8,384 research outputs found
Single-shot compressed ultrafast photography: a review
Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields
VIGAN: Missing View Imputation with Generative Adversarial Networks
In an era when big data are becoming the norm, there is less concern with the
quantity but more with the quality and completeness of the data. In many
disciplines, data are collected from heterogeneous sources, resulting in
multi-view or multi-modal datasets. The missing data problem has been
challenging to address in multi-view data analysis. Especially, when certain
samples miss an entire view of data, it creates the missing view problem.
Classic multiple imputations or matrix completion methods are hardly effective
here when no information can be based on in the specific view to impute data
for such samples. The commonly-used simple method of removing samples with a
missing view can dramatically reduce sample size, thus diminishing the
statistical power of a subsequent analysis. In this paper, we propose a novel
approach for view imputation via generative adversarial networks (GANs), which
we name by VIGAN. This approach first treats each view as a separate domain and
identifies domain-to-domain mappings via a GAN using randomly-sampled data from
each view, and then employs a multi-modal denoising autoencoder (DAE) to
reconstruct the missing view from the GAN outputs based on paired data across
the views. Then, by optimizing the GAN and DAE jointly, our model enables the
knowledge integration for domain mappings and view correspondences to
effectively recover the missing view. Empirical results on benchmark datasets
validate the VIGAN approach by comparing against the state of the art. The
evaluation of VIGAN in a genetic study of substance use disorders further
proves the effectiveness and usability of this approach in life science.Comment: 10 pages, 8 figures, conferenc
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Single-shot compressed ultrafast photography: a review
Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields
Super-Resolution Microscopy: A Virusâ Eye View of the Cell
It is difficult to observe the molecular choreography between viruses and host cell components, as they exist on a spatial scale beyond the reach of conventional microscopy. However, novel super-resolution microscopy techniques have cast aside technical limitations to reveal a nanoscale view of virus replication and cell biology. This article provides an introduction to super-resolution imaging; in particular, localisation microscopy, and explores the application of such technologies to the study of viruses and tetraspanins, the topic of this special issue
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