DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation

One key challenge of exemplar-guided image generation lies in establishing fine-grained correspondences between input and guided images. Prior approaches, despite the promising results, have relied on either estimating dense attention to compute per-point matching, which is limited to only coarse scales due to the quadratic memory cost, or fixing the number of correspondences to achieve linear complexity, which lacks flexibility. In this paper, we propose a dynamic sparse attention based Transformer model, termed Dynamic Sparse Transformer (DynaST), to achieve fine-level matching with favorable efficiency. The heart of our approach is a novel dynamic-attention unit, dedicated to covering the variation on the optimal number of tokens one position should focus on. Specifically, DynaST leverages the multi-layer nature of Transformer structure, and performs the dynamic attention scheme in a cascaded manner to refine matching results and synthesize visually-pleasing outputs. In addition, we introduce a unified training objective for DynaST, making it a versatile reference-based image translation framework for both supervised and unsupervised scenarios. Extensive experiments on three applications, pose-guided person image generation, edge-based face synthesis, and undistorted image style transfer, demonstrate that DynaST achieves superior performance in local details, outperforming the state of the art while reducing the computational cost significantly. Our code is available at https://github.com/Huage001/DynaSTComment: ECCV 202

Palladium, platinum, selenium and tellurium enrichment in the Jiguanzui-Taohuazui Cu-Au Deposit, Edong Ore District: Distribution and comparison with Cu-Mo deposits

The Jiguanzui-Taohuazui Cu-Au deposit is located in the Edong ore district, Middle–Lower Yangtze River metallogenic belt, eastern China. The deposit is palladium, platinum, selenium and tellurium enriched; however, the distribution of these metals is unclear. Three mineral assemblages of ore in the deposit have been identified, namely: a magnetite-bornite-chalcopyrite-(hematite) assemblage (Mt-Bn-Cp-Hm), a chalcopyrite-pyrite assemblage (Cp-Py), and a pyrite-chalcopyrite-(sphalerite) assemblage (Py-Cp-Sph). Forty-eight bulk ore assay results show high concentrations of up to 66.9 ppb for Pd, 5.9 ppb for Pt, 150 ppm for Se and 249 ppm for Te. The high temperature Mt-Bn-Cp-Hm assemblage (530–380 °C) is enriched in Pt and Pd, whereas the Py-Cp-Sph assemblage in the marble-replacement ore (300–220 °C) hosts the major Se and Te mineralization. Palladium, Pt, and Se are mostly hosted in sulfide minerals, whereas Te is hosted in tellurides and Bi-Te-S sulfosalt minerals. Building on previous experimental and thermodynamic calculations, we propose the major controls on the Pd and Pt distribution in the deposit are temperature and salinity, whereas the Se and Te mineralization is promoted by the precipitation of major sulfide phases such as pyrite, chalcopyrite and sphalerite. A comparison of the ores from the Jiguanzui-Taohuazui Cu-Au and Tongshankou Cu-Mo deposits in the Edong ore district shows that the Cu-Au deposit has higher PGE and Te, but similar Se concentrations. This scenario is consistent with the average grades and bulk ore contents of these elements from global (oxidized) porphyry (±skarn) Cu deposits. This suggests that the saturation of magmatic sulfides in the magma chamber as a result of higher proportion of crustal S-rich and/or reduced material contamination can be detrimental for PGE and Te enrichment processes, and thus, Cu-Au porphyry (±skarn) deposits have more potential for higher Pd and Te concentrations than the Cu-Mo deposits

A Survey of Neural Trees

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term as neural trees (NTs). This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability. We first propose a thorough taxonomy of NTs that expresses the gradual integration and co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their interpretability and performance, and suggest possible solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions towards this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl

Relationships Between Fungal and Plant Communities Differ Between Desert and Grassland in a Typical Dryland Region of Northwest China

The relationships between soil fungal and plant communities in the dryland have been well documented, yet the associated difference in relationships between soil fungal and plant communities among different habitats remains unclear. Here, we explored the relationships between plant and fungal functional communities, and the dominant factors of these fungal communities in the desert and grassland. Soil fungal functional communities were assessed based on fungal ITS sequence data which were obtained from our previous study. The results showed that the total, saprotrophic and pathotrophic fungal richness were predominantly determined by plant species richness and/or soil nutrients in the desert, but by MAP or soil CN in the grassland. AM fungal richness was only significantly related to soil nutrients in two habitats. The total and saprotrophic fungal species compositions were mainly shaped by abiotic and spatial factors in the desert, but by plant and abiotic factors in the grassland. Pathotrophic fungal species composition was more strongly correlated with plant and spatial factors in the desert, but with spatial and abiotic factors in the grassland. AM fungal species composition was more strongly correlated with MAP in the grassland, but with no factors in the desert. These results provide robust evidence that the relationships between soil fungal and plant communities, and the drivers of soil fungal communities differ between the desert and grassland. Furthermore, we highlight that the linkages between soil fungal and plant communities, and the drivers of soil fungal communities may also be affected by fungal traits (e.g., functional groups)

Development of a New Risk Score for Incident Type 2 Diabetes Using Updated Diagnostic Criteria in Middle-Aged and Older Chinese

Type 2 diabetes mellitus (T2DM) reaches an epidemic proportion among adults in China. However, no simple score has been created for the prediction of T2DM incidence diagnosed by updated criteria with hemoglobin A1c (HbA1c) ≥6.5% included in Chinese. In a 6-year follow-up cohort in Beijing and Shanghai, China, we recruited a total of 2529 adults aged 50–70 years in 2005 and followed them up in 2011. Fasting plasma glucose (FPG), HbA1c, and C-reactive protein (CRP) were measured and incident diabetes was identified by the recently updated criteria. Of the 1912 participants without T2DM at baseline, 924 were identified as having T2DM at follow-up, and most of them (72.4%) were diagnosed using the HbA1c criterion. Baseline body mass index, FPG, HbA1c, CRP, hypertension, and female gender were all significantly associated with incident T2DM. Based upon these risk factors, a simple score was developed with an estimated area under the receiver operating characteristic curve of 0.714 (95% confidence interval: 0.691, 0.737), which performed better than most of existing risk score models developed for eastern Asian populations. This simple, newly constructed score of six parameters may be useful in predicting T2DM in middle-aged and older Chinese