Theory of Linear Models for Estimating Regression Parameters with Applications to Two-Factor Studies with Unequal Sample Sizes

In this thesis we explored some topics in regression analysis. In particular, we studied what linear regression is from a matrix theory perspective, and applied analysis of variance in a setting with two factors and unbalanced sample sizes. In addition, we applied Box-Cox variable transformation as a solution when the regression model violated the normality and equal variance (also called homoscedasticity) assumption. Our main goal is to use these theories to construct models and investigate questions related to lifetime earnings of people living in America by using real data. In doing so, we used the statistical software R to perform calculation involved in variable selection models, to identify and quantify relationships between variables as well as to test hypotheses

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic

Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills

Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets. While offline reinforcement learning (RL) is well suited for these safety-critical tasks, it still struggles to plan over extended periods. In this work, we present a skill-based framework that enhances offline RL to overcome the long-horizon vehicle planning challenge. Specifically, we design a variational autoencoder (VAE) to learn skills from offline demonstrations. To mitigate posterior collapse of common VAEs, we introduce a two-branch sequence encoder to capture both discrete options and continuous variations of the complex driving skills. The final policy treats learned skills as actions and can be trained by any off-the-shelf offline RL algorithms. This facilitates a shift in focus from per-step actions to temporally extended skills, thereby enabling long-term reasoning into the future. Extensive results on CARLA prove that our model consistently outperforms strong baselines at both training and new scenarios. Additional visualizations and experiments demonstrate the interpretability and transferability of extracted skills

A molecular simulation analysis of producing monatomic carbon chains by stretching ultranarrow graphene nanoribbons

Atomistic simulations were utilized to develop fundamental insights regarding the elongation process starting from ultranarrow graphene nanoribbons (GNRs) and resulting in monatomic carbon chains (MACCs). There are three key findings. First, we demonstrate that complete, elongated, and stable MACCs with fracture strains exceeding 100% can be formed from both ultranarrow armchair and zigzag GNRs. Second, we demonstrate that the deformation processes leading to the MACCs have strong chirality dependence. Specifically, armchair GNRs first form DNA-like chains, then develop into monatomic chains by passing through an intermediate configuration in which monatomic chain sections are separated by two-atom attachments. In contrast, zigzag GNRs form rope-ladder-like chains through a process in which the carbon hexagons are first elongated into rectangles; these rectangles eventually coalesce into monatomic chains through a novel triangle-pentagon deformation structure under further tensile deformation. Finally, we show that the width of GNRs plays an important role in the formation of MACCs, and that the ultranarrow GNRs facilitate the formation of full MACCs. The present work should be of considerable interest due to the experimentally demonstrated feasibility of using narrow GNRs to fabricate novel nanoelectronic components based upon monatomic chains of carbon atoms.Comment: 11 pages, 6 figures, Nanotechnology accepted versio

Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction

Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While recent advancement in ADRD risk prediction have primarily relied on imaging analysis, yet not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative method to evaluate relationship importance and its influence on ADRD risk prediction, ensuring comprehensive interpretation. We employed Variationally Regularized Encoder-decoder Graph Neural Network (VGNN) for estimating ADRD likelihood. We created three scenarios to assess the model's efficiency, using Random Forest and Light Gradient Boost Machine as baselines. We further used our relation importance method to clarify the key relationships for ADRD risk prediction. VGNN surpassed other baseline models by 10% in the area under the receiver operating characteristic. The integration of the GNN model and relation importance interpretation could potentially play an essential role in providing valuable insight into factors that may contribute to or delay ADRD progression. Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches

Study on Fiber Clogging Mechanism in Sewage Pump Based on CFD–DEM Simulation

A large number of solid particles and fibrous impurities are always entrained in the fluid transported by a sewage pump, which can easily lead to the blockage of the sewage pump. In view of this, CFD–DEM simulations were conducted in this paper to reveal the fiber clogging mechanism in the sewage pump. A CFD–DEM coupling method with a fiber model was established and verified by an experimental benchmark, i.e., the rectangular flow channel. The method was then applied to a model sewage pump to, after mesh independence tests, analyze the effects of flow rate and fiber length on fiber motion and clogging. The results showed that the position of fiber retention coincides with the position of the vortex, mainly located at the inlet of the impeller, the head of the blade, the middle of the blade, and the tongue in the pump. In the case of a low flow rate, the fiber was more likely to cause blockage in the head of the blade, and in the case of a large flow rate, the fiber would wind around the tongue in the pump. At the same flow rate, long fiber was more likely to stay on the blade’s suction surface

Study on Fiber Clogging Mechanism in Sewage Pump Based on CFD–DEM Simulation

A large number of solid particles and fibrous impurities are always entrained in the fluid transported by a sewage pump, which can easily lead to the blockage of the sewage pump. In view of this, CFD–DEM simulations were conducted in this paper to reveal the fiber clogging mechanism in the sewage pump. A CFD–DEM coupling method with a fiber model was established and verified by an experimental benchmark, i.e., the rectangular flow channel. The method was then applied to a model sewage pump to, after mesh independence tests, analyze the effects of flow rate and fiber length on fiber motion and clogging. The results showed that the position of fiber retention coincides with the position of the vortex, mainly located at the inlet of the impeller, the head of the blade, the middle of the blade, and the tongue in the pump. In the case of a low flow rate, the fiber was more likely to cause blockage in the head of the blade, and in the case of a large flow rate, the fiber would wind around the tongue in the pump. At the same flow rate, long fiber was more likely to stay on the blade’s suction surface

Enhancement of ablation and ultrafast electron dynamics observation of nickel-based superalloy under double-pulse ultrashort laser irradiation

Temporally shaped femtosecond laser machining is a flexible and effective method to improve the efficiency and quality of cooling film holes. This study investigated the ablation of nickel-based superalloy by double-pulse femtosecond laser with different pulse-separations and fluences. Compared with single-pulse ablation, approximate 1.5 times enhancement of ablation area was obtained in double-pulse ablation with about 2 ps pulse-separation. By varying the pulse-separations, the ablation area can be tuned, and at the same time, the ablation depth can be kept for little fluctuation. An improved two-temperature model and time-resolved transient reflectivity technique were used for analyzing the ablation mechanisms. We found that more energy deposition can happen from electron system to lattice system for double-pulse ablation, which makes ablation area increase. However, mechanical relaxation started at around 2 ps, which could be suppressed by the pressure wave induced by the second sub-pulse, and finally achieved the maximum ablation area at about 2 ps pulse-separation. Besides, laser-induced subwavelength periodic surface structures were observed under irradiation of multiple pulses. The findings may aid in understanding the ablation mechanism between nickel-based superalloy and femtosecond laser, as well as in optimizing the processing of cooling film holes