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

Sleep When Everything Looks Fine: Self-Triggered Monitoring for Signal Temporal Logic Tasks

Online monitoring is a widely used technique in assessing if the performance of the system satisfies some desired requirements during run-time operation. Existing works on online monitoring usually assume that the monitor can acquire system information periodically at each time instant. However, such a periodic mechanism may be unnecessarily energy-consuming as it essentially requires to turn on sensors consistently. In this paper, we proposed a novel self-triggered mechanism for model-based online monitoring of discrete-time dynamical system under specifications described by signal temporal logic (STL) formulae. Specifically, instead of sampling the system state at each time instant, a self-triggered monitor can actively determine when the next system state is sampled in addition to its monitoring decision regarding the satisfaction of the task. We propose an effective algorithm for synthesizing such a self-triggered monitor that can correctly evaluate a given STL formula on-the-fly while maximizing the time interval between two observations. We show that, compared with the standard online monitor with periodic information, the proposed self-triggered monitor can significantly reduce observation burden while ensuring that no information of the STL formula is lost. Case studies are provided to illustrate the proposed monitoring mechanism