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Variational quantum semi-supervised classifier based on label propagation

Chinese Physics B 2023年第7期32卷 279-289页

作者：侯艳艳李剑陈秀波叶崇强 College of Information Science and Engineering ZaoZhuang UniversityZaozhuang 277160China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China Information Security Center State Key Laboratory Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China

Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data *** paper proposes a quantum semi-supervised classifier based on label *** the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)*** this method,a locally parameterized quantum circuit is created to reduce the parameters required in the ***,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum *** demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test *** work opens a new path to quantum machine learning based on graphs.

关键词： semi-supervised learning variational quantum algorithm parameterized quantum circuit

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Enhancing hydrolytic stability of dynamic imine bonds and polymers in acidic media with internal protecting groups

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Chinese Chemical Letters 2024年第5期35卷 161-165页

作者： Hang Chen Chengzhi Cui Hebo Ye Hanxun Zou Lei You College of Environmental and Biological Engineering Fujian Provincial Key Laboratory of Ecology-Toxicological Effects&Control for Emerging ContaminantsPutian UniversityPutian 351100China State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China College of Chemistry and Material Science Fujian Normal UniversityFuzhou 350007China Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350108China

Imine bonds are among the most explored building motifs in dynamic chemistry,polymers,and materials,and yet,their acid-resistance remains a longstanding *** we demonstrate a concept of internal protecting groups for improving the kinetic stability of dynamic imine bonds and *** examination of structure-reactivity relationship of a series of aldehydes/imines bearing a neighboring carboxyl allowed uncovering of required structural features for dynamically masking imine bonds with cyclic *** studies indicated that noncovalent interactions along with sterics control the ringchain equilibrium and the stability of imine *** incorporation of internal protecting groups into imine polymers further enabled their controlled stability in acidic ***,a combination of dynamic covalent network and coordination supramolecular network provided a facile means for the modulation of luminescent and mechanical properties of *** strategies and results reported should be beneficial to molecular assemblies,dynamic polymers,biological delivery,and intelligent materials.

关键词： Dynamic covalent chemistry Internal protecting group Kinetic stability Imine bond Dynamic polymer

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Optimal voltage compensation method for focal plane avalanche detector based on improved random forest 2

Optimal voltage compensation method for focal plane avalanch...

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2nd International Conference on Electronic information and Mechanical Manufacturing, EIMM 2022

作者： Chen, Mengqiang Yang, Jiazhi Yu, Guangwang Shen, Jie College of Information Science and Engineering Guilin University of Technology Guangxi Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guangxi Guilin541006 China

Working distance and background radiation greatly affect the signal-to-noise ratio of avalanche photodiode (APD) in the lidar detection system. The traditional method cannot adapt to a complex environment by offline compensation or pre-compensation according to the influence factors of the external environment. In this paper, an avalanche photodiode voltage compensation method based on the improved random forest is designed. Firstly, the distance image data is de-noised. Then the weight of each decision tree in the random forest was changed to improve the classification performance. The particle swarm optimization (PSO) algorithm was used to search for the optimal combination of parameters affecting classification accuracy and performance. Finally, the improved random forest algorithm is used to judge the current working state of APD at different distances, compensate for the bias voltage, and make APD work in the optimal state. The proposed method is compared with the k-nearest neighbor, support vector machine, and other commonly used classification algorithms, and the results verify the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Particle swarm optimization (PSO)

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Empowering self-sustained agriculture: an IoT field tracking model driven by fog computing

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Singh, Gurpreet Singh, Jaspreet Juneja, Sapna Gulzar, Yonis Gupta, Deepali Ghafoor, Kayhan Zrar Kumar, Mukesh Department of Computer Science and Engineering Chandigarh University Punjab Chandigarh140413 India KIET Group of Institutions Delhi-NCR Uttar Pradesh Ghaziabad201206 India Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Information and Communication Technology Eng Erbil Polytechnic University 42VQ+756 120 Meter St Erbil Erbil Governorate Iraq Mechanical Engineering College of Engineering Assosa University Assosa5220 Ethiopia

