中国的外商投资申诉机制: 外商投资治理改革的新起点?

This Perspective discusses the development and features of China's foreign investment complaint mechanism (FICMs) and highlights China's first national FICM's potential roles as an alternative of investment dispute prevention and settlement, a measure of investment facilitation, and a factor in advancing China's foreign investment governance reform

China’s foreign investment complaint mechanism: A new beginning of foreign investment governance reform?

This Perspective discusses the development and features of China's foreign investment complaint mechanism (FICMs) and highlights China's first national FICM's potential roles as an alternative of investment dispute prevention and settlement, a measure of investment facilitation, and a factor in advancing China's foreign investment governance reform

Modeling carbon uptake by vegetation of grassland ecosystems and its associated factors in China based on remote sensing

In order to reveal the spatial variation characteristics and influencing factors of grassland net primary productivity (NPP) in China, this paper uses remote sensing data, land use data and meteorological data to simulate and estimate China’s grassland net primary productivity from 2001 to 2019 using the Carnegie-Ames-Stanford Approach (CASA). The trend analysis and complex correlation analysis were used to analyze the relationship with the temporal and spatial changes of grassland NPP from the perspectives of climate factors, topography, longitude and latitude. The results show that: 1) In the past 19 years, the China’s grassland NPP has generally shown a fluctuating upward trend, the spatial distribution of NPP variation shows a characteristic of low in the west and high in the east, with the increased area accounting for 70.39% of the total grassland area, and the low NPP values are mainly distributed in the northwestern part of Tibet and Qinghai and the central part of Inner Mongolia, the average annual NPP is 257.13 g C·m−2·a−1. 2) The change of mean NPP value of grassland in China is more dependent on precipitation (p) than air temperature (T). 3) Grassland NPP showed a decreasing trend with the increase of altitude, and the NPP on the gradient with DEM between 200 m and 500 m was the highest (483.86 g·C·m−2·a−1); The maximum annual mean value (448.42 g C·m−2·a−1) is fallen over the sharp slope of 35°–45°; the NPP of grassland increases with the slope (from shade to sunny), and the NPP of grassland on the semi-sunny slope increases. The annual average NPP is the highest (270.87 g C·m−2·a−1). 4) The mean value of grassland NPP was negatively correlated with the change of latitude, and showed a “wave-like” downward trend from south to north; the mean value of grassland NPP was positively related to the change of longitude. The correlation relationship shows a “stepped” upward trend from west to east

R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation

Incremental Decoding is an effective framework that enables the use of an offline model in a simultaneous setting without modifying the original model, making it suitable for Low-Latency Simultaneous Speech Translation. However, this framework may introduce errors when the system outputs from incomplete input. To reduce these output errors, several strategies such as Hold-$n$, LA-$n$, and SP-$n$ can be employed, but the hyper-parameter $n$ needs to be carefully selected for optimal performance. Moreover, these strategies are more suitable for end-to-end systems than cascade systems. In our paper, we propose a new adaptable and efficient policy named "Regularized Batched Inputs". Our method stands out by enhancing input diversity to mitigate output errors. We suggest particular regularization techniques for both end-to-end and cascade systems. We conducted experiments on IWSLT Simultaneous Speech Translation (SimulST) tasks, which demonstrate that our approach achieves low latency while maintaining no more than 2 BLEU points loss compared to offline systems. Furthermore, our SimulST systems attained several new state-of-the-art results in various language directions.Comment: Preprin

INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion

Computer-aided translation (CAT) aims to enhance human translation efficiency and is still important in scenarios where machine translation cannot meet quality requirements. One fundamental task within this field is Word-Level Auto Completion (WLAC). WLAC predicts a target word given a source sentence, translation context, and a human typed character sequence. Previous works either employ word classification models to exploit contextual information from both sides of the target word or directly disregarded the dependencies from the right-side context. Furthermore, the key information, i.e. human typed sequences, is only used as prefix constraints in the decoding module. In this paper, we propose the INarIG (Iterative Non-autoregressive Instruct Generation) model, which constructs the human typed sequence into Instruction Unit and employs iterative decoding with subwords to fully utilize input information given in the task. Our model is more competent in dealing with low-frequency words (core scenario of this task), and achieves state-of-the-art results on the WMT22 and benchmark datasets, with a maximum increase of over 10% prediction accuracy.Comment: EMNLP202

