On the Effectiveness of Speech Self-supervised Learning for Music

Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trained on music recordings may have been mostly closed-sourced, recent speech models such as wav2vec2.0 have shown promise in music modelling. Nevertheless, research exploring the effectiveness of applying speech SSL models to music recordings has been limited. We explore the music adaption of SSL with two distinctive speech-related models, data2vec1.0 and Hubert, and refer to them as music2vec and musicHuBERT, respectively. We train $12$ SSL models with 95M parameters under various pre-training configurations and systematically evaluate the MIR task performances with 13 different MIR tasks. Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech. However, we identify the limitations of such existing speech-oriented designs, especially in modelling polyphonic information. Based on the experimental results, empirical suggestions are also given for designing future musical SSL strategies and paradigms

MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due to the distinctive challenges associated with modelling musical knowledge, particularly its tonal and pitched characteristics of music. To address this research gap, we propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training. In our exploration, we identified a superior combination of teacher models, which outperforms conventional speech and audio approaches in terms of performance. This combination includes an acoustic teacher based on Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical teacher based on the Constant-Q Transform (CQT). These teachers effectively guide our student model, a BERT-style transformer encoder, to better model music audio. In addition, we introduce an in-batch noise mixture augmentation to enhance the representation robustness. Furthermore, we explore a wide range of settings to overcome the instability in acoustic language model pre-training, which allows our designed paradigm to scale from 95M to 330M parameters. Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attains state-of-the-art (SOTA) overall scores. The code and models are online: https://github.com/yizhilll/MERT

Intrinsic Electronic Structure and Nodeless Superconducting Gap of YBa2Cu3O7−δ\mathrm{YBa_{2} Cu_{3} O_{7-\delta} } Observed by Spatially-Resolved Laser-Based Angle Resolved Photoemission Spectroscopy

The spatially-resolved laser-based high resolution ARPES measurements have been performed on the optimally-doped $\mathrm{YBa_{2} Cu_{3} O_{7-\delta} }$ (Y123) superconductor. For the first time, we found the region from the cleaved surface that reveals clear bulk electronic properties. The intrinsic Fermi surface and band structures of Y123 are observed. The Fermi surface-dependent and momentum-dependent superconducting gap is determined which is nodeless and consistent with the d+is gap form

Distinct distribution and prognostic significance of molecular subtypes of breast cancer in Chinese women: a population-based cohort study

<p>Abstract</p> <p>Background</p> <p>Molecular classification of breast cancer is an important prognostic factor. The distribution of molecular subtypes of breast cancer and their prognostic value has not been well documented in Asians.</p> <p>Methods</p> <p>A total of 2,791 breast cancer patients recruited for a population-based cohort study were evaluated for molecular subtypes of breast cancer by immunohistochemical assays. Data on clinicopathological characteristics were confirmed by centralized pathology review. The average follow-up of the patients was 53.4 months. Overall and disease-free survival by molecular subtypes of breast cancer were evaluated.</p> <p>Results</p> <p>The prevalence of the luminal A, luminal B, human epidermal growth factor receptor 2 (HER2), and triple-negative subtypes were 48.6%, 16.7%, 13.7%, and 12.9%, respectively. The luminal A subtype was more likely to be diagnosed in older women (P = 0.03) and had a stronger correlation with favorable clinicopathological factors (smaller tumor size, lower histologic grade, and earlier TNM stage) than the triple-negative or HER2 subtypes. Women with triple-negative breast cancer had a higher frequency of family history of breast cancer than women with other subtypes (P = 0.048). The 5-year overall/disease-free survival percentages for the luminal A, luminal B, HER2, and triple-negative subtypes were 92.9%/88.6%, 88.6%/85.1%, 83.2%/79.1%, and 80.7%/76.0%, respectively. A similar pattern was observed in multivariate analyses. Immunotherapy was associated with improved overall and disease-free survival for luminal A breast cancer, but reduced disease-free survival (HR = 2.21, 95% CI, 1.09-4.48) for the HER2 subtype of breast cancer.</p> <p>Conclusions</p> <p>The triple-negative and HER2 subtypes were associated with poorer outcomes compared with the luminal A subtype among these Chinese women. The HER2 subtype was more prevalent in this Chinese population compared with Western populations, suggesting the importance of standardized HER2 detection and anti-HER2 therapy to potentially benefit a high proportion of breast cancer patients in China.</p

A Second-Order Accurate Numerical Approximation for a Two-Sided Space-Fractional Diffusion Equation

In this paper, we investigate a practical numerical method for solving a one-dimensional two-sided space-fractional diffusion equation with variable coefficients in a finite domain, which is based on the classical Crank-Nicolson (CN) method combined with Richardson extrapolation. Second-order exact numerical estimates in time and space are obtained. The unconditional stability and convergence of the method are tested. Two numerical examples are also presented and compared with the exact solution

Research on infrared radiation characteristics of pre-cracked coal sample under loaded breaking

