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Your search keyword '"Zhou, Junting"' showing total 19 results
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19 results on '"Zhou, Junting"'

1. MuPT: A Generative Symbolic Music Pretrained Transformer

Author

Qu, Xingwei, Bai, Yuelin, Ma, Yinghao, Zhou, Ziya, Lo, Ka Man, Liu, Jiaheng, Yuan, Ruibin, Min, Lejun, Liu, Xueling, Zhang, Tianyu, Du, Xinrun, Guo, Shuyue, Liang, Yiming, Li, Yizhi, Wu, Shangda, Zhou, Junting, Zheng, Tianyu, Ma, Ziyang, Han, Fengze, Xue, Wei, Xia, Gus, Benetos, Emmanouil, Yue, Xiang, Lin, Chenghua, Tan, Xu, Huang, Stephen W., Chen, Wenhu, Fu, Jie, and Zhang, Ge

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our findings suggest that LLMs are inherently more compatible with ABC Notation, which aligns more closely with their design and strengths, thereby enhancing the model's performance in musical composition. To address the challenges associated with misaligned measures from different tracks during generation, we propose the development of a Synchronized Multi-Track ABC Notation (SMT-ABC Notation), which aims to preserve coherence across multiple musical tracks. Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set. Furthermore, we explore the implications of the Symbolic Music Scaling Law (SMS Law) on model performance. The results indicate a promising direction for future research in music generation, offering extensive resources for community-led research through our open-source contributions.

Published
2024

2. COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning

Author

Bai, Yuelin, Du, Xinrun, Liang, Yiming, Jin, Yonggang, Liu, Ziqiang, Zhou, Junting, Zheng, Tianyu, Zhang, Xincheng, Ma, Nuo, Wang, Zekun, Yuan, Ruibin, Wu, Haihong, Lin, Hongquan, Huang, Wenhao, Zhang, Jiajun, Chen, Wenhu, Lin, Chenghua, Fu, Jie, Yang, Min, Ni, Shiwen, and Zhang, Ge

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Recently, there have been significant advancements in large language models (LLMs), particularly focused on the English language. These advancements have enabled these LLMs to understand and execute complex instructions with unprecedented accuracy and fluency. However, despite these advancements, there remains a noticeable gap in the development of Chinese instruction tuning. The unique linguistic features and cultural depth of the Chinese language pose challenges for instruction tuning tasks. Existing datasets are either derived from English-centric LLMs or are ill-suited for aligning with the interaction patterns of real-world Chinese users. To bridge this gap, we introduce COIG-CQIA, a high-quality Chinese instruction tuning dataset. Our aim is to build a diverse, wide-ranging instruction-tuning dataset to better align model behavior with human interactions. To this end, we collect a high-quality human-written corpus from various sources on the Chinese Internet, including Q&A communities, Wikis, examinations, and existing NLP datasets. This corpus was rigorously filtered and carefully processed to form the COIG-CQIA dataset. Furthermore, we train models of various scales on different subsets of CQIA, following in-depth evaluation and analyses. The findings from our experiments offer valuable insights for selecting and developing Chinese instruction-tuning datasets. We also find that models trained on CQIA-Subset achieve competitive results in human assessment as well as knowledge and security benchmarks. Data are available at https://huggingface.co/datasets/m-a-p/COIG-CQIA

Published
2024

11. Optimizing Microfluidic Impedance Cytometry by Bypass Electrode Layout Design.

Author

Wu, Guangzu, Zhang, Zhiwei, Du, Manman, Wu, Dan, Zhou, Junting, Hao, Tianteng, and Xie, Xinwu

Subjects
ELECTRODES, CYTOMETRY, SIGNAL-to-noise ratio
Abstract

Microfluidic impedance cytometry (MIC) has emerged as a popular technique for single-cell analysis. Traditional MIC electrode designs consist of a pair of (or three) working electrodes, and their detection performance needs further improvements for microorganisms. In this study, we designed an 8-electrode MIC device in which the center pair was defined as the working electrode, and the connection status of bypass electrodes could be changed. This allowed us to compare the performance of layouts with no bypasses and those with floating or grounding electrodes by simulation and experiment. The results of detecting Φ 5 μm beads revealed that both the grounding and the floating electrode outperformed the no bypass electrode, and the grounding electrode demonstrated the best signal-to-noise ratio (SNR), coefficient of variation (CV), and detection sensitivity. Furthermore, the effects of different bypass grounding areas (numbers of grounding electrodes) were investigated. Finally, particles passing at high horizontal positions can be detected, and Φ 1 μm beads can be measured in a wide channel (150 μm) using a fully grounding electrode, with the sensitivity of bead volume detection reaching 0.00097%. This provides a general MIC electrode optimization technology for detecting smaller particles, even macromolecular proteins, viruses, and exosomes in the future. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Rational design and biological evaluation of a new class of thiazolopyridyl tetrahydroacridines as cholinesterase and GSK-3 dual inhibitors for Alzheimer’s disease

Author

Jiang, Xueyang, primary, Zhou, Junting, additional, Wang, Yang, additional, Chen, Lei, additional, Duan, Yan, additional, Huang, Jianping, additional, Liu, Chang, additional, Chen, Yao, additional, Liu, Wenyuan, additional, Sun, Haopeng, additional, Feng, Feng, additional, and Qu, Wei, additional

Published
2020
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19. Dynamic gene screening enabled identification of a 10-gene panel for early detection and progression assessment of gastric cancer.

Author

Long F, Li S, Xu Y, Liu M, Zhang X, Zhou J, Chen Y, Rong Y, Meng X, and Wang F

Abstract

Early diagnosis and progression assessment are critical for the timely detection and treatment of gastric cancer (GC) patients. Identification of diagnostic biomarkers for early detection of GC represents an unmet clinical need, and how these markers further influence GC progression is explored rarely. We performed dynamic gene screening based on high-throughput data analysis from patients with precancerous lesions and early gastric cancer (EGC) and identified a 10-gene panel by the lasso regression model. This panel demonstrated good diagnostic performance in TCGA (AUC = 0.95, sensitivity = 86.67 %, specificity = 90.63 %) and GEO (AUC = 0.84, sensitivity = 91.67 %, specificity = 78.13 %) cohorts. Moreover, three GC subtypes were clustered based on this panel, in which cluster 2 (C2) demonstrated the highest tumor progression level with a high expression of 10 genes, showing a decreased tumor mutation burden, significantly enriched epithelial-mesenchymal transition hallmark and increased immune exclusion/exhausted features. Finally, the cell localization of these panel genes was explored in scRNA-seq data based on more than 40,000 cells. The 10-gene panel is expected to be a new clinical early detection signature for GC and may aid in progression assessment and personalized treatment of patients., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Published
2022
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