Tailoring Non-Compact Spin Chains

We study three-point correlation functions of local operators in planar $\mathcal{N}=4$ SYM at weak coupling using integrability. We consider correlation functions involving two scalar BPS operators and an operator with spin, in the so called SL(2) sector. At tree level we derive the corresponding structure constant for any such operator. We also conjecture its one loop correction. To check our proposals we analyze the conformal partial wave decomposition of known four-point correlation functions of BPS operators. In perturbation theory, we extract from this decomposition sums of structure constants involving all primaries of a given spin and twist. On the other hand, in our integrable setup these sum rules are computed by summing over all solutions to the Bethe equations. A perfect match is found between the two approaches.Comment: 2 figure

Three Essays on the Economics of Education

This dissertation consists of three essays related to the field of economics of education. In chapter 2, using data from middle school students in China and exploiting the random assignment of students to classrooms within schools, I investigate the causal effect of peer groups on students’ scholastic achievement. I find that female student proportion in the classroom positively affects male students’ test scores and that the education level of peers’ parents improves the academic achievement of both male and female students. Students with highly-educated parents benefit more from classmates with higher parental education compared to students with relatively lower parental education. Investigation of mechanisms reveals that the peer effects can in part be explained by peers’ academic quality, classroom atmosphere, and behaviors of students’ classroom friends. Chapter 3 examines the causal impact of female education on fertility utilizing the Universal Primary Education (UPE) program in Malawi as a source of exogenous variation in schooling attainment. The results show that the UPE policy improved rural women’s educational attainment by 0.42 years and that an additional year of female education decreased women’s number of children ever born and living children by 0.39 and 0.33, respectively. An analysis of potential mechanisms suggests that the decreased fertility rates are driven by the reduction in women’s desired number of children, postponement of marriage and motherhood. There is no evidence that increased female education affects the characteristics of husband, women’s labor force participation, or modern contraceptive use. In chapter 4, I investigate the causal effect of maternal education on child mortality in Indonesia by using the one-time change in the length of the 1978 school year as a source of exogenous variation in education. The results show that the education reform increases women’s educational attainment by 0.82 years and an additional year of female education leads to a decrease in neonatal mortality by 0.8 percentage points. Mechanisms analysis suggests that higher female education postpones the timing of marriage and first birth, leads to higher quality of spouse and higher household wealth, and increases the use of prenatal health care and mass media

Refining the Spin Hamiltonian in the Spin-1/2 Kagome Lattice Antiferromagnet ZnCu3_{3}(OH)6_{6}Cl2_{2} using Single Crystals

We report thermodynamic measurements of the S=1/2 kagome lattice antiferromagnet ZnCu$_{3}$(OH)$_{6}$Cl$_{2}$, a promising candidate system with a spin-liquid ground state. Using single crystal samples, the magnetic susceptibility both perpendicular and parallel to the kagome plane has been measured. A small, temperature-dependent anisotropy has been observed, where $\chi_{z}/ \chi_{p} > 1$ at high temperatures and $\chi_{z}/ \chi_{p} < 1$ at low temperatures. Fits of the high-temperature data to a Curie-Weiss model also reveal an anisotropy. By comparing with theoretical calculations, the presence of a small easy-axis exchange anisotropy can be deduced as the primary perturbation to the dominant Heisenberg nearest neighbor interaction. These results have great bearing on the interpretation of theoretical calculations based on the kagome Heisenberg antiferromagnet model to the experiments on ZnCu$_{3}$(OH)$_{6}$Cl$_{2}$.Comment: 4 pages, 4 figure

Modeling and control of drillstring dynamics for vibration suppression

Drill-string vibrations could cause fatigue failure to downhole tools, bring damage to the wellbore, and decrease drilling efficiency; therefore, it is important to understand the drill-string dynamics through accurately modeling of the drill-string and bottom-hole assembly (BHA) dynamics, and then develop controllers to suppress the vibrations. Modeling drill-string dynamics for directional drilling operation is highly challenging because the drill-string and BHA bend with large curvatures. In addition, the interaction between the drill-string and wellbore wall could occur along the entire well. This fact complicates the boundary condition of modeling of drill-string dynamics. This dissertation presents a finite element method (FEM) model to characterize the dynamics of a directional drill-string. Based on the principle of virtual work, the developed method linearizes the drill-string dynamics around the central axis of a directional well, which significantly reduced the computational cost. In addition, a six DOF curved beam element is derived to model a curved drill-string. It achieves higher accuracy than the widely used straight beam element in both static and dynamic analyses. As a result, fewer curved beam elements are used to achieve the same accuracy, which further reduces the computational cost. During this research, a comprehensive drill-string and wellbore interaction model is developed as the boundary condition to simulate realistic drilling scenarios. Both static and dynamic analyses are carried out using the developed modeling framework. The static simulation can generate drill-string internal force as well as the drilling torque and drag force. The dynamic simulation can provide an insight of the underlying mechanism of drilling vibrations. Top drive controllers are also incorporated as torsional boundary conditions. The guidelines for tuning the control parameters are obtained from dynamic simulations. Drill-string vibrations can be suppressed through BHA configuration optimization. Based on the developed modeling framework, the BHA dynamic performance is evaluated using vibration indices. With an objective to minimize these indices, a genetic algorithm is developed to optimize the BHA stabilizer location for vibration suppression. After optimization, the BHA strain energy and the stabilizer side force, two of the vibration indices, are significantly reduced compared to the original design, which proves the BHA optimization method can lead to a significant reduction of undesirable drilling dynamics. At the end of this dissertation, reduced order models are also discussed for fast simulation and control design for real time operationMechanical Engineerin

On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data

Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception. Instead of deploying soft sensors on the Cloud, this study shift towards employing on-device soft sensors, promising heightened efficiency and bolstering data security. Our approach substantially improves energy efficiency by deploying Artificial Intelligence (AI) directly on devices within a wireless sensor network. Furthermore, the synergistic integration of the Microcontroller Unit and Field-Programmable Gate Array (FPGA) leverages the rapid AI inference capabilities of the latter. Empirical evidence from our real-world use case demonstrates that FPGA-based soft sensors achieve inference times ranging remarkably from 1.04 to 12.04 microseconds. These compelling results highlight the considerable potential of our innovative approach for executing real-time inference tasks efficiently, thereby presenting a feasible alternative that effectively addresses the latency challenges intrinsic to Cloud-based deployments.Comment: 8 pages, 6 figures, 1 Table, Accepted by the 1st AUTONOMOUS UBIQUITOUS SYSTEMS (AUTOQUITOUS) WORKSHOP of EAI MobiQuitous 2023 - 20th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Service

Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs

To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique. In the past, CNNs were frequently used because they are simple to optimise for special embedded hardware such as FPGAs. This work proposes a novel LSTM cell optimisation aimed at energy-efficient inference on end devices. Using the traffic speed prediction as a case study, a vanilla LSTM model with the optimised LSTM cell achieves 17534 inferences per second while consuming only 3.8 $\mu$J per inference on the FPGA XC7S15 from Spartan-7 family. It achieves at least 5.4$\times$ faster throughput and 1.37$\times$ more energy efficient than existing approaches.Comment: 12 pages, 7 figure