arXiv subjects

Hao Mei

Publications and source records attributed to Hao Mei.

13 recordsLinked to original sources

arXiv2025

Evaluating Surrogates in Individualized Treatment Rules

In many decision-making problems, the primary outcome is expensive, time-consuming, or difficult to observe, so individualized treatment rules (ITRs) may be instead learned from surrogate endpoints. However, a surrogate that is highly associated with the primary outcome, or even satisfies existing surrogate criteria, may not necessarily induce a treatment rule that performs well on the primary outcome, especially under treatment resource budget constraints. In this paper, we develop a principled framework for evaluating the decision-making value of surrogate endpoints. We introduce three ITR-oriented performance measures: surrogate regret, which assesses the expected loss from using the surrogate-optimal ITR instead of outcome-optimal ITR; surrogate gain, which quantifies the benefit of surrogate-optimal ITRs relative to the no-treatment baseline; and surrogate efficiency, which evaluates improvement over random treatment assignment. We also extend them to budget-constrained settings. We propose augmented inverse probability weighted (AIPW) estimators for these measures and establish their large-sample properties. We demonstrate the proposed approach on both simulations and an application to the Criteo dataset.

stat.ME↗

arXiv2025

Interconnections of Multimorbidity-Related Clinical Outcomes: Analysis of Health Administrative Claims Data with a Dynamic Network Approach

Given the rising complexity and burden of multimorbidity, it is crucial to provide evidence-based support for managing multimorbidity-related clinical outcomes. This study introduces a dynamic network approach to investigate conditional and time-varying interconnections in disease-specific clinical outcomes. Our method effectively tackles the issue of zero inflation, a frequent challenge in medical data that complicates traditional modeling techniques. The theoretical foundations of the proposed approach are rigorously developed and validated through extensive simulations. Using Taiwan's health administrative claims data from 2000 to 2013, we construct 14 yearly networks that are temporally correlated, featuring 125 nodes that represent different disease conditions. Key network properties, such as connectivity, module, and temporal variation are analyzed. To demonstrate how these networks can inform multimorbidity management, we focus on breast cancer and analyze the relevant network structures. The findings provide valuable clinical insights that enhance the current understanding of multimorbidity. The proposed methods offer promising applications in shaping treatment strategies, optimizing health resource allocation, and informing health policy development in the context of multimorbidity management.

stat.ME↗

arXiv2024

Rina: Enhancing Ring-AllReduce with In-network Aggregation in Distributed Model Training

Parameter Server (PS) and Ring-AllReduce (RAR) are two widely utilized synchronization architectures in multi-worker Deep Learning (DL), also referred to as Distributed Deep Learning (DDL). However, PS encounters challenges with the ``incast'' issue, while RAR struggles with problems caused by the long dependency chain. The emerging In-network Aggregation (INA) has been proposed to integrate with PS to mitigate its incast issue. However, such PS-based INA has poor incremental deployment abilities as it requires replacing all the switches to show significant performance improvement, which is not cost-effective. In this study, we present the incorporation of INA capabilities into RAR, called RAR with In-Network Aggregation (Rina), to tackle both the problems above. Rina features its agent-worker mechanism. When an INA-capable ToR switch is deployed, all workers in this rack run as one abstracted worker with the help of the agent, resulting in both excellent incremental deployment capabilities and better throughput. We conducted extensive testbed and simulation evaluations to substantiate the throughput advantages of Rina over existing DDL training synchronization structures. Compared with the state-of-the-art PS-based INA methods ATP, Rina can achieve more than 50\% throughput with the same hardware cost.

cs.NI↗

arXiv2023

LibSignal: An Open Library for Traffic Signal Control

This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators.

