Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning

Deep metric learning aims to construct an embedding space where samples of the same class are close to each other, while samples of different classes are far away from each other. Most existing deep metric learning methods attempt to maximize the difference of inter-class features. And semantic related information is obtained by increasing the distance between samples of different classes in the embedding space. However, compressing all positive samples together while creating large margins between different classes unconsciously destroys the local structure between similar samples. Ignoring the intra-class variance contained in the local structure between similar samples, the embedding space obtained from training receives lower generalizability over unseen classes, which would lead to the network overfitting the training set and crashing on the test set. To address these considerations, this paper designs a self-supervised generative assisted ranking framework that provides a semi-supervised view of intra-class variance learning scheme for typical supervised deep metric learning. Specifically, this paper performs sample synthesis with different intensities and diversity for samples satisfying certain conditions to simulate the complex transformation of intra-class samples. And an intra-class ranking loss function is designed using the idea of self-supervised learning to constrain the network to maintain the intra-class distribution during the training process to capture the subtle intra-class variance. With this approach, a more realistic embedding space can be obtained in which global and local structures of samples are well preserved, thus enhancing the effectiveness of downstream tasks. Extensive experiments on four benchmarks have shown that this approach surpasses state-of-the-art method

Fur in Magnetospirillum gryphiswaldense Influences Magnetosomes Formation and Directly Regulates the Genes Involved in Iron and Oxygen Metabolism

Magnetospirillum gryphiswaldense strain MSR-1 has the unique capability of taking up large amounts of iron and synthesizing magnetosomes (intracellular magnetic particles composed of Fe3O4). The unusual high iron content of MSR-1 makes it a useful model for studying biological mechanisms of iron uptake and homeostasis. The ferric uptake regulator (Fur) protein plays a key role in maintaining iron homeostasis in many bacteria. We identified and characterized a fur-homologous gene (MGR_1314) in MSR-1. MGR_1314 was able to complement a fur mutant of E. coli in iron-responsive manner in vivo. We constructed a fur mutant strain of MSR-1. In comparison to wild-type MSR-1, the mutant strain had lower magnetosome formation, and was more sensitive to hydrogen peroxide and streptonigrin, indicating higher intracellular free iron content. Quantitative real-time RT-PCR and chromatin immunoprecipitation analyses indicated that Fur protein directly regulates expression of several key genes involved in iron transport and oxygen metabolism, in addition it also functions in magnetosome formation in M. gryphiswaldense

NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole- scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image

First assessment of the CFOSAT scatterometer wind quality

International Ocean Vector Winds Science Team Meeting (IOVWST), 29-31 May 2019, Portland, Main

On the Quality of Cfosat Scatterometer Winds

2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 28 July - 2 August 2019, Yokohama, Japan.-- 4 pages, 4 figures, 1 tableThe sea surface winds from the CFOSAT scatterometer (CFOSCAT) are retrieved using the maximum likelihood estimator, and the inversion residual is used to sort the good-quality winds from the poor-quality ones. A two-dimensional variational analysis ambiguity removal (2DVAR) scheme is then applied over the CFOSCAT swath such that a unique wind field is selected from the available local scatterometer wind vector ambiguities. The preliminary results of CFOSCAT Level 2 (L2) processing show that the retrieved wind speed is overestimated under low-wind conditions (w 15 m/s). Moreover, the inversion residual for the sweet swath (where there are more than 10 views) is generally higher than that for the nadir/outer swath. These imply that observations with different geometries (views) at the same WVC are inconsistent with respect to the geophysical model function, and thus a comprehensive calibration is highly demanded. A more detailed assessment of the CFOSCAT wind quality will be carried out after calibration and validation campaig

Assessment of the CFOSAT scatterometer backscatter and wind quality

International Ocean Vector Winds Science Team Workshop (2018 IOVWST), 24-26 April 2018, BarcelonaPeer Reviewe

A Perspective on the performance of the CFOSAT rotating fan beam scatterometer

China France Oceanography SATellite (CFOSAT) pre-launch workshop, 8-9 October 2018, Brest, FrancePeer Reviewe

A Perspective on the Performance of the CFOSAT Rotating Fan-Beam Scatterometer

13 pages. 10 figures. 2 tables.-- © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The China-France Oceanography Satellite (CFOSAT) to be launched in October 2018 will carry two innovative payloads, i.e., the surface wave investigation and monitoring instrument and the rotating fan-beam scatterometer [CFOSAT scatterometer (CFOSCAT)]. Both instruments, operated in Ku-band microwave frequency, are dedicated to the measurement of sea surface wave spectra and wind vectors, respectively. This paper provides an overview of the system definition and characteristics of the CFOSCAT instrument. A prelaunch analysis is carried out to estimate the scatterometer backscatter and wind quality based on the developed CFOSCAT simulator prototype. The overall simulation includes two parts: first, a forward model is developed to simulate the ocean backscatter signals, accounting for both instrument and geophysical noise. Second, a wind inversion processor is used to retrieve wind vectors from the outputs of the forward model. The benefits and challenges of the novel observing geometries are addressed in terms of the CFOSCAT wind retrieval. The simulations show that the backscatter accuracy and the retrieved wind quality of CFOSCAT are quite promising and meet the CFOSAT mission requirementsPeer Reviewe