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Categorical Perception 分类知觉
Pub Date : 1900-01-01 DOI: 10.14264/uql.2020.641
Amira N Parker, G. Wallis, Rainer Obergrussberger, U. Siebeck
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引用次数: 0
APL paper data 应用程序纸张数据
Pub Date : 1900-01-01 DOI: 10.14264/ab6f55f
R. Navarathna, Tyler Jones
Contains data for Sample A, fidelities for the 24 hour plot and data for the phase robustness.
包含样本A的数据,24小时图的保真度和相位鲁棒性的数据。
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引用次数: 1
Incentives for forest restoration 森林恢复的激励措施
Pub Date : 1900-01-01 DOI: 10.14264/824d5f6
A. Tedesco, Jonathan Rhodes
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引用次数: 0
Anexos 附件
Pub Date : 1900-01-01 DOI: 10.14264/49ad9fe
Ana Carina Lopez de Winter, S. Furphy
Managing the current data deluge is a great challenge for users. Emails are constantly arriving, notifications of tweets and RSS feeds keep popping out, newspapers and blogs of different types publish potentially-relevant news every day, etc. If a user wants to keep track of certain topics in an efficient way, a careful filtering is needed in order to keep the number of items to review manageable, as otherwise the user may finally give up or just perform some random or casual reading. Automated tools can help the user to perform this initial selection, and thus to minimize the feeling of being overwhelmed that the user may experience. In this short paper, we present our ongoing work for the development of DodoAid, a recommender of digital objects that attempts to alleviate the current user’s overload when he/she wants to follow information about certain topics. Beyond the application of information retrieval and text mining techniques, it can also apply techniques from the field of recommender systems to suggest items that not only fit topics of interest for the user but are also expected to be valuable according to the individual user’s preferences, which can be learnt automatically in an implicit way.
管理当前的海量数据对用户来说是一个巨大的挑战。电子邮件不断到达,推特和RSS订阅的通知不断弹出,不同类型的报纸和博客每天都发布可能相关的新闻,等等。如果用户希望以有效的方式跟踪某些主题,则需要仔细过滤,以保持要查看的项目数量可管理,否则用户可能最终放弃或只是进行一些随机或随意的阅读。自动化工具可以帮助用户执行这个初始选择,从而最大限度地减少用户可能体验到的不知所措的感觉。在这篇短文中,我们介绍了我们正在进行的开发DodoAid的工作,DodoAid是一个数字对象的推荐器,它试图减轻当前用户想要关注某些主题信息时的过载。除了信息检索和文本挖掘技术的应用之外,它还可以应用推荐系统领域的技术,根据用户的个人偏好,建议既适合用户感兴趣的主题,又有价值的项目,这些项目可以以隐式的方式自动学习。
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引用次数: 0
Dosage dependent increases in mind wandering via prefrontal tDCS 剂量依赖性地通过前额叶tDCS增加走神
Pub Date : 1900-01-01 DOI: 10.14264/UQL.2019.295
H. Filmer, A. Griffin
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引用次数: 4
External Support to Non-State Armed Groups and Blowback 1980-2017: (Version 1.2) 1980-2017年对非国家武装团体的外部支持和反击:(1.2版)
Pub Date : 1900-01-01 DOI: 10.14264/UQL.2019.360
Mohsen Solhdoost
This dataset is developed on the basis of the particular cases included in External Support to Non-State Armed Groups (NSAGs) 1980-2017: A New Dataset created by Solhdoost (2019). This dataset focuses on activities undertaken by NSAGs and includes information on different categories of blowback that have occurred following external support to NSAGs covering the time period 1980-2017.
该数据集是根据《1980-2017年对非国家武装团体的外部支持:Solhdoost(2019)创建的新数据集》中包含的特定案例开发的。该数据集侧重于国家安全组织开展的活动,并包括1980-2017年期间国家安全组织获得外部支持后发生的不同类别反作用的信息。
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引用次数: 0
esys-escript 5.3
Pub Date : 1900-01-01 DOI: 10.14264/UQL.2019.510
Adam Ellery, Joel Fenwick, Lutz Gross
Escript is a programming tool for implementing mathematical models in python using the finite element method (FEM). As users do not access the data structures it is very easy to use and scripts can run on desktop computers as well as highly parallel supercomputer without changes. Application areas for escript include earth mantle convection, geophysical inversion, earthquakes, porous media flow, reactive transport, plate subduction, erosion, and tsunamis.
Escript是一个编程工具,用于在python中使用有限元方法(FEM)实现数学模型。由于用户不访问数据结构,它非常容易使用,脚本可以在桌面计算机以及高度并行的超级计算机上运行而无需更改。escript的应用领域包括地幔对流、地球物理反演、地震、多孔介质流动、反应输运、板块俯冲、侵蚀、海啸等。
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引用次数: 3
MRI collection of mouse model concussion 小鼠脑震荡模型的MRI采集
Pub Date : 1900-01-01 DOI: 10.14264/DE9AA47
X. To, F. Nasrallah
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引用次数: 1
Behavioral and electroencephalography recordings of human observers during a decision-making task with varying levels of task difficulty 在不同难度的决策任务中人类观察者的行为和脑电图记录
Pub Date : 1900-01-01 DOI: 10.14264/uql.2019.783
Dragan Rangelov, J. Mattingley, Rebecca K West
Using computational modelling of human behavior together with concurrent recording of brain activity by means of electroencephalography, this project aims to characterize neural and cognitive mechanisms of human decision making.
利用人类行为的计算模型和脑电图同时记录的大脑活动,本项目旨在描述人类决策的神经和认知机制。
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引用次数: 0
QTIM_FreeSurfer_Phenotypes QTIM_FreeSurfer_Phenotypes
Pub Date : 1900-01-01 DOI: 10.14264/uql.2019.868
Margaret J. Wright, K. Mcmahon, G. D. de Zubicaray, Paul M. Thompson
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引用次数: 0
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UQ eSpace
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