Highlights d SARS-CoV-2 infection in induced lung cells is characterized by phosphoproteomics d Analysis of response reveals host cell signaling and protein expression profile d Comparison to studies in undifferentiated cell lines shows unique pathology in iAT2s d Systems-level predictions find druggable pathways that can impede viral life cycle

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While the theoretical foundation of optimal camera placement has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor network. The discrete camera placement problem is NP-hard and many approximate solutions have been independently studied. The goal of this paper is to provide a comprehensive framework in comparing the merits of these techniques. We consider two general classes of camera placement problems and adapt some of the most commonly used approximation techniques in solving them. The accuracy, efficiency and scalability of each technique are analyzed and compared in depth. Extensive experimental results are provided to illustrate the strength and weakness of each method.

Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments. DTW is essentially a point-to-point matching method under some boundary and temporal consistency constraints. Although DTW obtains a global optimal solution, it does not necessarily achieve locally sensible matchings. Concretely, two temporal points with entirely dissimilar local structures may be matched by DTW. To address this problem, we propose an improved alignment algorithm, named shape Dynamic Time Warping (shapeDTW), which enhances DTW by taking point-wise local structural information into consideration. shapeDTW is inherently a DTW algorithm, but additionally attempts to pair locally similar structures and to avoid matching points with distinct neighborhood structures. We apply shapeDTW to align audio signal pairs having ground-truth alignments, as well as artificially simulated pairs of aligned sequences, and obtain quantitatively much lower alignment errors than DTW and its two variants. When shapeDTW is used as a distance measure in a nearest neighbor classifier (NN-shapeDTW) to classify time series, it beats DTW on 64 out of 84 UCR time series datasets, with significantly improved classification accuracies. By using a properly designed local structure descriptor, shapeDTW improves accuracies by more than 10% on 18 datasets. To the best of our knowledge, shapeDTW is the first distance measure under the nearest neighbor classifier scheme to significantly outperform DTW, which had been widely recognized as the best distance measure to date. Our code is publicly accessible at: https://github.com/jiapingz/shapeDTW. Jiaping Zhao received his bachelor and master degree from Wuhan University in 2008 and 2010 respectively. Currently he is a Ph.D student at iLab, University of Southern California, working under the supervision of Laurent Itti. His research interests include computer vision, data mining, visual attention and probabilistic graphical model. Laurent Itti received the MS degree in image processing from the Ecole Nationale Superiere des TeÂŽlecommunications in Paris in 1994 and the PhD degree in computation and neural systems from the California Institute of Technology in 2000. Now he is an professor of computer science, psychology, and neurosciences at the University of Southern California. His research interests include computational neuroscience, neural networks, visual attention, brain modelling. He is a member of the IEEE..

Hyperspectral stimulated Raman scattering (SRS) by spectral focusing can generate label-free chemical images through temporal scanning of chirped femtosecond pulses. Yet, pulse chirping decreases the pulse peak power and temporal scanning increases the acquisition time, resulting in a much slower imaging speed compared to single-frame SRS using femtosecond pulses. In this paper, we present a deep learning algorithm to solve the inverse problem of getting a chemically labeled image from a single-frame femtosecond SRS image. Our DenseNet-based learning method, termed as DeepChem, achieves high-speed chemical imaging with a large signal level. Speed is improved by 2 orders of magnitude with four subcellular components (lipid droplet, endoplasmic reticulum, nuclei, cytoplasm) classified in MIA PaCa-2 cells and other cell types which were not used for training. Lipid droplet dynamics and cellular response to dithiothreitol in live MIA PaCa-2 cells are demonstrated using this computationally multiplex method.

We present a randomly disordered silica-air optical fiber featuring a 28.5% air filling fraction in the structured region, and low attenuation below 1 dB per meter at visible wavelengths. The quality of images transported through this fiber is shown to be comparable to, or even better than, that of images sent through commercial multicore imaging fiber. We demonstrate robust high-quality optical image transfer through 90 cm-long fibers with disordered silica-air structure, more than an order of magnitude improvement compared to previous disordered fiber imaging distances. The effects of variations of wavelength and feature size on transported image quality are investigated experimentally.

Video inpainting is the process of repairing missing regions (holes) in videos. Most automatic techniques are computationally intensive and unable to repair large holes. To tackle these challenges, a computationally-efficient algorithm that separately inpaints foreground objects and background is proposed. Using Dynamic Programming, foreground objects are holistically inpainted with object templates that minimizes a sliding-window dissimilarity cost function. Static background are inpainted by adaptive background replacement and image inpainting.

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