Real-time visualization of Zn metal plating/stripping in aqueous batteries with high areal capacities

Zinc aqueous batteries have attracted great attention due to the earth abundance and the low redox potential of Zn metal. Utilizing Zn metal as an anode, however, causes low coulombic efficiency stemming from a dendritic Zn plating and formation of byproducts such as hydrogen gas, solid zinc hydroxide and salt-related compounds. One effective way of mitigating the issues is to modify the solvation structure of the electrolyte to increase the energy barrier of the water molecules for hydrolysis and electrolysis. Nevertheless, Zn aqueous batteries still indiscriminately utilize several types of electrolytes without elucidating the correlation between electrolyte composition and the electrochemistry of Zn metal. Here, we use operando optical microscopy to visualize the microstructural evolution of Zn metal, which strongly affects the electrochemical reversibility. In ZnSO4 electrolyte, large Zn platelets grow and form loose agglomerates vulnerable to unexpected delamination from the electrodes. In Zn(OTf)(2) electrolyte, Zn platelets nucleate more homogeneously and grow smaller, which forms denser agglomerates enabling more stable cycling. We further reveal that the formation of a stable solidelectrolyte interphase layer holds the key to the excellent performance of acetonitrile-hybrid water-in-salt electrolytes. Our results show the necessity of designing proper electrolytes to develop long-life Zn aqueous batteries.

Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data

We present a simple and efficient method to leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images. While significant recent advances have resulted in improved techniques for damage assessment using aerial or satellite imagery, they still suffer from poor robustness to domains where manual labeled data is unavailable, directly impacting post-disaster humanitarian assistance in such under-resourced geographies. Our contribution towards improving domain robustness in this scenario is two-fold. Firstly, we leverage the text-guided mask-based image editing capabilities of generative models and build an efficient and easily scalable pipeline to generate thousands of post-disaster images from low-resource domains. Secondly, we propose a simple two-stage training approach to train robust models while using manual supervision from different source domains along with the generated synthetic target domain data. We validate the strength of our proposed framework under cross-geography domain transfer setting from xBD and SKAI images in both single-source and multi-source settings, achieving significant improvements over a source-only baseline in each case

Reciprocal Attention Mixing Transformer for Lightweight Image Restoration

Although many recent works have made advancements in the image restoration (IR) field, they often suffer from an excessive number of parameters. Another issue is that most Transformer-based IR methods focus only on either local or global features, leading to limited receptive fields or deficient parameter issues. To address these problems, we propose a lightweight IR network, Reciprocal Attention Mixing Transformer (RAMiT). It employs our proposed dimensional reciprocal attention mixing Transformer (D-RAMiT) blocks, which compute bi-dimensional (spatial and channel) self-attentions in parallel with different numbers of multi-heads. The bi-dimensional attentions help each other to complement their counterpart's drawbacks and are then mixed. Additionally, we introduce a hierarchical reciprocal attention mixing (H-RAMi) layer that compensates for pixel-level information losses and utilizes semantic information while maintaining an efficient hierarchical structure. Furthermore, we revisit and modify MobileNet V1 and V2 to attach efficient convolutions to our proposed components. The experimental results demonstrate that RAMiT achieves state-of-the-art performance on multiple lightweight IR tasks, including super-resolution, color denoising, grayscale denoising, low-light enhancement, and deraining. Codes are available at https://github.com/rami0205/RAMiT.Comment: CVPR 2024 Workshop - NTIRE. Codes are available at https://github.com/rami0205/RAMi

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.Comment: main 8 page