Quantifying and Explaining Causal Effects of World Bank Aid Projects

In recent years, machine learning methods have enabled us to predict with good precision using large training data, such as deep learning. However, for many problems, we care more about causality than prediction. For example, instead of knowing that smoking is statistically associated with lung cancer, we are more interested in knowing that smoking is the cause of lung cancer. With causality, we can understand how the world progresses and how impacts are made on an outcome by influencing the cause. This thesis explores how to quantify the causal effects of a treatment on an observable outcome in the presence of heterogeneity. We focus on investigating the causal impacts that World Bank projects have on environmental changes. This high dimensional World Bank data set includes covariates from various sources and of different types, including time series data, such as the Normalized Difference Vegetation Index (NDVI) values, temperature and precipitation, spatial data such as longitude and latitude, and many other features such as distance to roads and rivers. We estimate the heterogeneous causal effect of World Bank projects on the change of NDVI values. Based on causal tree and causal forest proposed by Athey, we described the challenges we met and lessons we learned when applying these two methods to an actual World Bank data set. We show our observations of the heterogeneous causal effect of the World Bank projects on the change of environment. as we do not have the ground truth for the World Bank data set, we validate the results using synthetic data for simulation studies. The synthetic data is sampled from distributions fitted with the World Bank data set. We compared the results among various causal inference methods and observed that feature scaling is very important to generating meaningful data and results. in addition, we investigate the performance of the causal forest with various parameters such as leaf size, number of confounders, and data size. Causal forest is a black-box model, and the results from it cannot be easily interpreted. The results are also hard for humans to understand. By taking advantage of the tree structure, the neighbors of the project to be explained are selected. The weights are assigned to the neighbors according to dynamic distance metrics. We can learn a linear regression model with the neighbors and interpret the results with the help of the learned linear regression model. in summary, World Bank projects have small impacts on the change to the environment, and the result of an individual project can be interpreted using a linear regression model learned from closed projects

Investigation of the tetraquark states QqQˉqˉQq\bar{Q} \bar{q} in the improved chromomagnetic interaction model

In the framework of the improved chromomagnetic interaction model, we complete a systematic study of the $S$-wave tetraquark states $Qq\bar{Q}\bar{q}$ ($Q=c,b$, and $q=u,d,s$) with different quantum numbers, $J^{PC}=0^{+(+)}$, $1^{+(\pm)}$, and $2^{+(+)}$. The mass spectra of tetraquark states are predicted and the possible decay channels are analyzed by considering both the angular momentum and $\mathcal{C}$-parity conservation. The recently observed hidden-charm tetraquark states with strangeness, such as $Z_{cs}(3985)^-$, $X(3960)$, and $Z_{cs}(4220)^+$, can be well explained in our model. Besides, based on the wave function of each tetraquark state, we find that the low-lying states of each $Qq\bar{Q}\bar{q}$ configuration have a large overlap to the $Q\bar Q$ and $q\bar q$ meson basis, instead of $Q\bar q$ and $q\bar Q$ meson basis. This indicates one can search these tetraquark states in future experiments via the channel of $Q\bar Q$ and $q\bar q$ mesons.Comment: 11 pages, 9 figures, and 4 tables; accepted for publication in Chinese Physics

Spontaneously-Induced Dirac Boundary State and Digitization in a Nonlinear Resonator Chain

The low-energy excitations in many condensed matter and metamaterial systems can be well described by the Dirac equation. The mass term associated with these collective excitations, also known as the Dirac mass, can take any value and is directly responsible for determining whether the resultant band structure exhibits a band gap or a Dirac point with linear dispersion. Manipulation of this Dirac mass has inspired new methods of band structure engineering and electron confinement. Notably, it has been shown that a massless state necessarily localizes at any domain wall that divides regions with Dirac masses of different signs. These localized states are known as Jackiw-Rebbi-type (JR-type) Dirac boundary modes and their tunability and localization features have valuable technological potential. In this study, we experimentally demonstrate that nonlinearity within a 1D Dirac material can result in the spontaneous appearance of a domain boundary for the Dirac mass. Our experiments are performed in a dimerized magneto-mechanical metamaterial that allows complete control of both the magnitude and sign of the local material nonlinearity, as well as the sign of the Dirac mass. We find that the massless bound state that emerges at the spontaneously appearing domain boundary acts similarly to a dopant site within an insulator, causing the material to exhibit a dramatic binary switch in its conductivity when driven above an excitation threshold

Transfer Reinforcement Learning Based Negotiating Agent Framework

While achieving tremendous success, there is still a major issue standing out in the domain of automated negotiation: it is inefficient for a negotiating agent to learn a strategy from scratch when being faced with an unknown opponent. Transfer learning can alleviate this problem by utilizing the knowledge of previously learned policies to accelerate the current task learning. This work presents a novel Transfer Learning based Negotiating Agent (TLNAgent) framework that allows a negotiating agent to transfer previous knowledge from source strategies optimized by deep reinforcement learning, to boost its performance in new tasks. TLNAgent comprises three key components: the negotiation module, the adaptation module and the transfer module. To be specific, the negotiation module is responsible for interacting with the other agent during negotiation. The adaptation module measures the helpfulness of each source policy based on a fusion of two selection mechanisms. The transfer module is based on lateral connections between source and target networks and accelerates the agent’s training by transferring knowledge from the selected source strategy. Our comprehensive experiments clearly demonstrate that TL is effective in the context of automated negotiation, and TLNAgent outperforms state-of-the-art Automated Negotiating Agents Competition (ANAC) negotiating agents in various domains