Harnack inequality for a p-Laplacian equation with a source reaction term involving the product of the function and its gradient

A p-Laplacian type problem with a source reaction term involving the product of the function and its gradient is considered in this paper. A Harnack inequality is proved, and the main idea is based on de Giorgi-Nash-Moser iteration and Moser's iteration technique. As a consequence, $H\ddot{o}lder$ continuity and boundness for the solution of this problem also are obtained

Zero-Shot Emotion Transfer For Cross-Lingual Speech Synthesis

Zero-shot emotion transfer in cross-lingual speech synthesis aims to transfer emotion from an arbitrary speech reference in the source language to the synthetic speech in the target language. Building such a system faces challenges of unnatural foreign accents and difficulty in modeling the shared emotional expressions of different languages. Building on the DelightfulTTS neural architecture, this paper addresses these challenges by introducing specifically-designed modules to model the language-specific prosody features and language-shared emotional expressions separately. Specifically, the language-specific speech prosody is learned by a non-autoregressive predictive coding (NPC) module to improve the naturalness of the synthetic cross-lingual speech. The shared emotional expression between different languages is extracted from a pre-trained self-supervised model HuBERT with strong generalization capabilities. We further use hierarchical emotion modeling to capture more comprehensive emotions across different languages. Experimental results demonstrate the proposed framework's effectiveness in synthesizing bi-lingual emotional speech for the monolingual target speaker without emotional training data.Comment: Accepted by ASRU202

Preserving background sound in noise-robust voice conversion via multi-task learning

Background sound is an informative form of art that is helpful in providing a more immersive experience in real-application voice conversion (VC) scenarios. However, prior research about VC, mainly focusing on clean voices, pay rare attention to VC with background sound. The critical problem for preserving background sound in VC is inevitable speech distortion by the neural separation model and the cascade mismatch between the source separation model and the VC model. In this paper, we propose an end-to-end framework via multi-task learning which sequentially cascades a source separation (SS) module, a bottleneck feature extraction module and a VC module. Specifically, the source separation task explicitly considers critical phase information and confines the distortion caused by the imperfect separation process. The source separation task, the typical VC task and the unified task shares a uniform reconstruction loss constrained by joint training to reduce the mismatch between the SS and VC modules. Experimental results demonstrate that our proposed framework significantly outperforms the baseline systems while achieving comparable quality and speaker similarity to the VC models trained with clean data.Comment: Submitted to ICASSP 202

Shape and structure controlling of calcium oxalate crystals by a combination of additives in the process of biomineralization

The origin of complex hierarchical superstructures of biomaterials and their unique self-assembly mechanisms of formation are important in biological systems and have attracted considerable attention. In the present study, we investigated the morphological changes of calcium oxalate (CaO(x)) crystals induced by additives including chiral aspartic acid, sodium citrate, Mg(2+), casein and combinations of these molecules. The morphology and structure of CaO(x) were identified with the use of various techniques. The morphogenesis of CaO(x) crystals were significantly affected by chiral aspartic acid, sodium citrate or Mg(2+). However, they only formed calcium oxalate monohydrate (COM). It was observed that the chiral aspartic acid, sodium citrate and casein adhered to the surface of the crystals. The adherence of Mg(2+) to crystals was not evident. Casein significantly affected the formation of COM and calcium oxalate dihydrate (COD). The ratio of different CaO(x) crystal forms is associated with the casein concentration. In combination with Mg(2+) or citrate ions, casein showed improved formation of COD. The present study mimics biomineralization with a simple chemical approach and provides insight into the complicated system of CaO(x) biomineralization as well as facilitates the understanding of urinary stone treatment

Cross-BERT for Point Cloud Pretraining

Introducing BERT into cross-modal settings raises difficulties in its optimization for handling multiple modalities. Both the BERT architecture and training objective need to be adapted to incorporate and model information from different modalities. In this paper, we address these challenges by exploring the implicit semantic and geometric correlations between 2D and 3D data of the same objects/scenes. We propose a new cross-modal BERT-style self-supervised learning paradigm, called Cross-BERT. To facilitate pretraining for irregular and sparse point clouds, we design two self-supervised tasks to boost cross-modal interaction. The first task, referred to as Point-Image Alignment, aims to align features between unimodal and cross-modal representations to capture the correspondences between the 2D and 3D modalities. The second task, termed Masked Cross-modal Modeling, further improves mask modeling of BERT by incorporating high-dimensional semantic information obtained by cross-modal interaction. By performing cross-modal interaction, Cross-BERT can smoothly reconstruct the masked tokens during pretraining, leading to notable performance enhancements for downstream tasks. Through empirical evaluation, we demonstrate that Cross-BERT outperforms existing state-of-the-art methods in 3D downstream applications. Our work highlights the effectiveness of leveraging cross-modal 2D knowledge to strengthen 3D point cloud representation and the transferable capability of BERT across modalities

Editorial: Rock landslide risk assessment, stability analysis and monitoring for the development of early warning systems and reinforcement measures

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