arXiv subjects

Min Guo

Publications and source records attributed to Min Guo.

17 recordsLinked to original sources

arXiv2026

Stochastic first-passage modeling of single-event burnout in SiC power MOSFETs

Single-event burnout (SEB) in silicon carbide (SiC) power MOSFETs is often characterized by deterministic threshold quantities. Near the boundary between recovery and runaway, stochastic variability can make this threshold description probabilistic rather than sharp. This work introduces a first-passage perspective for stochastic threshold broadening in burnout. The process is described by a reduced electrothermal feedback-relaxation model with an absorbing boundary. The model combines carrier multiplication, avalanche feedback, localized heating, carrier loss, and thermal relaxation. Stochastic carrier and thermal terms represent unresolved event-level variability. The main finding is that finite fluctuations broaden the deterministic burnout threshold into a probabilistic transition band. Noise-induced subthreshold runaway also emerges, where nominally recoverable conditions can still fail through rare stochastic excursions. First-passage-time distributions resolve the time scale of burnout and survival probabilities further distinguish rapid feedback-dominated runaway from delayed stochastic failure. A feedback-relaxation phase diagram organizes recoverable, probabilistic, and rapidly unstable regimes. This framework provides a statistical-physics interpretation of threshold dispersion in single-event burnout of SiC power MOSFETs by linking coarse-grained electrothermal dynamics to probabilistic and time-resolved failure observables.

cond-mat.stat-mech↗

arXiv2026

Correlated decoherence in a common environment activated by relative motion

We study two spatially separated boundary subsystems coupled to a common structured environment under relative motion within a Gaussian open-system framework. By integrating out the environment, we obtain an influence functional governed by a dressed environmental correlator evaluated at the boundary positions. Its retarded and Hadamard components govern coherent mediation and correlated fluctuations, respectively. Relative motion activates the leading resonant contribution to correlated decoherence through Doppler-shifted spectral overlap of the boundary excitations. For identical boundary dispersions, this contribution has an ideal narrow-linewidth onset at $v>2u_\phi$. Below this value, no resonant momentum shell exists, whereas above it a finite shell opens and produces an enhanced correlated-decoherence contribution. Finite spectral linewidth generates subthreshold tails and rounds the ideal onset into a crossover centered on the same kinematic condition. The leading resonant contributions to motion-induced excitation production and correlated decoherence probe the same on-shell environmental structure. The proposed readout of this two-subsystem reduced-dynamics signature uses either differential-coherence measurements or a two-node noise cross-spectrum.

quant-ph↗

arXiv2026

Non-Markovian Entropy Dynamics in Living Systems from the Keldysh Formalism

Living systems are open nonequilibrium systems that continuously exchange energy, matter, and information with their environments, leading to stochastic dynamics with memory and active fluctuations. In this study, we develop a non-Markovian theoretical framework for the entropy dynamics of living systems based on the Keldysh functional formalism and stochastic thermodynamics. The approach naturally incorporates colored environmental noise, memory-dependent dissipation, and many-body interactions, yielding generalized Langevin dynamics and non-Markovian master equations. Within this framework we derive an exact frequency-domain expression for the entropy production rate and show that violations of the fluctuation-dissipation relation provide a direct thermodynamic signature of active biological fluctuations. We further demonstrate that environmental memory enhances low-frequency fluctuations and entropy production, leading to critical slowing down near dynamical instability. These results provide a microscopic physical foundation for the entropy "bathtub" picture of living systems and connect entropy evolution with development, aging, and death in nonequilibrium dynamics.

cond-mat.stat-mech↗

arXiv2024

LEARNER: A Transfer Learning Method for Low-Rank Matrix Estimation

Low-rank matrix estimation is a fundamental problem in statistics and machine learning with applications across biomedical sciences, including genetics, medical imaging, drug discovery, and electronic health record data analysis. In the context of heterogeneous data generated from diverse sources, a key challenge lies in leveraging data from a source population to enhance the estimation of a low-rank matrix in a target population of interest. We propose an approach that leverages similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population, which we refer to as LatEnt spAce-based tRaNsfer lEaRning (LEARNER). LEARNER is based on performing a low-rank approximation of the target population data which penalizes differences between the latent row and column spaces between the source and target populations. We present a cross-validation approach that allows the method to adapt to the degree of heterogeneity across populations. We conducted extensive simulations which found that LEARNER often outperforms the benchmark approach that only uses the target population data, especially as the signal-to-noise ratio in the source population increases. We also performed an illustrative application and empirical comparison of LEARNER and benchmark approaches in a re-analysis of summary statistics from a genome-wide association study in the BioBank Japan cohort. LEARNER is implemented in the R package learner and the Python package learner-py.

