Stronger intra-specific competition aggravates negative effects of drought on the growth of Cunninghamia lanceolata

Plant-plant competition is a dynamic and complicated process that is strongly influenced by abiotic conditions. Drought is a critical threat to forests, particularly to young plantation forests. Temporal changes in competition combined with the effects of drought may dramatically influence the physiological traits of plants. Cunninghamia lanceolata plants exposed to intra-specific competition and no-competition conditions were investigated under two soil water levels (well-watered and drought). Changes in plant-plant competition relationships and nitrogen uptake rates were measured at different harvest times. The effects of drought and plant competition on physiological traits, for example, leaf nitrogen allocation, δ13C, and levels of abscisic acid (ABA), indole acetic acid (IAA) and jasmonic acid (JA), were also explored. Our results indicated that C. lanceolata shifted from intense neighbor competition to facilitation under well-watered conditions, whereas under drought neighbor competition became much stronger at the second harvest compared to the first harvest. Strong competition significantly decreased N uptake under drought. Leaf NH4+, NO3- and N allocation to water-soluble proteins increased under drought at the first harvest, but significantly declined under prolonged drought. Leaf, stem and root starch concentrations were enhanced by drought. However, during prolonged drought, the root starch concentrations, leaf δ13C, leaf ABA and starch content of C. lanceolata were much lower under strong neighbor competition than in no-competition conditions, which demonstrated that the combined effects of drought and strong competition were more harmful to plant growth and survival compared to single effects. Our study demonstrated that drought combined with competition strongly affected the N uptake, N allocation and physiological traits of plants. Intense competition imposed by neighbors is a great threat to the growth and survival of young C. lanceolata plantations under prolonged drought.Peer reviewe

Influencing Factors of Catering O2O Customer Experience: An Approach Integrating Big Data Analytics with Grounded Theory

In the era of digital economy, catering O2O is developing rapidly. Catering O2O (catering online to offline), namely catering takeout in the paper, means that customers place an order through online ordering platform, and delivery persons deliver the food provided by catering enterprises offline. Catering O2O conforms to the trend of the digital economy era, but exposes a variety of problems, such as lower feedback rate of the platform, lower timeliness of acceptance and handling, lower customer feedback satisfaction, and poorer customer experience. As China\u27s leading e-commerce platform for life services, Meituan won the rating of not recommending to place an order in the report of "2020 China E-commerce User Experience and Complaint Monitoring". In order to improve customer experience and service satisfaction of catering O2O, this paper takes Meituan takeout as an example, integrates big data analytics and grounded theory to explore influencing factors of catering O2O customer experience. With the big data analytics method, the main influencing factors are obtained from 54250 customer reviews, and then the grounded theory method is used to conduct in-depth analysis on negative reviews, and influencing factors of O2O customer experience are verified and confirmed. The results show that the main influencing factors of catering O2O customer experience are catering food quality and delivery service quality and after-sale service quality. Catering food quality and delivery service quality have a significant impact on customer experience. Finally, from perspectives of catering O2O platforms and enterprises, the paper obtains management implications as follows: Catering O2O platforms should attach great importance on the service of contact points in distribution link, strengthen the last-mile delivery service quality, and improve the supervision and feedback mechanism; catering O2O enterprises should ensure the quality, portion and package of catering food, so as to improve customer experience and win electronic word-of-mouth and customer satisfaction

Software Engineers Response to Public Crisis: Lessons Learnt from Spontaneously Building an Informative COVID-19 Dashboard

The Coronavirus disease 2019 (COVID-19) outbreak quickly spread around the world, resulting in over 240 million infections and 4 million deaths by Oct 2021. While the virus is spreading from person to person silently, fear has also been spreading around the globe. The COVID-19 information from the Australian Government is convincing but not timely or detailed, and there is much information on social networks with both facts and rumors. As software engineers, we have spontaneously and rapidly constructed a COVID-19 information dashboard aggregating reliable information semi-automatically checked from different sources for providing one-stop information sharing site about the latest status in Australia. Inspired by the John Hopkins University COVID-19 Map, our dashboard contains the case statistics, case distribution, government policy, latest news, with interactive visualization. In this paper, we present a participant's in-person observations in which the authors acted as founders of https://covid-19-au.com/ serving more than 830K users with 14M page views since March 2020. According to our first-hand experience, we summarize 9 lessons for developers, researchers and instructors. These lessons may inspire the development, research and teaching in software engineer aspects for coping with similar public crises in the future

End-to-End Speech Recognition Contextualization with Large Language Models

In recent years, Large Language Models (LLMs) have garnered significant attention from the research community due to their exceptional performance and generalization capabilities. In this paper, we introduce a novel method for contextualizing speech recognition models incorporating LLMs. Our approach casts speech recognition as a mixed-modal language modeling task based on a pretrained LLM. We provide audio features, along with optional text tokens for context, to train the system to complete transcriptions in a decoder-only fashion. As a result, the system is implicitly incentivized to learn how to leverage unstructured contextual information during training. Our empirical results demonstrate a significant improvement in performance, with a 6% WER reduction when additional textual context is provided. Moreover, we find that our method performs competitively and improve by 7.5% WER overall and 17% WER on rare words against a baseline contextualized RNN-T system that has been trained on more than twenty five times larger speech dataset. Overall, we demonstrate that by only adding a handful number of trainable parameters via adapters, we can unlock contextualized speech recognition capability for the pretrained LLM while keeping the same text-only input functionality

Towards Selection of Text-to-speech Data to Augment ASR Training

This paper presents a method for selecting appropriate synthetic speech samples from a given large text-to-speech (TTS) dataset as supplementary training data for an automatic speech recognition (ASR) model. We trained a neural network, which can be optimised using cross-entropy loss or Arcface loss, to measure the similarity of a synthetic data to real speech. We found that incorporating synthetic samples with considerable dissimilarity to real speech, owing in part to lexical differences, into ASR training is crucial for boosting recognition performance. Experimental results on Librispeech test sets indicate that, in order to maintain the same speech recognition accuracy as when using all TTS data, our proposed solution can reduce the size of the TTS data down below its $30\,\%$, which is superior to several baseline methods

Anchored Speech Recognition with Neural Transducers

Neural transducers have achieved human level performance on standard speech recognition benchmarks. However, their performance significantly degrades in the presence of cross-talk, especially when the primary speaker has a low signal-to-noise ratio. Anchored speech recognition refers to a class of methods that use information from an anchor segment (e.g., wake-words) to recognize device-directed speech while ignoring interfering background speech. In this paper, we investigate anchored speech recognition to make neural transducers robust to background speech. We extract context information from the anchor segment with a tiny auxiliary network, and use encoder biasing and joiner gating to guide the transducer towards the target speech. Moreover, to improve the robustness of context embedding extraction, we propose auxiliary training objectives to disentangle lexical content from speaking style. We evaluate our methods on synthetic LibriSpeech-based mixtures comprising several SNR and overlap conditions; they improve relative word error rates by 19.6% over a strong baseline, when averaged over all conditions.Comment: To appear at IEEE ICASSP 202