检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Bilgin, Onur Aka, Pawel M. Vergutz, Thomas Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Reliable weather forecasting is of great importance in science, business, and society. The best performing data-driven models for weather prediction tasks rely on recurrent or convolutional neural networks, where some of which incorporate attention mechanisms. In this work, we introduce a novel model based on Transformer architecture for weather forecasting. The proposed Tensorial Encoder Transformer (TENT) model is equipped with tensorial attention and thus it exploits the spatiotemporal structure of weather data by processing it in multidimensional tensorial format. We show that compared to the classical encoder transformer, 3D convolutional neural networks, LSTM, and Convolutional LSTM, the proposed TENT model can better learn the underlying complex pattern of the weather data for the studied temperature prediction task. Experiments on two real-life weather datasets are performed. The datasets consist of historical measurements from weather stations in the USA, Canada and Europe. The first dataset contains hourly measurements of weather attributes for 30 cities in the USA and Canada from October 2012 to November 2017. The second dataset contains daily measurements of weather attributes of 18 cities across Europe from May 2005 to April 2020. Two attention scores are introduced based on the obtained tonsorial attention and are visualized in order to shed light on the decision-making process of our model and provide insight knowledge on the most important cities for the target cities. © 2021, CC BY-SA.

关键词： Weather forecasting

来源：评论

学校读者我要写书评

暂无评论

Deep brain state classification of MEG data

arXiv

引用

arXiv 2020年

作者： Abdellaoui, Ismail Alaoui Fernandez, Jesus Garcia Sahinli, Caner Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Neuroimaging techniques have shown to be useful when studying the brains activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), in combination with various deep artificial neural network models to perform brain decoding. More specifically, here we investigate to which extent can we infer the task performed by a subject based on its MEG data. Three models based on compact convolution, combined convolutional and long short-term architecture as well as a model based on multi-view learning that aims at fusing the outputs of the two stream networks are proposed and examined. These models exploit the spatio-temporal MEG data for learning new representations that are used to decode the relevant tasks across subjects. In order to realize the most relevant features of the input signals, two attention mechanisms, i.e. self and global attention, are incorporated in all the models. The experimental results of cross subject multi-class classification on the studied MEG dataset show that the inclusion of attention improves the generalization of the models across subjects. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Set matching: An enhancement of the Hales-Jewett pairing strategy

arXiv

引用

arXiv 2017年

作者： Uiterwijk, Jos W.H.M. Department of Data Science and Knowledge Engineering Maastricht University Netherlands

When solving k-in-a-Row games, the Hales-Jewett pairing strategy [4] is a well-known strategy to prove that specific positions are (at most) a draw. It requires two empty squares per possible winning line (group) to be marked, i.e., with a coverage ratio of 2.0. In this paper we present a new strategy, called Set Matching. A matching set consists of a set of nodes (the markers), a set of possible winning lines (the groups), and a coverage set indicating how all groups are covered after every first initial move. This strategy needs less than two markers per group. As such it is able to prove positions in k-in-a-Row games to be draws, which cannot be proven using the Hales-Jewett pairing strategy. We show several efficient configurations with their matching sets. These include Cycle Configurations, BiCycle Configurations, and PolyCycle Configurations involving more than two cycles. Depending on configuration, the coverage ratio can be reduced to 1.14. Many examples in the domain of solving k-in-a-Row games are given, including the direct proof (without further investigation) that the empty 4 × 4 board is a draw for 4-in-a-Row. Copyright © 2017, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-view random walk framework for search task discovery from click-through log 11

Multi-view random walk framework for search task discovery f...

引用

20th ACM Conference on Information and knowledge Management, CIKM'11

作者： Cui, Jianwei Liu, Hongyan Yan, Jun Ji, Lei Jin, Ruoming He, Jun Gu, Yingqin Chen, Zheng Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering School of Information Renmin University of China China Department of Management Science and Engineering Tsinghua University China Microsoft Research Asia Beijing China Computer Science Department Kent State University United States

ISBN: (纸本)9781450307178

Search engine users often have clear search tasks hidden behind their queries. Inspired by this, the modern search engines are providing an increasing number of services to help users simplify their key tasks. However, the problem of what are the major user search tasks with high traffic for which search engines should design special services is still underexplored. In this paper, we propose a novel Multi-view Random Walk (MRW) algorithm to measure the search task oriented similarity between queries, and then group search queries with similar tasks so that the major search tasks of users can be identified from search engine click-through log. The proposed MRW, which is a general framework to combine knowledge from different views in a random walk process, allows the random surfer to walk across different views to integrate information for search task discovery. Experimental results on click-through log of a commonly used commercial search engine show that our proposed MRW algorithm can effectively discover user search tasks. © 2011 ACM.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

Computable random variables and conditioning

arXiv

引用

arXiv 2020年

作者： Collins, Pieter Department of Data Science and Knowledge Engineering Maastricht University Netherlands

The aim of this paper is to present an elementary computable theory of random variables, based on the approach to probability via valuations. The theory is based on a type of lower-measurable sets, which are controlled limits of open sets, and extends existing work in this area by providing a computable theory of conditional random variables. The theory is based within the framework of type-two effectivity, so has an explicit direct link with Turing computation, and is expressed in a system of computable types and operations, so has a clean mathematical *** Codes 60A10 03D80 03D78 © 2020, CC BY.

