are available at this
.Comment: Project Page: this
$\href{https://wkbian.github.io/Projects/Context-TAP/}{webpage}
Incremental Learning from Scratch for Task-Oriented Dialogue Systems
Clarifying user needs is essential for existing task-oriented dialogue
systems. However, in real-world applications, developers can never guarantee
that all possible user demands are taken into account in the design phase.
Consequently, existing systems will break down when encountering unconsidered
user needs. To address this problem, we propose a novel incremental learning
framework to design task-oriented dialogue systems, or for short Incremental
Dialogue System (IDS), without pre-defining the exhaustive list of user needs.
Specifically, we introduce an uncertainty estimation module to evaluate the
confidence of giving correct responses. If there is high confidence, IDS will
provide responses to users. Otherwise, humans will be involved in the dialogue
process, and IDS can learn from human intervention through an online learning
module. To evaluate our method, we propose a new dataset which simulates
unanticipated user needs in the deployment stage. Experiments show that IDS is
robust to unconsidered user actions, and can update itself online by smartly
selecting only the most effective training data, and hence attains better
performance with less annotation cost.Comment: ACL201
Explaining the cosmic ray spectrum feature of Auger beyond the ankle with dip model plus the galactic propagation effect
The Auger Collaboration has recently published the energy spectrum of cosmic
rays above 1 EeV, which exhibits interesting features. These spectrum features
provide an opportunity to investigate the propagation of ultra-high-energy
cosmic rays (UHECRs). In this study, we have developed a model that
incorporates the dip model for UHECRs in the extragalactic propagation, while
accounting for the suppression due to diffusion and interactions within the
galaxy. Our model demonstrates excellent agreement with the energy spectrum
measured by Auger and supports a spectral index of 2 for the diffusion
coefficient in the galaxy starting from eV.Comment: 5 pages, 2 figure
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings
Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite
recent progress in neural-based CWS. The limited amount of annotated data in
the target domain has been the key obstacle to a satisfactory performance. In
this paper, we propose a semi-supervised word-based approach to improving
cross-domain CWS given a baseline segmenter. Particularly, our model only
deploys word embeddings trained on raw text in the target domain, discarding
complex hand-crafted features and domain-specific dictionaries. Innovative
subsampling and negative sampling methods are proposed to derive word
embeddings optimized for CWS. We conduct experiments on five datasets in
special domains, covering domains in novels, medicine, and patent. Results show
that our model can obviously improve cross-domain CWS, especially in the
segmentation of domain-specific noun entities. The word F-measure increases by
over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and
unsupervised cross-domain CWS approaches with a large margin. We make our code
and data available on Github
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