cs.LG↗

arXiv2023

Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control

Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizing traffic signal control, but current RL-based TSC methods are mainly trained in simulation and suffer from the performance gap between simulation and the real world. In this paper, we propose a simulation-to-real-world (sim-to-real) transfer approach called UGAT, which transfers a learned policy trained from a simulated environment to a real-world environment by dynamically transforming actions in the simulation with uncertainty to mitigate the domain gap of transition dynamics. We evaluate our method on a simulated traffic environment and show that it significantly improves the performance of the transferred RL policy in the real world.

cs.LG↗

arXiv2023

Uncertainty-aware Traffic Prediction under Missing Data

Traffic prediction is a crucial topic because of its broad scope of applications in the transportation domain. Recently, various studies have achieved promising results. However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations. In real-life scenarios, the deployment of sensors could be limited due to budget limitations and installation availability, which makes most current models not applicable. Though few pieces of literature tried to impute traffic states at the missing locations, these methods need the data simultaneously observed at the locations with sensors, making them not applicable to prediction tasks. Another drawback is the lack of measurement of uncertainty in prediction, making prior works unsuitable for risk-sensitive tasks or involving decision-making. To fill the gap, inspired by the previous inductive graph neural network, this work proposed an uncertainty-aware framework with the ability to 1) extend prediction to missing locations with no historical records and significantly extend spatial coverage of prediction locations while reducing deployment of sensors and 2) generate probabilistic prediction with uncertainty quantification to help the management of risk and decision making in the down-stream tasks. Through extensive experiments on real-life datasets, the result shows our method achieved promising results on prediction tasks, and the uncertainty quantification gives consistent results which highly correlated with the locations with and without historical data. We also show that our model could help support sensor deployment tasks in the transportation field to achieve higher accuracy with a limited sensor deployment budget.

cs.LG↗

arXiv2023

Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning

Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and mitigate congestion waste. In recent, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities' congestion headaches. However, there still exist performance gaps when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the system dynamic difference between the training simulator and the real-world environments. The Large Language Models (LLMs) are trained on mass knowledge and proved to be equipped with astonishing inference abilities. In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation. Accepting the cloze prompt template, and then filling in the answer based on accessible context, the pre-trained LLM's inference ability is exploited and applied to understand how weather conditions, traffic states, and road types influence traffic dynamics, being aware of this, the policies' action is taken and grounded based on realistic dynamics, thus help the agent learn a more realistic policy. We conduct experiments using DQN to show the effectiveness of the proposed PromptGAT's ability in mitigating the performance gap from simulation to reality (sim-to-real).

cs.AI↗

arXiv2023

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks

Graph regression is a fundamental task that has gained significant attention in various graph learning tasks. However, the inference process is often not easily interpretable. Current explanation techniques are limited to understanding Graph Neural Network (GNN) behaviors in classification tasks, leaving an explanation gap for graph regression models. In this work, we propose a novel explanation method to interpret the graph regression models (XAIG-R). Our method addresses the distribution shifting problem and continuously ordered decision boundary issues that hinder existing methods away from being applied in regression tasks. We introduce a novel objective based on the graph information bottleneck theory (GIB) and a new mix-up framework, which can support various GNNs and explainers in a model-agnostic manner. Additionally, we present a self-supervised learning strategy to tackle the continuously ordered labels in regression tasks. We evaluate our proposed method on three benchmark datasets and a real-life dataset introduced by us, and extensive experiments demonstrate its effectiveness in interpreting GNN models in regression tasks.

cs.LG↗

arXiv2023

Development of low-threshold detectors for low-mass dark matter searches with a p-type germanium detector operated at cryogenic temperature

This study investigates new technology for enhancing the sensitivity of low-mass dark matter detection by analyzing charge transport in a p-type germanium detector at 5.2 K. To achieve low-threshold detectors, precise calculations of the binding energies of dipole and cluster dipole states, as well as the cross-sections of trapping affected by the electric field, are essential. The detector was operated in two modes: depleted at 77 K before cooling to 5.2 K and cooled directly to 5.2 K with various bias voltages. Our results indicate that the second mode produces lower binding energies and suggests different charge states under varying operating modes. Notably, our measurements of the dipole and cluster dipole state binding energies at zero fields were $8.716\pm 0.435$ meV and $6.138\pm 0.308$ meV, respectively. These findings have strong implications for the development of low-threshold detectors for detecting low-mass dark matter in the future.