关键词： Random variables

来源：评论

学校读者我要写书评

暂无评论

RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Place Recognition Network 17th

RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Plac...

引用

17th European Conference on Computer Vision, ECCV 2022

作者： Fan, Zhaoxin Song, Zhenbo Zhang, Wenping Liu, Hongyan He, Jun Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Department of Management Science and Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031250552

Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous methods have made delightful achievements. However, how to deal with catastrophic collapse caused by rotation problems is still under-explored. In this paper, to tackle the issue, we propose a novel Point Cloud-based Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular, to solve the problem, we propose to learn rotation-invariant features in three steps. First, we design three kinds of novel Rotation-Invariant Features (RIFs), which are low-level features that can hold the rotation-invariant property. Second, using these RIFs, we design an attentive module to learn rotation-invariant kernels. Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process. By doing so, high-level scene-specific rotation-invariant features can be learned. We call the above process an Attentive Rotation-Invariant Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net, which takes ARIConv as a basic unit to construct a dense network architecture. Then, powerful global descriptors used for retrieval-based place recognition can be sufficiently extracted from RPR-Net. Experimental results on prevalent datasets show that our method achieves comparable results to existing state-of-the-art place recognition models and significantly outperforms other rotation-invariant baseline models when solving rotation problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Machine learning approach to algorithm selection for exact computation of Treewidth

引用

Algorithms 2019年第10期12卷

作者： Slavchev, Borislav Masliankova, Evelina Kelk, Steven Department of Data Science and Knowledge Engineering Maastricht University LK Maastricht6211 Netherlands

We present an algorithm selection framework based on machine learning for the exact computation of treewidth, an intensively studied graph parameter that is NP-hard to compute. Specifically, we analyse the comparative performance of three state-of-the-art exact treewidth algorithms on a wide array of graphs and use this information to predict which of the algorithms, on a graph by graph basis, will compute the treewidth the quickest. Experimental results show that the proposed meta-algorithm outperforms existing methods on benchmark instances on all three performance metrics we use: in a nutshell, it computes treewidth faster than any single algorithm in isolation. We analyse our results to derive insights about graph feature importance and the strengths and weaknesses of the algorithms we used. Our results are further evidence of the advantages to be gained by strategically blending machine learning and combinatorial optimisation approaches within a hybrid algorithmic framework. The machine learning model we use is intentionally simple to emphasise that speedup can already be obtained without having to engage in the full complexities of machine learning engineering. We reflect on how future work could extend this simple but effective, proof-of-concept by deploying more sophisticated machine learning models. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Parallel SimRank computation on large graphs with iterative aggregation

Parallel SimRank computation on large graphs with iterative ...

引用

16th ACM SIGKDD International Conference on knowledge Discovery and data Mining, KDD-2010

作者： He, Guoming Feng, Haijun Li, Cuiping Chen, Hong Key Lab. of Data Engineering and Knowledge Engineering of Ministry of Education China Department of Computer Science Renmin University of China China No. 59 Zhongguancun Street Haidian District Beijing China

ISBN: (纸本)9781450300551

Recently there has been a lot of interest in graph-based analysis. One of the most important aspects of graph-based analysis is to measure similarity between nodes in a graph. SimRank is a simple and influential measure of this kind, based on a solid graph theoretical model. However, existing methods on SimRank computation suffer from two limitations: 1) the computing cost can be very high in practice;and 2) they can only be applied on static graphs. In this paper, we exploit the inherent parallelism and high memory bandwidth of graphics processing units (GPU) to accelerate the computation of SimRank on large graphs. Furthermore, based on the observation that SimRank is essentially a first-order Markov Chain, we propose to utilize the iterative aggregation techniques for uncoupling Markov chains to compute SimRank scores in parallel for large graphs. The iterative aggregation method can be applied on dynamic graphs. Moreover, it can handle not only the link-updating problem but also the node-updating problem. Extensive experiments on synthetic and real data sets verify that the proposed methods are efficient and effective. © 2010 ACM.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

SEED: Entity oriented information search and exploration 22

SEED: Entity oriented information search and exploration

引用

22nd International Conference on Intelligent User Interfaces, IUI 2017

作者： Chen, Jun Jacucci, Giulio Chen, Yueguo Ruotsalo, Tuukka School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781450348935

Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge Graphs allow exploitation and recommendation of explicit links between the entities and other information to improve information access and ranking. However, less attention has been devoted to user interfaces for effectively presenting results, recommending related entities and explaining relations between entities. We introduce a system called SEED which is designed to support entity search and exploration in large knowledge Graphs. We demonstrate SEED using a dataset of hundreds of thousands of movie related entities from the DBpedia knowledge Graph. The system utilizes a graph embedding model for ranking entities and their relations, recommending related entities, and explaining their interrelations. Copyright is held by the author/owner(s).

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： Computerized tomography

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：