Study on Aesthetic Value of Subtitle Translation of Within and Beyond the Great Wall from the Perspective of Translation Aesthetics

With the advancement of cultural diversity and the increasingly frequent communication between the East and the West, more and more high-quality documentaries are presented to the foreigner. Chinese documentaries have become an important carrier for foreign audiences to understand Chinese culture, so the subtitle translation of documentary plays an important role in the spread of documentary. Translation aesthetics theory will help the study on the aesthetic values of subtitle translation and broaden the research scope of documentary subtitle translation. This paper selects the documentary Homeland Dreamland--Within and Beyond the Great Wall as research object to analyze the subtitle translation. From the perspective of translation aesthetics, this paper discusses how the aesthetic value of subtitle translation achieve and what translation methods translators use to show the aesthetic effect

Less disagreement, better forecasts: adjusted risk measures in the energy futures market

This paper develops a generic adjustment framework to improve in the market risk forecasts of diverse risk forecasting models, which indicates the degree to which risk is under- and overestimated. In the context of the energy commodity market, a market in which tail risk management is of crucial importance, the empirical analysis shows that after this adjustment framework is applied, the forecasting performance of various risk models generally improves, as verified by a battery of backtesting methods. Additionally, our method also lessens the risk model disagreement among post-adjusted risk forecasts

Weakly supervised conditional random fields model for semantic segmentation with image patches.

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models

PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection

Recently, polar-based representation has shown promising properties in perceptual tasks. In addition to Cartesian-based approaches, which separate point clouds unevenly, representing point clouds as polar grids has been recognized as an alternative due to (1) its advantage in robust performance under different resolutions and (2) its superiority in streaming-based approaches. However, state-of-the-art polar-based detection methods inevitably suffer from the feature distortion problem because of the non-uniform division of polar representation, resulting in a non-negligible performance gap compared to Cartesian-based approaches. To tackle this issue, we present PARTNER, a novel 3D object detector in the polar coordinate. PARTNER alleviates the dilemma of feature distortion with global representation re-alignment and facilitates the regression by introducing instance-level geometric information into the detection head. Extensive experiments show overwhelming advantages in streaming-based detection and different resolutions. Furthermore, our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on Waymo and ONCE validation set, thus achieving competitive results over the state-of-the-art methods.Comment: ICCV 202

The evolution of cloud and aerosol microphysics at the summit of Mt. Tai, China

The influence of aerosols, both natural and anthropogenic, remains a major area of uncertainty when predicting the properties and the behaviours of clouds and their influence on climate. In an attempt to better understand the microphysical properties of cloud droplets, the simultaneous variations in aerosol microphysics and their potential interactions during cloud life cycles in the North China Plain, an intensive observation took place from 17 June to 30 July 2018 at the summit of Mt. Tai. Cloud microphysical parameters were monitored simultaneously with number concentrations of cloud condensation nuclei (NCCN) at different supersaturations, PM2:5 mass concentrations, particle size distributions and meteorological parameters. Number concentrations of cloud droplets (NC), liquid water content (LWC) and effective radius of cloud droplets (reff) show large variations among 40 cloud events observed during the campaign. The low values of reff and LWC observed at Mt. Tai are comparable with urban fog. Clouds on clean days are more susceptible to the change in concentrations of particle number (NP), while clouds formed on polluted days might be more sensitive to meteorological parameters, such as updraft velocity and cloud base height. Through studying the size distributions of aerosol particles and cloud droplets, we find that particles larger than 150 nm play important roles in forming cloud droplets with the size of 5-10 μm. In general, LWC consistently varies with reff. As NC increases, reff changes from a trimodal distribution to a unimodal distribution and shifts to smaller size mode. By assuming a constant cloud thickness and ignoring any lifetime effects, increase in NC and decrease in reff would increase cloud albedo, which may induce a cooling effect on the local climate system. Our results contribute valuable information to enhance the understanding of cloud and aerosol properties, along with their potential interactions on the North China plain. © Author(s) 2020

Shufeng Jiedu Capsules Alleviate Lipopolysaccharide-Induced Acute Lung Inflammatory Injury via Activation of GPR18 by Verbenalin

Background/Aims: Acute respiratory tract infection (ARTI) is the most common reason for outpatient physician office visits. Although powerful and significant in the treatment of infections, antibiotics used for ARTI inappropriately have been an important contributor to antibiotic resistance. We previously reported that Shufeng Jiedu Capsule (SJC) can effectively amplify anti-inflammatory signaling during infection. In this study, we aimed to systematically explore its composition and the mechanism of its effects in ARTI. Methods: Pseudomonas aeruginosa (PAK) strain was used to generate a mouse model of ARTI, which were then treated with different drugs or compounds to determine the corresponding anti-inflammatory roles. High-performance liquid chromatography-quadrupole time of flight-tandem mass spectrometry. was conducted to detect the chemical compounds in SJC. RNAs from the lung tissues of mice were prepared for microarray analysis to reveal globally altered genes and the pathways involved after SJC treatment. Results: SJC significantly inhibited the expression and secretion of inflammatory factors from PAK-induced mouse lung tissues or lipopolysaccharide-induced peritoneal macrophages. Verbenalin, one of the bioactive compounds identified in SJC, also showed notable anti-inflammatory effects. Microarray data revealed numerous differentially expressed genes among the different treatment groups; here, we focused on studying the role of GPR18. We found that the anti-inflammatory role of verbenalin was attenuated in GPR18 knockout mice compared with wild-type mice, although no statistically significant difference was observed in the untreated PAK-induced mice types. Conclusion: Our data not only showed the chemical composition of SJC, but also demonstrated that verbenalin was a significant anti-inflammatory compound, which may function through GPR18

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report