Evaluation and projection of precipitation extremes under 1.5 degrees C and 2.0 degrees C GWLs over China using bias-corrected CMIP6 models

China is facing an increasing challenge from severe precipitation-related extremes with accelerating global warming. In this study, using a bias-corrected CMIP6 ensemble, future responses of precipitation extreme indices at 1.5°C and 2.0°C global warming levels (GWLs) under the SSP245, SSP370 and SSP585 scenarios are investigated. Despite different change magnitudes, extreme precipitation events will be more frequent and more intense over China as a whole under higher emissions and GWLs. The increase in annual total precipitation could attribute to a sharp increase in the intensity and days of very heavy precipitation in future global warming scenarios. Limiting global warming to 1.5°C and low emission pathways (i.e., SSP245) instead of 2°C and high emission pathways (i.e., SSP585) would have substantial benefits for China in terms of reducing occurrences of extreme precipitation events

How Extreme Events in China Would Be Affected by Global Warming-Insights From a Bias-Corrected CMIP6 Ensemble

In recent years, concurrent climate extreme conditions (i.e., hot-dry, cold-dry, hot-wet, and cold-wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra-extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias-corrected CMIP6 ensemble under two continuously-warming scenarios to evaluate the impact of global warming on ultra-extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot-wet events with concurrence in the same month and year would be expected for China under the continuously-warming scenarios. This is particularly obvious for the west where more than 6 hot-wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra-extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot-wet and cold-wet extremes concurring in the same month will be larger than those of dry-related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction

Spatiotemporal Changes of China's Carbon Emissions

Spatiotemporal changes in China's carbon emissions during the 11th and 12th Five‐Year Plan periods are quantified for the first time through a reconstructed nationwide high‐resolution gridded data set. The hot spots of carbon emissions in China have expanded by 28.5% (toward the west) in the north and shrunk by 18.7% in the south; meanwhile, the emission densities in North and South China have increased by 15.7% and 49.9%, respectively. This suggests a clear transition to a more intensive economic growth model in South China as a result of the energy conservation and emission reduction policies, while the expanded carbon hot spots in North China are mainly dominated by the Grand Western Development Program. The results also show that China's carbon emissions exhibit a typical spatially intensive, high‐emission pattern, which has undergone a slight relaxation (up to 3%) from 2007 to 2012 due to a typical urbanization process

Privacy-preserving design of graph neural networks with applications to vertical federated learning

The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM). The surging developments of graph representation learning (GRL) have opened up new opportunities for FRM applications under FL via efficiently utilizing the graph-structured data generated from underlying transaction networks. Meanwhile, transaction information is often considered highly sensitive. To prevent data leakage during training, it is critical to develop FL protocols with formal privacy guarantees. In this paper, we present an end-to-end GRL framework in the VFL setting called VESPER, which is built upon a general privatization scheme termed perturbed message passing (PMP) that allows the privatization of many popular graph neural architectures.Based on PMP, we discuss the strengths and weaknesses of specific design choices of concrete graph neural architectures and provide solutions and improvements for both dense and sparse graphs. Extensive empirical evaluations over both public datasets and an industry dataset demonstrate that VESPER is capable of training high-performance GNN models over both sparse and dense graphs under reasonable privacy budgets

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape variations and pose differences among objects in the same category. As a result, these methods underperform when handling unseen object instances in complex environments. In contrast, other approaches aim to achieve category-level estimation and reconstruction by leveraging normalized geometric structure priors, but the static prior-based reconstruction struggles with substantial intra-class variations. To solve these problems, we propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories. In DTF-Net, we design a deformable template field to represent the general category-wise shape latent features and intra-category geometric deformation features. The field establishes continuous shape correspondences, deforming the category template into arbitrary observed instances to accomplish shape reconstruction. We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene. We integrate a multi-modal representation extraction module to extract object features and semantic masks, enabling end-to-end inference. Moreover, during training, we implement a shape-invariant training strategy and a viewpoint sampling method to further enhance the model's capability to extract object pose features. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superiority of DTF-Net in both synthetic and real scenes. Furthermore, we show that DTF-Net effectively supports grasping tasks with a real robot arm.Comment: The first two authors are with equal contributions. Paper accepted by ACM MM 202

Cattle grazing mitigates the negative impacts of nitrogen addition on soil nematode communities

