7,309 research outputs found
Understanding Kernel Size in Blind Deconvolution
Most blind deconvolution methods usually pre-define a large kernel size to
guarantee the support domain. Blur kernel estimation error is likely to be
introduced, yielding severe artifacts in deblurring results. In this paper, we
first theoretically and experimentally analyze the mechanism to estimation
error in oversized kernel, and show that it holds even on blurry images without
noises. Then to suppress this adverse effect, we propose a low rank-based
regularization on blur kernel to exploit the structural information in degraded
kernels, by which larger-kernel effect can be effectively suppressed. And we
propose an efficient optimization algorithm to solve it. Experimental results
on benchmark datasets show that the proposed method is comparable with the
state-of-the-arts by accordingly setting proper kernel size, and performs much
better in handling larger-size kernels quantitatively and qualitatively. The
deblurring results on real-world blurry images further validate the
effectiveness of the proposed method.Comment: Accepted by WACV 201
Enhancement of MS Signal Processing for Improved Cancer Biomarker Discovery
Technological advances in proteomics have shown great potential in detecting cancer at the earliest stages. One way is to use the time of flight mass spectroscopy to identify biomarkers, or early disease indicators related to the cancer. Pattern analysis of time of flight mass spectra data from blood and tissue samples gives great hope for the identification of potential biomarkers among the complex mixture of biological and chemical samples for the early cancer detection. One of the keys issues is the pre-processing of raw mass spectra data. A lot of challenges need to be addressed: unknown noise character associated with the large volume of data, high variability in the mass spectroscopy measurements, and poorly understood signal background and so on. This dissertation focuses on developing statistical algorithms and creating data mining tools for computationally improved signal processing for mass spectrometry data. I have introduced an advanced accurate estimate of the noise model and a half-supervised method of mass spectrum data processing which requires little knowledge about the data
Neural Natural Language Inference Models Enhanced with External Knowledge
Modeling natural language inference is a very challenging task. With the
availability of large annotated data, it has recently become feasible to train
complex models such as neural-network-based inference models, which have shown
to achieve the state-of-the-art performance. Although there exist relatively
large annotated data, can machines learn all knowledge needed to perform
natural language inference (NLI) from these data? If not, how can
neural-network-based NLI models benefit from external knowledge and how to
build NLI models to leverage it? In this paper, we enrich the state-of-the-art
neural natural language inference models with external knowledge. We
demonstrate that the proposed models improve neural NLI models to achieve the
state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201
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