146 research outputs found
Relationship between the late-age hydration and strength development of cement-slag mortars
120-127The relationship between the late-age hydration and strength development of cement-slag mortars have been investigated by measuring the compressive strengths and the non-evaporable water contents. The results show that the late-age strength increases with increasing the slag content. Increasing the fineness of slag makes greater contribution to the late-age strength improvement at high water to binder ratio than that at low water to binder ratio. At lower water to binder ratio, the increasing rates of compressive strength and non-evaporable water content are smaller. There is a linear relationship between the increasing rate of compressive strength and the increasing rate of non-evaporable water contents. The slope is almost the same for all the samples at constant water to binder ratio and decreases with decreasing the water to binder ratio
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the
relationship between template matching and the recent popular correlation
filter based trackers (CFTs). Compared to the correlation operation in CFTs, a
sophisticated similarity metric termed "mutual buddies similarity" (MBS) is
proposed to exploit the relationship of multiple reciprocal nearest neighbors
for target matching. By doing so, our tracker obtains powerful discriminative
ability on distinguishing target and background as demonstrated by both
empirical and theoretical analyses. Besides, instead of utilizing single
template with the improper updating scheme in CFTs, we design a novel online
template updating strategy named "memory filtering" (MF), which aims to select
a certain amount of representative and reliable tracking results in history to
construct the current stable and expressive template set. This scheme is
beneficial for the proposed tracker to comprehensively "understand" the target
appearance variations, "recall" some stable results. Both qualitative and
quantitative evaluations on two benchmarks suggest that the proposed tracking
method performs favorably against some recently developed CFTs and other
competitive trackers.Comment: has been published on IEEE TI
Regularized Regression Problem in hyper-RKHS for Learning Kernels
This paper generalizes the two-stage kernel learning framework, illustrates
its utility for kernel learning and out-of-sample extensions, and proves
{asymptotic} convergence results for the introduced kernel learning model.
Algorithmically, we extend target alignment by hyper-kernels in the two-stage
kernel learning framework. The associated kernel learning task is formulated as
a regression problem in a hyper-reproducing kernel Hilbert space (hyper-RKHS),
i.e., learning on the space of kernels itself. To solve this problem, we
present two regression models with bivariate forms in this space, including
kernel ridge regression (KRR) and support vector regression (SVR) in the
hyper-RKHS. By doing so, it provides significant model flexibility for kernel
learning with outstanding performance in real-world applications. Specifically,
our kernel learning framework is general, that is, the learned underlying
kernel can be positive definite or indefinite, which adapts to various
requirements in kernel learning. Theoretically, we study the convergence
behavior of these learning algorithms in the hyper-RKHS and derive the learning
rates. Different from the traditional approximation analysis in RKHS, our
analyses need to consider the non-trivial independence of pairwise samples and
the characterisation of hyper-RKHS. To the best of our knowledge, this is the
first work in learning theory to study the approximation performance of
regularized regression problem in hyper-RKHS.Comment: 25 pages, 3 figure
Random Fourier Features for Asymmetric Kernels
The random Fourier features (RFFs) method is a powerful and popular technique
in kernel approximation for scalability of kernel methods. The theoretical
foundation of RFFs is based on the Bochner theorem that relates symmetric,
positive definite (PD) functions to probability measures. This condition
naturally excludes asymmetric functions with a wide range applications in
practice, e.g., directed graphs, conditional probability, and asymmetric
kernels. Nevertheless, understanding asymmetric functions (kernels) and its
scalability via RFFs is unclear both theoretically and empirically. In this
paper, we introduce a complex measure with the real and imaginary parts
corresponding to four finite positive measures, which expands the application
scope of the Bochner theorem. By doing so, this framework allows for handling
classical symmetric, PD kernels via one positive measure; symmetric,
non-positive definite kernels via signed measures; and asymmetric kernels via
complex measures, thereby unifying them into a general framework by RFFs, named
AsK-RFFs. Such approximation scheme via complex measures enjoys theoretical
guarantees in the perspective of the uniform convergence. In algorithmic
implementation, to speed up the kernel approximation process, which is
expensive due to the calculation of total mass, we employ a subset-based fast
estimation method that optimizes total masses on a sub-training set, which
enjoys computational efficiency in high dimensions. Our AsK-RFFs method is
empirically validated on several typical large-scale datasets and achieves
promising kernel approximation performance, which demonstrate the effectiveness
of AsK-RFFs
End-to-end Kernel Learning via Generative Random Fourier Features
Random Fourier features (RFFs) provide a promising way for kernel learning in
a spectral case. Current RFFs-based kernel learning methods usually work in a
two-stage way. In the first-stage process, learning the optimal feature map is
often formulated as a target alignment problem, which aims to align the learned
kernel with the pre-defined target kernel (usually the ideal kernel). In the
second-stage process, a linear learner is conducted with respect to the mapped
random features. Nevertheless, the pre-defined kernel in target alignment is
not necessarily optimal for the generalization of the linear learner. Instead,
in this paper, we consider a one-stage process that incorporates the kernel
learning and linear learner into a unifying framework. To be specific, a
generative network via RFFs is devised to implicitly learn the kernel, followed
by a linear classifier parameterized as a full-connected layer. Then the
generative network and the classifier are jointly trained by solving the
empirical risk minimization (ERM) problem to reach a one-stage solution. This
end-to-end scheme naturally allows deeper features, in correspondence to a
multi-layer structure, and shows superior generalization performance over the
classical two-stage, RFFs-based methods in real-world classification tasks.
Moreover, inspired by the randomized resampling mechanism of the proposed
method, its enhanced adversarial robustness is investigated and experimentally
verified.Comment: update revised versio
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