Uniaxial compression test was conducted on pre-cracked coal sample, and infrared radiation temperature and its change rule of the pre-cracked coal sample under loaded breaking were researched. The experimental results show that infrared radiation temperature curve will mutate in process of loaded breaking of the pre-cracked coal sample, and the infrared radiation temperatures of the pre-cracked coal samples with different angles will suddenly increase during main rupture. The mutation rate of infrared radiation temperature increases first and then decreases with increase of pre-cracked angle during main rupture, and the mutation rate of infrared radiation temperature of the pre-cracked coal sample with 45° is the largest. The change rule of infrared radiation of the pre-cracked coal sample is closely related to load and pre-crack, which can reflect internal rupture of macroscopic defective coal sample under load

ChatUniTest: a ChatGPT-based automated unit test generation tool

Unit testing is a crucial, yet often tedious and time-consuming task. To relieve developers from this burden, automated unit test generation techniques are developed. Existing automated unit test generation tools, such as program-analysis-based tools like EvoSuite and Randoop, lack program comprehension, resulting in unit tests with poor readability and limited assertions. Language-model-based tools, such as AthenaTest and A3Test, have limitations in the generation of correct unit tests. In this paper, we introduce ChatUniTest, a ChatGPT-based automated unit test generation tool developed under the Generation-Validation-Repair framework. ChatUniTest generates tests by parsing the project, extracting essential information, and creating an adaptive focal context that includes the focal method and its dependencies within the pre-defined maximum prompt token limit. The context is incorporated into a prompt and subsequently submitted to ChatGPT. Once ChatGPT's response is received, ChatUniTest proceeds to extract the raw test from the response. It then validates the test and employs rule-based repair to fix syntactic and simple compile errors, followed by ChatGPT-based repair to address challenging errors. Our rigorous evaluation demonstrates that ChatUniTest outperforms EvoSuite in branch and line coverage, surpasses AthenaTest and A3Test in focal method coverage, and effectively generates assertions while utilizing mock objects and reflection to achieve test objectives

Accuracy Improvement of a Laser Diode-Based System for Measuring the Geometric Errors of Machine Tools

Active methods are proposed to improve the measurement accuracy of a compact laser diode-based (LD-based) system, which is designed to measure the geometric errors of machine tools. The LD has some advantages, such as a small size, low cost and high efficiency. However, the laser spot of the LD is elliptical and the stability in the output power of the LD is low, which limits the accuracy of the measurement system, where the LD is used as the laser source. An active shaping method is proposed to shape the elliptical laser spot of the LD without adding additional optical elements. In addition, the laser beam drifts, including the linear drift and angular drift, are compensated in real-time by a proposed improved active error compensator, which consists of two drift feedback units and a Backpropagation Neural Networks-based PID controller, during the long-distance measurement. A series of experiments were conducted to verify the effectiveness of the proposed methods and the capability of the constructed LD-based system

Effect of fermented soybean meal supplementation on some growth performance, blood chemical parameters, and fecal microflora of finishing pigs

This study evaluated the effect of fermented soybean meal (FSBM) supplementation on growth performance, meat quality, blood biochemical parameters, and fecal microflora of finishing pigs. Thirty-two crossbred pigs (Duroc × Landrance × Yorkshire) (66-day-old, 67.95±0.25 kg) were randomly allocated to two treatments and fed diets containing soybean meal and FSBM. The average daily gain (ADG), average daily feed intake (ADFI), feed conversion ratio (FCR), blood biochemical parameters, and meat quality index were measured. At the end of experiment, the fecal microflora of finishing pigs was analyzed with 16S rDNA techniques. Results revealed that pigs fed FSBM had a greater ADG and lower cooking loss relative to control group (basal diets). Compared with the control group, the triglyceride content in the serum of the group fed FSBM increased significantly, and the creatinine content in the serum decreased notably. Fermented soybean meal enhanced the abundance of Bacteroidetes, Prevotellaceae, Bcteroidales, Bacteroidia, but inhibited the growth of Firmicutes, Clostridia, Clostridiales, and Ruminococcaceae in the intestine of pigs. Therefore, we can speculate that FSBM may play an important role in animal production. Dietary FSBM supplementation may be beneficial to some aspects of growth performance and the diversity of fecal microflora in finishing pig

Geometric and Volumetric Measurements of Orbital Structures in CT Scans in Thyroid Eye Disease Classification

This study examines the usefulness of both geometric and volumetric measurements of orbital soft tissues on CT scans to provide quantitative diagnostic guidance in image reading of thyroid eye disease (TED). Computed tomography (CT) images were obtained from 92 orbits and were classified as impaired motility (TED-IM) and normal motility (TED-NM). The TED-IM group was further divided into dysthyroid optic neuropathy (DON) and non-DON groups. There were 5 volumetric, 2 angular, and 3 ratio parameter measurements acquired from CT images to examine their feasibility in TED classification. We found that the mean volumes of extraocular muscle and retroorbital fat and their ratio to the orbital volume were significantly different between the two motility groups. The mean ratio of extraocular muscle volume in orbital apex and orbital apex volume (EMV-OA/OAV) was significantly larger in DON than non-DON patients (p &lt; 0.05). The population distribution among TED-NM, non-DON, and DON groups significantly varied for different angles between the optic nerve and medial rectus and lateral rectus. In conclusion, geometric and volumetric measurements using CT scans help to quantitatively classify TED