检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Niu, Shuaicheng Wu, Jiaxiang Xu, Guanghui Zhang, Yifan Guo, Yong Zhao, Peilin Wang, Peng Tan, Mingkui School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Tencent AI Lab China National University of Singapore Singapore Northwestern Polytechnical University China Pazhou Laboratory China

In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for learning: given the increasing data volume or the number of classes, one has to instantaneously adjust the neural model capacity to obtain promising performance. Existing methods either ignore the growing nature of data or seek to independently search an optimal architecture for a given dataset, and thus are incapable of promptly adjusting the architectures for the changed data. To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data. Specifically, we introduce an architecture adjuster to generate a suitable architecture for each data snapshot, based on the previous architecture and the different extent between current and previous data distributions. Furthermore, we propose an adaptation condition to determine the necessity of adjustment, thereby avoiding unnecessary and time-consuming adjustments. Extensive experiments on two growth scenarios (increasing data volume and number of classes) demonstrate the effectiveness of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Environmental analysis on co-processing of domestic waste in cement kiln 20th

Environmental analysis on co-processing of domestic waste in...

引用

20th Chinese Materials Conference, CMC 2019

作者： Yang, Xue Lin, Xinping Wang, Yanjing Liu, Yu Hao, Liwei Sun, Boxue National Engineering Laboratory for Industrial Big-data Application Technology Beijing100124 China Beijing University of Technology The Key Laboratory of Advanced Functional Materials Ministry of Education of China Beijing100124 China Beijing Building Materials Academy of Sciences Research State Key Laboratory of Solid Waste Reuse for Building Materials Beijing100041 China

ISBN: (纸本)9783035715668

The annual output of domestic waste has exceeded 400 million tons in China since 2016, which causes an urgent demand on harmless disposal in the current rapid process of urbanization. Compared with traditional disposal means (e.g. incineration and landfill), co-processing with cement production seems to be a better chose to meet the requirements of domestic waste treatment (i.e. harmless, volume reduction, waste utilization). However, co-processing technology also has some negative impact (e.g. extra energy consumption for pretreatment of domestic waste), so the environmental feasibility of domestic waste co-processing in cement kiln should be verified. In this paper, the influences caused by domestic waste co-processing on the cement clinker products including resource/energy consumption, as well as the pollutant emissions were quantify based on the investigation of typical plants in China. Moreover, the environment impact between landfill treatment and co-processing scenario were compared using life cycle assessment (LCA) method. The result shows that the energy consumption and CO2 emission per ton cement clinker production increased by 3.6% and 0.8% after co-process domestic waste, respectively. Furthermore, compared with sanitary landfill treatment, the co-processing in cement kiln will increase the impact of FFP, but reduce GWP and HTP impacts, especially significantly decrease the impact of LOP and SOP, bring in a good beneficial on material saving and energy saving. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Life cycle

来源：评论

学校读者我要写书评

暂无评论

Graph convolutional network for visual question answering based on fine-grained question representation 5

Graph convolutional network for visual question answering ba...

引用

5th IEEE International Conference on data Science in Cyberspace, DSC 2020

作者： Hu, Ze Wei, Jielong Huang, Qingbao Liang, Hanyu Zhang, Xingmao Liu, Qingguang Guangxi University School of Electrical Engineering Nanning Guangxi China School of Software Engineering South China University of Technology Guangzhou Guangdong China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Guangxi Arts University General Education Department Nanning Guangxi China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195582

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Recent research works mainly focus on the bilinear fusion methods and attention mechanism methods to explore the global features and local features, respectively. When an image exists multiple objects, the fine-grained question features can effectively identify the relationship between objects in an image. Therefore we propose a graph convolutional network based on fine-grained question representation (FQ-GCN). The object relation graph is firstly constructed. The fine-grained question features are used to explore the relations between objects in an image and prune unrelated edges between object nodes. Graph convolutional network is used to aggregate all neighborhood information of each object in an object graph. Experiments on the VQA 2.0 dataset show that the performance of our FQ-GCN model improves 2% ~ 3% than the classical methods. © 2020 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization

arXiv

引用

arXiv 2023年

作者： Wu, Yanan Chi, Zhixiang Wang, Yang Plataniotis, Konstantinos N. Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Edward S Rogers Sr. ECE Department University of Toronto TorontoM5S3G8 Canada Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and domain information is separately embedded in the weight matrix and batch normalization (BN) layer. Previous works normally update the whole network naively without explicitly decoupling the knowledge between label and domain. As a result, it leads to knowledge interference and defective distribution adaptation. In this work, we propose to reduce such learning interference and elevate the domain knowledge learning by only manipulating the BN layer. However, the normalization step in BN is intrinsically unstable when the statistics are re-estimated from a few samples. We find that ambiguities can be greatly reduced when only updating the two affine parameters in BN while keeping the source domain statistics. To further enhance the domain knowledge extraction from unlabeled data, we construct an auxiliary branch with label-independent self-supervised learning (SSL) to provide supervision. Moreover, we propose a bi-level optimization based on meta-learning to enforce the alignment of two learning objectives of auxiliary and main branches. The goal is to use the auxiliary branch to adapt the domain and benefit main task for subsequent inference. Our method keeps the same computational cost at inference as the auxiliary branch can be thoroughly discarded after adaptation. Extensive experiments show that our method outperforms the prior works on five WILDS real-world domain shift datasets. Our method can also be integrated with methods with label-dependent optimization to further push the performance boundary. Our code is available at https://***/ynanwu/MABN. © 2023, CC BY.

