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

3D vasculature segmentation using localized hybrid level-set method

Background: Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. Methods: This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images. Results: Experiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model. Conclusions: Experimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does

Measurement of web surface profiles using fringe projection

In this paper a full-field measurement technique, namely fringe projection technique was implemented to determine the three-dimensional surface profile for situations where webs are opaque or translucent. In this method, a grating is projected to the surface of a web. The projected grating will be distorted due to the non-flat surface. The image of the distorted gratings will be shifted on a reference grating on a computer to form Moire fringes. The phase of the fringes is determined and converted to the surface profile of the web. Calibration was conducted, and validation was made on surfaces with known geometry. Surface profiles of Polyvinyl Fluoride (PVF), nonwoven and polyethylene webs, as well as non-flat side walls of a wound roll determined as examples illustrating the technique. The accuracy of the method is discussed. The method has potential for industrial scale applications due to its ease in setup and speed in three-dimensional reconstruction.Mechanical and Aerospace Engineerin

Psoriasis comorbidity management in the COVID era: a pressing challenge

The global COVID-19 pandemic has presented a significant, ongoing challenge since its emergence in late 2019. Today, the Omicron strain, which is less lethal but more contagious than the original outbreak strain, continues to pose substantial health risks. In this background, the management of psoriatic comorbidities has become even more complex, particularly for patients with underlying inflammatory, metabolic, or cardiovascular diseases. This review aims to summarize current research on comorbid COVID-19 and psoriasis, and provide insights into the development of evidence-based management strategies. By providing appropriate patient instruction, implementing protective measures, and re-evaluating medication prescriptions based on each patient’s unique situation, healthcare professionals can effectively address the challenges faced by patients with comorbid psoriasis in the COVID-19 era

Time series prediction based on linear regression and SVR

The application of SVR in the time series prediction is increasingly popular. Because some time series prediction based on SVR wasn't very nice in the efficiency of the forecast, this article presents a new regression based on linear regression and SVR. The new regression separates time series into linear part and nonlinear part, then predicts the two parts respectively, and finally integrates the two parts to forecast. Experiments show that the new regression advances the precision of the forecasting compared to the common SVR

Type VI Secretion Systems Present New Insights on Pathogenic Yersinia

The type VI secretion system (T6SS) is a versatile secretion system widely distributed in Gram-negative bacteria that delivers multiple effector proteins into either prokaryotic or eukaryotic cells, or into the extracellular milieu. T6SS participates in various physiological processes including bacterial competition, host infection, and stress response. Three pathogenic Yersinia species, namely Yersinia pestis, Yersinia pseudotuberculosis, and Yersinia enterocolitica, possess different copies of T6SSs with distinct biological functions. This review summarizes the pathogenic, antibacterial, and stress-resistant roles of T6SS in Yersinia and the ion-transporting ability in Y. pseudotuberculosis. In addition, the T6SS-related effectors and regulators identified in Yersinia are discussed