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Essays in International Finance
This dissertation consists of three essays in international finance. The first two essays study emerging market sovereign risk with a focus on local currency denominated sovereign bonds. The third essay examines econometric tools for robust inference in the presence of missing observations, an issue frequently encountered by researchers in international finance.Economic
TacIPC: Intersection- and Inversion-free FEM-based Elastomer Simulation For Optical Tactile Sensors
Tactile perception stands as a critical sensory modality for human
interaction with the environment. Among various tactile sensor techniques,
optical sensor-based approaches have gained traction, notably for producing
high-resolution tactile images. This work explores gel elastomer deformation
simulation through a physics-based approach. While previous works in this
direction usually adopt the explicit material point method (MPM), which has
certain limitations in force simulation and rendering, we adopt the finite
element method (FEM) and address the challenges in penetration and mesh
distortion with incremental potential contact (IPC) method. As a result, we
present a simulator named TacIPC, which can ensure numerically stable
simulations while accommodating direct rendering and friction modeling. To
evaluate TacIPC, we conduct three tasks: pseudo-image quality assessment,
deformed geometry estimation, and marker displacement prediction. These tasks
show its superior efficacy in reducing the sim-to-real gap. Our method can also
seamlessly integrate with existing simulators. More experiments and videos can
be found in the supplementary materials and on the website:
https://sites.google.com/view/tac-ipc
Intersection-free Robot Manipulation with Soft-Rigid Coupled Incremental Potential Contact
This paper presents a novel simulation platform, ZeMa, designed for robotic
manipulation tasks concerning soft objects. Such simulation ideally requires
three properties: two-way soft-rigid coupling, intersection-free guarantees,
and frictional contact modeling, with acceptable runtime suitable for deep and
reinforcement learning tasks. Current simulators often satisfy only a subset of
these needs, primarily focusing on distinct rigid-rigid or soft-soft
interactions. The proposed ZeMa prioritizes physical accuracy and integrates
the incremental potential contact method, offering unified dynamics simulation
for both soft and rigid objects. It efficiently manages soft-rigid contact,
operating 75x faster than baseline tools with similar methodologies like
IPC-GraspSim. To demonstrate its applicability, we employ it for parallel grasp
generation, penetrated grasp repair, and reinforcement learning for grasping,
successfully transferring the trained RL policy to real-world scenarios
An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm
In a distribution system, sparse reliable samples and inconsistent fault characteristics always appear in the dataset of neural network fault detection models because of high impedance fault (HIF) and system structural changes. In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. First, the GANRA generates enough high-quality analogous fault data to solve a shortage of realistic fault data for the fault detection modelâs training. Second, an evolution strategy is proposed to help the GANRA improve the fault detection neural networkâs accuracy and generalization by searching for GANâs initial parameters. Finally, Convolutional Neural Network (CNN) is considered as the identification fault model in simulation experiments to verify the validity of the evolution strategy and the GANRA under the HIF environment. The results show that the GANRA can optimize the initial parameters of GAN and effectively reduce the calculation time, the sample size, and the number of learning iterations needed for dataset generation in the new grid structures
Altered intestinal microbiota enhances adenoid hypertrophy by disrupting the immune balance
IntroductionAdenoid hypertrophy (AH) is a common upper respiratory disorder in children. Disturbances of gut microbiota have been implicated in AH. However, the interplay of alteration of gut microbiome and enlarged adenoids remains elusive.Methods119 AH children and 100 healthy controls were recruited, and microbiome profiling of fecal samples in participants was performed using 16S rRNA gene sequencing. Fecal microbiome transplantation (FMT) was conducted to verify the effects of gut microbiota on immune response in mice.ResultsIn AH individuals, only a slight decrease of diversity in bacterial community was found, while significant changes of microbial composition were observed between these two groups. Compared with HCs, decreased abundances of Akkermansia, Oscillospiraceae and Eubacterium coprostanoligenes genera and increased abundances of Bacteroides, Faecalibacterium, Ruminococcus gnavus genera were revealed in AH patients. The abundance of Bacteroides remained stable with age in AH children. Notably, a microbial marker panel of 8 OTUs were identified, which discriminated AH from HC individuals with an area under the curve (AUC) of 0.9851 in the discovery set, and verified in the geographically different validation set, achieving an AUC of 0.9782. Furthermore, transfer of mice with fecal microbiota from AH patients dramatically reduced the proportion of Treg subsets within peripheral blood and nasal-associated lymphoid tissue (NALT) and promoted the expansion of Th2 cells in NALT.ConclusionThese findings highlight the effect of the altered gut microbiota in the AH pathogenesis
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