Selective Refinement Network for High Performance Face Detection

High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.Comment: The first two authors have equal contributions. Corresponding author: Shifeng Zhang ([email protected]

PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery

Satellite imagery analysis plays a vital role in remote sensing, but the information loss caused by cloud cover seriously hinders its application. This study presents a high-performance cloud removal architecture called Progressive Multi-scale Attention Autoencoder (PMAA), which simultaneously leverages global and local information. It mainly consists of a cloud detection backbone and a cloud removal module. The cloud detection backbone uses cloud masks to reinforce cloudy areas to prompt the cloud removal module. The cloud removal module mainly comprises a novel Multi-scale Attention Module (MAM) and a Local Interaction Module (LIM). PMAA establishes the long-range dependency of multi-scale features using MAM and modulates the reconstruction of the fine-grained details using LIM, allowing for the simultaneous representation of fine- and coarse-grained features at the same level. With the help of diverse and multi-scale feature representation, PMAA outperforms the previous state-of-the-art model CTGAN consistently on the Sen2_MTC_Old and Sen2_MTC_New datasets. Furthermore, PMAA has a considerable efficiency advantage, with only 0.5% and 14.6% of the parameters and computational complexity of CTGAN, respectively. These extensive results highlight the potential of PMAA as a lightweight cloud removal network suitable for deployment on edge devices. We will release the code and trained models to facilitate the study in this direction.Comment: 8 pages, 5 figure

Spatially Related Sampling Uncertainty in the Assessment of Labile Soil Carbon and Nitrogen in an Irish Forest Plantation

The importance of labile soil carbon (C) and nitrogen (N) in soil biogeochemical processes is now well recognized. However, the quantification of labile soil C and N in soils and the assessment of their contribution to ecosystem C and N budgets is often constrained by limited information on spatial variability. To address this, we examined spatial variability in dissolved organic carbon (DOC) and dissolved total nitrogen (DTN) in a Sitka spruce forest in central Ireland. The results showed moderate variations in the concentrations of DOC and DTN based on the mean, minimum, and maximum, as well as the coefficients of variation. Residual values of DOC and DTN were shown to have moderate spatial autocorrelations, and the nugget sill ratios were 0.09% and 0.10%, respectively. Distribution maps revealed that both DOC and DTN concentrations in the study area decreased from the southeast. The variability of both DOC and DTN increased as the sampling area expanded and could be well parameterized as a power function of the sampling area. The cokriging technique performed better than the ordinary kriging for predictions of DOC and DTN, which are highly correlated. This study provides a statistically based assessment of spatial variations in DOC and DTN and identifies the sampling effort required for their accurate quantification, leading to improved assessments of forest ecosystem C and N budgets.University College DublinChina Scholarship Council (CSC

High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement: Establishing a Novel Baseline and Benchmark

The extraction of lakes from remote sensing images is a complex challenge due to the varied lake shapes and data noise. Current methods rely on multispectral image datasets, making it challenging to learn lake features accurately from pixel arrangements. This, in turn, affects model learning and the creation of accurate segmentation masks. This paper introduces a unified prompt-based dataset construction approach that provides approximate lake locations using point, box, and mask prompts. We also propose a two-stage prompt enhancement framework, LEPrompter, which involves prompt-based and prompt-free stages during training. The prompt-based stage employs a prompt encoder to extract prior information, integrating prompt tokens and image embeddings through self- and cross-attention in the prompt decoder. Prompts are deactivated once the model is trained to ensure independence during inference, enabling automated lake extraction. Evaluations on Surface Water and Qinghai-Tibet Plateau Lake datasets show consistent performance improvements compared to the previous state-of-the-art method. LEPrompter achieves mIoU scores of 91.48% and 97.43% on the respective datasets without introducing additional parameters or GFLOPs. Supplementary materials provide the source code, pre-trained models, and detailed user studies.Comment: 8 pages, 7 figure

