检索结果-内蒙古大学图书馆

18th International Conference on Computational methods in Systems Biology, CMSB 2020

作者： Danos, Vincent Heindel, Tobias Honorato-Zimmer, Ricardo Stucki, Sandro CNRS ENS-PSL INRIA Paris France Institute of Commercial Information Technology and Quantitative Methods Technische Universität Berlin Berlin Germany Centro Interdisciplinario de Neurociencia de Valparaíso Universidad de Valparaíso Valparaiso Chile Department of Computer Science and Engineering University of Gothenburg Gothenburg Sweden

ISBN: (纸本)9783030603267

In this paper, we combine ideas from two different scientific traditions: 1) graph transformation systems (GTSs) stemming from the theory of formal languages and concurrency, and 2) mean field approximations (MFAs), a collection of approximation techniques ubiquitous in the study of complex dynamics. Using existing tools from algebraic graph rewriting, as well as new ones, we build a framework which generates rate equations for stochastic GTSs and from which one can derive MFAs of any order (no longer limited to the humanly computable). The procedure for deriving rate equations and their approximations can be automated. An implementation and example models are available online at https://***/fragger. We apply our techniques and tools to derive an expression for the mean velocity of a two-legged walker protein on DNA. © 2020, Springer Nature Switzerland AG.

关键词： Formal languages

来源：评论

学校读者我要写书评

暂无评论

Third-order Volterra Model Based on DUPSO for EEG Signal Denoising 2019

Third-order Volterra Model Based on DUPSO for EEG Signal Den...

引用

Proceedings of the 2019 International Conference on Video, Signal and Image Processing

作者： Xia Wu Yumei Zhang Xiaojun Wu Key Laboratory of Modern Teaching technology Ministry of Education (Shaanxi Normal University) and School of Computer Science Shaanxi Normal University Shaanxi CHN

ISBN: (纸本)9781450371483

In order to gain a high-performance analysis for electroencephalogram (EEG) signal, denoising has become a hot topic in this field. Due to the good performance of Volterra model, it has been widely applied to remove the noise in EEG signals. However, the inherent dimensionality problem of Volterra series makes the existing research mostly choose low order Volterra, which will sacrifice some accuracy. In addition, the realistic acquisition of EEG signals cannot satisfy the ideal conditions of phase space reconstruction, which makes it difficult to reconstruct phase space in the analysis process. For the purpose to solve these two questions, we introduce a third-order Volterra filter (TOVF) model to study the denoising problem of EEG and apply a dissipative uniform searching particle swarm optimization (DUPSO) algorithm to optimize the model's coefficients. Then a denoising model based on DUPSO third-order Volterra filter (DUPSO-TOVF) can be obtained. Simulating results show that the DUPSO-TOVF model has a significant increase in the SNR and decrease in the MSE when compared with UPSO second-order Volterra filter (UPSO-SOVF) and PSO-SOVF. Besides, the calculation time of DUPSO-TOVF model is not much different from the two compared models', which means the proposed model not only has the highest precision among the compared models but also can avoid the dimension disaster effectively.

关键词： Third-order Volterra filter EEG signal DUPSO algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficient sensor management for multitarget tracking in passive sensor networks via Cauchy-Schwarz divergence

arXiv

引用

arXiv 2020年

作者： Zhu, Yun Key Laboratory of Modern Teaching Technology Ministry of Education School of Computer Science Shaanxi Normal University Xi’an CityShaanxi Province China

This paper presents an efficient sensor management approach for multi-target tracking in passive sensor networks. Compared with active sensor networks, passive sensor networks have larger uncertainty due to the nature of passive sensing. Multi-target tracking in passive sensor networks is challenging because the multi-sensor multi-target fusion problem is difficult and sensor management is necessary to achieve good tradeoffs between tracking accuracy and energy consumption or other costs. To address this problem, we present an efficient information-theoretic approach to manage the sensors for better tracking of the unknown and time-varying number of targets. This is accomplished with two main technical innovations. The first is a tractable information-based multi-sensor selection solution via a partially observed Markov decision process framework. The Cauchy-Schwarz divergence is used as the criterion to select informative sensors sequentially from the candidates. The second is a novel dual-stage fusion strategy based on the iterated-corrector multi-sensor generalized labeled multi-Bernoulli filter. Since the performance of the iterated-corrector scheme is greatly influenced by the order of sensor updates, the selected sensors are first ranked in order of their abilities to detect targets according to the Cauchy-Schwarz divergence, followed the iterated-corrector update. The computation costs of ranking the sensors are negligible, since the Cauchy-Schwarz divergence has been computed in the multi-sensor selection procedure. Simulation results validate the effectiveness and efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Sensor networks

