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arXiv 2024年

作者： Xia, Xin Zhang, Yajie Zeng, Xiangxiang Zhang, Xingyi Zheng, Chunhou Su, Yansen The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China College of Computer Science and Electronic Engineering Hunan University Changsha410012 China

Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development. Various artificial intelligence technologies have demonstrated high effectiveness and efficiency on molecular optimization tasks. However, few of these technologies focus on balancing property optimization with constraint satisfaction, making it difficult to obtain high-quality molecules that not only possess desirable properties but also meet various constraints. To address this issue, we propose a constrained multi-property molecular optimization framework (CMOMO), which is a flexible and efficient method to simultaneously optimize multiple molecular properties while satisfying several druglike constraints. CMOMO improves multiple properties of molecules with constraints based on dynamic cooperative optimization, which dynamically handles the constraints across various scenarios. Besides, CMOMO evaluates multiple properties within discrete chemical spaces cooperatively with the evolution of molecules within an implicit molecular space to guide the evolutionary search. Experimental results show the superior performance of the proposed CMOMO over five state-of-the-art molecular optimization methods on two benchmark tasks of simultaneously optimizing multiple non-biological activity properties while satisfying two structural constraints. Furthermore, the practical applicability of CMOMO is verified on two practical tasks, where it identified a collection of candidate ligands of β2-adrenoceptor GPCR and candidate inhibitors of glycogen synthase kinase-3β with high properties and under drug-like constraints. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Non-line-of-Sight Recognition Algorithm Based on Deep Learning

Non-line-of-Sight Recognition Algorithm Based on Deep Learni...

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International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2022

作者： Zhang, Chengjun Wang, Hongmei Wang, Zhiwei Wang, Faguang Min, Minghui Li, Shiyin School of Information and Control Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education and School of Cyber Science and Engineering Wuhan University Wuhan430072 China

ISBN: (纸本)9789819904150

With the development of the information age, large indoor places such as hospitals, mines and factories have higher and higher requirements for indoor positioning. GPS and other common outdoor positioning methods cannot meet the requirements of accurate indoor navigation. Ultra-wideband is widely used in indoor places because of its strong anti-interference and high accuracy. Accurate identification of errors in UWB indoor non-visual range helps to improve the data for positioning. Therefore, this study combined with convolutional neural network to solve the problem of low recognition rate of non-line-of-sight signals in indoor scenes, and proposed an improved convolutional neural network based on empirical mode decomposition (EMD), wavelet domain denoising (WD) and improved CNN) fusion model EMD-WD-CNN. First, the channel impulse response signal of UWB is denoised based on the EMD-WD algorithm, and then the denoised signal is used as the input of the CNN model for training. The comparison based on similar algorithms shows that the improved CNN model can significantly improve the line-of-sight and NLOS recognition rate of different media in indoor environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Empirical mode decomposition

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An ultra-wideband high-isolation dual-port MIMO antenna design

An ultra-wideband high-isolation dual-port MIMO antenna desi...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Shuangqing Cao Minquan Li Zhenfa Zi Zhonghui Li Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China School of Physics and Materials Engineering Hefei Normal University Hefei China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

In this paper, an antenna for ultra-wideband multiple-input and multiple-output (UWB-MIMO) is designed. The design comprises two hexagonal monopole antennas. In order to achieve ultra-wideband characteristics and mitigate the coupling, a stepped microstrip line structure for impedance matching and T-shaped defective ground structure are employed, which optimizes the broadband characteristics and achieves high isolation characteristics. The antenna achieves a wide impedance bandwidth of 3.5 GHz to 13.9 GHz (relative bandwidth of 119%). Furthermore, the isolation between the two feed ports of the antenna exceeds 18 dB, the envelope correlation coefficient (ECC) is less than 0.012, and it exhibits favorable radiation characteristics.

关键词： Correlation coefficient Time-frequency analysis Impedance matching Bandwidth Microstrip antennas Ultra wideband antennas Envelope detectors Impedance Microstrip Gain

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Dynamic QoS-Driven Framework for Co-Scheduling of Distributed Long-Running Applications on Shared Clusters

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IEEE Transactions on Cloud computing 2025年

作者： Zhu, Jianyong Wang, Hongtao Su, Pan Wang, Yang Pan, Weihua North China Electric Power University Department of Computer China Hebei Key Laboratory of Knowledge Computing for Energy & Power China Ministry of Education Engineering Research Center of Intelligent Computing for Complex Energy Systems China

