Gamified Live-streaming: Is Avatar Better than Human Being?

Live-streaming has emerged as a popular direct selling channel to foster synchronous interaction between streamers and consumers, with the avatar streamer largely underexplored. Using the data from a fashion retailer, we adopt the Generalized Synthetic Control (GSC) method to examine the effect of gamified and human live-streaming on product sales and return rate. We find that (1) the gamified live-streaming reduces product sales and the return rate simultaneously; (2) human live-streaming boosts product sales but increases the return rate, and (3) the dual-type live-streaming can increase product sales and decrease return rates. Furthermore, we proposed that the reason for the differentiated effects between gamified and human live-streaming could be driven by the impulse-buying behavior of viewers only in human live-streaming. Our findings contribute to the growing literature on the business value of AI technology and gamification in live-streaming and shed light on practical decisions made by online retailers

Improving Language Model-Based Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts

Zero-shot text-to-speech (TTS) synthesis aims to clone any unseen speaker's voice without adaptation parameters. By quantizing speech waveform into discrete acoustic tokens and modeling these tokens with the language model, recent language model-based TTS models show zero-shot speaker adaptation capabilities with only a 3-second acoustic prompt of an unseen speaker. However, they are limited by the length of the acoustic prompt, which makes it difficult to clone personal speaking style. In this paper, we propose a novel zero-shot TTS model with the multi-scale acoustic prompts based on a neural codec language model VALL-E. A speaker-aware text encoder is proposed to learn the personal speaking style at the phoneme-level from the style prompt consisting of multiple sentences. Following that, a VALL-E based acoustic decoder is utilized to model the timbre from the timbre prompt at the frame-level and generate speech. The experimental results show that our proposed method outperforms baselines in terms of naturalness and speaker similarity, and can achieve better performance by scaling out to a longer style prompt.Comment: Submitted to ICASSP 202

Variation in Soil Fungal Composition Associated with the Invasion of Stellera chamaejasme L. in Qinghai-Tibet Plateau Grassland

Stellera chamaejasme L. is the most problematic weed in China's grasslands. Its root exudates affect co-occurring plants and thus may also affect soil fungi. Soils (0-20 cm depth) on two adjacent sites, one invaded the other uninvaded, were compared for a range of physiochemical parameters and by DNA sequencing of fungal communities. At the invaded site, relationships between S. chamaejasme abundance, soil physiochemical factors, and fungal communities were further investigated to determine whether these relationships corroborated conclusions on the basis of site differences that could be translated into functional variation. Results showed that the invaded soils had lower N, P, organic matter, fungal alpha diversity, and relative abundance of arbuscular mycorrhizal fungi (AMF), but greater abundance of pathogenic fungi. Organic matter and P were the edaphic factors most strongly linked to site differences in total fungal ommunities. Within the invaded site, organic matter rather than S. chamaejasme cover was closely linked to total fungal composition. However, on this site, a number of fungal species that had various ecological functions and that differentiated the two sites were related to S. chamaejasme cover. This study indicates that lower fertility soils may be more susceptible to invasion by S. chamaejasme. Although the influence of S. chamaejasme on total fungal community composition was limited, there was evidence of effects on particular fungal species. Further research is needed to determine whether these effects influence S. chamaejasme invasiveness.This work was supported by the National Natural Science Foundation of China (31402133), Special Aid Fund for Qinghai Province (2020-QY-210), and Key Laboratory Research Fund of Department of Education of Shaanxi Province (18JS110) to W.H.; National Natural Science Foundation of China (41871335) to Y.L.; Special Fund for Agro-scientific Research in the Public Interest of China (201203062) to Y.W., W.H., and J.S. We acknowledge the support of a Stapledon Fellowship and BBSRC (Biotechnology and Biological Sciences Research Council) exchange grant BB/M027945/1, which helped in the formation of this manuscript. A.D. is supported by the WEFO/ERDF (Welsh European Funding Office/European Regional Development Fund) funded Flexis project

Skywork: A More Open Bilingual Foundation Model

In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date. We introduce a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively. We show that our model not only excels on popular benchmarks, but also achieves \emph{state of the art} performance in Chinese language modeling on diverse domains. Furthermore, we propose a novel leakage detection method, demonstrating that test data contamination is a pressing issue warranting further investigation by the LLM community. To spur future research, we release Skywork-13B along with checkpoints obtained during intermediate stages of the training process. We are also releasing part of our SkyPile corpus, a collection of over 150 billion tokens of web text, which is the largest high quality open Chinese pre-training corpus to date. We hope Skywork-13B and our open corpus will serve as a valuable open-source resource to democratize access to high-quality LLMs