ResLT: Residual Learning for Long-tailed Recognition

Deep learning algorithms face great challenges with long-tailed data distribution which, however, is quite a common case in real-world scenarios. Previous methods tackle the problem from either the aspect of input space (re-sampling classes with different frequencies) or loss space (re-weighting classes with different weights), suffering from heavy over-fitting to tail classes or hard optimization during training. To alleviate these issues, we propose a more fundamental perspective for long-tailed recognition, {i.e., from the aspect of parameter space, and aims to preserve specific capacity for classes with low frequencies. From this perspective, the trivial solution utilizes different branches for the head, medium, tail classes respectively, and then sums their outputs as the final results is not feasible. Instead, we design the effective residual fusion mechanism -- with one main branch optimized to recognize images from all classes, another two residual branches are gradually fused and optimized to enhance images from medium+tail classes and tail classes respectively. Then the branches are aggregated into final results by additive shortcuts. We test our method on several benchmarks, {i.e., long-tailed version of CIFAR-10, CIFAR-100, Places, ImageNet, and iNaturalist 2018. Experimental results manifest that our method achieves new state-of-the-art for long-tailed recognition. Code will be available at \url{https://github.com/FPNAS/ResLT}

Characteristics of multiple‐year nitrous oxide emissions from conventional vegetable fields in southeastern China

The annual and interannual characteristics of nitrous oxide (N2O) emissions from conventional vegetable fields are poorly understood. We carried out 4 year measurements of N2O fluxes from a conventional vegetable cultivation area in the Yangtze River delta. Under fertilized conditions subject to farming practices, approximately 86% of the annual total N2O release occurred following fertilization events. The direct emission factors (EFd) of the 12 individual vegetable seasons investigated ranged from 0.06 to 14.20%, with a mean of 3.09% and a coefficient of variation (CV) of 142%. The annual EFd varied from 0.59 to 4.98%, with a mean of 2.88% and an interannual CV of 74%. The mean value is much larger than the latest default value (1.00%) of the Intergovernmental Panel on Climate Change. Occasional application of lagoon‐stored manure slurry coupled with other nitrogen fertilizers, or basal nitrogen addition immediately followed by heavy rainfall, accounted for a substantial portion of the large EFds observed in warm seasons. The large CVs suggest that the emission factors obtained from short‐term observations that poorly represent seasonality and/or interannual variability will inevitably yield large uncertainties in inventory estimation. The results of this study indicate that conventional vegetable fields associated with intensive nitrogen addition, as well as occasional applications of manure slurry, may substantially account for regional N2O emissions. However, this conclusion needs to be further confirmed through studies at multiple field sites. Moreover, further experimental studies are needed to test the mitigation options suggested by this study for N2O emissions from open vegetable fields

Generalized Parametric Contrastive Learning

In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our GPaCo/PaCo loss under a balanced setting. Our analysis demonstrates that GPaCo/PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed benchmarks manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models from CNNs to vision transformers trained with GPaCo loss show better generalization performance and stronger robustness compared with MAE models. Moreover, GPaCo can be applied to the semantic segmentation task and obvious improvements are observed on the 4 most popular benchmarks. Our code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.Comment: TPAMI 2023. arXiv admin note: substantial text overlap with arXiv:2107.1202

Pressure-induced spin reorientation transition in layered ferromagnetic insulator Cr2Ge2Te6

Anisotropic magnetoresistance (AMR) of Cr2Ge2Te6 (CGT), a layered ferromagnetic insulator, is investigated under an applied hydrostatic pressure up to 2 GPa. The easy axis direction of the magnetization is inferred from the AMR saturation feature in the presence and absence of the applied pressure. At zero applied pressure, the easy axis is along the c-direction or perpendicular to the layer. Upon application of a hydrostatic pressure>1 GPa, the uniaxial anisotropy switches to easy-plane anisotropy which drives the equilibrium magnetization from the c-axis to the ab-plane at zero magnetic field, which amounts to a giant magnetic anisotropy energy change (>100%). As the temperature is increased across the Curie temperature, the characteristic AMR effect gradually decreases and disappears. Our first-principles calculations confirm the giant magnetic anisotropy energy change with moderate pressure and assign its origin to the increased off-site spin-orbit interaction of Te atoms due to a shorter Cr-Te distance. Such a pressure-induced spin reorientation transition is very rare in three-dimensional ferromagnets, but it may be common to other layered ferromagnets with similar crystal structures to CGT, and therefore offers a unique way to control magnetic anisotropy

The Controversy, Challenges, and Potential Benefits of Putative Female Germline Stem Cells Research in Mammals

The conventional view is that female mammals lose their ability to generate new germ cells after birth. However, in recent years, researchers have successfully isolated and cultured a type of germ cell from postnatal ovaries in a variety of mammalian species that have the abilities of self-proliferation and differentiation into oocytes, and this finding indicates that putative germline stem cells maybe exist in the postnatal mammalian ovaries. Herein, we review the research history and discovery of putative female germline stem cells, the concept that putative germline stem cells exist in the postnatal mammalian ovary, and the research progress, challenge, and application of putative germline stem cells in recent years

Peach allergen Pru p 1 content is generally low in fruit but with large variation in different varieties

Background: Pru p 1 is a major allergen in peach and nectarine, and the different content in varieties may affect the degree of allergic reactions. This study aimed to quantify Pru p 1 levels in representative peach varieties and select hypoallergenic Pru p 1 varieties. Methods: To obtain monoclonal and polyclonal antibodies, mice and rabbits, respectively, were immunized with recombinant Pru p 1.01 and Pru p 1.02. The Pru p 1 levels in fruits from 83 representative peach varieties was quantified by sandwich enzyme‐linked immunosorbent assay (sELISA). nPru p 1 was obtained through specific monoclonal antibody affinity purification and confirmed by Western blot and mass spectrometry. The variable Pru p 1 content of selected varieties was evaluated by Western blot and the expression level of encoding Pru p 1 genes by quantitative polymerase chain reaction. Results: A sELISA method with monoclonal and polyclonal antibodies was built for quantifying Pru p 1 levels in peach. Pru p 1 was mainly concentrated in the peel (0.20–73.44 μg/g, fresh weight), being very low in the pulp (0.05–9.62 μg/g) and not detected in wild peach. For the 78 peach and nectarine varieties, Pru p 1 content varied widely from 0.12 to 6.45 μg/g in whole fruit. We verified that natural Pru p 1 is composed of 1.01 and 1.02 isoallergens, and the Pru p 1 expression level and Pru p 1 band intensity in the immunoblots were in agreement with protein quantity determined by ELISA for some tested varieties. In some cases, the reduced levels of Pru p 1 did not coincide with low Pru p 3 in the same variety in whole fruit, while some ancient wild peach and nectarines contained low levels of both allergens, and late‐ripening yellow flesh varieties were usually highly allergenic. Conclusion: Pru p 1 content is generally low in peach compared to Pru p 3. Several hypoallergenic Pru p 1 and Pru p 3 varieties, “Zi Xue Tao,” “Wu Yue Xian,” and “May Fire,” were identified, which could be useful in trials for peach allergy patients.info:eu-repo/semantics/publishedVersio