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arXiv 2021年

作者： Zhang, Kun Lv, Guangyi Wang, Meng Chen, Enhong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science and Technology University of Science and Technology of China China

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by advancement of representation learning techniques and inspiration of human behaviors. Among all these methods, attention mechanism plays an essential role by selecting important parts effectively. However, current attention methods either focus on all the important parts in a static way or only select one important part at one attention step dynamically, which leaves a large space for further improvement. To this end, in this paper, we design a novel Dynamic Gaussian Attention Network (DGA-Net) to combine the advantages of current static and dynamic attention methods. More specifically, we first leverage pre-trained language model to encode the input sentences and construct semantic representations from a global perspective. Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective. Finally, we combine the global information and detailed local information together to decide the semantic relation of sentences comprehensively and precisely. Extensive experiments on two popular sentence semantic matching tasks demonstrate that our proposed DGA-Net is effective in improving the ability of attention mechanism. © 2021, CC BY.

关键词： Gaussian distribution

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Selection for Cheaper Amino Acids Drives Nucleotide Usage at the Start of Translation in Eukaryotic Genes

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 949-957页

作者： Na L.Gao Zilong He Qianhui Zhu Puzi Jiang Songnian Hu Wei-Hua Chen Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Institute for Computer Science and Cluster of Excellence on Plant Sciences Heinrich Heine UniversityD-40225 DuesseldorfGermany CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and EngineeringBeihang UniversityBeijing 100191China University of Chinese Academy of Sciences Beijing 100049China

Coding regions have complex interactions among multiple selective forces,which are manifested as biases in nucleotide *** studies have revealed a decreasing GC gradient from the 5′-end to 3′-end of coding regions in various *** confirmed that this gradient is universal in eukaryotic genes,but the decrease only starts from the~25th *** trend is mostly found in nonsynonymous(ns)sites at which the GC gradient is universal across the eukaryotic *** GC contents at ns sites result in cheaper amino acids,indicating a universal selection for energy efficiency toward the N-termini of encoded *** a genome,the decreasing GC gradient is intensified from lowly to highly expressed genes(more and more protein products),further supporting this *** reveals a conserved selective constraint for cheaper amino acids at the translation start that drives the increased GC contents at ns *** GC contents can facilitate transcription but result in a more stable local secondary structure around the start codon and subsequently impede translation ***,the GC gradients at four-fold and two-fold synonymous sites vary across *** could decrease or increase,suggesting different constraints acting at the GC contents of different codon sites in different *** study reveals that the overall GC contents at the translation start are consequences of complex interactions among several major biological processes that shape the nucleotide sequences,especially efficient energy usage.

关键词： Macroevolution Prioritization of selective forces Energy efficiency Transcription Translation initiation

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An Autoencoder-based Transceiver for UAV-to-Ground Free Space Optical Communication

An Autoencoder-based Transceiver for UAV-to-Ground Free Spac...

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OptoElectronics and Communications Conference, OECC

作者： Qianwu Zhang Guanwen Chen Boyang Liu Xuzhuang Zhi Shucheng Zhan Jing Zhang Ning Jiang Bingyao Cao Zhengxuan Li Key Laboratory of Specialty Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

An end-to-end autoencoder-based transceiver for UAV-to-ground free space optical communication is proposed. Simulation results show $\sim 12 \mathrm{~dB}$ improvement compared to the PPM transmitter and $\sim 51.3 \%$...

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Causal structure learning of nonlinear additive noise model based on streaming feature

Causal structure learning of nonlinear additive noise model ...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Jing Yang Gaojin Fan Anbo Shen Aiguo Wang Ministry of Education Key Laboratory of Knowledge Engineering With Big Data(Hefei University of Technology) School of Computer Science and Information Engineering Hefei University of Technology Hefei P. R. China School of Electronic and Information Engineering Foshan University China

ISBN: (纸本)9781665424288

The current streaming feature structure learning needs to be improved in the processing of nonlinear continuous data and the dynamic acquisition of causal structures. In this paper, we propose a causal structure learning algorithm, CANSF, based on the streaming feature of additive noise models. We have made three contributions. First, by using the information carried by the noise of nonlinear continuous data, we propose a real correlation identification method based on logarithmic likelihood, which can identify the real correlation and redundant features of target features, and dynamically select parent and child nodes for each feature. Second, based on regression analysis, a method to determine the causal direction is proposed, which can be used for dynamic orientation. Third, a learning method of causal structure based on streaming features is proposed, which can obtain the Causal structure diagram directly and dynamically.

关键词： Additive noise Learning systems Correlation Heuristic algorithms Conferences data models Regression analysis

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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach

The State of Charge Estimation of Lithium-ion Batteries Usin...

