1,784 research outputs found
Few-shot Text Classification with Dual Contrastive Consistency
In this paper, we explore how to utilize pre-trained language model to
perform few-shot text classification where only a few annotated examples are
given for each class. Since using traditional cross-entropy loss to fine-tune
language model under this scenario causes serious overfitting and leads to
sub-optimal generalization of model, we adopt supervised contrastive learning
on few labeled data and consistency-regularization on vast unlabeled data.
Moreover, we propose a novel contrastive consistency to further boost model
performance and refine sentence representation. After conducting extensive
experiments on four datasets, we demonstrate that our model (FTCC) can
outperform state-of-the-art methods and has better robustness.Comment: 8 pages, 2 figures, under revie
Time-periodic solution to nonhomogeneous isentropic compressible Euler equations with time-periodic boundary conditions
In this paper, we study one-dimensional nonhomogeneous isentropic
compressible Euler equations with time-periodic boundary conditions. With the
aid of the energy methods, we prove the existence and uniqueness of the
time-periodic supersonic solutions after some certain time
Global existence and stability of subsonic time-periodic solution to the damped compressible Euler equations in a bounded domain
In this paper, we consider the one-dimensional isentropic compressible Euler
equations with source term in a bounded domain,
which can be used to describe gas transmission in a nozzle.~The model is
imposed a subsonic time-periodic boundary condition.~Our main results reveal
that the time-periodic boundary can trigger an unique subsonic time-periodic
smooth solution and this unique periodic solution is stable under small
perturbations on initial and boundary data.~To get the existence of subsonic
time-periodic solution, we use the linear iterative skill and transfer the
boundary value problem into two initial value ones by using the hyperbolic
property of the system. Then the corresponding linearized system can be
decoupled.~The uniqueness is a direct by-product of the stability. There is no
small assumptions on the damping coefficient
Multi-symplectic discontinuous Galerkin methods for the stochastic Maxwell equations with additive noise
One- and multi-dimensional stochastic Maxwell equations with additive noise
are considered in this paper. It is known that such system can be written in
the multi-symplectic structure, and the stochastic energy increases linearly in
time. High order discontinuous Galerkin methods are designed for the stochastic
Maxwell equations with additive noise, and we show that the proposed methods
satisfy the discrete form of the stochastic energy linear growth property and
preserve the multi-symplectic structure on the discrete level. Optimal error
estimate of the semi-discrete DG method is also analyzed. The fully discrete
methods are obtained by coupling with symplectic temporal discretizations. One-
and two-dimensional numerical results are provided to demonstrate the
performance of the proposed methods, and optimal error estimates and linear
growth of the discrete energy can be observed for all cases
Relation Strength-Aware Clustering of Heterogeneous Information Networks with Incomplete Attributes
With the rapid development of online social media, online shopping sites and
cyber-physical systems, heterogeneous information networks have become
increasingly popular and content-rich over time. In many cases, such networks
contain multiple types of objects and links, as well as different kinds of
attributes. The clustering of these objects can provide useful insights in many
applications. However, the clustering of such networks can be challenging since
(a) the attribute values of objects are often incomplete, which implies that an
object may carry only partial attributes or even no attributes to correctly
label itself; and (b) the links of different types may carry different kinds of
semantic meanings, and it is a difficult task to determine the nature of their
relative importance in helping the clustering for a given purpose. In this
paper, we address these challenges by proposing a model-based clustering
algorithm. We design a probabilistic model which clusters the objects of
different types into a common hidden space, by using a user-specified set of
attributes, as well as the links from different relations. The strengths of
different types of links are automatically learned, and are determined by the
given purpose of clustering. An iterative algorithm is designed for solving the
clustering problem, in which the strengths of different types of links and the
quality of clustering results mutually enhance each other. Our experimental
results on real and synthetic data sets demonstrate the effectiveness and
efficiency of the algorithm.Comment: VLDB201
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