Cloud service providers typically co-locate various workloads within the same production cluster to improve resource utilization and reduce operational costs. These workloads primarily consist of batch analysis jobs composed of multiple parallel short-running tasks and long-running applications (LRAs) that continuously reside in the system. The adoption of microservice architecture has led to the emergence of distributed long-running applications (DLRAs), which enhance deployment flexibility but pose challenges in detecting and investigating QoS violations due to workload variability and performance propagation across microservices. State-of-the-art resource managers are only responsible for resource allocation among applications/jobs and do not prioritize runtime QoS aspects, such as applicationlevel latency. To address this, we introduce Prank, a QoSdriven resource management framework for co-located workloads. Prank incorporates a non-intrusive performance anomaly detection mechanism for DLRAs and proposes a root cause localization algorithm based on PageRank-weighted analysis of performance anomalies. Moreover, it dynamically balances resource allocation between DLRAs and co-located batch jobs on nodes hosting critical microservices, optimizing for both DLRA performance and overall cluster efficiency. Experimental results demonstrate that Prank outperforms state-of-the-art baselines, reducing DLRA tail latency by over 38% while increasing batch job completion time by no more than 21% on average © 2013 IEEE.

关键词： Resource allocation

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Talents Training in Antenna engineering 5

Talents Training in Antenna Engineering

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Li, Yingsong Huang, Zhixiang Jiang, Tao Li, Yibing Ye, Fang Chung, Kwok L. College of Information and Communication Engineering Harbin Engineering University Harbin15001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China School of Computer Science and Engineering Huizhou University Huizhou516007 China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

We propose the antenna developments from nature to engineering, which is a natural method to train student for designing antenna. Also, the interest of the students can be stimulated. Using this method, the students can get how to design antennas and will be interested on the antenna engineering. Then, young talents can be brought up. By using this strategy, a nature Logo is introduced and cooperated with the proposed talent training method to get new antennas. Also, the results for the developed antenna is also simulated and analyzed based on the theory from the text book. In the future, the proposed talents training techniques or method can also be expanded to other different research directions. © 2022 IEEE.

关键词： Antennas

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An Unsupervised Domain Adaptation Model Based on Multi-Level Joint Alignment for Multi-Modal Cardiac Image Segmentation

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Procedia Computer Science 2023年 226卷 106-112页

作者： Jiapeng Li Yimou Lv Lisheng Xu Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang 110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110169 China

Unsupervised Domain Adaptation has greatly boosted the performance of multi-modal medical segmentation when there are only source domain labels and no labels in the target domain. Many previous work relies on Convolutional Neural Networks for distribution or instance alignment, however, the receptive field of CNN makes it overly concerned with texture and local semantic information of images, while losing the global style information and semantic information and lacking relationships between local and global semantics. In order to address these issues, we propose a two-stage, multi-level framework for unsupervised domain adaptation, which consists of an image translation network and a Transformer-based domain adaptation segmentation network, jointly aligning the data distribution in the source and target domains from the image, feature and output level through adversarial learning. Experimental results indicate that our method can achieve satisfactory results and outperform other state-of-the-art medical image segmentation methods.

关键词： Unsupervised domain adaptation Cardiac image segmentation Adversarial learning Multi-modal segmentation

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Incorporating dynamic attention gating mechanism and pre-trained embedding for Chinese clinical named entity recognition

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Procedia Computer Science 2023年 226卷 113-119页

作者： Zhenming Qiu Anyu Pan Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang 110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110169 China

Clinical Named Entity Recognition (CNER) serves as a cornerstone in clinical research and healthcare informatics, with its primary goal being the discernment and categorization of clinical terminologies present within Electronic Medical Records (EMRs). While deep learning has significantly improved CNER's performance, these models still face challenges in identifying rare or unseen entities. Our study unveils an innovative DAGM-BiLSTM-CRF model that seamlessly integrates data-driven deep learning strategies with human knowledge to surmount this limitation. The distinctive features of this model lie in its integration of a pre-trained character embedding layer, adept at encapsulating nuanced semantic variations at the granularity of individual characters. Furthermore, it incorporates a bidirectional Long Short-Term Memory (BiLSTM) layer, proficient in deriving contextually relevant information by traversing the sequence from both endpoints. The most noteworthy aspect of our model is the inclusion of a Dynamic Attention Gating Mechanism (DAGM) that dynamically recalibrates attention weights based on the traits of the input sequence and a Conditional Random Fields (CRF) layer that considers interdependencies among output labels within a sequence. The evaluation was carried out on the CCKS-2017 CNER benchmark dataset to underscore the superiority of our model over existing cutting-edge methods, thereby validating its proficiency in deciphering the intricacies of Chinese electronic medical records.

