SPACEFLIGHT HYPERION DATA RADIATION CALIBRATION PRELIMINARY STUDY

Radiometric calibration is the basis of both hyperspectral data applications and quantitative remote sensing. While at present, there is no good and suitable method for radiometric calibration in the aerospace hyperspectral data. The paper uses the highest spectral resolution Hyperion EO-1 satellite data in current world Spaceflight sensor as information source, using a variety of calibration models, which including of the calibration model based on atmospheric radiation theory, conversion model based on the characteristics of the image itself(Flat Field Model, IAR Reflectance Model, Log Residuals Modified Model, e.g.), linear model based on the experience of the ground calibration and so on, implementation Spaceflight hyperspectral data calibration study. Through the above model calibration contrast, and combined analysis with the field measured spectral curve, then we draw the conclusion that the calibration model based on atmospheric radiation theory is the best radiation calibration method for Spaceflight Hyperion data, so we provide the quality assurance for the effective application of Spaceflight Hyperion data. 1

Bis[2,4-dibromo-6-(ethyl­imino­methyl)phenolato-κ2 N,O]cobalt(II)

In the title compound, [Co(C9H8Br2NO)2], the CoII atom, located on a twofold axis, is in a pseudo-tetra­hedral environment, with two bidentate 2,4-dibromo-6-(ethyl­imino­meth­yl)phenolate Schiff base ligands acting as chelates through their phenolate O and azomethine N atoms. C—H⋯O hydrogen bonds link the complex mol­ecules to form a chain parallel to the b axis

Learning to Reweight with Deep Interactions

Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training data using a teacher model, receives much attention due to its simplicity and effectiveness. In existing learning to reweight works, the teacher model only utilizes shallow/surface information such as training iteration number and loss/accuracy of the student model from training/validation sets, but ignores the internal states of the student model, which limits the potential of learning to reweight. In this work, we propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model, and the teacher model returns adaptive weights of training samples to enhance the training of the student model. The teacher model is jointly trained with the student model using meta gradients propagated from a validation set. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.Comment: Accepted to AAAI-202

Modeling and simulation of extended ant colony labor division for benefit distribution of the all-for-one tourism supply chain with front and back decoupling

This paper takes the supply chain alliance under the decoupling of the front and back of the all-for-one tourism as the research object. Considering the three behavior stimuli of self-benefit, altruism, and invariance, this article resets the attributes such as environmental stimuli and response threshold of ants based on the characteristics of the all-for-one tourism supply chain with shared services as the core under the decoupling of the front and back. Moreover, it introduces dual intervention factors to coordinate the benefit distribution process of different member companies, takes fairness as the main goal of benefit distribution, introduces relative deprivation as the measure index of fairness, and establishes a dynamic all-for-one tourism supply chain alliance benefit distribution model. The experimental results show that the extended model has good flexibility of benefit distribution and realizes the fair distribution of supply chain benefits

IFI16 directly senses viral RNA and enhances RIG-I transcription and activation to restrict influenza virus infection

The retinoic acid-inducible gene I (RIG-I) receptor senses cytoplasmic viral RNA and activates type I interferons (IFN-I) and downstream antiviral immune responses. How RIG-I binds to viral RNA and how its activation is regulated remains unclear. Here, using IFI16 knockout cells and p204-deficient mice, we demonstrate that the DNA sensor IFI16 enhances IFN-I production to inhibit influenza A virus (IAV) replication. IFI16 positively upregulates RIG-I transcription through direct binding to and recruitment of RNA polymerase II to the RIG-I promoter. IFI16 also binds to influenza viral RNA via its HINa domain and to RIG-I protein with its PYRIN domain, thus promoting IAV-induced K63-linked polyubiquitination and RIG-I activation. Our work demonstrates that IFI16 is a positive regulator of RIG-I signalling during influenza virus infection, highlighting its role in the RIG-I-like-receptor-mediated innate immune response to IAV and other RNA viruses, and suggesting its possible exploitation to modulate the antiviral response

The enormous repetitive Antarctic krill genome reveals environmental adaptations and population insights

Antarctic krill (Euphausia superba) is Earth’smost abundant wild animal, and its enormous biomass is vital to the Southern Ocean ecosystem. Here, we report a 48.01-Gb chromosome-level Antarctic krill genome, whose large genome size appears to have resulted from inter-genic transposable element expansions. Our assembly reveals the molecular architecture of the Antarctic krill circadian clock and uncovers expanded gene families associated with molting and energy metabolism, providing insights into adaptations to the cold and highly seasonal Antarctic environment. Population-level genome re-sequencing from four geographical sites around the Antarctic continent reveals no clear population structure but highlights natural selection associated with environmental variables. An apparent drastic reduction in krill population size 10 mya and a subsequent rebound 100 thousand years ago coincides with climate change events. Our findings uncover the genomic basis of Antarctic krill adaptations to the Southern Ocean and provide valuable resources for future Antarctic research