Content adaptive sparse illumination for Fourier ptychography

Fourier Ptychography (FP) is a recently proposed technique for large field of view and high resolution imaging. Specifically, FP captures a set of low resolution images under angularly varying illuminations and stitches them together in Fourier domain. One of FP's main disadvantages is its long capturing process due to the requisite large number of incident illumination angles. In this letter, utilizing the sparsity of natural images in Fourier domain, we propose a highly efficient method termed as AFP, which applies content adaptive sparse illumination for Fourier ptychography by capturing the most informative parts of the scene's spatial spectrum. We validate the effectiveness and efficiency of the reported framework with both simulations and real experiments. Results show that the proposed AFP could shorten the acquisition time of conventional FP by around 30%-60%

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding models, random walk (RW)-based is one of the most popular and successful groups. However, it is challenged by two issues when applied on graphs with rich texts: (i) sampling efficiency: deriving from the training objective of RW-based models (e.g., DeepWalk and node2vec), we show that RW-based models are likely to generate large amounts of redundant training samples due to three main drawbacks. (ii) text utilization: these models have difficulty in dealing with zero-shot scenarios where graph embedding models have to infer graph structures directly from texts. To solve these problems, we propose a novel framework, namely Text-driven Graph Embedding with Pairs Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an inductive graph embedding approach, to generate node embeddings from texts. Since each node contains rich texts, TGE is able to generate high-quality embeddings and provide reasonable predictions on existence of links to unseen nodes. We evaluate TGE-PS on several real-world datasets, and experiment results demonstrate that TGE-PS produces state-of-the-art results on both traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019

Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient

Fourier ptychographic microscopy (FPM) is a novel computational coherent imaging technique for high space-bandwidth product imaging. Mathematically, Fourier ptychographic (FP) reconstruction can be implemented as a phase retrieval optimization process, in which we only obtain low resolution intensity images corresponding to the sub-bands of the sample's high resolution (HR) spatial spectrum, and aim to retrieve the complex HR spectrum. In real setups, the measurements always suffer from various degenerations such as Gaussian noise, Poisson noise, speckle noise and pupil location error, which would largely degrade the reconstruction. To efficiently address these degenerations, we propose a novel FP reconstruction method under a gradient descent optimization framework in this paper. The technique utilizes Poisson maximum likelihood for better signal modeling, and truncated Wirtinger gradient for error removal. Results on both simulated data and real data captured using our laser FPM setup show that the proposed method outperforms other state-of-the-art algorithms. Also, we have released our source code for non-commercial use

Motion-corrected Fourier ptychography

Fourier ptychography (FP) is a recently proposed computational imaging technique for high space-bandwidth product imaging. In real setups such as endoscope and transmission electron microscope, the common sample motion largely degrades the FP reconstruction and limits its practicability. In this paper, we propose a novel FP reconstruction method to efficiently correct for unknown sample motion. Specifically, we adaptively update the sample's Fourier spectrum from low spatial-frequency regions towards high spatial-frequency ones, with an additional motion recovery and phase-offset compensation procedure for each sub-spectrum. Benefiting from the phase retrieval redundancy theory, the required large overlap between adjacent sub-spectra offers an accurate guide for successful motion recovery. Experimental results on both simulated data and real captured data show that the proposed method can correct for unknown sample motion with its standard deviation being up to 10% of the field-of-view scale. We have released our source code for non-commercial use, and it may find wide applications in related FP platforms such as endoscopy and transmission electron microscopy

PUMA amplifies necroptosis signaling by activating cytosolic DNA sensors.

Necroptosis, a form of regulated necrotic cell death, is governed by RIP1/RIP3-mediated activation of MLKL. However, the signaling process leading to necroptotic death remains to be elucidated. In this study, we found that PUMA, a proapoptotic BH3-only Bcl-2 family member, is transcriptionally activated in an RIP3/MLKL-dependent manner following induction of necroptosis. The induction of PUMA, which is mediated by autocrine TNF-α and enhanced NF-κB activity, contributes to necroptotic death in RIP3-expressing cells with caspases inhibited. On induction, PUMA promotes the cytosolic release of mitochondrial DNA and activation of the DNA sensors DAI/Zbp1 and STING, leading to enhanced RIP3 and MLKL phosphorylation in a positive feedback loop. Furthermore, deletion of PUMA partially rescues necroptosis-mediated developmental defects in FADD-deficient embryos. Collectively, our results reveal a signal amplification mechanism mediated by PUMA and cytosolic DNA sensors that is involved in TNF-driven necroptotic death in vitro and in vivo