Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse gro...

Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods are being published for analyzing CyTOF data attempt to improve automation, scalability, performance, and interpretation of data generated in large studies. However, most current tools are less suitable for r...

We propose an anomaly detection method using the reconstruction probability from the variational autoencoder. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. The reconstruction probability has a theoretical background making it a more principled and objective anomaly score than the reconstruction error, which is...

We introduce and study the properties of Boolean autoencoder circuits. In particular, we show that the Boolean autoencoder circuit problem is equivalent to a clustering problem on the hypercube. We show that clustering m binary vectors on the n-dimensional hypercube into k clusters is NP-hard, as soon as the number of clusters scales like m (ε > 0), and thus the general Boolean autoencoder prob...

In this paper, we propose the “adversarial autoencoder” (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution. Matching the aggregated posterior to the prior ensures that generating fro...

There is an increasing interest in the machine learning community to automatically learn feature representations directly from the (unlabeled) data instead of using hand-designed features. The autoencoder is one method that can be used for this purpose. However, for data sets with a high degree of noise, a large amount of the representational capacity in the autoencoder is used to minimize the ...

We have stacked subwavelength gratings (SWGs) on a single substrate to create a compact, integrated circular polarization filter. The SWGs consist of a wire grid polarizer and a broadband form-birefringent quarter-wave plate (QWP). Rigorous coupled-wave analysis was used to design the QWP for operation over the 3.5-5.0-mum wavelength range. The fabricated silicon broadband QWP exhibited a phase...

The novel Coronavirus 2019 (COVID-19) has spread rapidly and become a pandemic around the world. So far, about 44 million cases have been registered, causing more than one deaths worldwide. COVID-19 had devastating impact on every nation, particularly economic sector. To identify infected human being prevent virus from spreading further, easy, precise screening is required. can be potentially d...

Original scientific paper The model of autoencoder is one of the most typical deep learning models that have been mainly used in unsupervised feature learning for many applications like recognition, identification and mining. Autoencoder algorithms are compute-intensive tasks. Building large scale autoencoder model can satisfy the analysis requirement of huge volume data. But the training time ...