Traduzir a poesia brasileira

É constrangedor confessar, mas antes de eu ir ao Brasil em 2003, ignorava por completo a riqueza da poesia modernista desse grande país. Na realidade, eu não era o único. O desconhecimento total da poesia brasileira entre os poetas contemporâneos chineses ainda é uma situação comum. Antes de eu deixar o Brasil em 2005, além de Vinícius de Moraes, eu também havia traduzido alguns poemas de Manuel Bandeira, Carlos Drummond de Andrade, Mário Quintana, Paulo Leminski, Ana Cristina César. Contudo, logo descobri minha meta de tradução: João Cabral de Melo Neto, mestre da poesia modernista brasileira

An Efficient Method for Traffic Image Denoising

AbstractIn this paper, a novel method for traffic image denoising based on the low-rank decomposition is proposed. Firstly, the low-rank decomposition is carried out. Under the sparse and low-rank constraints of low-rank decomposition, the foreground images with complanate background and moving vehicles and the background images with similar road scene are obtained. Then the foreground image is segmented into blocks of a certain size. The variance of each block is calculated, among that the minimum is considered the estimate of the noise power. KSVD algorithm is performed for the foreground image denoising. Furthermore, the noisy pixel discrimination algorithm is performed to distinguish the noisy pixels from the noiseless pixels and the eight- neighborhood weight interpolation algorithm is performed to reconstruct the noisy pixels, where the weighted coefficients are inversely proportional to the Euclidean distances between the pixels. And PCA recovery combined with noisy pixel discrimination and eight-neighborhood weight interpolation is adopted for the background image denoising. Finally, our proposed method is conducted based on the traffic videos obtained under the same view and angle. Moreover, our proposed method is compared with several state-of-the-art denoising methods including BM3D, KSVD and PCA recovery. The experiment results illustrate that our proposed method can more effectively remove the noise, preserve the useful information and achieve a better performance in terms of both PSNR index and visual qualities

Thermodynamic, Kinetic, and Transport Contributions to Hydrogen Evolution Activity and Electrolyte-Stability Windows for Water-in-Salt Electrolytes

Concentrated water-in-salt electrolytes (WiSEs) are used in aqueous batteries and to control electrochemical reactions for fuel production. The hydrogen evolution reaction is a parasitic reaction at the negative electrode that limits cell voltage in WiSE batteries and leads to self-discharge, and affects selectivity for electrosynthesis. Mitigating and modulating these processes is hampered by a limited fundamental understanding of HER kinetics in WiSEs. Here, we quantitatively assess how thermodynamics, kinetics, and interface layers control the apparent HER activities in 20 m LiTFSI. When the LiTFSI concentration is increased from 1 to 20 m, an increase in proton activity causes a positive shift in the HER equilibrium potential of 71 mV. The exchange current density, io, derived from the HER branch for 20 m LiTFSI in 98% purity (0.56 ± 0.05 μA/cmPt2), however, is 8 times lower than for 20 m LiTFSI in 99.95% (4.7 ± 0.2 μA/cmPt2) and 32 times lower than for 1 m LiTFSI in 98% purity (18 ± 1 μA/cmPt2), demonstrating that the WiSE's impurities and concentration are both central in significantly suppressing HER kinetics. The ability and applicability of the reported methods are extended by examining additional WiSEs formulations made of acetates and nitrates

Zero-shot information extraction from radiological reports using ChatGPT

Electronic health records contain an enormous amount of valuable information, but many are recorded in free text. Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis. However, the traditional information extraction components, such as named entity recognition and relation extraction, require annotated data to optimize the model parameters, which has become one of the major bottlenecks in building information extraction systems. With the large language models achieving good performances on various downstream NLP tasks without parameter tuning, it becomes possible to use large language models for zero-shot information extraction. In this study, we aim to explore whether the most popular large language model, ChatGPT, can extract useful information from the radiological reports. We first design the prompt template for the interested information in the CT reports. Then, we generate the prompts by combining the prompt template with the CT reports as the inputs of ChatGPT to obtain the responses. A post-processing module is developed to transform the responses into structured extraction results. We conducted the experiments with 847 CT reports collected from Peking University Cancer Hospital. The experimental results indicate that ChatGPT can achieve competitive performances for some extraction tasks compared with the baseline information extraction system, but some limitations need to be further improved