Increasing availability of cost effective IoT (Internet of Things) devices have led to the development of IoT- based farm management systems. Smart Agriculture is a demanding approach that employs the use of technology-enabled information and communication to monitor farms. In India, somewhere around 60% of the organization’s operations mostly dependent on agriculture directly or indirectly. A nation’s advancement is typically influenced by concerns with agriculture. The most effective approach to addressing agriculture’s challenges is always to improve traditional agricultural practices as well as modernize them into smart agriculture. The smart agriculture technique offers beneficial data collection, highly precise control as well as automated monitoring strategies. This research proposed a IoT assisted fog-based model that promises to provide the cost-effective smart agriculture system. Fog platform brings computation and storing nearer at the network’s the border, allowing for the efficient processing of data produced by IoT devices across smart agricultural systems. The bibliometric evaluation of the Web of science database for the adoption of the IoT-Fog base infrastructure in the creation of smart agriculture systems is also highlighted in this paper. The proposed solution when implemented as well as when contrasted with existing IoT-based agriculture system exhibit better efficiency. The result revealed that proposed system brings down response time, error rate and standard deviation to significant level and improves the accuracy of current system exponentially. © The Author(s) 2025.

关键词： Agricultural robots

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Disentangling Spatio-Temporal Knowledge for Weakly Supervised Object Detection and Segmentation in Surgical Video

Disentangling Spatio-Temporal Knowledge for Weakly Supervise...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Liao, Guiqiu Jogan, Matjaz Koushik, Sai Eaton, Eric Hashimoto, Daniel A. University of Pennsylvania Pcaso Laboratory Department of Surgery United States University of Pennsylvania Department of Electrical and Systems Engineering United States University of Pennsylvania Department of Computer and Information Science United States

ISBN: (纸本)9798331510831

Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object presence. WSVOS in surgical videos is, however, more challenging due to the complex interaction of multiple transient objects, such as surgical tools moving in and out of the surgical field. In this scenario, state-of-the-art WSVOS methods struggle to learn accurate segmentation maps. We address this problem by introducing ViDeo Spatio-Temporal disentanglement Networks (VDST-Net), a framework to disentangle complex spatio-temporal object interactions using semi-decoupled knowledge distillation to predict high-quality class activation maps (CAMs). A teacher network is designed to help a temporal-reasoning student network resolve activation conflicts, as the student leverages temporal dependencies when specifics about object location and timing in the video are not provided. We demonstrate the efficacy of our framework on a challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames, and compare our method to state-of-the-art methods on surgical data and on a public dataset commonly used to benchmark WSVOS. Our method outperforms state-of-the-art techniques and generates accurate segmentation masks under video-level weak supervision. Our code is available at: https://***/PCASOlab/VDST-net. © 2025 IEEE.

关键词： Image segmentation

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ECMpy 2.0:A Python package for automated construction and analysis of enzyme-constrained models

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Synthetic and systems Biotechnology 2024年第3期9卷 494-502页

作者： Zhitao Mao Jinhui Niu Jianxiao Zhao Yuanyuan Huang Ke Wu Liyuan Yun Jirun Guan Qianqian Yuan Xiaoping Liao Zhiwen Wang Hongwu Ma Biodesign Center Key Laboratory of Engineering Biology for Low-carbon ManufacturingTianjin Institute of Industrial BiotechnologyChinese Academy of SciencesTianjin300308China National Center of Technology Innovation for Synthetic Biology Tianjin300308China Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(Ministry of Education) Tianjin UniversityTianjin300072China College of Biotechnology Tianjin University of Science and TechnologyTianjin300457China Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhenChina Tianjin Agricultural College Tianjin300384China Haihe Laboratory of Synthetic Biology Tianjin300308China

Genome-scale metabolic models(GEMs)have been widely employed to predict microorganism ***,GEMs only consider stoichiometric constraints,leading to a linear increase in simulated growth and product yields as substrate uptake rates *** divergence from experimental measurements prompted the creation of enzyme-constrained models(ecModels)for various species,successfully enhancing chemical *** upon studies that allocate macromolecule resources,we developed a Python-based workflow(ECMpy)that constructs an enzyme-constrained *** involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in ***,this procedure de-mands manual collection of enzyme kinetic parameter information and subunit composition details,making it rather *** this work,we’ve enhanced the ECMpy toolbox to version 2.0,broadening its scope to automatically generate ecGEMs for a wider array of *** 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters,which significantly enhances parameter ***,ECMpy 2.0 introduces common analytical and visualization features for ecModels,rendering computational results more user ***,ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic *** 2.0 is available at https://***/tibbdc/ECMpy or as a pip package(https://***/project/ECMpy/).

