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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xie, Jiancong Zhang, Zhe Li, Youyou Rao, Jiahua Yang, Yuedong Sun Yat-sen University School of Computer Science and Engineering China University of Chicago Department of Chemistry IL United States Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing of Moe China

ISBN: (纸本)9798350386226

Precise prediction of anti-cancer drug responses has become a crucial obstruction in anti-cancer drug design and clinical applications. In recent years, various deep learning methods have been applied to drug response prediction and become more accurate. However, they are still criticized as being non-transparent. To offer reliable drug response prediction in real-world applications, there is still a pressing demand to develop a model with high predictive performance as well as interpretability. In this study, we propose DrugVNN, an end-to-end interpretable drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN) and learns drug representation through a node-edge communicative message passing network (CMPNN). Additionally, between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs. By evaluating on the GDSC dataset, DrugVNN achieved state-of-the-art performance. Moreover, DrugVNN can identify active genes and relevant signaling pathways for specific drug-cell line pairs with supporting evidence in the literature, implying the interpretability of our model. © 2024 IEEE.

关键词： Prediction models

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Circuit Simulation and Optimization of Quantum Search Algorithm 6

Circuit Simulation and Optimization of Quantum Search Algori...

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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Liu, Xiaonan Zhao, Chenyan Xie, Haoshan Liu, Zhengyu State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University China School of Computer and Artificial Intelligence ZhengZhou University China

ISBN: (纸本)9781450397148

At present, the scale of quantum computers in the real sense is still small, and quantum simulation has become one of the important ways of quantum theory research, grover quantum search algorithm is suitable for the search problem of disordered database. Firstly, according to the implementation principle of Grover algorithm and Boolean logic relationship, the design idea of multi-objective Oracle is analyzed, based on IBMQ quantum cloud platform, the quantum circuit of Grover algorithm with multi-objective items is simulated. Based on the characteristics of Grover algorithm and the simulation process of quantum gate, the action of multiple identical quantum gates is combined to reduce the update times of probability amplitude and improve the simulation efficiency. The libquantum quantum simulator is used for experiments and the target item is successfully searched, which proves the feasibility of the optimization method and provides reference for the simulation and optimization of other quantum algorithms. © 2022 Association for computing Machinery.

关键词： Timing circuits

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Byzantine-robust distributed support vector machine

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Science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

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Multi-scale Motion Feature Integration for Action Recognition 9

Multi-scale Motion Feature Integration for Action Recognitio...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Lai, Jinming Zheng, Huicheng Dang, Jisheng Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Guangdong Province Key Laboratory of Information Security Technology China

ISBN: (纸本)9798350317251

Analyzing video data with intricate temporal structures and extracting comprehensive motion information remains a significant challenge. In this work, we introduce the multi-scale motion feature integration (MMFI) network, which leverages two key modules for motion analysis: the progressive local context aggregation (PLCA) module and the multi-scale motion excitation (MSME) module. The PLCA module captures frame-level motion details by incrementally processing frame-wise differences near the input frame in the early stages of the network. The MSME module provides motion-attentive channel weights in deeper layers with higher dimensions, incorporating short- and long-range segment-level motion information. These modules synergistically capture motion details across various scales. Our approach is evaluated on the large-scale video dataset Something-Something V1, yielding state-of-the-art performance with minimal computational overhead. © 2023 IEEE.

关键词： Deep learning

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Improvement and optimization of consensus algorithm based on PBFT 4

Improvement and optimization of consensus algorithm based on...

