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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： He, Chunhua Xu, Yingyu Wu, Heng Huang, Qinwen Zhao, Qiancheng Yan, Guizhen Guangdong University of Technology School of Computer China Ceprei Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory China Guangdong University of Technology School of Automation China Peking University School of Integrated Circuits Beijing China National Key Lab of Micro/Nano Fabrication Technology Beijing China

ISBN: (纸本)9798331508890

This paper presents a novel single-loop narrow-band force rebalance control method with temperature self-compensation for a MEMS gyroscope. The narrow-bandwidth force rebalance control for the sense mode is achieved using a simple bandpass filter (BPF) and without modulation or demodulation. The center frequency of the BPF is adjusted automatically to match the resonant frequency of the sense mode. Meanwhile, its gain is tuned based on the real-time quality factor of the sense mode. Experimental results demonstrate that the bias drift of the gyroscope with self-compensation is reduced from 1320 deg/h to 16 deg/h, while the scale factor drift is reduced from 0.616 mV/deg/s to 0.075 mV/deg/s. © 2025 IEEE.

关键词： Gyroscopes

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

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IEEE Transactions on computers 2025年第7期74卷 2168-2182页

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

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Characteristics, Classification and Challenges in Searching for CEMP Stars

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Research in Astronomy and Astrophysics 2025年第5期25卷 184-193页

作者： Lefeng He Xiangru Li Xiaoming Kong A-Li Luo Haifeng Yang School of Computer Science South China Normal University School of Mechanical Electrical&Information EngineeringShandong University Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Key Lab of Optical Astronomy National Astronomical ObservatoriesChinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Technology Taiyuan University of Science and Technology

The study of carbon-enhanced metal-poor (CEMP) stars is of great significance for understanding the chemical evolution of the early universe and stellar *** stars are characterized by carbon overabundance and are classified into several subclasses based on the abundance patterns of neutron-capture elements,including CEMP-s,CEMP-no,CEMP-r,and CEMP-r/*** subclasses provide important insights into the formation of thefirst stars,early stellar nucleosynthesis,and supernova ***,one of the major challenges in CEMP star research is the relatively small sample size of identified stars,which limits statistical analyses and hinders a comprehensive understanding of their ***,a series of large-scale spectroscopic survey projects have been launched and developed in recent years,providing unprecedented opportunities and technical challenges for the search and study of CEMP *** this end,this paper draws on the progress and future prospects of existing methods in constructing large CEMP data sets and offers an in-depth discussion from a technical standpoint,focusing on the strengths and *** addition,we review recent advancements in the identification of CEMP stars,emphasizing the growing role of machine learning in processing and analyzing the increasingly large data sets generated by modern astronomical *** to traditional spectral analysis methods,machine learning offers greater efficiency in handling complex data,automatic extraction of stellar parameters,and improved prediction *** these advancements,the research faces persistent challenges,including the scarcity of labeled samples,limitations imposed by low-resolution spectra,and the lack of interpretability in machine learning *** address these issues,the paper proposes potential solutions and future research directions aimed at advancing the study of CEMP stars and enhancing our understanding of their role in the chemical evolutio

关键词： methods: data analysis methods: observational stars: abundances stars: carbon surveys

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A range and radial velocity measurement method based on up-chirp and down-chirp LFM signal in pulsed radar

A range and radial velocity measurement method based on up-c...

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IET International Radar Conference 2023, IRC 2023

作者： Chen, Hao Lei, Yi Key Lab of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781839539954

Range accuracy and radial velocity accuracy are important indexes in pulsed radar. However, in pulsed radar using linear frequency modulation (LFM) pulse, the range-Doppler coupling of LFM signal will lead to large range error in high-speed target measurement. The target's radial velocity is computed using range differentiation in traditional method, which is also not accurate. This paper presents a measurement method based on up-chirp and down-chirp LFM signal to get the target's accurate range and radial velocity simultaneously. Experiments confirm that the proposed method outperforms traditional method in both range accuracy and radial velocity accuracy. © The Institution of Engineering & technology 2023.

关键词： Chirp modulation

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Wei Ping, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350386226

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM © 2024 IEEE.

关键词： Contrastive Learning

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Improved Training of GMM-Based TSK Fuzzy System from Stability Perspective 18

Improved Training of GMM-Based TSK Fuzzy System from Stabili...