stat.ME↗

arXiv2024

CathAction: A Benchmark for Endovascular Intervention Understanding

Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive annotations necessary for broader endovascular intervention understanding. To tackle these limitations, we introduce CathAction, a large-scale dataset for catheterization understanding. Our CathAction dataset encompasses approximately 500,000 annotated frames for catheterization action understanding and collision detection, and 25,000 ground truth masks for catheter and guidewire segmentation. For each task, we benchmark recent related works in the field. We further discuss the challenges of endovascular intentions compared to traditional computer vision tasks and point out open research questions. We hope that CathAction will facilitate the development of endovascular intervention understanding methods that can be applied to real-world applications. The dataset is available at https://airvlab.github.io/cathaction/.

cs.CV↗

arXiv2024

Motion-driven quantum dissipation in an open electronic system with nonlocal interaction

In this paper, we study excitations and dissipation in two infinite parallel metallic plates undergoing relative motion. The degrees of freedom of the electrons in both plates are modeled using the 1+2 dimensional Dirac field, and a nonlocal potential is selected to describe the interaction between the two plates. The internal relative motion is introduced via a Galilean boost, with one plate assumed to slide relative to the other. We then calculate the effective action of the system and derive the vacuum occupation number in momentum space using a perturbative method. Numerical plots reveal that the vacuum occupation number, as a function of momentum, is isotropic for a motion speed $v = 0$ and anisotropic for nonzero $v$. The relative motion induces energy transfer between the plates, leading to on-shell excitations in a manner analogous to the dissipative process of the Schwinger effect. Consequently, we study the motion-induced dissipation effects and the dissipative forces through the quantum action. Numerical results demonstrate that both the imaginary part of the quantum action due to the motion boost and the dissipative force exhibit a threshold as functions of $v$, and both are positively correlated with $v$.

quant-ph↗

arXiv2024

Topological skyrmions in monolayer multiferroic MoPtGe2S6

Two-dimensional (2D) multiferroic materials with coexisting ferroelectricity and ferromagnetism have garnered substantial attention for their intriguing physical properties and diverse promising applications in spintronics. For example, multiferroic materials with electronically controlled broken central symmetry provide a versatile platform for designing and manipulating topological skyrmions and diverse spintronic applications. Here, we investigate the complex magnetic properties of room-temerature multiferroic material MoPtGe2S6 and its electrical control of topological skyrmions using first-principles calculations and atomistic micromagnetic simulations. A sizable Dzyaloshinskii-Moriya interaction (DMI) (2.1 meV) is found in the multiferroic material MoPtGe2S6 with an electrically polarized ground state. The magnetic skyrmions can be stabilized in monolayer MoPtGe2S6 under zero magnetic field, and the chirality of skyrmions can be reversed with electric field-induced flipping of electrical polarization due to the reversed chirality of the DMI. Furthermore, an external magnetic fielc can reverse the magnetization direction and topological charge of the skyrmions as well as tune the size of skyrmions. These results demonstrate that the monolayer MoPtGe2S6 can enrich the 2D skyrmion community and pave the way for electronically controlled spintronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Phase Diverse Phase Retrieval for Microscopy: Comparison of Gaussian and Poisson Approaches