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4th International Conference on Communications, Information System and Computer engineering, CISCE 2022

作者： Zhao, Liang Li, Bin Zhou, Qinglei Chen, Xiaojie Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

This paper proposes an improved consensus algorithm based on PBFT(EBCR-PBFT). Firstly, The Modified Random Select(MRS) function is used to perform preliminary screening of network nodes, so as to solve the problem of multiple and redundant network nodes. Secondly, based on the idea of clustering, logical stratification of blockchain network nodes can reduce communication times and improve network efficiency. Thirdly, the core module of MRS is optimized by FPGA to further improve the efficiency of the algorithm. At the same time, the improved dynamic reputation evaluation model is introduced to evaluate and feedback the node behavior in time to ensure the smooth operation of the system. Experiments show that the proposed and optimized consensus algorithm can effectively improve the consensus efficiency, timely punish malicious nodes, increase the reliability of nodes, communication overhead, throughput, delay and other high performance, has a certain application value. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Memory-Guided Contrastive and Triplet Separation for Weakly-Supervised Disease Detection

Memory-Guided Contrastive and Triplet Separation for Weakly-...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： She, Jinwen Li, Qiong Ma, Andy J. Sun Yat-sen University Cancer Center Guangzhou China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Guangdong Province Key Laboratory of Information Security Technology Key Laboratory of Machine Intelligence and Advanced Computing China

ISBN: (纸本)9798350386226

Weakly-supervised disease detection has the great potential to alleviate the time-consuming and labor-intensive burden of manual annotations in instance level. While existing methods extract normality prototypes encoding normal patterns to improve the detection performance, they may fail to detect subtle anomalies without considering the diverse abnormal patterns. In this paper, we propose to recognize both normal and abnormal patterns for feature representation learning in disease detection based on a dual memory network. To learn discriminative memory banks and classifiers, dual memory loss is incorporated with a feature magnitude separation loss for model training. With the proposed contrastive feature separation loss and triplet feature separation loss, feature discriminability is further improved to obtain a more separable decision boundary. Additionally, we collect a large-scale CT dataset to evaluate lung tumor detection. Extensive experiments on the publicly available PANDA-MIL dataset and the collected LUNG-MIL dataset demonstrate the superiority of our proposed method compared to the state-of-the-art approaches for weakly-supervised disease detection. © 2024 IEEE.

关键词： Contrastive Learning

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Perception-Enhanced Generative Transformer for Key Information Extraction from Documents 27th

Perception-Enhanced Generative Transformer for Key Informat...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Runbo Ou Yang, Jun Jie Gao, Chen Qin, Xugong Zeng, Gangyan Hu, Xiaoxu Zhang, Peng School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Viterbi School of Engineering University of Southern California Los Angeles United States National Computer Network Response Technical Team/Coordination Center Technology Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (纸本)9783031781186

Key information extraction (KIE) from scanned documents has attracted significant attention due to practical real-world applications. Despite impressive results achieved by incorporating multimodal information within the generative framework, existing methods fail to understand complex layouts and fuzzy semantics in document images. To settle these issues, we propose a perception-enhanced generative transformer (PEGT), which improves the model through fine-grained multimodal modeling and pre-training tasks tailored for the generative framework. Firstly, we introduce a pre-trained vision-language model to provide transferable knowledge for visual text perceptron. Then two auxiliary pre-training tasks including absolute position prediction (APP) and semantic relationship reasoning (SRR) are designed for the generative framework. APP learns to predict which grids the texts fall into, improving the model on utilization of the position information. SRR exploits prior information of semantic relationships, injecting the ability for better semantic discrimination into PEGT. Finally, well-designed prompts are leveraged to unleash the potential of PEGT for extracting key information from documents. Extensive experiments on several public datasets show that PEGT effectively generalizes over different types of documents. Especially, PEGT achieves state-of-the-art results in terms of F-measure, i.e., 97.47%, 98.04%, and 84.32% on the SROIE, CORD, and FUNSD datasets, demonstrating the superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Model-Free and Pseudoinverse-Free Zhang Neurodynamics Scheme for Robotic Arms’ Path Tracking Control