Relational Learning for Joint Head and Human Detection

Head and human detection have been rapidly improved with the development of deep convolutional neural networks. However, these two tasks are often studied separately without considering their inherent correlation, leading to that 1) head detection is often trapped in more false positives, and 2) the performance of human detector frequently drops dramatically in crowd scenes. To handle these two issues, we present a novel joint head and human detection network, namely JointDet, which effectively detects head and human body simultaneously. Moreover, we design a head-body relationship discriminating module to perform relational learning between heads and human bodies, and leverage this learned relationship to regain the suppressed human detections and reduce head false positives. To verify the effectiveness of the proposed method, we annotate head bounding boxes of the CityPersons and Caltech-USA datasets, and conduct extensive experiments on the CrowdHuman, CityPersons and Caltech-USA datasets. As a consequence, the proposed JointDet detector achieves state-of-the-art performance on these three benchmarks. To facilitate further studies on the head and human detection problem, all new annotations, source codes and trained models will be public

Global patterns of plant and microbial biomass in response to CO2 fumigation

IntroductionThe stimulation of plant and microbial growth has been widely observed as a result of elevated CO2 concentrations (eCO2), however, this stimulation could be influenced by various factors and their relative importance remains unclear.MethodsA global meta-analysis was performed using 884 lines of observations collected from published papers, which analyzed the eCO2 impact on plant and microbial biomass.ResultsA significant positive impact of eCO2 was observed on various biomass measures, including aboveground biomass (20.5%), belowground biomass (42.6%), soil microbial biomass (10.4%), fungal biomass (11.0%), and bacterial biomass (9.2%). It was found that eCO2 levels above 200 ppm had a greater impact on plant biomass compared to concentrations at or below 200 ppm. On the other hand, studies showed that positive effects on microbial biomass were more prominent at lower eCO2 levels (≤200 ppm) than at higher levels (>200 ppm), which could be explained by soil nitrogen limitations. Importantly, our results indicated that aboveground biomass was controlled more by climatic and experimental conditions, while soil properties strongly impacted the stimulation of belowground and microbial biomass.DiscussionOur results provided evidence of the eCO2 fertilization effect across various ecosystem types, experimental methods, and climates, and provided a quantitative estimate of plant and soil microbial biomass sensitivity to eCO2. The results obtained in this study suggest that ecosystem models should consider climatic and edaphic factors to more accurately predict the effects of global climate change and their impact on ecosystem functions

Structure determination of the zeolite IM-5 using electron crystallography

The structure of the complex zeolite IM-5 (Cmcm, a = 14.33(4) Å, b = 56.9(2) Å, c = 20.32(7) Å) was determined by combining selected area electron diffraction (SAED), 3D reconstruction of high resolution transmission electron microscopy (HRTEM) images from different zone axes and distance least squares (DLS) refinement. The unit cell parameters were determined from SAED. The space group was determined from extinctions in the SAED patterns and projection symmetries of HRTEM images. Using the structure factor amplitudes and phases of 144 independent reflections obtained from HRTEM images along the [100], [010] and [001] directions, a 3D electrostatic potential map was calculated by inverse Fourier transformation. From this 3D potential map, all 24 unique Si positions could be determined. Oxygen atoms were added between each Si-Si pair and further refined together with the Si positions by distance-least-squares. The final structure model deviates on average 0.16 Å for Si and 0.31 Å for O from the structure refined using X-ray powder diffraction data. This method is general and offers a new possibility for determining the structures of zeolites and other materials with complex structure

A complicated quasicrystal approximant ∊16 predicted by the strong-reflections approach

The structure of the quasicrystal approximant ∊16 was predicted by the strong-reflections approach based on the known approximant ∊6

Supra-molecular assembly of aromatic proton sponges to direct the crystallization of extra-large-pore zeotypes