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第34届中国控制与决策会议

作者： Wei He Hongyan Ma Yingda Zhang Shuai Wang Jiaming Dou Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education

Accurate state of charge(SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or ***,it is difficult to get an accurate value of SOC since it is an inner state of a battery cell,which cannot be directly *** order to improve the estimation accuracy of SOC,this paper develops a SOC estimation model for a lithium-ion battery using a Particle Swarm Optimization-Extreme Learning Machine(PSO-ELM) *** PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM *** inputs to the PSO-ELM model are the battery voltage,current,and temperature,and the output is the actual SOC *** performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error(MSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and SOC *** results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion Battery State of Charge Particle Swarm Optimization Algorithm Extreme Learning Machine

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The Enhanced N-Butanol Gas Sensing Properties Based on Perovskite-Type Znsno3 Nanoflowers

SSRN

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SSRN 2024年

作者： Meng, Fanli Zhou, Fangling Mu, Zhuangzhuang Yuan, Zhenyu Zhang, Renze Li, Yudong Zhu, Hongmin Yan, Xin College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang110819 China

N-butanol is a volatile organic compound (VOC) that is harmful to human health, and the development of gas sensors for n-butanol vapor detection is necessary. In this work, the nanoflowers-like ZnSnO3 was synthesized via a one-step hydrothermal route followed by an annealing process. The results revealed that optimizing the hydrothermal time to 12 hours resulted in ZnSnO3-2 exhibiting the highest gas response (40.48) towards 100ppm n-butanol at 275°C, boasting a detection limit of 500 ppb. Furthermore, ZnSnO3-2 exhibits excellent gas selectivity and long-term stability. The improved sensing performance of ZnSnO3-2 was attributed to its large specific surface area and abundant oxygen vacancies. Additionally, the gas sensing mechanism for enhancing n-butanol detection was comprehensively analyzed. These results highlight the potential of nanoflower-like ZnSnO3 as a promising candidate for n-butanol gas sensing material, offering both high sensitivity and selectivity. Moreover, this research provides valuable guidance for the development and optimization of gas sensing materials for applications in environmental monitoring and industrial safety. © 2024, The Authors. All rights reserved.

关键词： Perovskite

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Tumor State-Space Network for High and Low Grade Glioma Classification

Tumor State-Space Network for High and Low Grade Glioma Clas...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Qijian Chen Lihui Wang Zeyu Deng Li Wang Chen Ye YueMin Zhu Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 University Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurately predicting the grade of gliomas is crucial for choosing right treatment plans. While current methods using radiomics and deep learning can predict glioma grades effectively using magnetic resonance imaging (MRI), they overlook dynamic changes in the tumor and its neighboring areas. To address this issue, we propose a tumor state-space network (TSSNet), which fully exploits changes in the tumor region and its surroundings through dynamical updating strategy. The experimental results demonstrated that TSSNet achieves 90.32% accuracy and 93.55% area under the curve (AUC) for the prediction of high-grade and low-grade gliomas, respectively, which are 2.4% and 2.5% higher than the best state-of-the-art deep learning models. Moreover, the proposed state-space module can effectively enhance prediction performance, which is advantageous for personalized glioma treatment.

关键词： Deep learning Accuracy Magnetic resonance imaging Signal processing Predictive models Tumors Radiomics

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Determination of Trace Al3+ in Upconversion Nanoparticles-Xylenol Orange System Based on Inner Filter Effect

SSRN

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SSRN 2023年

作者： Wang, Jinfeng Jiang, Rui Feng, Bin Mou, Panpan Zhou, Shuo Zhang, Xianbo Zhou, Yan Chen, Guosong Lin, Donghai School of Chemistry and Molecular Engineering Nanjing Tech University Nanjing211816 China School of Energy and Materials Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional Materials Shanghai Key Laboratory of Engineering Materials Application and Evaluation Shanghai Polytechnic University Shanghai201209 China Nanjing Tech University China

This paper reports a novel method that utilized the inner filter effect between upconversion nanoparticles (UCNPs) and xylenol orange-aluminum (XO-Al3+) for the sensitive and specific detection of trace Al3+. Under excitation by near-infrared light at 980 nm, Yb3+ and Er3+ doped UCNPs emit fluorescence at 543 nm and 658 nm, respectively. Upon the addition of Al3+ to the UCNPs-XO system, a red XO-Al3+ complex was formed, which exhibited a maximum absorption peak at 550 nm. This complex quenched the upconversion fluorescence at 543 nm of UCNPs while leaving the fluorescence at 658 nm unaffected. Thus, the concentration of Al3+ could be determined by calculating the decrease of I543 / I658. Under optimized conditions, the I543 / I658 ratio of the UCNPs-XO-Al3+ system demonstrated a well-defined linear relationship within the concentration range of 0-30 μM Al3+, with a detection limit of 0.19 μM. The findings of this study highlight the remarkable sensitivity, excellent specificity, and robust linear relationship exhibited by the upconversion fluorescence detection method, therefore, providing a feasible new approach for the detection of trace amounts of Al3+. © 2023, The Authors. All rights reserved.

关键词： Aluminum compounds

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Detecting and Corrupting Convolution-based Unlearnable Examples

arXiv

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arXiv 2023年

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Review of Research on Robot Visual Slam

Review of Research on Robot Visual Slam

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第34届中国控制与决策会议

作者： YANG Xin WANG Li-Na China Jiliang University College of Mechanical and Electrical Engineering Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University

SLAM technology,which is called simultaneous localization and mapping technology,is to make the robot running in the environment self-locate according to the estimation of its position and posture and the existing map,and at the same time build the map incrementally on this basis,and finally achieve the purpose of building the map and positioning for the whole ***,this paper summarizes the history and development of SLAM ***,it introduces monocular vision SLAM and RGB D SLAM through two kinds of vision SLAM ***,it expounds the content of cooperation between vision technology and single robot,and finds related problems,and looks forward to the future.

关键词： SLAM machine vision single robot cooperation problem

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