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10201-10212页

作者： Jielong Chen Yan Pan Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

Path tracking control of robotic arms is regarded as a fundamental problem in the field of robotics. However, obtaining an accurate model of the robotic arm in practical engineering poses significant challenges. As a result, model-free schemes have become a focus of investigation. In contrast to traditional model-free schemes used for estimating the Jacobian matrix of the robotic arm, in this work, a novel estimator directly for the pseudoinverse (PI) of the Jacobian matrix based on Zhang neurodynamics (ZN) is proposed for the first time. In addition, a novel model-free and PI-free ZN (MFPIFZN) scheme for path tracking control of robotic arms is proposed. The MFPIFZN scheme not only significantly reduces the operation complexity by eliminating the requirement to compute the PI of the Jacobian matrix but also enhances the accuracy by eliminating the potential errors that may arise from the computation of the PI. Theoretical analyses provide guarantees for the convergence and stability of the MFPIFZN scheme. Finally, experimental results conducted on planar four-link and Kinova Jaco2 robotic arms vividly illustrate the excellent performance of the MFPIFZN scheme. Comparison experiments with four other model-free schemes further confirm the superiority of the MFPIFZN scheme.

关键词： Robots Manipulators Jacobian matrices Program processors Computational modeling Convergence Mathematical models Neurodynamics Accuracy Vectors

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Enhancing Large-Scale Pre-Trained Models with Mixer for Far-Field ASR

Enhancing Large-Scale Pre-Trained Models with Mixer for Far-...

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Artificial intelligence and Computer engineering (ICAICE), International Conference on

作者： Tong Niu Yaqi Chen Dan Qu Hengbo Hu Information Engineering University Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Although the significant improvements of Automatic Speech Recognition systems is achieved by the end-to-end modeling technology, especially the use of large-scale pretrained models. Far-Field Speech Recognition still faces the challenges due to signal degradation, such as low signal-to-noise ratio, reverberation, and background noise. To overcome these issues, we introduce an innovative data augmentation technique called Mixer to enhance the performance of large-scale pretrained models in FSR, by interpolated and mixed the features to represent the speech samples and the corresponding losses. Experimental results show that compared to the Whisper acrossed all version, including SpecAugment, the proposed method has reduced 3.6% Word Error Rate.

关键词： Degradation Error analysis Speech enhancement Data augmentation Data models Reverberation Background noise Mixers Faces Signal to noise ratio

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Using Knowledge Induction Strategies: Llms Can Do Better in Knowledge-Driven Dialogue Tasks

SSRN

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SSRN 2023年

作者： Peng, Sisi Qu, Dan Zhang, Hao Zhang, Wenlin Li, Shunhang Information Engineering University Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China

Acquiring and utilizing knowledge is essential for dialogue systems to generate high-quality responses. In recent years, Large Language Models (LLMs) have garnered significant attention in research due to their ability to learn general knowledge from vast amounts of training data and complex textual semantics using deep neural network structures. However, LLMs face certain limitations in knowledge-driven dialogue tasks, including a lack of factual knowledge and poor utilization of commonsense knowledge. Many efforts alleviate these issues by providing an external knowledge base, which is time-consuming and labor-intensive. To address these shortcomings, this paper introduces two Knowledge Induction (KI) strategies: Explicit Knowledge Induction (EKI) and Implicit Knowledge Induction (IKI), to mine LLMs’ internel knowledge and stimulate their own reasoning ability. EKI involves an iterative automatic prompt generation process that guides LLMs in generating explicit knowledge, while IKI utilizes Chain-Of-Thought (CoT) prompts to construct dialogue thinking chains. These strategies empower LLMs to accurately and comprehensively leverage internal knowledge, thereby enabling them to tackle knowledge-driven dialogue tasks without relying on external knowledge bases. Furthermore, the detailed conditional constraints embedded in the prompts facilitate the control of knowledge generation formats, streamlining the knowledge post-processing process. To validate the efficacy of the KI strategies, a series of experiments were performed across multiple models and public datasets. The experimental results demonstrate that the KI strategies effectively enhance knowledge utilization and improve the quality of dialogue generation. Specifically, ChatGLM achieved significant improvements in BLEU (+3.86/+0.83), ROUGE (+7.55/+7.94), and MAUVE (+0.1/+0.08) metrics. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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