Livestock grazing and atmospheric nitrogen (N) deposition have been reported as important factors affecting soil communities. However, how different large herbivore grazing and N addition may interact to affect soil biota in grassland ecosystems is unclear. Nematodes are the most abundant metazoan in soil ecosystems, play critical roles in regulating carbon and nutrient dynamics, and are valuable bioindicators. We examined the independent and interactive effects of grazing regimes (no grazing; sheep grazing; cattle grazing; mixed grazing of sheep and cattle) and N addition (ambient N; N addition) on soil nematodes in a meadow steppe. We found that grazing and N addition interacted to influence total nematode abundance, trophic group abundance, generic richness, diversity and several nematode-based indices (maturity index, channel ratio, enrichment index). In cattle grazing treatment, N addition significantly increased total nematode abundance, and the abundance of bacterial feeders, plant feeders, and omnivore-predators, and generic richness. By contrast, in the sheep and mixed grazing treatments, N addition had a negative effect on the same variables. Moreover, N addition reduced nematode maturity, enrichment and structure indices, and enhanced nematode channel ratio, in most grazing treatments, except mixed grazing where N addition had no effect on these variables. Structural equation modeling (SEM) revealed that N addition indirectly reduced nematode abundance and richness through increased soil NO3−-N content, whereas the effects of grazing were associated with increased relative biomass of grasses. Our results suggested that the response of soil nematodes to N addition strongly depended on herbivore assemblages. Nitrogen addition enhanced soil nematode diversity and maintained a relatively complex and mature soil food web in the presence of cattle rather than sheep grazing. Furthermore, our study highlighted that under N deposition, cattle grazing could benefit the soil nematode community

Physical Mapping of a Novel Locus Conferring Leaf Rust Resistance on the Long Arm of Agropyron cristatum Chromosome 2P

Wheat leaf rust is one of the most common wheat diseases worldwide and can cause up to 40% wheat yield loss. To combat the growth and spread of leaf rust disease, continual exploration and identification of new and effective resistance genes are needed. Here, we report for the first time a locus conferring leaf rust resistance located on the long arm of Agropyron cristatum chromosome 2P in Triticum aestivum–A. cristatum 2P translocation lines. This study used 50 leaf rust races, including two Chinese major dominant leaf rust races, named by THT and PHT, and other 48 different leaf rust races collected from 11 provinces, 1autonomous region and 1 municipality of China to test the resistance to T. aestivum–A. cristatum 2P chromosome translocation lines and their backcross populations, the results indicated that the novel leaf rust resistance locus was immune or nearly immune to all tested leaf rust races. Four long arm translocation lines with different breakpoints of A. cristatum chromosome 2PL and their backcross populations were tested with leaf rust race THT at the seedling and adult stages and genotyped with 2P-specific STS markers. The results showed that the novel leaf rust resistance locus of the T. aestivum–A. cristatum 2P translocation lines was located in the chromosomal bin FL 0.66–0.86 of 2PL. Therefore, T. aestivum–A. cristatum 2P chromosome translocation lines conferring leaf rust resistance locus could provide a novel disease-resistance resource for future wheat breeding programs

QTL Mapping of Six Spike and Stem Traits in Hybrid Population of Agropyron Gaertn. in Multiple Environments

Most Agropyron Gaertn. species are excellent sources of forage. The derivative lines of wheat-Agropyron cristatum show elite agronomic traits, and some are valuable for wheat breeding. The species of Agropyron Gaertn. was mainly recognized by the spike morphology in traditional taxon. Six traits, including spike length (SL), ear stem length (ESL), the second internodes length (SIL), spikelet number per spike (SNS), floret number per spikelet (FNS), and grain number per spikelet (GNS), are vital to morphology studies and also influences the forage crop yield. To elucidate the genetic basis of spike and stem traits, a quantitative trait locus (QTL) analysis was conducted in a cross-pollinated (CP) hybrid population derived from a cross between two diverse parents, Agropyron mongolicum Keng Z2098 and A. cristatum (L.) Gaertn. Z1842, evaluated across three ecotopes (Langfang, Changli, and Guyuan of Hebei, China) over 3 years (from 2014 to 2016). Construction of a high-density linkage map was based on 1,023 single-nucleotide polymorphism (SNP) markers, covering 907.8 cM of the whole Agropyron genome. A total of 306 QTLs with single QTL in different environments explaining 0.07–33.21% of the phenotypic variation were detected for study traits. Seven major-effect QTLs were identified, including one for ESL on chromosome 3, one for SIL on chromosome 5, three for SL (two on chromosome 2 and one on chromosome 4), and two for SNS on chromosomes 3 and 7. Also, seven stable QTLs, including four for ESL, one for SL, one for GNS, and one for FNS, were mainly mapped on chromosomes 2, 3, 4, 5, and 7, respectively, elucidating 0.25–14.98% of the phenotypic variations. On the use of Agropyron CP hybrid population to identify QTL determining spike and stem traits for the first time, these QTLs for six traits would provide a theoretical reference for the molecular marker-assisted selection in the improvement of forage and cereal crop species