来源：评论

学校读者我要写书评

暂无评论

DPA-2:a large atomic model as a multitask learner

引用

npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

来源：评论

学校读者我要写书评

暂无评论

Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

引用

Blockchain, Smart Healthcare and Emerging Technologies (SmartBlock4Health), IEEE International Conference on

作者： Hanan Hussein Al-asadi Maria Goga Haider Abdullah Ali Ramona Cristina Popa Awodele Oludele Faculty of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest Romania University of Technology-Iraq Baghdad Iraq UTCB Department for Training of Teaching Personnel Technical University of Civil Engineering of Bucharest Bucharest Romania Madenat Alelem University College Baghdad Iraq Faculty of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest Romania computer science department Babcock University Ogun State Nigeria

ISBN: (纸本)9781665460156

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy.

关键词： Smart agriculture Machine learning algorithms Costs Green buildings Soil Control systems Agriculture

来源：评论

学校读者我要写书评

暂无评论

Text classification based on Multi-granularity attention Hybrid Neural Network

arXiv

引用

arXiv 2020年

作者： Liu, Zhenyu Huang, Haiwei Lu, Chaohong Lyu, Shengfei Tao, Zhenchao Department of Science and Technology Teaching China University of Political Science and Law Beijing100088 China School of Computer and Science Technology University of Science and Technique of China Hefei Anhui230027 China First Affiliated Hospital University of Science and Technique of China Hefei Anhui230027 China

Neural network-based approaches have become the driven forces for Natural Language Processing (NLP) tasks. Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet). RNNs are good at modeling long-term dependencies over input texts, but preclude parallel computation. ConvNets do not have memory capability and it has to model sequential data as un-ordered features. Therefore, ConvNets fail to learn sequential dependencies over the input texts, but it is able to carry out high-efficient parallel computation. As each neural architecture, such as RNN and ConvNets, has its own pro and con, integration of different architectures is assumed to be able to enrich the semantic representation of texts, thus enhance the performance of NLP tasks. However, few investigation explores the reconciliation of these seemingly incompatible architectures. To address this issue, we propose a hybrid architecture based on a novel hierarchical multi-granularity attention mechanism, named Multi-granularity Attention-based Hybrid Neural Network (MahNN). The attention mechanism is to assign different weights to different parts of the input sequence to increase the computation efficiency and performance of neural models. In MahNN, two types of attentions are introduced: the syntactical attention and the semantical attention. The syntactical attention computes the importance of the syntactic elements (such as words or sentence) at the lower symbolic level and the semantical attention is used to compute the importance of the embedded space dimension corresponding to the upper latent semantics. We adopt the text classification as an exemplifying way to illustrate the ability of MahNN to understand texts. The experimental results on a variety of datasets demonstrate that MahNN outperforms most of the state-of-the-arts for text classification. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Design of Named Entity Recognition Model for Awingi Language Using Deep Learning Approach

Design of Named Entity Recognition Model for Awingi Language...

引用

IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Abebe Belew Kassahun Eshetie Derb Amogne Andualem Ayalew Ayodeji Olalekan Salau Sepiribo Lucky Braide Murtala Aminu Buhari Teaching Learning and Technology Team Leader ICT Executive Injibara University Injibara Ethiopia Department of Information Technology School of Computing Institute of Technology Debre Markos University Debre Markos Ethiopia Department of Information System College of Engineering and Technology Injibara University Injibara Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Rivers State University Port Harcourt Nigeria School of Science Engineering & Environment University of Salford United Kingdom

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Named entity recognition is a field of NLP that aims to detect and extract relevant information from unstructured text documents. However, some Ethiopian languages were getting started in different research fields. One of the languages spoken in Ethiopia is called Awingi language has a rich morphology and it is highly lacking in computational linguistic tools. There is some NER research done in Ethiopian language using different approaches and classification techniques, but since there was a lack of study conducted on NER for the Awingi language. This study aims to design a named entity recognition model for the Awngi Language using a deep learning approach with word2vect word embedding techniques. The data collected from AMICO Hiber Awiꬼgi program was posted on social media pages, Awngi Language and Literature Department of Injibara University, and Awi Zone Educational and Training Department. Therefore, the study uses a newly tagged dataset with 37,133 tokens. Presently, researchers focused on discovering four main named entities such as persons, locations, Dates, and organizations from unstructured Awiꬼgi text by using the three algorithms namely convolutional neural networks, long short-term memory, and bi-directional long-short-term memory. For the feature engineering task, Word2vec and one-hot encoding are utilized. To identify the best-performing model, the researchers have conducted several experiments. Over other algorithms, the Bi-LSTM algorithm before sampling and word embedding performed better results, i.e 95.4%, 92.4%, 95.3%, and 91.2% Accuracy, precision, recall, and f1-score, respectively, and after sampling and word embedding it performed with 99.4%, 97.3%, 99.2%, and 97.2% Accuracy, precision, recall, and f1-score, respectively.

