52 research outputs found
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 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 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
(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
Electronic Origin of High-Tc Maximization and Persistence in Trilayer Cuprate Superconductors
In high temperature cuprate superconductors, it was found that the
superconducting transition temperature Tc depends on the number of CuO2 planes
(n) in the structural unit and the maximum Tc is realized in the trilayer
system (n=3). It was also found that the trilayer superconductors exhibit an
unusual phase diagram that Tc keeps nearly constant in the overdoped region
which is in strong contrast to the Tc decrease usually found in other cuprate
superconductors. The electronic origin of the Tc maximization in the trilayer
superconductors and its high Tc persistence in the overdoped region remains
unclear. By taking high resolution laser-based angle resolved photoemission
(ARPES) measurements, here we report our revelation of the microscopic origin
of the unusual superconducting properties in the trilayer superconductors. For
the first time we have observed the trilayer splitting in Bi2Sr2Ca2Cu3O10+d
(Bi2223) superconductor. The observed Fermi surface, band structures,
superconducting gap and the selective Bogoliubov band hybridizations can be
well described by a three-layer interaction model. Quantitative information of
the microscopic processes involving intra- and interlayer hoppings and pairings
are extracted. The electronic origin of the maximum Tc in Bi2223 and the
persistence of the high Tc in the overdoped region is revealed. These results
provide key insights in understanding high Tc superconductivity and pave a way
to further enhance Tc in the cuprate superconductors
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
Toxic heavy metal ions contamination in the aqueous environment, its toxicity and methods of microbial remediation
Heavy metal compounds are used in a variety of industrial processes, including tanning, chrome plating, anti-corrosion treatments, and wood preservation. Heavy metal ion pollution in water and wastewater is often caused by industrial effluent discharge into open water sources. Toxic heavy metal ions such as As (III), Cr (VI), Cd (II), and Pb (II) are well-known and enter the body through a variety of pathways, including the food chain, respiration, skin absorption, and drinking water. These heavy metal ions produce oxidative stress in cells, resulting in cell organelle destruction. Heavy metals produce toxicity and may cause genetic material mutation or change, histone modification, and epigenetic alteration at various stages. Furthermore, heavy metals are linked to heart failure, renal damage, liver failure, and a variety of skin problems. For heavy metals cleanup, several standard approaches are utilized. Nonetheless, these technologies are costly and result in toxic sludge after treatment. As a result, there is an urgent need for an appropriate, environmentally safe, and efficient heavy metal removal technology. For heavy metal removal, microbial-based approaches are regarded as both environmentally benign and cost-effective. This review focuses on heavy metal pollution in water, its harmful consequences, and heavy metal cleanup by microbiological means
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
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
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 45Ā° 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
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
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