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Regimes of polymer behaviour in drop-on-demand ink-jetting
Three regimes of fast DoD jetting behaviour for solutions of mono-disperse linear polymers have been linked to the underlying polymer molecular chains and their fully extended length L in good solvents. This allows scaling laws in molecular weight to be predicted and applied to experimental jetting results from different DoD print heads. The higher extensional flows encountered in high speed jetting in viscous solvents can fully stretch linear molecules outside the nozzle, permitting jetting of higher polymer content than for purely elastic behaviour. These results are significant for DoD printing at raised jet speeds and will apply to any DoD print head jetting linear polymer solutions.This work was supported by EPSRC grant number RG5560
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
When labeled training data is scarce, a promising data augmentation approach
is to generate visual features of unknown classes using their attributes. To
learn the class conditional distribution of CNN features, these models rely on
pairs of image features and class attributes. Hence, they can not make use of
the abundance of unlabeled data samples. In this paper, we tackle any-shot
learning problems i.e. zero-shot and few-shot, in a unified feature generating
framework that operates in both inductive and transductive learning settings.
We develop a conditional generative model that combines the strength of VAE and
GANs and in addition, via an unconditional discriminator, learns the marginal
feature distribution of unlabeled images. We empirically show that our model
learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA
and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e.
inductive and transductive (generalized) zero- and few-shot learning settings.
We also demonstrate that our learned features are interpretable: we visualize
them by inverting them back to the pixel space and we explain them by
generating textual arguments of why they are associated with a certain label.Comment: Accepted at CVPR 201
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics
Anomaly detection is the process of finding data points that deviate from a
baseline. In a real-life setting, anomalies are usually unknown or extremely
rare. Moreover, the detection must be accomplished in a timely manner or the
risk of corrupting the system might grow exponentially. In this work, we
propose a two level framework for detecting anomalies in sequences of discrete
elements. First, we assess whether we can obtain enough information from the
statistics collected from the discriminator's layers to discriminate between
out of distribution and in distribution samples. We then build an unsupervised
anomaly detection module based on these statistics. As to augment the data and
keep track of classes of known data, we lean toward a semi-supervised
adversarial learning applied to discrete elements.Comment: 5 pages, 53rd Annual Conference on Information Sciences and Systems,
CISS 201
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