Advanced Sulfide Solid Electrolyte Enabled by Nitrogen Doping

All-solid-state lithium batteries (ASSLBs) are being considered as the ultimate solution to the safety issue of current lithium ion batteries which use flammable organic electrolyte. The properties of the solid electrolyte, the most important component in ASSLBs, largely affect the electrode/electrolyte interfacial behavior and eventually the performance of ASSLBs. Sulfide electrolytes are considered as one of the most promising solid electrolyte for ASSLBs because of its excellent mechanical properties. However, they suffer from poor electrochemical stability and lithium dendrite formation. In this thesis, I demonstrated that the nitrogen-doped sulfide solid electrolyte system (75-1.5x)Li2S-25P2S5-xLi3N showed comprehensive performance improvements. The ionic conductivity is enhanced and the interfacial resistance between Li anode and solid electrolyte gets reduced by over 80%. The dendrite formation in solid electrolyte could also be effectively suppressed with the critical current density enhanced by 70%. The simultaneous improvement make it a promising solid electrolyte for ASSLBs

Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method

Recovering the shape and appearance of real-world objects from natural 2D images is a long-standing and challenging inverse rendering problem. In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras. Our method follows an analysis-by-synthesis approach and consists of two phases. In the initialization phase, we use traditional SfM and MVS methods to reconstruct a virtual scene roughly matching the real scene. Then in the optimization phase, we adopt a hybrid approach to refine the geometry and reflectance, where the geometry is first optimized using an approximate differentiable rendering method, and the reflectance is optimized afterward using a physically-based differentiable rendering method. Our hybrid approach combines the efficiency of approximate methods with the high-quality results of physically-based methods. Extensive experiments on synthetic and real data demonstrate that our method can produce reconstructions with similar or higher quality than state-of-the-art methods while being more efficient.Comment: IJCAI202

A conserved but plant-specific CDK-mediated regulation of DNA replication protein A2 in the precise control of stomatal terminal division

The R2R3-MYB transcription factor FOUR LIPS (FLP) controls the stomatal terminal division through transcriptional repression of the cell cycle genes CYCLIN-DEPENDENT KINASE (CDK) B1s (CDKB1s), CDKA; 1, and CYCLIN A2s (CYCA2s). We mutagenized the weak mutant allele flp-1 seeds with ethylmethane sulfonate and screened out a flp-1 suppressor 1 (fsp1) that suppressed the flp-1 stomatal cluster phenotype. FSP1 encodes RPA2a subunit of Replication Protein A (RPA) complexes that play important roles in DNA replication, recombination, and repair. Here, we show that FSP1/RPA2a functions together with CDKB1s and CYCA2s in restricting stomatal precursor proliferation, ensuring the stomatal terminal division and maintaining a normal guard-cell size and DNA content. Furthermore, we provide direct evidence for the existence of an evolutionarily conserved, but plant-specific, CDK-mediated RPA regulatory pathway. Serine-11 and Serine-21 at the N terminus of RPA2a are CDK phosphorylation target residues. The expression of the phosphorylation-mimic variant RPA2a(S11,21/D) partially complemented the defective cell division and DNA damage hypersensitivity in cdkb1;1 1;2 mutants. Thus, our study provides a mechanistic understanding of the CDK-mediated phosphorylation of RPA in the precise control of cell cycle and DNA repair in plants

Continuous Occupancy Mapping in Dynamic Environments Using Particles

Particle-based dynamic occupancy maps were proposed in recent years to model the obstacles in dynamic environments. Current particle-based maps describe the occupancy status in discrete grid form and suffer from the grid size problem, wherein a large grid size is unfavorable for motion planning, while a small grid size lowers efficiency and causes gaps and inconsistencies. To tackle this problem, this paper generalizes the particle-based map into continuous space and builds an efficient 3D egocentric local map. A dual-structure subspace division paradigm, composed of a voxel subspace division and a novel pyramid-like subspace division, is proposed to propagate particles and update the map efficiently with the consideration of occlusions. The occupancy status of an arbitrary point in the map space can then be estimated with the particles' weights. To further enhance the performance of simultaneously modeling static and dynamic obstacles and minimize noise, an initial velocity estimation approach and a mixture model are utilized. Experimental results show that our map can effectively and efficiently model both dynamic obstacles and static obstacles. Compared to the state-of-the-art grid-form particle-based map, our map enables continuous occupancy estimation and substantially improves the performance in different resolutions.Comment: This paper has been accepted by IEEE Transactions on Robotic

SRIBO: An Efficient and Resilient Single-Range and Inertia Based Odometry for Flying Robots

Positioning with one inertial measurement unit and one ranging sensor is commonly thought to be feasible only when trajectories are in certain patterns ensuring observability. For this reason, to pursue observable patterns, it is required either exciting the trajectory or searching key nodes in a long interval, which is commonly highly nonlinear and may also lack resilience. Therefore, such a positioning approach is still not widely accepted in real-world applications. To address this issue, this work first investigates the dissipative nature of flying robots considering aerial drag effects and re-formulates the corresponding positioning problem, which guarantees observability almost surely. On this basis, a dimension-reduced wriggling estimator is proposed accordingly. This estimator slides the estimation horizon in a stepping manner, and output matrices can be approximately evaluated based on the historical estimation sequence. The computational complexity is then further reduced via a dimension-reduction approach using polynomial fittings. In this way, the states of robots can be estimated via linear programming in a sufficiently long interval, and the degree of observability is thereby further enhanced because an adequate redundancy of measurements is available for each estimation. Subsequently, the estimator's convergence and numerical stability are proven theoretically. Finally, both indoor and outdoor experiments verify that the proposed estimator can achieve decimeter-level precision at hundreds of hertz per second, and it is resilient to sensors' failures. Hopefully, this study can provide a new practical approach for self-localization as well as relative positioning of cooperative agents with low-cost and lightweight sensors