Generating Functions for Hyperbolic Plane Tessellations

For a hyperbolic plane tessellation there is a generating function with respect to the distance. This generating function is the same as the growth function of a group of isometries of hyperbolic plane that acts regularly in the tessellation. For most of the tessellation the generating functions have a symmetric form. In this thesis we will show the computation of the generating function for the hyperbolic plane tessellation and find the tessellations that have a symmetric form in the generating functions

POLYNOMIAL REPRESENTATION RING VERSUS COHOMOLOGY RING OF FLAG VARIETIES FOR CLASSICAL AND EXCEPTIONAL GROUPS

To any almost faithful representation of a complex, connected, reductive algebraic group Gof highest weight λ, one can associate a dominant Springer morphism from the group to its Liealgebra g. This map can be used to give a natural definition of polynomial representations forsimple Lie groups. Given a parabolic subgroup P of G, Kumar showed there is a surjective algebrahomomorphism ξP from the polynomial representations of a Levi subgroup of P to the cohomologyof G/P. Kumar-Rogers gave explicit determination of this morphism ξP for classical groups oftype B, C and their maximal parabolic subgroup P, and extend ξP to the corresponding stablecohomology rings. In this work we extend the result to classical groups of type D, and study themorphism ξP for exceptional groups of type G, F, E and their maximal parabolic subgroup P.Doctor of Philosoph

Transformer-based stereo-aware 3D object detection from binocular images

Transformers have shown promising progress in various visual object detection tasks, including monocular 2D/3D detection and surround-view 3D detection. More importantly, the attention mechanism in the Transformer model and the image correspondence in binocular stereo are both similarity-based. However, directly applying existing Transformer-based detectors to binocular stereo 3D object detection leads to slow convergence and significant precision drops. We argue that a key cause of this defect is that existing Transformers ignore the stereo-specific image correspondence information. In this paper, we explore the model design of Transformers in binocular 3D object detection, focusing particularly on extracting and encoding the task-specific image correspondence information. To achieve this goal, we present TS3D, a Transformer-based Stereo-aware 3D object detector. In the TS3D, a Disparity-Aware Positional Encoding (DAPE) module is proposed to embed the image correspondence information into stereo features. The correspondence is encoded as normalized sub-pixel-level disparity and is used in conjunction with sinusoidal 2D positional encoding to provide the 3D location information of the scene. To extract enriched multi-scale stereo features, we propose a Stereo Preserving Feature Pyramid Network (SPFPN). The SPFPN is designed to preserve the correspondence information while fusing intra-scale and aggregating cross-scale stereo features. Our proposed TS3D achieves a 41.29% Moderate Car detection average precision on the KITTI test set and takes 88 ms to detect objects from each binocular image pair. It is competitive with advanced counterparts in terms of both precision and inference speed

The Emerging of Hydrovoltaic Materials as a Future Technology: A Case Study for China

Water contains tremendous energy in various forms, but very little of this energy has yet been harvested. Nanostructured materials can generate electricity by water-nanomaterial interaction, a phenomenon referred to as hydrovoltaic effect, which potentially extends the technical capability of water energy harvesting. In this chapter, starting by describing the fundamental principle of hydrovoltaic effect, including water-carbon interactions and fundamental mechanisms of harvesting water energy with nanostructured materials, experimental advances in generating electricity from water flows, waves, natural evaporation, and moisture are then reviewed. We further discuss potential applications of hydrovoltaic technologies, analyze main challenges in improving the energy conversion efficiency and scaling up the output power, and suggest prospects for developments of the emerging technology, especially in China

DFormer: Diffusion-guided Transformer for Universal Image Segmentation

This paper introduces an approach, named DFormer, for universal image segmentation. The proposed DFormer views universal image segmentation task as a denoising process using a diffusion model. DFormer first adds various levels of Gaussian noise to ground-truth masks, and then learns a model to predict denoising masks from corrupted masks. Specifically, we take deep pixel-level features along with the noisy masks as inputs to generate mask features and attention masks, employing diffusion-based decoder to perform mask prediction gradually. At inference, our DFormer directly predicts the masks and corresponding categories from a set of randomly-generated masks. Extensive experiments reveal the merits of our proposed contributions on different image segmentation tasks: panoptic segmentation, instance segmentation, and semantic segmentation. Our DFormer outperforms the recent diffusion-based panoptic segmentation method Pix2Seq-D with a gain of 3.6% on MS COCO val2017 set. Further, DFormer achieves promising semantic segmentation performance outperforming the recent diffusion-based method by 2.2% on ADE20K val set. Our source code and models will be publicly on https://github.com/cp3wan/DForme

Reconstruction of plant microstructure using distance weighted tessellation algorithm optimized by virtual segmentation

