当代中国政治话语中和 谐文化因子解读 = Interpretation on the Cultural Factors of "Harmony" in Contemporary Chinese Political Discourses

当代中国政治话语建立在中国文化价值基 础之上，通过“以人为本”、“和而不同”、“德 法和谐”、“中国梦”等和谐话语体现出来。 本研究力图从语言和文化的视角对上述和谐文 化价值因子进行分析解读，探究其形成的历史 文化渊源，挖掘其文化内涵，寻找其普世价值 意义，从而进一步促进世界文化多样性的和谐 构建。 The contemporary Chinese political discourse, with Chinese cultural values as its foundation, demonstrates such discursive ideas as ‘people foremost’, ‘harmony within diversity’, ‘co-existence between morals and law’, ‘China’s Dream’, and so on and so forth. This paper makes an interpretation of these harmonious cultural factors by exploring the historical origins as well as cultural connotation, trying to seek its universal values for further promotion of the harmonious construction of global cultural diversity

NoreSpeech: Knowledge Distillation based Conditional Diffusion Model for Noise-robust Expressive TTS

Expressive text-to-speech (TTS) can synthesize a new speaking style by imiating prosody and timbre from a reference audio, which faces the following challenges: (1) The highly dynamic prosody information in the reference audio is difficult to extract, especially, when the reference audio contains background noise. (2) The TTS systems should have good generalization for unseen speaking styles. In this paper, we present a \textbf{no}ise-\textbf{r}obust \textbf{e}xpressive TTS model (NoreSpeech), which can robustly transfer speaking style in a noisy reference utterance to synthesized speech. Specifically, our NoreSpeech includes several components: (1) a novel DiffStyle module, which leverages powerful probabilistic denoising diffusion models to learn noise-agnostic speaking style features from a teacher model by knowledge distillation; (2) a VQ-VAE block, which maps the style features into a controllable quantized latent space for improving the generalization of style transfer; and (3) a straight-forward but effective parameter-free text-style alignment module, which enables NoreSpeech to transfer style to a textual input from a length-mismatched reference utterance. Experiments demonstrate that NoreSpeech is more effective than previous expressive TTS models in noise environments. Audio samples and code are available at: \href{http://dongchaoyang.top/NoreSpeech\_demo/}{http://dongchaoyang.top/NoreSpeech\_demo/}Comment: Submitted to ICASSP202

A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations

Many scientific and engineering problems require to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, choosing an appropriate prior distribution is an important task. In particular we consider problems where the function to infer is subject to sharp jumps which render the commonly used Gaussian measures unsuitable. On the other hand, the so-called total variation (TV) prior can only be defined in a finite dimensional setting, and does not lead to a well-defined posterior measure in function spaces. In this work we present a TV-Gaussian (TG) prior to address such problems, where the TV term is used to detect sharp jumps of the function, and the Gaussian distribution is used as a reference measure so that it results in a well-defined posterior measure in the function space. We also present an efficient Markov Chain Monte Carlo (MCMC) algorithm to draw samples from the posterior distribution of the TG prior. With numerical examples we demonstrate the performance of the TG prior and the efficiency of the proposed MCMC algorithm

Fast Gibbs sampling for high-dimensional Bayesian inversion

Solving ill-posed inverse problems by Bayesian inference has recently attracted considerable attention. Compared to deterministic approaches, the probabilistic representation of the solution by the posterior distribution can be exploited to explore and quantify its uncertainties. In applications where the inverse solution is subject to further analysis procedures, this can be a significant advantage. Alongside theoretical progress, various new computational techniques allow to sample very high dimensional posterior distributions: In [Lucka2012], a Markov chain Monte Carlo (MCMC) posterior sampler was developed for linear inverse problems with $\ell_1$-type priors. In this article, we extend this single component Gibbs-type sampler to a wide range of priors used in Bayesian inversion, such as general $\ell_p^q$ priors with additional hard constraints. Besides a fast computation of the conditional, single component densities in an explicit, parameterized form, a fast, robust and exact sampling from these one-dimensional densities is key to obtain an efficient algorithm. We demonstrate that a generalization of slice sampling can utilize their specific structure for this task and illustrate the performance of the resulting slice-within-Gibbs samplers by different computed examples. These new samplers allow us to perform sample-based Bayesian inference in high-dimensional scenarios with certain priors for the first time, including the inversion of computed tomography (CT) data with the popular isotropic total variation (TV) prior.Comment: submitted to "Inverse Problems

Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset

Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam, sourced from the Chinese National Medical Licensing Examination. CMExam consists of 60K+ multiple-choice questions for standardized and objective evaluations, as well as solution explanations for model reasoning evaluation in an open-ended manner. For in-depth analyses of LLMs, we invited medical professionals to label five additional question-wise annotations, including disease groups, clinical departments, medical disciplines, areas of competency, and question difficulty levels. Alongside the dataset, we further conducted thorough experiments with representative LLMs and QA algorithms on CMExam. The results show that GPT-4 had the best accuracy of 61.6% and a weighted F1 score of 0.617. These results highlight a great disparity when compared to human accuracy, which stood at 71.6%. For explanation tasks, while LLMs could generate relevant reasoning and demonstrate improved performance after finetuning, they fall short of a desired standard, indicating ample room for improvement. To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations. The experiments and findings of LLM evaluation also provide valuable insights into the challenges and potential solutions in developing Chinese medical QA systems and LLM evaluation pipelines. The dataset and relevant code are available at https://github.com/williamliujl/CMExam

Topological insulator Bi2Te3 films synthesized by metal organic chemical vapor deposition

Topological insulator (TI) materials such as Bi2Te3 and Bi2Se3 have attracted strong recent interests. Large scale, high quality TI thin films are important for developing TI-based device applications. In this work, structural and electronic properties of Bi2Te3 thin films deposited by metal organic chemical vapor deposition (MOCVD) on GaAs (001) substrates were characterized via X-ray diffraction (XRD), Raman spectroscopy, angle-resolved photoemission spectroscopy (ARPES), and electronic transport measurements. The characteristic topological surface states (SS) with a single Dirac cone have been clearly revealed in the electronic band structure measured by ARPES, confirming the TI nature of the MOCVD Bi2Te3 films. Resistivity and Hall effect measurements have demonstrated relatively high bulk carrier mobility of ~350 cm^2/Vs at 300K and ~7,400 cm^2/Vs at 15 K. We have also measured the Seebeck coefficient of the films. Our demonstration of high quality topological insulator films grown by a simple and scalable method is of interests for both fundamental research and practical applications of thermoelectric and TI materials.Comment: 14 pages, 4 figure

Estimating perfluorocarbon emission factors for industrial rare earth metal electrolysis

Rare earth (RE) metals have been widely applied in new materials, leading to their drastic production increase in the last three decades. In the production process featured by the molten-fluoride electrolysis technology, perfluorocarbon (PFC) emissions are significant and therefore deserve full accounting in greenhouse gas (GHG) emission inventories. Yet, in the ‘2006 IPCC Guidelines for National Greenhouse Gas Inventories’, no method currently exists to account for PFC emissions from rare earth metal production. This research aims to determine emission factors for industrial rare earth metals production through on-site monitoring and lab analysis of PFC concentrations in the exhaust gases from rare earth metal electrolysis. Continuous FTIR measurements and time-integrated samples (analysed off-site by high-precision Medusa GC–MS) were conducted over 24–60 h periods from three rare earth companies in China, covering production of multiple rare earth metals/alloys including Pr-Nd, La and Dy-Fe. The study confirmed that PFC emissions are generated during electrolysis, typically in the form of CF4 (∼90% wt of detected PFCs), C2F6 (∼10%) and C3F8 (<1%); trace levels of c-C4F8 and C4F10 were also detected. In general, PFC emission factors vary with rare earth metal produced and from one facility to another, ranging from 26.66 to 109.43 g/t-RE for CF4 emissions, 0.26 to 10.95 g/t-RE for C2F6, and 0.03 to 0.27 g/t-RE for C3F8. Converted to 211.60 to 847.41 kg CO2-e/t-RE for total PFCs, this emissions intensity for rare earths electrolysis is of lower (for most RE production) or similar (Dy-Fe production) level of magnitude to industrial aluminium electrolysis