Article Review-Impact Investing

Impact investing was a generally adopted investment strategy that not only aimed to gain financial returns from investment but also attempted to create positive and measurable social or environmental impacts through the investments being made. Investors who followed the impact investing strategy would take into consideration the company’s commitment and engagement in social and environmental responsibility. Impact investing strategy challenged the return-based investment strategy, with observations that many institutional investors, including banks, capital funds and public holding finance companies, were entering the impact investing market. However, whether and to what extent investors were willing to trade their pecuniary gains for the positive social or environmental impacts remained to be explored

Machine learning and residential real estate: Three applications

This thesis is a composite of empirical studies for three topics, automated valuation model (AVM), residential property price index (RPPI), and the analysis of land development. Firstly, machine learning techniques are applied to develop the implementations of AVMs, whose purpose is to provide a price estimate of a particular property at a specified time. The main objective is to minimize human intervention in price estimation when the presence of missing values remains a major challenge in the process. Then, the proposed AVM implementation is applied to compiling the residential property price index, which tracks the trend of market values, cooperating with the classic indexing approaches. The main objective is to investigate whether more accurate price predictions lead to a better price index and examine how well the machine learning techniques explain the time effects. Thirdly, land development is a "real option" that allows the landowner to decide whether and when to develop the vacant land by spending money. The analysis of land development is to examine the real option, including the valuation of the option and the optimal timing to exercise the option. The research uses machine learning techniques with the factors on both the investment output (residential buildings) side and the investment cost (construction cost) side, such as the growths and uncertainties of property prices and construction costs

Efficient Attention: Attention with Linear Complexities

Dot-product attention has wide applications in computer vision and natural language processing. However, its memory and computational costs grow quadratically with the input size. Such growth prohibits its application on high-resolution inputs. To remedy this drawback, this paper proposes a novel efficient attention mechanism equivalent to dot-product attention but with substantially less memory and computational costs. Its resource efficiency allows more widespread and flexible integration of attention modules into a network, which leads to better accuracies. Empirical evaluations demonstrated the effectiveness of its advantages. Efficient attention modules brought significant performance boosts to object detectors and instance segmenters on MS-COCO 2017. Further, the resource efficiency democratizes attention to complex models, where high costs prohibit the use of dot-product attention. As an exemplar, a model with efficient attention achieved state-of-the-art accuracies for stereo depth estimation on the Scene Flow dataset. Code is available at https://github.com/cmsflash/efficient-attention.Comment: To appear at WACV 202

What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization

In this paper, we conduct a comprehensive study of In-Context Learning (ICL) by addressing several open questions: (a) What type of ICL estimator is learned by large language models? (b) What is a proper performance metric for ICL and what is the error rate? (c) How does the transformer architecture enable ICL? To answer these questions, we adopt a Bayesian view and formulate ICL as a problem of predicting the response corresponding to the current covariate, given a number of examples drawn from a latent variable model. To answer (a), we show that, without updating the neural network parameters, ICL implicitly implements the Bayesian model averaging algorithm, which is proven to be approximately parameterized by the attention mechanism. For (b), we analyze the ICL performance from an online learning perspective and establish a $\mathcal{O}(1/T)$ regret bound for perfectly pretrained ICL, where $T$ is the number of examples in the prompt. To answer (c), we show that, in addition to encoding Bayesian model averaging via attention, the transformer architecture also enables a fine-grained statistical analysis of pretraining under realistic assumptions. In particular, we prove that the error of pretrained model is bounded by a sum of an approximation error and a generalization error, where the former decays to zero exponentially as the depth grows, and the latter decays to zero sublinearly with the number of tokens in the pretraining dataset. Our results provide a unified understanding of the transformer and its ICL ability with bounds on ICL regret, approximation, and generalization, which deepens our knowledge of these essential aspects of modern language models

An application of machine learning in real estate economics: What extra benefits could machine learning techniques provide?

Funding Information: The authors would like to thank the financial supports from the School of Accounting, Economics and Finance, Curtin University and the Business School, University of Aberdeen

L’utilisation de la comédie musicale dans l’enseignement du Français Langue Étrangère, dans le cas particulier de la Chine

Au cours des dernières années, la comédie musicale française a gagné en notoriété dans le monde entier. Elles ont donné envie à beaucoup de jeunes Chinois de connaître cette belle langue romantique. Face à cette vague d’apprentissage du français, je me demande s'il est possible pour la comédie musicale d’entrer dans l’enseignement du français langue étrangère (FLE) en Chine. Ce mémoire tentera de démontrer la légitimité de la comédie musicale dans le cadre de l’enseignement des langues étrangères, à l’appui des avantages qu’elle pourrait apporter. De plus, j’essaierai de proposer une unité didactique modèle qui la justifiera. Dans la première partie du mémoire, je rappellerai le contexte social chinois qui facilite l’utilisation de la comédie musicale française en classe de FLE. Puis, la deuxième partie se concentrera sur les théories et les observations de spécialistes, qui pourront justifier la légitimité de cette nouvelle ressource dans le cadre de l’enseignement. La troisième partie présentera comment les enseignants devront organiser des unités didactiques en s’appuyant sur les thèmes fournis par la comédie musicale. Enfin, la dernière partie visera à démontrer le moyen d’utilisation de ce genre de support au travers d’un exemple concret