Abstract(#br)The accurate reconstruction model of plant microstructure is important for obtaining the mechanical properties of plant tissues. In this paper, a virtual segmentation technique is proposed to optimize Delaunay triangulation. Based on the optimized Delaunay triangulation, an Optimized Distance Weighted Tessellation (ODWT) algorithm is developed. Two different structures, namely carrot and retting maize vascular bundles, were reconstructed via the ODWT algorithm. The accuracy of ODWT is evaluated statistically by comparing with Centroid-based Voronoi Tessellation (CVT) and Area Weighted Tessellation (AWT). The results show that ODWT has distinct advantages over CVT and AWT. It is worth mentioning that ODWT has better performance than CVT when there exists large diversity in adjacent cell area. It is found that CVT and AWT fail to reconstruct cells with elongated and concave shapes, while ODWT shows excellent feasibility and reliability. Furthermore, ODWT is capable of establishing finite tissue boundary, which CVT and AWT have failed to realize. The purpose of this work is to develop an algorithm with higher accuracy to implement the preprocessing for further numerical study of plants properties. The comparison results of the simulated values of the longitudinal tensile modulus with the experimental value show that ODWT algorithm can improve the prediction accuracy of multi-scale models on mechanical properties

Durable superhydrophobic polyvinylidene fluoride membranes via facile spray-coating for effective membrane distillation

Membrane wetting and fouling substantially limits application and deployment of membrane distillation process. Designing high-performance superhydrophobic membranes offers an effective solution to solve the challenge. In this work, a highly durable superhydrophobic surface (water contact angle of 170.8 ± 1.3°) was constructed via a facile and rapid spray-coating of extremely hydrophobic SiO2 nanoparticles onto a porous polyvinylidene fluoride (PVDF) substrate for membrane distillation. The superhydrophobic membrane coated by fluorinated SiO2 nanoparticles exhibited a superior physicochemical stability in a wide range of extreme environments (i.e., NaOH, HCl, hot water, rust water, humic acid solution, ultrasonication, and high-speed water scouring). During 8-h continuous membrane distillation desalination experiment, the coated superhydrophobic membrane experienced a consistently stable water vapor flux (ca. 19.1 kg·m−2·h−1) and desalination efficiency (99.99 %). Additionally, such a stable superhydrophobicity endowed the spray-coated PVDF membrane to overcome membrane wetting and fouling during membrane distillation of highly saline solutions containing foulants (i.e., humic acid and rust). Results reported in this study provides a useful concept and strategy in facile construction of robust superhydrophobic membranes via spray-coating for effective membrane distillation.</p

Research progress on value-added utilization of carbon dioxide through bio-electro-catalysis

China is the world’s largest CO2 emitter and coal consumer, and its coal dominated energy structure is difficult to be changed in the short term. In the context of carbon peaking and carbon neutrality, the capture and storage or conversion of carbon dioxide into renewable fuels and chemicals can reduce dependence on fossil fuels and at the same time reduce CO2 emissions, providing key technical support for the green transition of coal-fired power plants and energy chemicals. Electrocatalysis and microbial conversion are important ways to produce renewable fuels and chemicals from carbon dioxide. The reaction rate of electrocatalytic reduction of CO2 is high, but the products are mostly limited to C1 and C2 products. Microbial CO2 fixation has the advantages of high selectivity and variety of products. However, the low electron transfer and energy supply lead to a long reaction period in the microbial CO2 fixation. Integration of electrocatalysis and microbial conversion can play their advantages to efficiently produce the value-added multi-carbon products. In this paper, firstly, the reaction principles, typical products of electrocatalysis and microbial CO2 fixation under a single technical route were introduced respectively. The catalyst and reactor of electrocatalytic CO2 reduction were discussed, and the microbial species and biological metabolic pathways of microbial fixation of CO2 were summarized. Secondly, two methods of the integration of electrocatalysis and microbial conversion was reviewed, and the system structure, working principle, electrode materials and value-added products were analyzed. Finally, the technology readiness level of different coupling methods was compared, and the future prospects were highlighted from four aspects: the catalysts for electrocatalytic CO2 reduction, the engineered microbial strains, the design and integration of coupling systems and the linkage between academic research and industry

SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior

Novel View Synthesis (NVS) for street scenes play a critical role in the autonomous driving simulation. The current mainstream technique to achieve it is neural rendering, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Although thrilling progress has been made, when handling street scenes, current methods struggle to maintain rendering quality at the viewpoint that deviates significantly from the training viewpoints. This issue stems from the sparse training views captured by a fixed camera on a moving vehicle. To tackle this problem, we propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data. Specifically, we first fine-tune a Diffusion Model by adding images from adjacent frames as condition, meanwhile exploiting depth data from LiDAR point clouds to supply additional spatial information. Then we apply the Diffusion Model to regularize the 3DGS at unseen views during training. Experimental results validate the effectiveness of our method compared with current state-of-the-art models, and demonstrate its advance in rendering images from broader views