464 research outputs found
To what extent can a future public transport system be designed to cater for private travel preferences? - The role of individuals’ attitude in two suburban neighbourhoods - Kangjian, Shanghai and Bull Creek, Perth
This thesis researches the role of individual factors, such as travel attitudes, in choice of transport mode and analyses the extent to which public transport planning practice caters such passenger factors. Consideration is given to the potential for combining the policy aspirations of government with the individual needs of residents
Inference for mixtures of symmetric distributions
This article discusses the problem of estimation of parameters in finite
mixtures when the mixture components are assumed to be symmetric and to come
from the same location family. We refer to these mixtures as semi-parametric
because no additional assumptions other than symmetry are made regarding the
parametric form of the component distributions. Because the class of symmetric
distributions is so broad, identifiability of parameters is a major issue in
these mixtures. We develop a notion of identifiability of finite mixture
models, which we call k-identifiability, where k denotes the number of
components in the mixture. We give sufficient conditions for k-identifiability
of location mixtures of symmetric components when k=2 or 3. We propose a novel
distance-based method for estimating the (location and mixing) parameters from
a k-identifiable model and establish the strong consistency and asymptotic
normality of the estimator. In the specific case of L_2-distance, we show that
our estimator generalizes the Hodges--Lehmann estimator. We discuss the
numerical implementation of these procedures, along with an empirical estimate
of the component distribution, in the two-component case. In comparisons with
maximum likelihood estimation assuming normal components, our method produces
somewhat higher standard error estimates in the case where the components are
truly normal, but dramatically outperforms the normal method when the
components are heavy-tailed.Comment: Published at http://dx.doi.org/10.1214/009053606000001118 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
An XPS Study of the Radiation-induced Effect on the Thermal Degradation and Charring of Butadiene and its Copolymers
A pseudo-in-situ XPS approach shows that cross-linking induced by irradiation may lead to char formation even though it shows only a small or no effect on the onset temperature of degradation
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
Data mixing augmentation has proved effective in training deep models. Recent
methods mix labels mainly based on the mixture proportion of image pixels. As
the main discriminative information of a fine-grained image usually resides in
subtle regions, methods along this line are prone to heavy label noise in
fine-grained recognition. We propose in this paper a novel scheme, termed as
Semantically Proportional Mixing (SnapMix), which exploits class activation map
(CAM) to lessen the label noise in augmenting fine-grained data. SnapMix
generates the target label for a mixed image by estimating its intrinsic
semantic composition, and allows for asymmetric mixing operations and ensures
semantic correspondence between synthetic images and target labels. Experiments
show that our method consistently outperforms existing mixed-based approaches
on various datasets and under different network depths. Furthermore, by
incorporating the mid-level features, the proposed SnapMix achieves top-level
performance, demonstrating its potential to serve as a solid baseline for
fine-grained recognition. Our code is available at
https://github.com/Shaoli-Huang/SnapMix.git.Comment: Accepted by AAAI202
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