Text Style Transfer Back-Translation

Back Translation (BT) is widely used in the field of machine translation, as it has been proved effective for enhancing translation quality. However, BT mainly improves the translation of inputs that share a similar style (to be more specific, translation-like inputs), since the source side of BT data is machine-translated. For natural inputs, BT brings only slight improvements and sometimes even adverse effects. To address this issue, we propose Text Style Transfer Back Translation (TST BT), which uses a style transfer model to modify the source side of BT data. By making the style of source-side text more natural, we aim to improve the translation of natural inputs. Our experiments on various language pairs, including both high-resource and low-resource ones, demonstrate that TST BT significantly improves translation performance against popular BT benchmarks. In addition, TST BT is proved to be effective in domain adaptation so this strategy can be regarded as a general data augmentation method. Our training code and text style transfer model are open-sourced.Comment: acl2023, 14 pages, 4 figures, 19 table

UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data. But when only pre-training on Pseudo Paired Data, previous models have negative effect on correction. While fine-tuning on Original Paired Data, the source side data must be transcribed by a well-trained ASR model, which takes a lot of time and not universal. In this paper, we propose UCorrect, an unsupervised Detector-Generator-Selector framework for ASR Error Correction. UCorrect has no dependency on the training data mentioned before. The whole procedure is first to detect whether the character is erroneous, then to generate some candidate characters and finally to select the most confident one to replace the error character. Experiments on the public AISHELL-1 dataset and WenetSpeech dataset show the effectiveness of UCorrect for ASR error correction: 1) it achieves significant WER reduction, achieves 6.83\% even without fine-tuning and 14.29\% after fine-tuning; 2) it outperforms the popular NAR correction models by a large margin with a competitive low latency; and 3) it is an universal method, as it reduces all WERs of the ASR model with different decoding strategies and reduces all WERs of ASR models trained on different scale datasets.Comment: Accepted in ICASSP 202

Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere

Physical properties of transition metal oxides synthesized by floating zone method and spark plasma sintering

Transition metal oxides have attracted growing attention over the last few decades because of rich physical properties they exhibit. Perovskite structure transition metal oxides AMO₃ are of particular interest to the design of functional materials in modern techniques, since a variety of ways can be used to tune the physical properties of AMO₃. Single crystals of Y₁₋ₓLaₓTiO₃ are grown by floating zone method to study the magnetic transition from ferromagnetic in YTiO₃ to G-type antiferromagnetic in LaTiO₃. Y₁₋ₓ LaₓTiO₃ shows similar magnetic phase diagram with RTiO₃ family, and the magnetism and the transition temperature can be finely tuned by varying the La doping x. By measuring the change of magnetic transition temperatures on single crystal samples under uniaxial stress, the correlation between the lattice distortions and the cooperative orbital ordering can be distinguished. Double perovskite CaMnTi₂O₆ is the first columnar A-site ordered perovskite exhibiting ferroelectric property. Spark plasma sintering (SPS) is used to successfully synthesize gram-level Ca₂₋ₓMnₓTi₂O₆, which has the same crystal structure and similar high-T [subscript c] ferroelectric property. Through neutron diffraction, the detailed information of the structure is obtained, and the driving force for ferroelectricity is identified. Inspired by the successful synthesis of double perovskite Ca₂₋ₓMnₓTi₂O₆, perovskites La₁₋ₓPrₓRuO₃ are obtained by SPS as well. The substitution of La by smaller rare earth ion Pr gives rise to the crossover from itinerant to localized electronic behavior. A systematical study of physical properties is made and an unusual second-order metal insulator transition is found in La₁₋ₓPrₓRuO₃. The A²⁺V₂O₄ spinels have the smallest gap caused by electron-electron correlations in the single-valent spinels, and the V-V bond length in these spinels decreases as the A-site cation is replaced by cations in the order of A = Cd, Mn, Fe, Mg, Zn, Co. The density functional theory (DFT) calculation and transport properties of CoV₂O₄ under pressure indicate that CoV₂O₄ might be at the crossover between localized electron and itinerant electronic behavior. In order to clarify this, the series of AV₂O₄ spinels (A = Cd, Mn, Fe, Mg, Zn, Co) are studied with in situ high-pressure x-ray and neutron diffraction at different temperatures.Materials Science and Engineerin