Large Language Models at Work in China's Labor Market

This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following Eloundou et al. (2023)'s methodology. We then aggregate occupation exposure to the industry level to obtain industry exposure scores. The results indicate a positive correlation between occupation exposure and wage levels/experience premiums, suggesting higher-paying and experience-intensive jobs may face greater displacement risks from LLM-powered software. The industry exposure scores align with expert assessments and economic intuitions. We also develop an economic growth model incorporating industry exposure to quantify the productivity-employment trade-off from AI adoption. Overall, this study provides an analytical basis for understanding the labor market impacts of increasingly capable AI systems in China. Key innovations include the occupation-level exposure analysis, industry aggregation approach, and economic modeling incorporating AI adoption and labor market effects. The findings will inform policymakers and businesses on strategies for maximizing the benefits of AI while mitigating adverse disruption risks

Design of new drugs for medullary thyroid carcinoma

Medullary thyroid carcinoma (MTC) is one of the common malignant endocrine tumors, which seriously affects human health. Although surgical resection offers a potentially curative therapeutic option to some MTC patients, most patients do not benefit from it due to the difficulty to access the tumors and tumor metastasis. The survival rate of MTC patients has improved with the recent advances in the research, which has improved our understanding of the molecular mechanism underlying MTC and enabled the development and approval of novel targeted drugs. In this article, we reviewed the molecular mechanisms related to MTC progression and the principle for the design of molecular targeted drugs, and proposed some future directions for prospective studies exploring targeted drugs for MTC

Extremely large magnetoresistance in topologically trivial semimetal α\alpha-WP2_2

Extremely large magnetoresistance (XMR) was recently discovered in many non-magnetic materials, while its underlying mechanism remains poorly understood due to the complex electronic structure of these materials. Here, we report an investigation of the $\alpha$-phase WP$_2$, a topologically trivial semimetal with monoclinic crystal structure (C2/m), which contrasts to the recently discovered robust type-II Weyl semimetal phase in $\beta$-WP$_2$. We found that $\alpha$-WP$_2$ exhibits almost all the characteristics of XMR materials: the near-quadratic field dependence of MR, a field-induced up-turn in resistivity following by a plateau at low temperature, which can be understood by the compensation effect, and high mobility of carriers confirmed by our Hall effect measurements. It was also found that the normalized MRs under different magnetic fields has the same temperature dependence in $\alpha$-WP$_2$, the Kohler scaling law can describe the MR data in a wide temperature range, and there is no obvious change in the anisotropic parameter $\gamma$ value with temperature. The resistance polar diagram has a peanut shape when field is rotated in $\textit{ac}$ plane, which can be understood by the anisotropy of Fermi surface. These results indicate that both field-induced-gap and temperature-induced Lifshitz transition are not the origin of up-turn in resistivity in the $\alpha$-WP$_2$ semimetal. Our findings establish $\alpha$-WP$_2$ as a new reference material for exploring the XMR phenomena.Comment: 18 pages, 12 figure

NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers

Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is important to capture the diversity in human speech such as speaker identities, prosodies, and styles (e.g., singing). Current large TTS systems usually quantize speech into discrete tokens and use language models to generate these tokens one by one, which suffer from unstable prosody, word skipping/repeating issue, and poor voice quality. In this paper, we develop NaturalSpeech 2, a TTS system that leverages a neural audio codec with residual vector quantizers to get the quantized latent vectors and uses a diffusion model to generate these latent vectors conditioned on text input. To enhance the zero-shot capability that is important to achieve diverse speech synthesis, we design a speech prompting mechanism to facilitate in-context learning in the diffusion model and the duration/pitch predictor. We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, robustness, and voice quality in a zero-shot setting, and performs novel zero-shot singing synthesis with only a speech prompt. Audio samples are available at https://speechresearch.github.io/naturalspeech2.Comment: A large-scale text-to-speech and singing voice synthesis system with latent diffusion model

PromptTTS 2: Describing and Generating Voices with Text Prompt

Speech conveys more information than just text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompt for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompt based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available online\footnote{https://speechresearch.github.io/prompttts2}.Comment: Demo page: https://speechresearch.github.io/prompttts

Population genetic structure of Hymenopellis radicata germplasm resources based on genome re-sequencing

