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What Have Urban Digital Twins Contributed to Urban Planning and Decision Making? From a Systematic Literature Review Toward a Socio-Technical Research and Development Agenda
Published on 2025-02-13 by Shervin Azadi, Dena Kasraian, Pirouz Nourian, Pieter van Wesemael @MDPI
Abstract: Urban digital twins (UDTs) were first discussed in 2018. Seven years later, we ask: What has been their contribution to urban planning and decision making so far? Here, we systematically review 88 peer-reviewed articles to map and compare UDTs’ ambitions with their realized contributions. Our results indicate that despite the vast technical developments, socio-technical challenges have remained largely unaddressed, causing many of UDTs’ ambitions to remain unrealized.[...]
Our summary: Systematic literature review identifies challenges in realizing the ambitions of Urban Digital Twins for urban planning and decision making, proposing an Augmented Urban Planning agenda.
Urban Digital Twins, Urban Planning, Decision Making, Socio-Technical
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
Published on 2024-05-25 by Sohee Yang, Jonghyeon Kim, Joel Jang, Seonghyeon Ye, Hyunji Lee, Minjoon Seo @MIT
Abstract: Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal prompt among the candidates for a given task but have failed to provide a comprehensive and fair comparison between each other. In this paper, we propose a unified framework to interpret and evaluate the existing probability-based prompt selection methods by performing extensive experiments on 13 common and diverse NLP ta[...]
Our summary: Evaluation of probability-based prompt selection methods through unified framework, Improving prompt selection effectiveness through combinatorial variants of mutual information, Introducing Calibration by Marginalization method for unbiased prompt selection, Achieving high performance in prompt selection without calibration by maximizing mutual information.
prompt selection, probability-based, unified evaluation, analysis, NLP tasks
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Metric-Free Learning Network with Dual Relations Propagation for Few-Shot Aspect Category Sentiment Analysis
Published on 2024-02-03 by Shiman Zhao, Yutao Xie, Wei Chen, Tengjiao Wang, Jiahui Yao, Jiabin Zheng @MIT
Abstract: Few-shot Aspect Category Sentiment Analysis (ACSA) is a crucial task for aspect-based sentiment analysis, which aims to detect sentiment polarity for a given aspect category in a sentence with limited data. However, few-shot learning methods focus on distance metrics between the query and support sets to classify queries, heavily relying on aspect distributions in the embedding space. Thus, they suffer from overlapping distributions of aspect embeddings caused by irrelevant sentiment noise among[...]
Our summary: Metric-Free Learning Network with Dual Relations Propagation for Few-Shot Aspect Category Sentiment Analysis. Crucial task for aspect-based sentiment analysis. Proposes metric-free method using Dual Relations Propagation. Achieves improvement in accuracy and F1 score.
learning, network, relations, propagation, sentiment
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