Essays on the Secular Stagnation Hypothesis and the Cross-Section of Assets Returns

This thesis studies the relations between growth and cross-sectional assets prices. I develop four discrete-time models in both the exchange and the production economy. Chapter 3 introduces the model with two Lucas trees and studies the interactions between two trees in terms of their price dividend ratios and returns. Chapter 4 explores a production economy with multiple balanced growth paths. The model shows that pessimistic beliefs may trigger persistent slumps, low interest rates and high risk premia. Chapter 5 extends the model used in chapter 4 to the Epstein and Zin framework and calibrates the model to match the historical data moments. Chapter 6 considers a model with two parallel sectors in the production economy and examines the cross-sectional co-movements between growth and asset returns

Preliminary Investigation of Impact of Technological Impairment on Trajectory-Based Operations

The Next Generation Air Transportation System (NextGen) incorporates collaborative air traffic management and Trajectory-Based Operations (TBO) in order to significantly increase the capacity, efficiency, and predictability of operations in the National Airspace System (NAS), without decreasing safety. This is enabled by airspace users and service providers sharing knowledge about operations that allows prediction of the complete 4D flight trajectory with as little uncertainty as possible. Additionally, new software and hardware technology is critical to reaching NextGen goals, especially with regard to TBO. What if the technologies that are critical for TBO were to be impaired or fail completely? Should there be a malfunction of a piece of the technology, it must be ensured that the whole system does not break down completely or suffer severe impairment. Instead, operations need to be maintained proportionally to the problem and safety needs to be ensured (graceful degradation). This paper proposes a systematic framework to investigate the vulnerability of TBO to technology disruption, and determine the impact of technological impairment on TBO. Two representative technologies are chosen for detailed investigation and the impact of their impairment on the degradation of TBO is illustrated using a weather-related scenario. XXXX There are several possible directions of future work. We believe it is desirable to develop methods to quantitatively assess the impact of technological disruption on TBO and to have the simulation tools to validate the impact. The availability of prognostics and health management methods could be leveraged to predict technological failure/disruption, thus predicting how TBO will be a ected, and possibly pro-actively mitigating the impact. It is important to develop large-scale scenarios where the e ect of technological impairment is prominent, and identify methods to quantitatively assess the extent of TBO degradation. An important goal of such an investigation is the development of failure-resistant resilient trajectory-based oper- ations. Resilience14, 15 is the property of a system to \bounce back" and resume at least a signi cant portion of its functionalities after degradation due to technological impairment(s). A systems resilience includes properties such as \bu ering capacity" (quantifying disruptions the system can absorb or adapt to without a fundamental breakdown in performance or in the systems structure), \ exibility" (ability to restructure itself in response to external changes or pressures), "margin" (how closely the system is currently operating rela- tive to one or another kind of performance boundary), \tolerance" (whether the system gracefully degrades as stress/pressure increase, or collapses quickly when pressure exceeds adaptive capacity), etc. Future work needs to focus on quantifying and improving the resilience of TBO, and identifying resilient design solutions for aviation

Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge

In this paper, we develop a generic methodology to encode hierarchical causality structure among observed variables into a neural network in order to improve its predictive performance. The proposed methodology, called causality-informed neural network (CINN), leverages three coherent steps to systematically map the structural causal knowledge into the layer-to-layer design of neural network while strictly preserving the orientation of every causal relationship. In the first step, CINN discovers causal relationships from observational data via directed acyclic graph (DAG) learning, where causal discovery is recast as a continuous optimization problem to avoid the combinatorial nature. In the second step, the discovered hierarchical causality structure among observed variables is systematically encoded into neural network through a dedicated architecture and customized loss function. By categorizing variables in the causal DAG as root, intermediate, and leaf nodes, the hierarchical causal DAG is translated into CINN with a one-to-one correspondence between nodes in the causal DAG and units in the CINN while maintaining the relative order among these nodes. Regarding the loss function, both intermediate and leaf nodes in the DAG graph are treated as target outputs during CINN training so as to drive co-learning of causal relationships among different types of nodes. As multiple loss components emerge in CINN, we leverage the projection of conflicting gradients to mitigate gradient interference among the multiple learning tasks. Computational experiments across a broad spectrum of UCI data sets demonstrate substantial advantages of CINN in predictive performance over other state-of-the-art methods. In addition, an ablation study underscores the value of integrating structural and quantitative causal knowledge in enhancing the neural network's predictive performance incrementally

Fowler-Nordheim-like local injection of photoelectrons from a silicon tip

Tunneling between a photo-excited p-type silicon tip and a gold surface is studied as a function of tip bias, tip/sample distance and light intensity. In order to extend the range of application of future spin injection experiments, the measurements are carried out under nitrogen gas at room temperature. It is found that while tunneling of valence band electrons is described by a standard process between the semiconductor valence band and the metal, the tunneling of photoelectrons obeys a Fowler-Nordheim-like process directly from the conduction band. In the latter case, the bias dependence of the photocurrent as a function of distance is in agreement with theoretical predictions which include image charge effects. Quantitative analysis of the bias dependence of the dark and photocurrent spectra gives reasonable values for the distance, and for the tip and metal work functions. For small distances image charge effects induce a vanishing of the barrier and the bias dependence of the photocurrent is exponential. In common with many works on field emission, fluctuations in the tunneling currents are observed. These are mainly attributed to changes in the prefactor for the tunneling photocurrent, which we suggest is caused by an electric-field-induced modification of the thickness of the natural oxide layer covering the tip apex.Comment: 12 pages, 11 figures. Submitted to Phys. Rev.

A Bio-Inspired Method for the Constrained Shortest Path Problem

The constrained shortest path (CSP) problem has been widely used in transportation optimization, crew scheduling, network routing and so on. It is an open issue since it is a NP-hard problem. In this paper, we propose an innovative method which is based on the internal mechanism of the adaptive amoeba algorithm. The proposed method is divided into two parts. In the first part, we employ the original amoeba algorithm to solve the shortest path problem in directed networks. In the second part, we combine the Physarum algorithm with a bio-inspired rule to deal with the CSP. Finally, by comparing the results with other method using an examples in DCLC problem, we demonstrate the accuracy of the proposed method

Design and Performance of Anticracking Asphalt-Treated Base

To enhance the crack resistance of asphalt-treated base (ATB), a type of gapped and semiopened gradation ATB mixture, GSOG, was designed. Its design method was proposed based on the volume design method and performance tests. Firstly, several gradations were designed preliminarily in which middle particle sizes of coarse aggregates were partially or completely gapped according to the gradation specification. Secondly, their voids in coarse aggregates (VCA) were determined through dry rod compaction test on coarse aggregates, and then their theoretical voids were calculated. Gradations whose theoretical voids met the requirements were selected to fabricate specimens with Superpave Gyratory Compactor, and their voids were determined using vacuum sealing method and submerged weight in water method. Finally, gradations whose voids meet requirements were selected to fabricate different types of specimens for various performance tests, and the optimal gradation can be selected comprehensively considering their performances, especially focusing on their crack resistance. According to this gradation design method, the gradation of GSOG-25 was designed, and its performances, including high-temperature stability, water stability, fatigue, and antireflection crack resistance, were measured and compared to ordinary ATB-25. The results demonstrate that the performance of GSOG-25 is much better than that of ordinary ATB-25, especially in anticracking capacity