Mercury speciation, transformation, and transportation in soils, atmospheric flux, and implications for risk management : a critical review

Mercury (Hg) is a potentially harmful trace element in the environment and one of the World Health Organization's foremost chemicals of concern. The threat posed by Hg contaminated soils to humans is pervasive, with an estimated 86 Gg of anthropogenic Hg pollution accumulated in surface soils worldwide. This review critically examines both recent advances and remaining knowledge gaps with respect to cycling of mercury in the soil environment, to aid the assessment and management of risks caused by Hg contamination. Included in this review are factors affecting Hg release from soil to the atmosphere, including how rainfall events drive gaseous elemental mercury (GEM) flux from soils of low Hg content, and how ambient conditions such as atmospheric O3 concentration play a significant role. Mercury contaminated soils constitute complex systems where many interdependent factors, including the amount and composition of soil organic matter and clays, oxidized minerals (e.g. Fe oxides), reduced elements (e.g. S2−), as well as soil pH and redox conditions affect Hg forms and transformation. Speciation influences the extent and rate of Hg subsurface transportation, which has often been assumed insignificant. Nano-sized Hg particles as well as soluble Hg complexes play important roles in soil Hg mobility, availability, and methylation. Finally, implications for human health and suggested research directions are put forward, where there is significant potential to improve remedial actions by accounting for Hg speciation and transportation factors

Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information

For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) plays an important role in producing natural and intelligible speech. Although inter-utterance linguistic information can influence the speech interpretation of the target utterance, previous works on PSP mainly focus on utilizing intrautterance linguistic information of the current utterance only. This work proposes to use inter-utterance linguistic information to improve the performance of PSP. Multi-level contextual information, which includes both inter-utterance and intrautterance linguistic information, is extracted by a hierarchical encoder from character level, utterance level and discourse level of the input text. Then a multi-task learning (MTL) decoder predicts prosodic boundaries from multi-level contextual information. Objective evaluation results on two datasets show that our method achieves better F1 scores in predicting prosodic word (PW), prosodic phrase (PPH) and intonational phrase (IPH). It demonstrates the effectiveness of using multi-level contextual information for PSP. Subjective preference tests also indicate the naturalness of synthesized speeches are improved.Comment: Accepted by Interspeech202

Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension

Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the target language. In this paper, we propose a novel multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to disassociate semantics from syntax in representations learned by multilingual pre-trained models. To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement. Experimental results on three multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the effectiveness of our proposed approach over models based on mBERT and XLM-100. Code is available at:https://github.com/wulinjuan/SSDM_MRC.Comment: Accepted to ACL 2022 (main conference

Towards Improving the Expressiveness of Singing Voice Synthesis with BERT Derived Semantic Information

This paper presents an end-to-end high-quality singing voice synthesis (SVS) system that uses bidirectional encoder representation from Transformers (BERT) derived semantic embeddings to improve the expressiveness of the synthesized singing voice. Based on the main architecture of recently proposed VISinger, we put forward several specific designs for expressive singing voice synthesis. First, different from the previous SVS models, we use text representation of lyrics extracted from pre-trained BERT as additional input to the model. The representation contains information about semantics of the lyrics, which could help SVS system produce more expressive and natural voice. Second, we further introduce an energy predictor to stabilize the synthesized voice and model the wider range of energy variations that also contribute to the expressiveness of singing voice. Last but not the least, to attenuate the off-key issues, the pitch predictor is re-designed to predict the real to note pitch ratio. Both objective and subjective experimental results indicate that the proposed SVS system can produce singing voice with higher-quality outperforming VISinger