SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments

Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds. Modernized factories have advanced process control algorithms such as model predictive control (MPC). However, there is little exploration of applying deep reinforcement learning to control manufacturing plants. One of the reasons is the lack of high fidelity simulations and standard APIs for benchmarking. To bridge this gap, we develop an easy-to-use library that includes five high-fidelity simulation environments: BeerFMTEnv, ReactorEnv, AtropineEnv, PenSimEnv and mAbEnv, which cover a wide range of manufacturing processes. We build these environments on published dynamics models. Furthermore, we benchmark online and offline, model-based and model-free reinforcement learning algorithms for comparisons of follow-up research.

To Paraphrase or Not To Paraphrase: User-Controllable Selective Paraphrase Generation

In this article, we propose a paraphrase generation technique to keep the key phrases in source sentences during paraphrasing. We also develop a model called TAGPA with such technique, which has multiple pre-configured or trainable key phrase detector and a paraphrase generator. The paraphrase generator aims to keep the key phrases and increase the diversity of the paraphrased sentences. The key phrases can be entities provided by our user, like company names, people's names, domain-specific terminologies, etc., or can be learned from a given dataset.

Latent Dirichlet Allocation with Residual Convolutional Neural Network Applied in Evaluating Credibility of Chinese Listed Companies

This project demonstrated a methodology to estimating cooperate credibility with a Natural Language Processing approach. As cooperate transparency impacts both the credibility and possible future earnings of the firm, it is an important factor to be considered by banks and investors on risk assessments of listed firms. This approach of estimating cooperate credibility can bypass human bias and inconsistency in the risk assessment, the use of large quantitative data and neural network models provides more accurate estimation in a more efficient manner compare to manual assessment. At the beginning, the model will employs Latent Dirichlet Allocation and THU Open Chinese Lexicon from Tsinghua University to classify topics in articles which are potentially related to corporate credibility. Then with the keywords related to each topics, we trained a residual convolutional neural network with data labeled according to surveys of fund manager and accountant's opinion on corporate credibility. After the training, we run the model with preprocessed news reports regarding to all of the 3065 listed companies, the model is supposed to give back companies ranking based on the level of their transparency.

Representation Learning Models for Entity Search

We focus on the problem of learning distributed representations for entity search queries, named entities, and their short descriptions. With our representation learning models, the entity search query, named entity and description can be represented as low-dimensional vectors. Our goal is to develop a simple but effective model that can make the distributed representations of query related entities similar to the query in the vector space. Hence, we propose three kinds of learning strategies, and the difference between them mainly lies in how to deal with the relationship between an entity and its description. We analyze the strengths and weaknesses of each learning strategy and validate our methods on public datasets which contain four kinds of named entities, i.e., movies, TV shows, restaurants and celebrities. The experimental results indicate that our proposed methods can adapt to different types of entity search queries, and outperform the current state-of-the-art methods based on keyword matching and vanilla word2vec models. Besides, the proposed methods can be trained fast and be easily extended to other similar tasks.