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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Zhou, Erhao Wang, Guanjin Wang, Shitong School of Information Technology Murdoch University Australia School of AI and Computer Science Jiangnan University Wuxi China TaiHu Univ. Key Construction Lab. of IoT Application Technologies Wuxi China

ISBN: (纸本)9798350318401

With the help of the conditional mean, the mathematically equivalent bridge between a Gaussian mixture model (GMM) and a Takagi-Sugeno-Kang (TSK) fuzzy system has been built to form a GMM-based fuzzy system so as to share mutual support for their respective training from rich achievements about GMM and fuzzy system. However, only the conditional mean without the consideration of stability (i.e., conditional variance) may perhaps hinder the modeling performance of the GMM-based fuzzy system, especially for noisy data scenarios. In this study, with strict mathematical derivations, it is revealed that the stability of a GMM-based fuzzy system depends on the three factors, i.e., stability of each fuzzy rule, stability of each input/output subspace associated with each fuzzy rule, and weighted stability of all fuzzy rules and their input/output subspaces. Accordingly, based on the derived result about the above stabilities, the training method of the proposed EGMM-based (Enhanced GMM-based) TSK fuzzy system, which considers both accuracy and stability is developed to improve the modeling performance of the original GMM-based TSK fuzzy system by optimizing the GMM's parameters therein. The effectiveness of the proposed training method is manifested by the experimental results on 10 real regression datasets. © 2023 IEEE.

关键词： Fuzzy rules

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Few Shot Contrastive Spectral Clustering with Meta Learning and Neighbor Mining

Few Shot Contrastive Spectral Clustering with Meta Learning ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Nongxiao Wang Xulun Ye Jieyu Zhao Qing Wang Department of Computer Science and Technology Ningbo University China Key Lab of Mobile Network Application Technology of Zhejiang Province Ningbo China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Spectral clustering is widely used in the field of unsupervised learning and has been successfully applied in various data analysis tasks. However, in practical scenarios, it’s common to get few labeled data at low cost, resulting limited labeled data in few categories, while the rest are completely unlabeled. Relying solely on the unsupervised clustering framework can’t leverage the existing supervised information. Using a supervised meta-learning paradigm alone poses challenges when dealing with the majority categories of completely unlabeled data. Therefore, we propose a few-shot contrastive spectral clustering combined with meta learning and neighbor mining framework (FCSMN). First we leverage few shot labeled information to obtain a meta-learning feature embedding with strong generalization capabilities, and acquire semantic feature spaces using an unsupervised contrastive learning model. Then, we mine neighbors in these two feature spaces separately and align them. Next, we jointly reinforce intra-cluster compactness and inter-cluster separability at both instance and cluster level. To satisfies the conditions of spectral clustering, we impose orthogonality constraints at the last layer. Experiments on three datasets demonstrate the effectiveness our proposed method.

关键词： Metalearning Costs Semantics Neural networks Contrastive learning Data augmentation Data mining

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MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS 11

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTUR...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Zaixi Min, Yaosen Zheng, Shuxin Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Institute of Interdisciplinary Information Sciences Tsinghua University China Microsoft Research United States

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragment-based LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures. Our code is publicly available at https://***/zaixizhang/FLAG. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Molecules

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Fine-grained distributed averaging for large-scale radio interferometric measurement sets

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Research in Astronomy and Astrophysics 2021年第4期21卷 93-100页

作者： Shou-Lin Wei Kai-Da Luo Feng Wang Hui Deng Ying Mei Faculty of Information Engineering and Automation Computer Technology Application Key Lab of Yunnan ProvinceKunming University of Science and TechnologyKunming 650500China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China

The Square Kilometre Array(SKA)would be the world’s largest radio telescope with eventually over a square kilometre of collecting ***,there are enormous challenges in its data *** use of modern distributed computing techniques to solve the problem of massive data processing in the SKA is one of the most important *** this study,basing on the Dask distribution computational framework,and taking the visibility function integral processing as an example,we adopt a multi-level parallelism method to implement distributed averaging over time and *** Array was used to implement super large matrix or arrays with supported *** maximize the usage of memory,we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance *** validity of the proposed pattern was also verified by using the Common Astronomy Software application(CASA),wherein we analyze the smearing effects on images reconstructed from different resolution visibilities.

关键词： techniques:interferometric methods:data analysis methods:numerical instrumentation:interferometers

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CoCGAN: Contrastive Learning for Adversarial Category Text Generation 29

CoCGAN: Contrastive Learning for Adversarial Category Text G...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Sheng, Xin Xu, Linli Xu, Yinlong Bao, Changcun Chen, Huang Ren, Bo Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

The task of generating texts of different categories has attracted more and more attention in the area of natural language generation recently. Meanwhile, generative adversarial net (GAN) has demonstrated its effectiveness on text generation, and is further applied to category text generation in later works. Different from existing methods, which mainly consider the pairwise relations between the text embedding and the corresponding fixed one-hot class label (data-to-class relations), this paper proposes a novel Contrastive Category Generative Adversarial Net (CoCGAN) to incorporate contrastive learning into adversarial category text generation, considering more flexible data-to-class relations as well as relations between the multiple text embeddings in the same batch (data-to-data relations). The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples. Accordingly, the generator tries to produce more realistic samples which can confuse the discriminator. Experimental results on both synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generation models. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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