The combination of different experimental techniques, such as solid C-13 and H-1 magic-angle spinning NMR spectroscopy, fluorescence spectroscopy and powder X-ray diffraction, together with theoretical calculations allows the determination of the unique structure directing the role of the bulky aromatic proton sponge 1,8-bis(dimethylamino)naphthalene (DMAN) towards the extra-large-pore ITQ-51 zeolite through supra-molecular assemblies of those organic molecules.This work has been supported by the Spanish Government through Consolider Ingenio 2010-Multicat, the 'Severo Ochoa Programme' (SEV 2012-0267), MAT2012-37160; UPV through PAID-06-11 (no. 1952); the Swedish Research Council (VR) and the Swedish Governmental Agency for Innovation Systems (VINNOVA).Martínez Franco, R.; Sun, J.; Sastre Navarro, GI.; Yun, Y.; Zou, X.; Moliner Marin, M.; Corma Canós, A. (2014). Supra-molecular assembly of aromatic proton sponges to direct the crystallization of extra-large-pore zeotypes. Proceedings of the Royal Society of London. Series A, Mathematical and physical sciences. 470:1-13. https://doi.org/10.1098/rspa.2014.0107S113470Moliner, M., Rey, F., & Corma, A. (2013). Towards the Rational Design of Efficient Organic Structure-Directing Agents for Zeolite Synthesis. Angewandte Chemie International Edition, 52(52), 13880-13889. doi:10.1002/anie.201304713Freyhardt, C. C., Tsapatsis, M., Lobo, R. F., Balkus, K. J., & Davis, M. E. (1996). A high-silica zeolite with a 14-tetrahedral-atom pore opening. Nature, 381(6580), 295-298. doi:10.1038/381295a0Wagner, P., Yoshikawa, M., Tsuji, K., Davis, M. E., Wagner, P., Lovallo, M., & Taspatsis, M. (1997). CIT-5: a high-silica zeolite with 14-ring pores. Chemical Communications, (22), 2179-2180. doi:10.1039/a704774fBurton, A., Elomari, S., Chen, C.-Y., Medrud, R. C., Chan, I. Y., Bull, L. M., … Vittoratos, E. S. (2003). SSZ-53 and SSZ-59: Two Novel Extra-Large Pore Zeolites. Chemistry - A European Journal, 9(23), 5737-5748. doi:10.1002/chem.200305238Corma, A., Díaz-Cabañas, M. J., Rey, F., Nicolopoulus, S., & Boulahya, K. (2004). ITQ-15: The first ultralarge pore zeolite with a bi-directional pore system formed by intersecting 14- and 12-ring channels, and its catalytic implications. Chem. Commun., (12), 1356-1357. doi:10.1039/b406572gSun, J., Bonneau, C., Cantín, Á., Corma, A., Díaz-Cabañas, M. J., Moliner, M., … Zou, X. (2009). The ITQ-37 mesoporous chiral zeolite. Nature, 458(7242), 1154-1157. doi:10.1038/nature07957Jiang, J., Jorda, J. L., Yu, J., Baumes, L. A., Mugnaioli, E., Diaz-Cabanas, M. J., … Corma, A. (2011). Synthesis and Structure Determination of the Hierarchical Meso-Microporous Zeolite ITQ-43. Science, 333(6046), 1131-1134. doi:10.1126/science.1208652Kubota, Y., Helmkamp, M. M., Zones, S. I., & Davis, M. E. (1996). Properties of organic cations that lead to the structure-direction of high-silica molecular sieves. Microporous Materials, 6(4), 213-229. doi:10.1016/0927-6513(96)00002-8Corma, A., Rey, F., Rius, J., Sabater, M. J., & Valencia, S. (2004). Supramolecular self-assembled molecules as organic directing agent for synthesis of zeolites. Nature, 431(7006), 287-290. doi:10.1038/nature02909Gómez-Hortigüela, L., López-Arbeloa, F., Corà, F., & Pérez-Pariente, J. (2008). Supramolecular Chemistry in the Structure Direction of Microporous Materials from Aromatic Structure-Directing Agents. Journal of the American Chemical Society, 130(40), 13274-13284. doi:10.1021/ja8023725Gómez-Hortigüela, L., Hamad, S., López-Arbeloa, F., Pinar, A. B., Pérez-Pariente, J., & Corà, F. (2009). Molecular Insights into the Self-Aggregation of Aromatic Molecules in the Synthesis of Nanoporous Aluminophosphates: A Multilevel Approach. Journal of the American Chemical Society, 131(45), 16509-16524. doi:10.1021/ja906105xStaab, H. A., & Saupe, T. (1988). ?Proton Sponges? and the Geometry of Hydrogen Bonds: Aromatic Nitrogen Bases with Exceptional Basicities. Angewandte Chemie International Edition in English, 27(7), 865-879. doi:10.1002/anie.198808653Leslie, M., & Gillan, N. J. (1985). The energy and elastic dipole tensor of defects in ionic crystals calculated by the supercell method. Journal of Physics C: Solid State Physics, 18(5), 973-982. doi:10.1088/0022-3719/18/5/005Catlow, C. R. A., & Cormack, A. N. (1987). Computer modelling of silicates. International Reviews in Physical Chemistry, 6(3), 227-250. doi:10.1080/01442358709353406Gale, J. D. (1997). GULP: A computer program for the symmetry-adapted simulation of solids. Journal of the Chemical Society, Faraday Transactions, 93(4), 629-637. doi:10.1039/a606455hGale, J. D., & Rohl, A. L. (2003). The General Utility Lattice Program (GULP). Molecular Simulation, 29(5), 291-341. doi:10.1080/0892702031000104887Kiselev, A. V., Lopatkin, A. A., & Shulga, A. A. (1985). Molecular statistical calculation of gas adsorption by silicalite. Zeolites, 5(4), 261-267. doi:10.1016/0144-2449(85)90098-3Sastre, G., Lewis, D. W., & Catlow, C. R. A. (1996). Structure and Stability of Silica Species in SAPO Molecular Sieves. The Journal of Physical Chemistry, 100(16), 6722-6730. doi:10.1021/jp953362fYanai, T., Tew, D. P., & Handy, N. C. (2004). A new hybrid exchange–correlation functional using the Coulomb-attenuating method (CAM-B3LYP). Chemical Physics Letters, 393(1-3), 51-57. doi:10.1016/j.cplett.2004.06.011Tawada, Y., Tsuneda, T., Yanagisawa, S., Yanai, T., & Hirao, K. (2004). A long-range-corrected time-dependent density functional theory. The Journal of Chemical Physics, 120(18), 8425-8433. doi:10.1063/1.1688752Schäfer, A., Horn, H., & Ahlrichs, R. (1992). Fully optimized contracted Gaussian basis sets for atoms Li to Kr. The Journal of Chemical Physics, 97(4), 2571-2577. doi:10.1063/1.463096Wozniak, K., He, H., Klinowski, J., Jones, W., & Barr, T. L. (1995). ESCA, Solid-State NMR, and X-ray Diffraction Monitor the Hydrogen Bonding in a Complex of 1,8-Bis(dimethylamino)naphthalene with 1,2-Dichloromaleic Acid. The Journal of Physical Chemistry, 99(40), 14667-14677. doi:10.1021/j100040a014Rodriguez, I., Sastre, G., Corma, A., & Iborra, S. (1999). Catalytic Activity of Proton Sponge: Application to Knoevenagel Condensation Reactions. Journal of Catalysis, 183(1), 14-23. doi:10.1006/jcat.1998.2380Wozniak, K., He, H., Klinowski, J., Barr, T. L., & Milart, P. (1996). ESCA and Solid-State NMR Studies of Ionic Complexes of 1,8-Bis(dimethylamino)naphthalene. The Journal of Physical Chemistry, 100(27), 11420-11426. doi:10.1021/jp9537320Wo?naik, K., Krygowski, T. M., Pawlak, D., Kolodziejski, W., & Grech, E. (1997). Solid-state NMR and x-ray diffraction studies of ionic complex of 1,8-bis(dimethylamino)naphthalene (DMAN) with picrolonic acid. Journal of Physical Organic Chemistry, 10(11), 814-824. doi:10.1002/(sici)1099-1395(199711)10:113.0.co;2-