Through whole-genome re-sequencing of 18 Hymenopellis radicata germplasm resources collected from diverse regions in China, we identified significant variations in the form of Single Nucleotide Polymorphisms (SNPs) and Insertions and Deletions (InDels). These variations were comprehensively annotated, shedding light on the mutation types present in the entire genome of the H. radicata germplasm. This analysis revealed the number and position information of each mutation and provided insights into the overall genomic landscape of H. radicata germplasm. Utilizing SNP data, we delved into the population structure of the 18 H. radicata germplasm resources. The results indicated the presence of 2,335,179 Indel sites and 12,050,448 SNP sites. The population structure analysis unveiled two distinct subgroups among the H. radicata germplasm resources. Phenotypic statistics, principal component analysis, and phylogenetic tree results echoed the findings of the population structure analysis. Different strains of H. radicata from various regions in China exhibited notable differences in genetic diversity, mycelial growth rate, yield, and fruiting body characteristics. Significant disparities were observed between the two subgroups, while strains within each subgroup shared common characteristics. This research establishes a solid foundation for integrating H. radicata into diverse breeding programs. The data underscore the potential of H. radicata for genetic improvement and exploitation in breeding initiatives, paving the way for future advancements in this field

Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech

Recently, leveraging BERT pre-training to improve the phoneme encoder in text to speech (TTS) has drawn increasing attention. However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input. Pre-training only with phonemes as input can alleviate the input mismatch but lack the ability to model rich representations and semantic information due to limited phoneme vocabulary. In this paper, we propose MixedPhoneme BERT, a novel variant of the BERT model that uses mixed phoneme and sup-phoneme representations to enhance the learning capability. Specifically, we merge the adjacent phonemes into sup-phonemes and combine the phoneme sequence and the merged sup-phoneme sequence as the model input, which can enhance the model capacity to learn rich contextual representations. Experiment results demonstrate that our proposed Mixed-Phoneme BERT significantly improves the TTS performance with 0.30 CMOS gain compared with the FastSpeech 2 baseline. The Mixed-Phoneme BERT achieves 3x inference speedup and similar voice quality to the previous TTS pre-trained model PnG BERTComment: submitted to interspeech 202

Achieving blood pressure control targets in hypertensive patients of rural China - A pilot randomized trial

Background: This study aimed to test the feasibility and titration methods used to achieve specific blood pressure (BP) control targets in hypertensive patients of rural China. Methods: A randomized, controlled, open-label trial was conducted in Rongcheng, China. We enrolled 105 hypertensive participants aged over 60 years, and who had no history of stroke or cardiovascular disease. The patients were randomly assigned to one of three systolic-BP target groups: standard: 140 to \u3c 150 mmHg; moderately intensive: 130 to \u3c 140 mmHg; and intensive: \u3c 130 mmHg. The patients were followed for 6 months. Discussion: The optimal target for systolic blood pressure (SBP) lowering is still uncertain worldwide and such information is critically needed, especially in China. However, in China the rates of awareness, treatment and control are only 46.9%, 40.7%, and 15.3%, respectively. It is challenging to achieve BP control in the real world and it is very important to develop population-specific BP-control protocols that fully consider the population\u27s characteristics, such as age, sex, socio-economic status, compliance with medication, education level, and lifestyle. This randomized trial showed the feasibility and safety of the titration protocol to achieve desirable SBP targets (\u3c 150, \u3c 140, and \u3c 130 mmHg) in a sample of rural, Chinese hypertensive patients. The three BP target groups had similar baseline characteristics. After 6 months of treatment, the mean SBP measured at an office visit was 137.2 mmHg, 131.1 mmHg, and 124.2 mmHg, respectively, in the three groups. Home BP and central aortic BP measurements were also obtained. At 6 months, home BP measurements (2 h after drug administration) showed a mean SBP of 130.9 mmHg in the standard group, 124.9 mmHg in the moderately intensive group, and 119.7 mmHg in the intensive group. No serious adverse events were recorded over the 6-month study period. Rates of adverse events, including dry cough, palpitations, and arthralgia, were low and showed no significant differences between the three groups. This trial provided real-world experience and laid the foundation for a future, large-scale, BP target study. Trial registration: Feasibility Study of the Intensive Systolic Blood Pressure Control; ClinicalTrials.gov, ID: NCT02817503. Registered retrospectively on 29 June 2016