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Titlebook: Housing Markets and Housing Institutions: An International Comparison; Bj?rn H?rsman,John M. Quigley Book 1991 Kluwer Academic Publishers

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樓主: digestive-tract
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發(fā)表于 2025-3-26 22:32:09 | 只看該作者
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發(fā)表于 2025-3-27 01:18:51 | 只看該作者
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發(fā)表于 2025-3-27 07:05:30 | 只看該作者
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.
34#
發(fā)表于 2025-3-27 11:24:03 | 只看該作者
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
35#
發(fā)表于 2025-3-27 17:29:24 | 只看該作者
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
36#
發(fā)表于 2025-3-27 21:37:55 | 只看該作者
37#
發(fā)表于 2025-3-27 22:27:06 | 只看該作者
Alex Anas,Ulf Jirlow,Bj?rn H?rsman,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
38#
發(fā)表于 2025-3-28 03:44:50 | 只看該作者
39#
發(fā)表于 2025-3-28 06:37:49 | 只看該作者
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
40#
發(fā)表于 2025-3-28 13:50:44 | 只看該作者
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
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