The Impact of Modifications in Forest Litter Inputs on Soil N2O Fluxes: A Meta-Analysis

Although litter can regulate the global climate by influencing soil N2O fluxes, there is no consensus on the major drivers or their relative importance and how these impact at the global scale. In this paper, we conducted a meta-analysis of 21 global studies to quantify the impact of litter removal and litter doubling on soil N2O fluxes from forests. Overall, our results showed that litter removal significantly reduced soil N2O fluxes (−19.0%), while a doubling of the amount of litter significantly increased soil N2O fluxes (30.3%), based on the results of a small number of studies. Litter removal decreased the N2O fluxes from tropical forest and temperate forest. The warmer the climate, the greater the soil acidity, and the larger the soil C:N ratio, the greater the impact on N2O emissions, which was particularly evident in tropical forest ecosystems. The decreases in soil N2O fluxes associated with litter removal were greater in acid soils (pH 15. Litter removal decreased soil N2O fluxes from coniferous forests (−21.8%) and broad-leaved forests (−17.2%) but had no significant effect in mixed forests. Soil N2O fluxes were significantly reduced in experiments where the duration of litter removal was <1 year. These results showed that modifications in ecosystem N2O fluxes due to changes in the ground litter vary with forest type and need to be considered when evaluating current and future greenhouse gas budgets.Beijing Academy of Agriculture and Forestry Sciences (BAAFS)Natural Science Foundation of Changsh