physics.ins-det↗

arXiv2023

Reinforcement Learning Approaches for Traffic Signal Control under Missing Data

The emergence of reinforcement learning (RL) methods in traffic signal control tasks has achieved better performance than conventional rule-based approaches. Most RL approaches require the observation of the environment for the agent to decide which action is optimal for a long-term reward. However, in real-world urban scenarios, missing observation of traffic states may frequently occur due to the lack of sensors, which makes existing RL methods inapplicable on road networks with missing observation. In this work, we aim to control the traffic signals in a real-world setting, where some of the intersections in the road network are not installed with sensors and thus with no direct observations around them. To the best of our knowledge, we are the first to use RL methods to tackle the traffic signal control problem in this real-world setting. Specifically, we propose two solutions: the first one imputes the traffic states to enable adaptive control, and the second one imputes both states and rewards to enable adaptive control and the training of RL agents. Through extensive experiments on both synthetic and real-world road network traffic, we reveal that our method outperforms conventional approaches and performs consistently with different missing rates. We also provide further investigations on how missing data influences the performance of our model.

cs.LG↗

arXiv2023

Development of Low-Threshold Detectors for Low-Mass Dark Matter Searches Using an N-Type Germanium Detector at 5.2 K

We investigated charge transport in an n-type germanium detector at 5.2 K to explore new technology for enhancing low-mass dark matter detection sensitivity. Calculations of dipole and cluster dipole state binding energies and electric field-dependent trapping cross-sections are critical to developing low-threshold detectors. The detector operates in two modes: depleting at 77K before cooling, or directly cooling to 5.2 K and applying different bias voltages. Results indicated lower binding energy of charge states in the second mode, at zero field and under an electric field, suggesting different charge states formed under different operating modes. Measured cluster dipole and dipole state binding energies at zero field were 7.884$\pm$0.644 meV and 8.369$\pm$0.748 meV, respectively, signifying high low-threshold potential for low-mass dark matter searches in the future.

physics.ins-det↗

arXiv2022

Modeling Network-level Traffic Flow Transitions on Sparse Data

Modeling how network-level traffic flow changes in the urban environment is useful for decision-making in transportation, public safety and urban planning. The traffic flow system can be viewed as a dynamic process that transits between states (e.g., traffic volumes on each road segment) over time. In the real-world traffic system with traffic operation actions like traffic signal control or reversible lane changing, the system's state is influenced by both the historical states and the actions of traffic operations. In this paper, we consider the problem of modeling network-level traffic flow under a real-world setting, where the available data is sparse (i.e., only part of the traffic system is observed). We present DTIGNN, an approach that can predict network-level traffic flows from sparse data. DTIGNN models the traffic system as a dynamic graph influenced by traffic signals, learns the transition models grounded by fundamental transition equations from transportation, and predicts future traffic states with imputation in the process. Through comprehensive experiments, we demonstrate that our method outperforms state-of-the-art methods and can better support decision-making in transportation.

cs.LG↗

arXiv2015

Crystal growth and detector performance of large size high-purity Ge crystals

High-purity germanium crystals approximately 12 cm in diameter were grown in a hydrogen atmosphere using the Czochralski method. The dislocation density of the crystals was determined to be in the range of 2000 - 4200 cm-2, which meets a requirement for use as a radiation detector. The axial and radial distributions of impurities in the crystals were measured and are discussed. A planar detector was also fabricated from one of the crystals and then evaluated for electrical and spectral performance. Measurements of gamma-ray spectra from Cs-137 and Am-241 sources demonstrate that the detector has excellent energy resolution.

physics.ins-det↗