关键词： Time domain analysis

来源：评论

学校读者我要写书评

暂无评论

LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid Network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

关键词：

来源：评论

学校读者我要写书评

暂无评论

ADVISor: Automatic Visualization Answer for Natural-Language Question on Tabular data

ADVISor: Automatic Visualization Answer for Natural-Language...

引用

Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Can Liu Yun Han Ruike Jiang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Beijing China

We propose an automatic pipeline to generate visualization with annotations to answer natural-language questions raised by the public on tabular data. With a pre-trained language representation model, the input natural language questions and table headers are first encoded into vectors. According to these vectors, a multi-task end-to-end deep neural network extracts related data areas and corresponding aggregation type. We present the result with carefully designed visualization and annotations for different attribute types and tasks. We conducted a comparison experiment with state-of-the-art works and the best commercial tools. The results show that our method outperforms those works with higher accuracy and more effective visualization.

关键词： Annotations Natural languages Pipelines Neural networks data visualization Tools data mining

来源：评论

学校读者我要写书评

暂无评论

Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement

Multi-Channel Hypergraph Convolution Group Recommendation wi...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Tianhao Chen Qian Gao Jun Fan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards efficient local causal structure learning

arXiv

引用

arXiv 2021年

作者： Yang, Shuai Wang, Hao Yu, Kui Cao, Fuyuan Wu, Xindong The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China The School of Computer and Information Technology Shanxi University Taiyuan030006 China The Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei230601 China Mininglamp Academy of Sciences Mininglamp Technology Beijing100102 China

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

A study on improved mrmf algorithm on uncertain graph 2nd

A study on improved mrmf algorithm on uncertain graph

引用

2nd International Cognitive Cities Conference, IC3 2019

作者： Wen, Kejie Yang, Juan Liu, Wei Yang, Yanchun Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing211189 China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China

ISBN: (纸本)9789811561122

With the increased size and density of graph, the performance of the space decomposition-based algorithm (SDBA) decreases rapidly in terms of the most reliable maximum flow problem (MRMF). To solve this problem, the double filter space decomposition-based algorithm (DF-SDBA) is proposed by applying the probability filter and the cut-set filter. First, among the intervals to be processed in the process of space decomposition, DF-SDBA algorithm uses the probability constraint to filter out the intervals which own smaller probability of the lower boundary distribution than the current maximum flow, reducing the number of iterations effectively. Then the cut-set constraint is applied to unspecified intervals. According to the rule that all the edges in the cut-sets must exist in the upper bound of each interval, the unspecified intervals are filtered out, cutting down the number of partition intervals once again. The experimental results reveal that compared with the SDBA algorithm, the DF-SDBA algorithm can reduce the number of the intervals needed to be divided can obtain better performance. © Springer Nature Singapore Pte Ltd 2020.

关键词： Flow graphs

来源：评论

学校读者我要写书评

暂无评论

Parallel generated method of transcriptional regulatory networks

Parallel generated method of transcriptional regulatory netw...

引用

作者： Liu, Shuai Ta, Na Lu, Mengye Liu, Gaocheng Bai, Weiling Li, Wenhui College of Computer Science Inner Mongolia University Hohhot010012 China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot010012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China

Generated method of transcriptional regulatory networks remains an important research in biology. Many approaches have been proposed to construct transcriptional regulatory networks. However, with the increase of ChIP-seq and RNA-seq data, the speed of constructing transcriptional regulation networks is still a challenge. Moreover, parallel computing lacks application in constructing gene regulatory networks through the analysis of the relationships between transcription factors (TFs) and target genes (TGs). Therefore, in this paper, a parallel generated method of transcriptional regulatory networks was proposed. First, two datasets, Michigan Cancer Foundation – 7 (MCF-7) and Cardiomyocytes (CM) were applied. Then, a parallel method was used to generate transcriptional regulatory network with their transcription factors (TFs) and target genes (TGs). Finally, experimental results showed that 61% regulatory relations in MCF-7 were validated in the Gene Expression Omnibus (GEO), while 29% results needed further experimental verification. Besides, 56% regulatory relations in CM were consistent with GEO, while 33% results were not yet verified. Furthermore, speed of parallel algorithm was faster than traditional serial algorithm in generating transcriptional regulatory networks. © 2018 John Wiley & Sons, Ltd.

关键词： Transcription factors

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：