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

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

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

Develop a Hazard Index Using Machine Learning Approach for the Hazard Identification of Chemical Logistic Warehouses

With the rapid development of chemical process plants, the safe storage of hazardous chemicals become an essential topic. Several chemical warehouse incidents related to fire and explosion have been reported recently. Therefore, an accurate hazard identification method for the logistic warehouse is needed not only for the facility to develop a proper emergency response plan but also for the residents who live near the facility to have an effective hazard communication. Furthermore, the government can better allocate the resources for first responders to make fire protection strategies, and the stakeholders can lead to improved risk management. The storage of hazardous chemicals in a warehouse is a complex problem. The potential hazards include flammability, reactivity, and interaction among different types of hazardous chemicals. Hazard index is a helpful tool to identify and quantify the hazard in a facility or a process unit. Various hazard indices are developed in history. However, the challenge for this research is to improve the current method with the novel technique to implement our purpose. The first objective of this research is to develop a “Storage Hazard Factor” (SHF) to evaluate and rank the inherent hazards of chemicals stored in logistic warehouses. In the factor calculation, the inherent hazard of chemicals is determined by various parameters (e.g., the NFPA rating, the flammability limit, and the protective action criteria values, etc.) and validated by the comparison with other indices. The current criteria for flammable hazard ratings are based on flash point, which is proved to be insufficient. Two machine learning based methods will be used for the classification of liquid flammability considering aerosolization based on DIPPR 801 database. Subsequently, SHF and other warehouse safety penalty factors (e.g., the quantity of the chemicals, the distance to the nearest fire department, etc.) are utilized to identify the Logistic Warehouse Hazard Index (LWHI) of the facilities. In the last chapter, LWHI is applied to an actual case from Houston Chronicle, and several statistical analyses are used to prove that the LWHI is helpful for hazard identification to emergency responders and hazard communication to the public