检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Basilakos, Spyros Paliathanasis, Andronikos Saridakis, Emmanuel N. National Observatory of Athens Lofos Nymfon Athens11852 Greece Academy of Athens Research Center for Astronomy and Applied Mathematics Soranou Efesiou 4 Athens11527 Greece School of Sciences European University Cyprus Diogenes Street Engomi Nicosia1516 Cyprus Institute of Systems Science Department of Mathematics Faculty of Applied Sciences Durban University of Technology Durban4000 South Africa School for Data Science and Computational Thinking Stellenbosch University 44 Banghoek Rd Stellenbosch7600 South Africa Departamento de Matemáticas Universidad Católica del Norte Avda. Angamos 0610 Casilla Antofagasta1280 Chile CAS Key Laboratory for Researches in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Anhui Hefei230026 China

We show that f(Q) cosmology with a non-trivial connection, namely the Connection II of the literature, is dynamically equivalent with a quintom-like model. In particular, we show that the scalar field arising from the non-linear f(Q) form, and the scalar field associated to the non-trivial connection, are combined to provide one canonical and one phantom field in the minisuperspace Lagrangian. Hence, the combination of two well-behaved fields appears effectively as a phantom field. This is fundamentally different from the usual approach in phantom and quintom cosmology, in which the phantom field is introduced ad hoc, and thus it may open a new way to handle the phantom fields, namely as effective realizations of the connection-related canonical fields. Copyright © 2025, The Authors. All rights reserved.

关键词： Phantoms

来源：评论

学校读者我要写书评

暂无评论

ICTNET at TREC 2017 Dynamic Domain Track 26

ICTNET at TREC 2017 Dynamic Domain Track

引用

26th Text REtrieval Conference, TREC 2017

作者： Zhang, Weimin Hu, Yaokang Jia, Rongqian Wang, Xianfa Zhang, Le Feng, Yue Yu, Sihao Xue, Yuanhai Yu, Xiaoming Liu, Yue Cheng, Xueqi Institute of Computing Technology CAS China Key Laboratory of Web Data Science and Technology CAS China University of Chinese Academy of Sciences China

来源：评论

学校读者我要写书评

暂无评论

User Profiling for CSDN:keyphrase Extraction,User Tagging and User Growth Value Prediction

引用

data Intelligence 2019年第2期1卷 137-159页

作者： Guoliang Xing Hao Gao Qi Cao Xinyu Yue Bingbing Xu Keting Cen Huawei Shen Key Laboratory of Network Data Science and Technology Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

The Chinese Software Developer network(CSDN)is one of the largest information technology communities and service platforms in *** paper describes the user profiling for CSDN,an evaluation track of SMP Cup *** contains three tasks:(1)user document keyphrase extraction,(2)user tagging and(3)user growth value *** the first task,we treat keyphrase extraction as a classification problem and train a Gradient-Boosting-Decision-Tree model with comprehensive *** the second task,to deal with class imbalance and capture the interdependency between classes,we propose a two-stage framework:(1)for each class,we train a binary classifier to model each class against all of the other classes independently;(2)we feed the output of the trained classifiers into a softmax classifier,tagging each user with multiple *** the third task,we propose a comprehensive architecture to predict user growth *** contributions in this paper are summarized as follows:(1)we extract various types of features to identify the key factors in user value growth;(2)we use the semi-supervised method and the stacking technique to extend labeled data sets and increase the generality of the trained model,resulting in an impressive performance in our *** the competition,we achieved the first place out of 329 teams.

关键词： User profiling keyphrase extraction User tagging Growth value prediction Word embedding

来源：评论

学校读者我要写书评

暂无评论

A Novel Identification Method for Frequently Switched Encrypted Video Streaming in Complex network Environment 22

A Novel Identification Method for Frequently Switched Encryp...

引用

2022 Asia Conference on Electrical, Power and Computer Engineering, EPCE 2022

作者： Zhang, Y.U. Ma, Xiaowei Leng, Dongpeng Yu, Peiran College of Cyber Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (纸本)9781450396127

Nowadays, to provide stable video streaming and smooth user experience among different scenarios, more and more online video sharing and social media platforms adopt the Dynamic Adaptive Streaming over HTTP (DASH), which can transmit and play videos of appropriate quality levels according to the user's network conditions. For sake of illegal videos spreading on the platforms, the identification of encrypted DASH video streaming is of great importance. However, related works on the identification of encrypted DASH video streaming are seldom concerned with the streaming under poor network conditions that the video quality levels are switched relatively frequently. Therefore, we propose a novel video fingerprint extraction method as well as a novel multi-layer fuzzy matching scheme. So far as we know, we are the first to solve the identification of encrypted DASH video streaming under poor network conditions. And to reduce the overhead we establish a local video fingerprint database using the fingerprint extraction method. Our scheme can identify the encrypted DASH video streaming of frequently switched quality levels using very short-time network traffic sniffing. Experiments show that the time required for traffic sniffing is just 6 seconds and the accuracy exceeds 98%, which is significantly better than state-of-art methods. Besides, our scheme performs well on the identification of streaming in multiple scenarios. © 2022 ACM.

