Does China's overseas lending favor One Belt One Road countries?

The One Belt One Road initiative is found to promote China’s overseas lending in the belt road countries, especially for countries along the continental route. Such effect strengthens and persists for at least three years. Our findings show that launching a national strategy could be a decisive determinant of one country’s outbound loans

Does China's overseas lending favors One Belt One Road countries?

The One Belt One Road initiative is found to promote China’s overseas lending in the belt road countries, especially for countries along the continental route. Such effect strengthens and persists for at least three years. Our findings show that launching a national strategy could be a decisive determinant of one country’s outbound loans

Fluorescence-based quantification of messenger RNA and plasmid DNA decay kinetics in extracellular biological fluids and cell extracts

Extracellular and intracellular degradation of nucleic acids remains an issue in non-viral gene therapy. Understanding biodegradation is critical for the rational design of gene therapeutics in order to maintain stability and functionality at the target site. However, there are only limited methods available that allow determining the stability of genetic materials in biological environments. In this context, the decay kinetics of fluorescently labeled plasmid DNA (pDNA) and messenger RNA (mRNA) in undiluted biological samples (i.e., human serum, human ascites, bovine vitreous) and cell extracts is studied using fluorescence correlation spectroscopy (FCS) and single particle tracking (SPT). It is demonstrated that FCS is suitable to follow mRNA degradation, while SPT is better suited to investigate pDNA integrity. The half-life of mRNA and pDNA is approximate to 1-2 min and 1-4 h in biological samples, respectively. The resistance against biodegradation drastically improves by complexation with lipid-based carriers. Taken together, FCS and SPT are able to quantify the integrity of mRNA and pDNA, respectively, as a function of time, both in the extracellular biological fluids and cell extracts. This in turn allows to focus on the important but less understood issue of nucleic acids degradation in more detail and to rationally optimize gene delivery system as therapeutics

Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks

As more practical and scalable quantum computers emerge, much attention has been focused on realizing quantum supremacy in machine learning. Existing quantum ML methods either (1) embed a classical model into a target Hamiltonian to enable quantum optimization or (2) represent a quantum model using variational quantum circuits and apply classical gradient-based optimization. The former method leverages the power of quantum optimization but only supports simple ML models, while the latter provides flexibility in model design but relies on gradient calculation, resulting in barren plateau (i.e., gradient vanishing) and frequent classical-quantum interactions. To address the limitations of existing quantum ML methods, we introduce Quark, a gradient-free quantum learning framework that optimizes quantum ML models using quantum optimization. Quark does not rely on gradient computation and therefore avoids barren plateau and frequent classical-quantum interactions. In addition, Quark can support more general ML models than prior quantum ML methods and achieves a dataset-size-independent optimization complexity. Theoretically, we prove that Quark can outperform classical gradient-based methods by reducing model query complexity for highly non-convex problems; empirically, evaluations on the Edge Detection and Tiny-MNIST tasks show that Quark can support complex ML models and significantly reduce the number of measurements needed for discovering near-optimal weights for these tasks.Comment: under revie

Political Tensions and Corporate Cross-border Financing: Evidence from the China-U.S. Trade War

A growing body of literature has explored the effects of political tensions on international trade and consumers’ behavior. Still, little is known whether or to what extend it matters to corporations’ cross-border financing activities. This study fills such gap in the literature by investigating the impacts of the recent China-U.S. trade war on Chinese firms’ international syndicated loans. This quasi-nature experiment facilitates the difference-in-differences (DD) identification strategy and we use Chinese corporations seeking international borrowing as the treatment group and non-Chinese counterparties as the control group. Our analysis is taken at both the aggregate level and the deal level. Preliminary results suggest significant negative aggregate consequences, including the number of loan initiations as well as their amount. Deal level estimations exhibit the similar pattern: loan spreads and maturities were adversely affected; and sizes of syndicates became bigger and the probability of secured loan occurrence was higher for Chinese corporations. To substantiate the argument that the observed gloom was caused by the trade war, we adopt the triple difference-in-differences (DDD) estimation method by exploiting U.S. borrowers as an additional level of variation

VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis Enhanced by Digital Signal Processing Synthesizer