关键词： Deep learning Training Humanities Accuracy Social networking (online) Morphology Named entity recognition Organizations Encoding Long short term memory

来源：评论

学校读者我要写书评

暂无评论

What is STEM? Preservice elementary teachers’ conceptions of integrated STEM education

引用

teaching and Teacher Education 2025年 165卷

作者： Jeanna R. Wieselmann Deepika Menon Brynn C. Price Allison Johnson Sumreen Asim Sarah Haines Grace Morison Department of Teaching and Learning Southern Methodist University United States Department of Teaching Learning & Teacher Education and Center for Science Mathematics & Computer Education University of Nebraska – Lincoln United States SMU Libraries Southern Methodist University United States School of Education Indiana University Southeast United States Department of Biological Sciences Towson University United States

Teachers’ conceptions can relate to their teaching practices, but integrated science, technology, engineering, and mathematics (STEM) education does not have a consistent definition. This study included 627 preservice elementary preservice teachers across 11 different teacher preparation programs to identify common conceptions of integrated STEM education and how these conceptions shifted during science methods coursework. Findings from survey and interview data showed that participants commonly referred to the STEM acronym and surface-level integration across and beyond STEM disciplines. Key experiences that shaped their conceptions and preparedness to teach included planning and implementing STEM lessons and discussing quality integrated STEM instruction.

关键词： STEM integration Preservice teacher preparation Teacher conceptions Preservice elementary teachers

来源：评论

学校读者我要写书评

暂无评论

An Efficient Polynomial-Time Approximation Scheme for Parallel Multi-Stage Open Shops

SSRN

引用

SSRN 2022年

作者： Dong, Jianming Jin, Ruyan Lin, Guohui Su, Bing Tong, Weitian Xu, Yao School of Management and E-Business Contemporary Business and Trade Research Center Zhejiang Gongshang University Zhejiang Hangzhou310018 China Public course teaching and Research Office Taizhou Vocational and Technical College Zhejiang Taizhou318000 China Department of Computing Science University of Alberta EdmontonABT6G 2E8 Canada School of Economics and Management Xi’an Technological University Xi’an China Department of Computer Science Georgia Southern University StatesboroGA30458 United States

Various new scheduling problems have been arising from real-world applications and spawning new research areas in the scheduling field. We study the parallel multi-stage open shops problem, which generalizes the classic open shop scheduling and parallel machine scheduling problems. Given m identical k-stage open shops and a set of n jobs, we aim to process all jobs on these open shops with the minimum makespan, i.e., the completion time of the last job, under the constraint that job preemption is not allowed. We present an efficient polynomial-time approximation scheme (EPTAS) for the case when both m and k are constant. The main idea for our EPTAS is the combination of several categorization, scaling, and linear programming rounding techniques. Jobs and/or operations are first scaled and then categorized carefully into multiple types so that different types of jobs and/or operations are scheduled appropriately without increasing the makespan too much. © 2022, The Authors. All rights reserved.

关键词： Linear programming

来源：评论

学校读者我要写书评

暂无评论

Evaluation of storage and functioning characteristics of artificial neural networks on the basis of a neurocomputer device

引用

IOP Conference Series: Materials science and Engineering 2020年第1期734卷

作者： V A Romanchuk Department of computer science computer engineering and methods of teaching of computer science Ryazan State University named for S. Yesenin Ryazan Russia

A solution to the problem of evaluating the temporal and quantitative characteristics of the storage and processing of artificial neural networks based on a neurocomputer device is proposed. The most popular and used topologies of artificial neural networks are considered, for which analytical relationships are presented that allow evaluating the training cycle of an artificial neural network, the amount of memory needed and the amount of transmitted data. The proposed relationships differ in that approaches and characteristics unique to neuroprocessor devices are offered for evaluation when implementing the calculations presented in the neural network logical basis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：