Less is More: Underlying Mechanism of Zn Electrode Long-Term Stability using Sodium L-Ascorbate as Electrolyte Additive.

Structured passivation layers and hydrated Zn2+ solvation structure strongly influence Zn depositions on Zn electrodes and then the cycle life and electrochemical performance of aqueous zinc ion batteries. To achieve these, the electrolyte additive of sodium L-ascorbate (Ass) is introduced into aqueous zinc sulfate (ZnSO4, ZS) electrolyte solutions. Combined experimental characterizations with theoretical calculations, the unique passivation layers with vertical arrayed micro-nano structure are clearly observed, as well as the hydrated Zn2+ solvation structure is changed by replacing two ligand water molecules with As-, thus regulating the wettability and interfacial electric field intensity of Zn surfaces, facilitating rapid ionic diffusions within electrolytes and electrodes together with the inhibited side reactions and uniform depositions of Zn2+. When tested in Zn||Zn symmetric cell, the electrolyte containing Ass is extraordinarily stably operated for the long time ≈3700 h at both 1 mA cm-2 and 1 mAh cm-2. In Zn||MnO2 full coin cells, the energy density can still maintain as high as ≈184 Wh kg-1 at the power density high up to 2 kW kg-1, as well as the capacity retention can reach up to 80.5% even after 1000 cycles at 2 A g-1, which are substantially superior to the control cells.

MNSS: Neural Supersampling Framework for Real-Time Rendering on Mobile Devices.

Although neural supersampling has achieved great success in various applications for improving image quality, it is still difficult to apply it to a wide range of real-time rendering applications due to the high computational power demand. Most existing methods are computationally expensive and require high-performance hardware, preventing their use on platforms with limited hardware, such as smartphones. To this end, we propose a new supersampling framework for real-time rendering applications to reconstruct a high-quality image out of a low-resolution one, which is sufficiently lightweight to run on smartphones within a real-time budget. Our model takes as input the renderer-generated low resolution content and produces high resolution and anti-aliased results. To maximize sampling efficiency, we propose using an alternate sub-pixel sample pattern during the rasterization process. This allows us to create a relatively small reconstruction model while maintaining high image quality. By accumulating new samples into a high-resolution history buffer, an efficient history check and re-usage scheme is introduced to improve temporal stability. To our knowledge, this is the first research in pushing real-time neural supersampling on mobile devices. Due to the absence of training data, we present a new dataset containing 57 training and test sequences from three game scenes. Furthermore, based on the rendered motion vectors and a visual perception study, we introduce a new metric called inter-frame structural similarity (IF-SSIM) to quantitatively measure the temporal stability of rendered videos. Extensive evaluations demonstrate that our supersampling model outperforms existing or alternative solutions in both performance and temporal stability.

GlyphCreator: Towards Example-based Automatic Generation of Circular Glyphs.

Circular glyphs are used across disparate fields to represent multidimensional data. However, although these glyphs are extremely effective, creating them is often laborious, even for those with professional design skills. This paper presents GlyphCreator, an interactive tool for the example-based generation of circular glyphs. Given an example circular glyph and multidimensional input data, GlyphCreator promptly generates a list of design candidates, any of which can be edited to satisfy the requirements of a particular representation. To develop GlyphCreator, we first derive a design space of circular glyphs by summarizing relationships between different visual elements. With this design space, we build a circular glyph dataset and develop a deep learning model for glyph parsing. The model can deconstruct a circular glyph bitmap into a series of visual elements. Next, we introduce an interface that helps users bind the input data attributes to visual elements and customize visual styles. We evaluate the parsing model through a quantitative experiment, demonstrate the use of GlyphCreator through two use scenarios, and validate its effectiveness through user interviews.