关键词： Enzyme-constrained model Python package Automated construction Multiple analysis functions

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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An LLM-based Framework for Biomedical Terminology Normalization in Social Media via Multi-Agent Collaboration 31

An LLM-based Framework for Biomedical Terminology Normalizat...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Fan, Yongqi Xue, Kui Li, Zelin Zhang, Xiaofan Ruan, Tong School of Information Science and Engineering East China University of Science and Technology Shanghai China Intelligent Healthcare Shanghai Artificial Intelligence Laboratory Shanghai China Northwestern University United States Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798891761964

Biomedical Terminology Normalization aims to identify the standard term in a specified termbase for non-standardized mentions from social media or clinical texts, employing the mainstream "Recall and Re-rank" framework. Instead of the traditional pretraining-finetuning paradigm, we would like to explore the possibility of accomplishing this task through a tuning-free paradigm using powerful Large Language Models (LLMs), hoping to address the costs of re-training due to discrepancies of both standard termbases and annotation protocols. Another major obstacle in this task is that both mentions and terms are short texts. Short texts contain an insufficient amount of information that can introduce ambiguity, especially in a biomedical context. Therefore, besides using the advanced embedding model, we implement a Retrieval-Augmented Generation (RAG) based knowledge card generation module. This module introduces an LLM agent that expands the short texts into accurate, harmonized, and more informative descriptions using a search engine and a domain knowledge base. Furthermore, we present an innovative tuning-free agent collaboration framework for the biomedical terminology normalization task in social media. By leveraging the internal knowledge and the reasoning capabilities of LLM, our framework conducts more sophisticated recall, ranking and re-ranking processes with the collaboration of different LLM agents. Experimental results across multiple datasets indicate that our approach exhibits competitive performance. We release our code and data on the github repository JOHNNY-fans/RankNorm. © 2025 Association for Computational Linguistics.

关键词： Terminology

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GestureTeach: A gesture guided online teaching interactive model

GestureTeach: A gesture guided online teaching interactive m...

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作者： Liu, Hongjun Yao, Chao Zhang, Yalan Ban, Xiaojuan Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial Intelligence School of Computer and Communication Engineering School of Intelligence Science and Technology Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Institute for Materials Intelligent Technology Liaoning Academy of Materials Shenyang China

Online education has become more popular and effective due to the availability of high-speed internet and technological innovations, which allow people from different locations to access educational resources and opportunities. However, online classes often face challenges such as limited interactivity and display options, which can affect the quality and effectiveness of the online learning experience. In this article, we propose GestureTeach, a new pedagogical paradigm that enables free handwriting interaction and animation generation for online teaching. GestureTeach uses gestures as a natural and intuitive way of interaction, which enhances the teacher's intention expression and the student's engagement. GestureTeach also generates animations from handwritten sketches, which improves the display effects of the interaction and the student's knowledge comprehension. We conducted a two-stage study with 15 teachers and 90 students to evaluate the effectiveness of GestureTeach in facilitating classroom interaction. The results show that GestureTeach is preferred by both teachers and students over traditional online teaching methods and has the potential to transform the online teaching landscape by providing a seamless and interactive experience. © 2023 John Wiley & Sons Ltd.

关键词： Students

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Device-Free 3D Drone Localization in RIS-Assisted mmWave MIMO Networks

Device-Free 3D Drone Localization in RIS-Assisted mmWave MIM...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： He, Jiguang Vanwynsberghe, Charles Chen, Hui Huang, Chongwen Fakhreddine, Aymen Great Bay University School of Computing and Information Technology Dongguan China Technology Innovation Institute Abu Dhabi9639 United Arab Emirates Chalmers University of Technology Department of Electrical Engineering Gothenburg Sweden Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Klagenfurt Institute of Networked and Embedded Systems Klagenfurt Austria

ISBN: (纸本)9798350351255

In this paper, we investigate the potential of reconfigurable intelligent surfaces (RISs) in facilitating passive/device-free three-dimensional (3D) drone localization within existing cellular infrastructure operating at millimeter-wave (mmWave) frequencies and employing multiple antennas at the transceivers. The developed localization system operates in the bi-static mode without requiring direct communication between the drone and the base station. We analyze the theoretical performance limits via Fisher information analysis and Cramér Rao lower bounds (CRLBs). Furthermore, we develop a low-complexity yet effective drone localization algorithm based on coordinate gradient descent and examine the impact of factors such as radar cross section (RCS) of the drone and training overhead on system performance. It is demonstrated that integrating RIS yields significant benefits over its RIS-free counterpart, as evidenced by both theoretical analyses and numerical simulations. © 2024 IEEE.

关键词： Cramer-Rao bounds

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