Revisiting Non-Autoregressive Translation at Scale

In real-world systems, scaling has been critical for improving the translation quality in autoregressive translation (AT), which however has not been well studied for non-autoregressive translation (NAT). In this work, we bridge the gap by systematically studying the impact of scaling on NAT behaviors. Extensive experiments on six WMT benchmarks over two advanced NAT models show that scaling can alleviate the commonly-cited weaknesses of NAT models, resulting in better translation performance. To reduce the side-effect of scaling on decoding speed, we empirically investigate the impact of NAT encoder and decoder on the translation performance. Experimental results on the large-scale WMT20 En-De show that the asymmetric architecture (e.g. bigger encoder and smaller decoder) can achieve comparable performance with the scaling model, while maintaining the superiority of decoding speed with standard NAT models. To this end, we establish a new benchmark by validating scaled NAT models on the scaled dataset, which can be regarded as a strong baseline for future works. We release code and system outputs at https://github.com/DeepLearnXMU/Scaling4NAT.Comment: 13 pages, Findings of ACL 202

DiffusionRig: Learning Personalized Priors for Facial Appearance Editing

We address the problem of learning person-specific facial priors from a small number (e.g., 20) of portrait photos of the same person. This enables us to edit this specific person's facial appearance, such as expression and lighting, while preserving their identity and high-frequency facial details. Key to our approach, which we dub DiffusionRig, is a diffusion model conditioned on, or "rigged by," crude 3D face models estimated from single in-the-wild images by an off-the-shelf estimator. On a high level, DiffusionRig learns to map simplistic renderings of 3D face models to realistic photos of a given person. Specifically, DiffusionRig is trained in two stages: It first learns generic facial priors from a large-scale face dataset and then person-specific priors from a small portrait photo collection of the person of interest. By learning the CGI-to-photo mapping with such personalized priors, DiffusionRig can "rig" the lighting, facial expression, head pose, etc. of a portrait photo, conditioned only on coarse 3D models while preserving this person's identity and other high-frequency characteristics. Qualitative and quantitative experiments show that DiffusionRig outperforms existing approaches in both identity preservation and photorealism. Please see the project website: https://diffusionrig.github.io for the supplemental material, video, code, and data.Comment: CVPR 2023. Project website: https://diffusionrig.github.i

APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding

Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to directly model global dependencies, which results in a limited effective receptive field. Besides, how to effectively incorporate local and global components also remains challenging. To tackle these problems, we propose Asymmetric Parallel Point Transformer (APPT). Specifically, we introduce Global Pivot Attention to extract global features and enlarge the effective receptive field. Moreover, we design the Asymmetric Parallel structure to effectively integrate local and global information. Combined with these designs, APPT is able to capture features globally throughout the entire network while focusing on local-detailed features. Extensive experiments show that our method outperforms the priors and achieves state-of-the-art on several benchmarks for 3D point cloud understanding, such as 3D semantic segmentation on S3DIS, 3D shape classification on ModelNet40, and 3D part segmentation on ShapeNet

A novel “holey-LFP / graphene / holey-LFP” sandwich nanostructure with significantly improved rate capability for lithium storage

The development of high-performance and new-structure electrode materials is vital for the wide application of rechargeable lithium batteries in electric vehicles. In this work, we design a special composite electrode structure with the macroporous three-dimensional graphene areogel framework supporting mesoporous LiFePO4 nanoplate. It is realized using a simple sol-gel deposition method. The highly conductivity graphene nanosheets assemble into an interconnected three-dimensional macroporous areogel framework, while LiFePO4 grows along the graphene nanosheets and generates a mesoporous nanoplate structure. In comparison with LiFePO4, this unique sandwich nanostructure offers a greatly increased electronic conductivity thanks to the framework of graphene nanosheets. Also, the bimodal porous structure of the composite remarkably increases the interface between the electrode/electrolyte and facilitates the transport of Li+ throughout the electrode, enabling the superior specific capacity, rate characteristic and cyclic retention

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Characterization of the microbiota in long- and short-term natural indigo fermentation

The duration for which the indigo-reducing state maintenance in indigo natural fermentation in batch dependent. The microbiota was analyzed in two batches of sukumo fermentation fluids that lasted for different durations (Batch 1: less than 2 months; Batch 2: nearly 1 year) to understand the mechanisms underlying the sustainability and deterioration of this natural fermentation process. The transformation of the microbiota suggested that the deterioration of the fermentation fluid is associated with the relative abundance of Alcaligenaceae. Principal coordinates analysis (PCoA) showed that the microbial community maintained a very stable state in only the long-term Batch 2. Therefore, entry of the microbiota into a stable state under alkaline anaerobic condition is an important factor for maintenance of indigo fermentation for long duration. This is the first report on the total transformation of the microbiota for investigation of long-term maintenance mechanisms and to address the problem of deterioration in indigo fermentation