End-to-end singing voice synthesis (SVS) model VISinger can achieve better performance than the typical two-stage model with fewer parameters. However, VISinger has several problems: text-to-phase problem, the end-to-end model learns the meaningless mapping of text-to-phase; glitches problem, the harmonic components corresponding to the periodic signal of the voiced segment occurs a sudden change with audible artefacts; low sampling rate, the sampling rate of 24KHz does not meet the application needs of high-fidelity generation with the full-band rate (44.1KHz or higher). In this paper, we propose VISinger 2 to address these issues by integrating the digital signal processing (DSP) methods with VISinger. Specifically, inspired by recent advances in differentiable digital signal processing (DDSP), we incorporate a DSP synthesizer into the decoder to solve the above issues. The DSP synthesizer consists of a harmonic synthesizer and a noise synthesizer to generate periodic and aperiodic signals, respectively, from the latent representation z in VISinger. It supervises the posterior encoder to extract the latent representation without phase information and avoid the prior encoder modelling text-to-phase mapping. To avoid glitch artefacts, the HiFi-GAN is modified to accept the waveforms generated by the DSP synthesizer as a condition to produce the singing voice. Moreover, with the improved waveform decoder, VISinger 2 manages to generate 44.1kHz singing audio with richer expression and better quality. Experiments on OpenCpop corpus show that VISinger 2 outperforms VISinger, CpopSing and RefineSinger in both subjective and objective metrics.Comment: Submitted to ICASSP 202

AdaVITS: Tiny VITS for Low Computing Resource Speaker Adaptation

Speaker adaptation in text-to-speech synthesis (TTS) is to finetune a pre-trained TTS model to adapt to new target speakers with limited data. While much effort has been conducted towards this task, seldom work has been performed for low computational resource scenarios due to the challenges raised by the requirement of the lightweight model and less computational complexity. In this paper, a tiny VITS-based TTS model, named AdaVITS, for low computing resource speaker adaptation is proposed. To effectively reduce parameters and computational complexity of VITS, an iSTFT-based wave construction decoder is proposed to replace the upsampling-based decoder which is resource-consuming in the original VITS. Besides, NanoFlow is introduced to share the density estimate across flow blocks to reduce the parameters of the prior encoder. Furthermore, to reduce the computational complexity of the textual encoder, scaled-dot attention is replaced with linear attention. To deal with the instability caused by the simplified model, instead of using the original text encoder, phonetic posteriorgram (PPG) is utilized as linguistic feature via a text-to-PPG module, which is then used as input for the encoder. Experiment shows that AdaVITS can generate stable and natural speech in speaker adaptation with 8.97M model parameters and 0.72GFlops computational complexity.Comment: Accepted by ISCSLP 202

DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention

Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial constraints in resource allocation for training and data accessibility, impacting their adaptability and scalability across varied interaction realms. To address this, we present the DeepSpeed-VisualChat framework, designed to optimize Large Language Models (LLMs) by incorporating multi-modal capabilities, with a focus on enhancing the proficiency of Large Vision and Language Models in handling interleaved inputs. Our framework is notable for (1) its open-source support for multi-round and multi-image dialogues, (2) introducing an innovative multi-modal causal attention mechanism, and (3) utilizing data blending techniques on existing datasets to assure seamless interactions in multi-round, multi-image conversations. Compared to existing frameworks, DeepSpeed-VisualChat shows superior scalability up to 70B parameter language model size, representing a significant advancement in multi-modal language models and setting a solid foundation for future explorations

Life cycle environmental impact comparison of bioelectrochemical systems for wastewater treatment

Bioelectrochemical systems (BESs) are developed to transform the energy harvested from the biomass into electricity. Different types of BESs including microbial fuel cell (MFC), microbial electrolysis cell (MEC), and microbial desalination cell (MDC) are under intensive research and development; however, their life cycle environmental impacts have not been systematically compared to identify the most environmentally friendly BES. To understand and eventually help reduce the environmental impacts of different BESs, life cycle assessment (LCA) models were developed in this study to assess and compare their potential environmental impacts. The results indicate that the MEC has better environmental performance than the MFC and MDC due to the large hydrogen peroxide production in the operation phase. The environmental performance of the MFC and MDC can be improved by the increase of power density, but their environmental impacts, at a relatively high power density that can be achieved by current technology, are still higher than the environmental impacts of the MEC at current power density. When the environmental impacts are benchmarked with those of the traditional wastewater treatment methods, the MEC has a better environmental performance, whereas the MFC and MDC have relatively large environmental impacts.This publication is made possible by NPRP # 6-289-2-125 from Qatar national research fund (a member of Qatar foundation)