arXiv subjects

Mingyang Cai

Publications and source records attributed to Mingyang Cai.

6 recordsLinked to original sources

arXiv2025

Breaking the material-limited temperature coefficient of resistance via carrier feedback in a single transistor

The temperature coefficient of resistance (TCR) is one of the most fundamental properties of a material. Semiconductor materials exhibiting high TCR are promising candidates for applications in high-resolution thermal imaging for autonomous systems, high-precision temperature sensing, and neuromorphic computing. However, the TCR magnitude is typically below 5%/K near 300 K for thermal imaging materials, such as vanadium oxide and amorphous silicon. Inspired by the distinctive characteristic of feedback in electronic circuits, we demonstrate a voltage-tunable TCR of up to 150%/K near 300 K in a two-terminal InGaAs/InP n-p-n transistor, enabled by an internal coherent carrier feedback mechanism. In this device, current amplification arises from a synergistic interplay between temperature-dependent transistor gain and avalanche multiplication. Carriers amplified at the emitter-base junction via the transistor effect are injected into the collector-base junction, where avalanche multiplication generates additional carriers. These excess carriers are then fed back to the emitter-base junction, triggering further transistor amplification. This regenerative positive feedback loop results in a high and bias-tunable temperature coefficient of resistance (TCR). This work reveals the potential of device engineering in overcoming the fundamental material-level physical limits of temperature properties.

physics.app-ph↗

arXiv2024

A Semi-supervised CART Model for Covariate Shift

Machine learning models used in medical applications often face challenges due to the covariate shift, which occurs when there are discrepancies between the distributions of training and target data. This can lead to decreased predictive accuracy, especially with unknown outcomes in the target data. This paper introduces a semi-supervised classification and regression tree (CART) that uses importance weighting to address these distribution discrepancies. Our method improves the predictive performance of the CART model by assigning greater weights to training samples that more accurately represent the target distribution, especially in cases of covariate shift without target outcomes. In addition to CART, we extend this weighted approach to generalized linear model trees and tree ensembles, creating a versatile framework for managing the covariate shift in complex datasets. Through simulation studies and applications to real-world medical data, we demonstrate significant improvements in predictive accuracy. These findings suggest that our weighted approach can enhance reliability in medical applications and other fields where the covariate shift poses challenges to model performance across various data distributions.

cs.LG↗

arXiv2022

How to relate potential outcomes: Estimating individual treatment effects under a given specified partial correlation

In most medical research, the average treatment effect is used to evaluate a treatment's performance. However, precision medicine requires knowledge of individual treatment effects: What is the difference between a unit's measurement under treatment and control conditions? In most treatment effect studies, such answers are not possible because the outcomes under both experimental conditions are not jointly observed. This makes the problem of causal inference a missing data problem. We propose to solve this problem by imputing the individual potential outcomes under a specified partial correlation (SPC), thereby allowing for heterogeneous treatment effects. We demonstrate in simulation that our proposed methodology yields valid inferences for the marginal distribution of potential outcomes. We highlight that the posterior distribution of individual treatment effects varies with different specified partial correlations. This property can be used to study the sensitivity of optimal treatment outcomes under different correlation specifications. In a practical example on HIV-1 treatment data, we demonstrate that the proposed methodology generalises to real-world data. Imputing under the SPC, therefore, opens up a wealth of possibilities for studying heterogeneous treatment effects on incomplete data and the further adaptation of individual treatment effects.

stat.CO↗

arXiv2022

Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking

Missing data are often dealt with multiple imputation. A crucial part of the multiple imputation process is selecting sensible models to generate plausible values for incomplete data. A method based on posterior predictive checking is proposed to diagnose imputation models based on posterior predictive checking. To assess the congeniality of imputation models, the proposed diagnostic method compares the observed data with their replicates generated under corresponding posterior predictive distributions. If the imputation model is congenial with the substantive model, the observed data are expected to be located in the centre of corresponding predictive posterior distributions. Simulation and application are designed to investigate the proposed diagnostic method for parametric and semi-parametric imputation approaches, continuous and discrete incomplete variables, univariate and multivariate missingness patterns. The results show the validity of the proposed diagnostic method.

stat.CO↗

arXiv2022

Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors

Fully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables. However, FCS may not converge to a stationary distribution. Many authors have studied the convergence properties of FCS when priors of conditional models are non-informative. We extend to the case of informative priors. This paper evaluates the convergence properties of the normal linear model with normal-inverse gamma prior. The theoretical and simulation results prove the convergence of FCS and show the equivalence of prior specification under the joint model and a set of conditional models when the analysis model is a linear regression with normal inverse-gamma priors.

stat.CO↗

arXiv2022

A blended distance to define "people-like-me"

Curve matching is a prediction technique that relies on predictive mean matching, which matches donors that are most similar to a target based on the predictive distance. Even though this approach leads to high prediction accuracy, the predictive distance may make matches look unconvincing, as the profiles of the matched donors can substantially differ from the profile of the target. To counterbalance this, similarity between the curves of the donors and the target can be taken into account by combining the predictive distance with the Mahalanobis distance into a `blended distance' measure. The properties of this measure are evaluated in two simulation studies. Simulation study I evaluates the performance of the blended distance under different data-generating conditions. The results show that blending towards the Mahalanobis distance leads to worse performance in terms of bias, coverage, and predictive power. Simulation study II evaluates the blended metric in a setting where a single value is imputed. The results show that a property of blending is the bias-variance trade off. Giving more weight to the Mahalanobis distance leads to less variance in the imputations, but less accuracy as well. The main conclusion is that the high prediction accuracy achieved with the predictive distance necessitates the variability in the profiles of donors.

stat.ME↗