Cavity effect on phase noise of Fabry-Perot modulator-based optical frequency comb

We study previously unconsidered filtering effect of a Fabry-Perot (FP) cavity on the phase noise of optical frequency comb generated with an FP-based electro-optic modulator. We found that phase noise can be suppressed by up to 30 dB for offset frequencies >FSR/finesse

Neural Sequence-to-grid Module for Learning Symbolic Rules

Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning. In particular, even state-of-the-art neural networks fail to achieve \textit{out-of-distribution} (OOD) generalization of symbolic reasoning tasks, whereas humans can easily extend learned symbolic rules. To resolve this difficulty, we propose a neural sequence-to-grid (seq2grid) module, an input preprocessor that automatically segments and aligns an input sequence into a grid. As our module outputs a grid via a novel differentiable mapping, any neural network structure taking a grid input, such as ResNet or TextCNN, can be jointly trained with our module in an end-to-end fashion. Extensive experiments show that neural networks having our module as an input preprocessor achieve OOD generalization on various arithmetic and algorithmic problems including number sequence prediction problems, algebraic word problems, and computer program evaluation problems while other state-of-the-art sequence transduction models cannot. Moreover, we verify that our module enhances TextCNN to solve the bAbI QA tasks without external memory.Comment: 9 pages, 9 figures, AAAI 202

PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing

Reconstructing RGB image from RAW data obtained with a mobile device is related to a number of image signal processing (ISP) tasks, such as demosaicing, denoising, etc. Deep neural networks have shown promising results over hand-crafted ISP algorithms on solving these tasks separately, or even replacing the whole reconstruction process with one model. Here, we propose PyNET-CA, an end-to-end mobile ISP deep learning algorithm for RAW to RGB reconstruction. The model enhances PyNET, a recently proposed state-of-the-art model for mobile ISP, and improve its performance with channel attention and subpixel reconstruction module. We demonstrate the performance of the proposed method with comparative experiments and results from the AIM 2020 learned smartphone ISP challenge. The source code of our implementation is available at https://github.com/egyptdj/skyb-aim2020-publicComment: ECCV 2020 AIM workshop accepted versio

NRG1 functions downstream of EDS1 to regulate TIR-NLR-mediated plant immunity in Nicotiana benthamiana.

Effector-triggered immunity (ETI) in plants involves a large family of nucleotide-binding leucine-rich repeat (NLR) immune receptors, including Toll/IL-1 receptor-NLRs (TNLs) and coiled-coil NLRs (CNLs). Although various NLR immune receptors are known, a mechanistic understanding of NLR function in ETI remains unclear. The TNL Recognition of XopQ 1 (Roq1) recognizes the effectors XopQ and HopQ1 from Xanthomonas and Pseudomonas, respectively, which activates resistance to Xanthomonas euvesicatoria and Xanthomonas gardneri in an Enhanced Disease Susceptibility 1 (EDS1)-dependent way in Nicotiana benthamiana In this study, we found that the N. benthamiana N requirement gene 1 (NRG1), a CNL protein required for the tobacco TNL protein N-mediated resistance to tobacco mosaic virus, is also essential for immune signaling [including hypersensitive response (HR)] triggered by the TNLs Roq1 and Recognition of Peronospora parasitica 1 (RPP1), but not by the CNLs Bs2 and Rps2, suggesting that NRG1 may be a conserved key component in TNL signaling pathways. Besides EDS1, Roq1 and NRG1 are necessary for resistance to Xanthomonas and Pseudomonas in N. benthamiana NRG1 functions downstream of Roq1 and EDS1 and physically associates with EDS1 in mediating XopQ-Roq1-triggered immunity. Moreover, RNA sequencing analysis showed that XopQ-triggered gene-expression profile changes in N. benthamiana were almost entirely mediated by Roq1 and EDS1 and were largely regulated by NRG1. Overall, our study demonstrates that NRG1 is a key component that acts downstream of EDS1 to mediate various TNL signaling pathways, including Roq1 and RPP1-mediated HR, resistance to Xanthomonas and Pseudomonas, and XopQ-regulated transcriptional changes in N. benthamiana

Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning

The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short, and there are just too many to remember. In such a case, the users may want to ask contextual queries that describe the features they are looking for, but the standard term frequency-based search cannot process them. This paper presents a novel retrieval system for mobile features that accepts intuitive and contextual search queries. We trained a relevance model via contrastive learning from a pre-trained language model to perceive the contextual relevance between query embeddings and indexed mobile features. Also, to make it run efficiently on-device using minimal resources, we applied knowledge distillation to compress the model without degrading much performance. To verify the feasibility of our method, we collected test queries and conducted comparative experiments with the currently deployed search baselines. The results show that our system outperforms the others on contextual sentence queries and even on usual keyword-based queries.Comment: 7 pages, IAAI 202