195 research outputs found

    Heterogeneous Information and Appraisal Smoothing

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    This study examines the heterogeneous appraiser behavior and its implication on the traditional appraisal smoothing theory. We show that the partial adjustment model is consistent with the traditional appraisal smoothing argument only when all the appraisers choose the same smoothing technique. However, if appraiser behavior is heterogeneous and exhibits cross-sectional variation due to the difference in their access to, and interpretation of information, the model actually leads to a mixed outcome: The variance of the appraisal-based returns can be higher or lower than the variance of transaction-based return depending on the degree of such heterogeneity. Using data from the residential market, we find that, contrary to what the traditional appraisal smoothing theory would predict, appraisal-based indices may not suffer any “smoothing” bias. These findings suggest that the traditional appraisal smoothing theory, which fails to consider the heterogeneity of appraiser behaviors, exaggerates the effect of appraisal smoothing.

    IC classifier: a classifier for 3D industrial components based on geometric prior using GNN

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    In this paper, we propose an approach to address the problem of classifying 3D industrial components by introducing a novel framework named IC-classifier (Industrial Component classifier). Our framework is designed to focus on the object's local and global structures, emphasizing the former by incorporating specific local features for embedding the model. By utilizing graphical neural networks and embedding derived from geometric properties, IC-classifier facilitates the exploration of the local structures of the object while using geometric attention for the analysis of global structures. Furthermore, the framework uses point clouds to circumvent the heavy computation workload. The proposed framework's performance is benchmarked against state-of-the-art models, demonstrating its potential to compete in the field.Comment: 15 pages including citations, 3 pages of figure

    How Tax Credits Have Affected the Rehabilitation of the Boston Office Market

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    This paper is concerned with the extent to which rehabilitation tax credits affect the conditional probability of commercial real estate rehabilitation. Very little has been written about the rehabilitation tax credit, despite the fact that it has been a feature of the U.S. tax code since 1978. Our analysis suggests that rehabilitation tax credits have been a significant determinant of the conditional probability of rehabilitation in the Boston office market. We also find that a significant portion of rehabilitation tax-credit investment is investment that would have been invested elsewhere, about 60 to 65 percent in certain periods, but rising to as high as 90 percent in other periods. We find that the rehabilitation tax credit has a significant and substantial influence on the conditional probability of rehabilitation. We also find that the greatest amount of slippage, not too surprisingly, generally occurs when the tax credit is low and when the gain from rehabilitation before the tax credit is high.

    Ultraconserved coding regions outside the homeobox of mammalian Hox genes

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    <p>Abstract</p> <p>Background</p> <p>All bilaterian animals share a general genetic framework that controls the formation of their body structures, although their forms are highly diversified. The Hox genes that encode transcription factors play a central role in this framework. All Hox proteins contain a highly conserved homeodomain encoded by the homeobox motif, but the other regions are generally assumed to be less conserved. In this study, we used comparative genomic methods to infer possible functional elements in the coding regions of mammalian Hox genes.</p> <p>Results</p> <p>We identified a set of ultraconserved coding regions (UCRs) outside the homeobox of mammalian Hox genes. Here a UCR is defined as a region of at least 120 nucleotides without synonymous and nonsynonymous nucleotide substitutions among different orders of mammals. Further analysis has indicated that these UCRs occur only in placental mammals and they evolved apparently after the split of placental mammals from marsupials. Analysis of human SNP data suggests that these UCRs are maintained by strong purifying selection.</p> <p>Conclusion</p> <p>Although mammalian genomes are known to contain ultraconserved non-coding elements (UNEs), this paper seems to be the first to report the UCRs in protein coding genes. The extremely high degree of sequence conservation in non-homeobox regions suggests that they might have important roles for the functions of Hox genes. We speculate that UCRs have some gene regulatory functions possibly in relation to the development of the intra-uterus child-bearing system.</p

    Learning to Prove Trigonometric Identities

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    Automatic theorem proving with deep learning methods has attracted attentions recently. In this paper, we construct an automatic proof system for trigonometric identities. We define the normalized form of trigonometric identities, design a set of rules for the proof and put forward a method which can generate theoretically infinite trigonometric identities. Our goal is not only to complete the proof, but to complete the proof in as few steps as possible. For this reason, we design a model to learn proof data generated by random BFS (rBFS), and it is proved theoretically and experimentally that the model can outperform rBFS after a simple imitation learning. After further improvement through reinforcement learning, we get AutoTrig, which can give proof steps for identities in almost as short steps as BFS (theoretically shortest method), with a time cost of only one-thousandth. In addition, AutoTrig also beats Sympy, Matlab and human in the synthetic dataset, and performs well in many generalization tasks