关键词： Clinical named entity recognition deep learning dynamic attention gating mechanism

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Allogeneic gene-edited HIV-specific CAR-T cells secreting PD-1 blocking scFv enhance specific cytotoxic activity against HIV Envt cells in vivo

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Virologica Sinica 2023年第2期38卷 285-295页

作者： Hanyu Pan Xinyi Yang Jing Wang Huitong Liang Zhengtao Jiang Lin Zhao Yanan Wang Zhiming Liang Xiaoting Shen Qinru Lin Yue Liang Jinglong Yang Panpan Lu Yuqi Zhu Min Li Pengfei Wang Jianqing Xu Hongzhou Lu Huanzhang Zhu State Key Laboratory of Genetic Engineering And Key Laboratory of Medical Molecular Virology of Ministry of Education/HealthSchool of Life SciencesFudan UniversityShanghai200438China Shanghai Institute of Infectious Disease and Biosecurity School of Life SciencesFudan UniversityShanghai200438China Department of Infectious Disease Key Laboratory of Medical Molecular Virology of Ministry of Education/HealthSchool of Basic Medical Sciences and Shanghai Public Health Clinical CenterFudan UniversityShanghai201508China Scientific Research Center Shanghai Public Health Clinical CenterFudan UniversityShanghai201508China Department of Infectious Diseases and Immunology Shanghai Public Health Clinical CenterFudan UniversityShanghai201508China Department of Infectious Diseases and Nursing Research Institution National Clinical Research Center for Infectious DiseasesThe Third People's Hospital of ShenzhenShenzhen518112China

HIV-specific chimeric antigen receptor(CAR)T-cells have been developed to target HIV-1 infected CD4t T-cells that express HIV Env ***,T cell exhaustion and the patient-specific autologous paradigm of CAR-T cell hurdled clinical ***,we created HIV-specific CAR-T cells using human peripheral blood mononuclear cells and a 3BNC117-E27(3BE)CAR construct that enabled the expression of programmed cell death protein(PD-1)-blocking scFv E27 and the single-chain variable fragment of the HIV-1-specific broadly neutralizing antibody 3BNC117 to target native HIV *** with T cells expressing 3BNC117-CAR alone,3BE CAR-T cells showed greater cytotoxic activity against HIV Envt cells with stronger proliferation capability,higher killing efficiency,and enhanced cytokine secretion in the presence of HIV Env-expressing ***,we manufactured TCR-deficient 3BE CAR-T cells through gene editing and demonstrated that these CAR-T cells could effectively kill HIV Env^(+) cells in vivo without the occurrence of severe graft-versus-host disease(GvHD)in NSG *** data suggest that we have provided a feasible approach to the generation of“off-theshelf”anti-HIV CAR-T cells in combination with PD-1 checkpoint blockade immunotherapy,which can be a powerful therapeutic candidate for the functional cure of HIV.

关键词： Anti-HIV CAR-T PD-1 blocking TCR disruption Cellular immunotherapy

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

Joint Automatic Speech Recognition And Structure Learning Fo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hu, Jiliang Li, Zuchao Shen, Mengjia Ai, Haojun Li, Sheng Zhang, Jun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

ISBN: (纸本)9798350368741

Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets. © 2025 IEEE.

关键词： Information Extraction Speech Recognition Spoken Language Understanding

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Development of synthetic biology tools to engineer Pichia pastoris as a chassis for the production of natural products

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Synthetic and Systems Biotechnology 2021年第2期6卷 110-119页

作者： Jucan Gao Lihong Jiang Jiazhang Lian Key Laboratory of Biomass Chemical Engineering of Ministry of Education College of Chemical and Biological EngineeringZhejiang UniversityHangzhou310027China Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou310027China

The methylotrophic yeast Pichia pastoris(*** phaffii)is one of the most commonly used hosts for industrial production of recombinant *** a non-conventional yeast,*** has unique biological characteristics and its expression system has been well *** the advances in synthetic biology,more efforts have been devoted to developing *** into a chassis for the production of various high-value compounds,such as natural *** review begins with the introduction of synthetic biology tools for the engineering of ***,including vectors,promoters,and terminators for heterologous gene expression as well as Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated System(CRISPR/Cas)for genome *** review is then followed by examples of the production of value-added natural products in metabolically engineered *** ***,challenges and outlooks in developing *** as a synthetic biology chassis are prospected.

关键词： Pichia pastoris Natural products Synthetic biology CRISPR/Cas9 Heterologous gene expression

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