关键词： Video streaming

来源：评论

学校读者我要写书评

暂无评论

HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning

HiSMatch: Historical Structure Matching based Temporal Knowl...

引用

2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Li, Zixuan Hou, Zhongni Guan, Saiping Jin, Xiaolong Peng, Weihua Bai, Long Lyu, Yajuan Li, Wei Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Baidu Inc

A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (subject, relation, object, timestamp) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (query entity, query relation, ?, future timestamp) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the Historical Structure Matching (HiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

ICTNET at TREC 2019 Deep Learning Track 28

ICTNET at TREC 2019 Deep Learning Track

引用

28th Text REtrieval Conference, TREC 2019

作者： Chen, Jiangui Cai, Yinqiong Jiang, Haoquan University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

We participated in the Deep Learning Track at TREC 2019. We built a ranking system which combines a search component based on BM25 and a semantic matching component using pretraining knowledge. Our best run achieves NDCG@10 of 0.6650, MAP of 0.2035. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Sparse Word Embeddings Using 1 Regularized Online Learning

Sparse Word Embeddings Using 1 Regularized Online Learning

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Sun, Fei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the l regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with 1 regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state-of-the-art results with better interpretability using less than 10% non-zero elements. © 2016 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

ICTNET at Trec 2019 Incident Streams Track 28

ICTNET at Trec 2019 Incident Streams Track

引用

28th Text REtrieval Conference, TREC 2019

作者： Guangsheng, Kuang Kun, Zhang Jiabao, Zhang Xin, Zheng University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

Social medial become our public ways to share our information in our lives. Crisis management via social medial is becoming indispensable for its tremendous information. While deep learning shows surprising outcome in many tasks. So in this paper, we cope this learning task with neural network in the view of classification problem. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Wang, Di Hu, Meiqi Jin, Yao Miao, Yuchun Yang, Jiaqi Xu, Yichu Qin, Xiaolei Ma, Jiaqi Sun, Lingyu Li, Chenxing Fu, Chuan Chen, Hongruixuan Han, Chengxi Yokoya, Naoto Zhang, Jing Xu, Minqiang Liu, Lin Zhang, Lefei Wu, Chen Du, Bo Tao, Dacheng Zhang, Liangpei Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring. However, existing HSI processing methods are predominantly task-specific and scene-dependent, which severely limits their ability to transfer knowledge across tasks and scenes, thereby reducing the practicality in real-world applications. To address these challenges, we present HyperSIGMA, a vision transformer-based foundation model that unifies HSI interpretation across tasks and scenes, scalable to over one billion parameters. To overcome the spectral and spatial redundancy inherent in HSIs, we introduce a novel sparse sampling attention (SSA) mechanism, which effectively promotes the learning of diverse contextual features and serves as the basic block of HyperSIGMA. HyperSIGMA integrates spatial and spectral features using a specially designed spectral enhancement module. In addition, we construct a large-scale hyperspectral dataset, HyperGlobal-450 K, for pre-training, which contains about 450 K hyperspectral images, significantly surpassing existing datasets in scale. Extensive experiments on various high-level and low-level HSI tasks demonstrate HyperSIGMA's versatility and superior representational capability compared to current state-of-the-art methods. Moreover, HyperSIGMA shows significant advantages in scalability, robustness, cross-modal transferring capability, real-world applicability, and computational efficiency. The code and models will be released at HyperSIGMA. © 1979-2012 IEEE.

关键词： Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

ICTNET at TREC 2019 News Track 28

ICTNET at TREC 2019 News Track

引用

28th Text REtrieval Conference, TREC 2019

作者： Ding, Yuyang Lian, Xiaoying Zhou, Houquan Liu, Zhaoge Ding, Hanxing Hou, Zhongni University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

This paper describes our work in the background linking task and entity ranking task in TREC 2018 News Track. We explore four methods in background linking task and two methods in entity ranking task. All of our methods largely rely on off-the-shell open-source components(e.g., Solr for indexing the documents), and follow the basic thoughts-BM25 and relevance feedback. These methods differ in how they analyze the given input to obtain a query and to what extent the returned results are re-ranked taking meta data of the document(e.g., publish dates) into account. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

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：