找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Housing Markets and Housing Institutions: An International Comparison; Björn Hårsman,John M. Quigley Book 1991 Kluwer Academic Publishers

[复制链接]
楼主: digestive-tract
发表于 2025-3-26 22:32:09 | 显示全部楼层
发表于 2025-3-27 01:18:51 | 显示全部楼层
发表于 2025-3-27 07:05:30 | 显示全部楼层
The Vienna Housing Market: Structure, Problems, and Policies,on has a strong tradition in Austria. Rent control and tenant security legislation, in varying forms, have been continuing features of government housing policy since the First World War, as have government incentives for new housing construction.
发表于 2025-3-27 11:24:03 | 显示全部楼层
Glasgow: From Mean City to Miles Better,g conditions. More recently the city has gained a reputation for innovative rehabilitation programs. With a current population of just under three-quarters of a million people, Glasgow forms the core of a relatively large metropolitan area. The functional metropolitan area, which is roughly cotermin
发表于 2025-3-27 17:29:24 | 显示全部楼层
er, GAEs tend to over-emphasize proximity information at the expense of structural information, leading to relatively poor performance on some downstream tasks such as node classification. To address this issue, we propose a novel GAE framework via community, named Community aware Masked Graph AutoE
发表于 2025-3-27 21:37:55 | 显示全部楼层
发表于 2025-3-27 22:27:06 | 显示全部楼层
Alex Anas,Ulf Jirlow,Björn Hårsman,Folke Snickarsods. Recent years have witnessed a surge in datasets and computational methods for detecting abusive language, reflecting the growing interest in combating online abuse. Deep learning, in particular, has emerged as a powerful tool for addressing this pervasive issue. This paper presents a novel cont
发表于 2025-3-28 03:44:50 | 显示全部楼层
发表于 2025-3-28 06:37:49 | 显示全部楼层
Leo van Wissen,Peter Nijkamp,Annemarie Rimaods. Recent years have witnessed a surge in datasets and computational methods for detecting abusive language, reflecting the growing interest in combating online abuse. Deep learning, in particular, has emerged as a powerful tool for addressing this pervasive issue. This paper presents a novel cont
发表于 2025-3-28 13:50:44 | 显示全部楼层
John A. Hird,John M. Quigley,Michael L. Wiseman, etc.. Serving as a low-latency data source, they provide a crucial avenue for understanding the progression and consequences of disasters. Therefore, recognizing these incidents from images on social media is a highly valuable research problem. Traditional methods involve collecting raw images fro
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 吾爱论文网 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
QQ|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-8-5 09:17
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表