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  1. This research presents a comprehensive review of the research on smart urban energy retrofit decision-making. Based on the analysis of 91 journal articles over the past decade, the study identifies and discusses five key categories of approaches to retrofit decision-making, including simulation, optimization, assessment, system integration, and empirical study. While substantial advancements have been made in this field, opportunities for further growth remain. Findings suggest directions for future research and underscore the importance of interdisciplinary collaboration, data-driven evaluation methodologies, stakeholder engagement, system integration, and robust and adaptable retrofit solutions in the field of urban energy retrofitting. This review provides valuable insights for researchers, policymakers, and practitioners interested in advancing the state of the art in this critical area of research to facilitate more effective, sustainable, and efficient solutions for urban energy retrofits. 
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    Free, publicly-accessible full text available June 1, 2024
  2. Recently, evidence for a conducting surface state (CSS) below 19 K was reported for the correlatedd-electron small gap semiconductor FeSi. In the work reported herein, the CSS and the bulk phase of FeSi were probed via electrical resistivity ρ measurements as a function of temperatureT, magnetic fieldBto 60 T, and pressurePto 7.6 GPa, and by means of a magnetic field-modulated microwave spectroscopy (MFMMS) technique. The properties of FeSi were also compared with those of the Kondo insulator SmB6to address the question of whether FeSi is ad-electron analogue of anf-electron Kondo insulator and, in addition, a “topological Kondo insulator” (TKI). The overall behavior of the magnetoresistance of FeSi at temperatures above and below the onset temperatureTS= 19 K of the CSS is similar to that of SmB6. The two energy gaps, inferred from the ρ(T) data in the semiconducting regime, increase with pressure up to about 7 GPa, followed by a drop which coincides with a sharp suppression ofTS. Several studies of ρ(T) under pressure on SmB6reveal behavior similar to that of FeSi in which the two energy gaps vanish at a critical pressure near the pressure at whichTSvanishes, although the energy gaps in SmB6initially decrease with pressure, whereas in FeSi they increase with pressure. The MFMMS measurements showed a sharp feature atTS≈ 19 K for FeSi, which could be due to ferromagnetic ordering of the CSS. However, no such feature was observed atTS≈ 4.5 K for SmB6.

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  3. This paper studies continual learning (CL) for sentiment classification (SC). In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain. Two natural questions are: Can the system transfer the knowledge learned in the past from the previous tasks to the new task to help it learn a better model for the new task? And, can old models for previous tasks be improved in the process as well? This paper proposes a novel technique called KAN to achieve these objectives. KAN can markedly improve the SC accuracy of both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of KAN is demonstrated through extensive experiments. 
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  4. Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential of turning customer reviews into a large source of knowledge that can be exploited to answer user questions. We call this problem Review Reading Comprehension (RRC). To the best of our knowledge, no existing work has been done on RRC. In this work, we first build an RRC dataset called ReviewRC based on a popular benchmark for aspect-based sentiment analysis. Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC. To show the generality of the approach, the proposed post-training is also applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis. Experimental results demonstrate that the proposed post-training is highly effective. 
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