Phase diversity is a widefield aberration correction method that uses multiple images to estimate the phase aberration at the pupil plane of an imaging system by solving an optimization problem. This estimated aberration can then be used to deconvolve the aberrated image or to reacquire it with aberration corrections applied to a deformable mirror. The optimization problem for aberration estimation has been formulated for both Gaussian and Poisson noise models but the Poisson model has never been studied in microscopy nor compared with the Gaussian model. Here, the Gaussian- and Poisson-based estimation algorithms are implemented and compared for widefield microscopy in simulation. The Poisson algorithm is found to match or outperform the Gaussian algorithm in a variety of situations, and converges in a similar or decreased amount of time. The Gaussian algorithm does perform better in low-light regimes when image noise is dominated by additive Gaussian noise. The Poisson algorithm is also found to be more robust to the effects of spatially variant aberration and phase noise. Finally, the relative advantages of re-acquisition with aberration correction and deconvolution with aberrated point spread functions are compared.

eess.IV↗

arXiv2023

ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting

Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence. In this paper, we propose a plug-and-play module named Action Prompt Module (APM) that effectively mines different kinds of action clues for 3D HPE. The highlight is that, the mining scheme of APM can be widely adapted to different frameworks and bring consistent benefits. Specifically, we first present a novel Action-related Text Prompt module (ATP) that directly embeds action labels and transfers the rich language information in the label to the pose sequence. Besides, we further introduce Action-specific Pose Prompt module (APP) to mine the position-aware pose pattern of each action, and exploit the correlation between the mined patterns and input pose sequence for further pose refinement. Experiments show that APM can improve the performance of most video-based 2D-to-3D HPE frameworks by a large margin.

cs.CV↗

arXiv2023

Codes and Pseudo-Geometric Designs from the Ternary $m$-Sequences with Welch-type decimation $d=2\cdot 3^{(n-1)/2}+1$

Pseudo-geometric designs are combinatorial designs which share the same parameters as a finite geometry design, but which are not isomorphic to that design. As far as we know, many pseudo-geometric designs have been constructed by the methods of finite geometries and combinatorics. However, none of pseudo-geometric designs with the parameters $S\left (2, q+1,(q^n-1)/(q-1)\right )$ is constructed by the approach of coding theory. In this paper, we use cyclic codes to construct pseudo-geometric designs. We firstly present a family of ternary cyclic codes from the $m$-sequences with Welch-type decimation $d=2\cdot 3^{(n-1)/2}+1$, and obtain some infinite family of 2-designs and a family of Steiner systems $S\left (2, 4, (3^n-1)/2\right )$ using these cyclic codes and their duals. Moreover, the parameters of these cyclic codes and their shortened codes are also determined. Some of those ternary codes are optimal or almost optimal. Finally, we show that one of these obtained Steiner systems is inequivalent to the point-line design of the projective space $\mathrm{PG}(n-1,3)$ and thus is a pseudo-geometric design.

cs.IT↗

arXiv2023

Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation

There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies. However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topology in the self-attention mechanism. To tackle this issue, in this paper, we propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D HPE. Specifically, we first develop novel pose-oriented self-attention mechanism and distance-related position embedding for POT to explicitly exploit the human skeleton topology. The pose-oriented self-attention mechanism explicitly models the topological interactions between body joints, whereas the distance-related position embedding encodes the distance of joints to the root joint to distinguish groups of joints with different difficulties in regression. Furthermore, we present an Uncertainty-Guided Refinement Network (UGRN) to refine pose predictions from POT, especially for the difficult joints, by considering the estimated uncertainty of each joint with uncertainty-guided sampling strategy and self-attention mechanism. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods with reduced model parameters on 3D HPE benchmarks such as Human3.6M and MPI-INF-3DHP

cs.CV↗

arXiv2022

On Existence of alpha-Core Solutions for Games with Finite or Infinite Players

This gives two existence results of alpha-core solutions by introducing P-open conditions and strong P-open conditions into games without ordered preferences. The existence of alpha-core solutions is obtained for games with infinite-players. Secondly, it provides a short proof of Kajii's (Journal of Economic Theory 56, 194-205, 1992) existence theorem for alpha-core solutions, further, the Kajii's theorem is equivalent to the Browder fixed point theorem. In addition, the obtained existence results can include many typical results for alpha-core solutions and some recent existence results as special cases.

econ.TH↗

arXiv2021

Hierarchical Graph Networks for 3D Human Pose Estimation

Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton. However, we argue that this skeletal topology is too sparse to reflect the body structure and suffer from serious 2D-to-3D ambiguity problem. To overcome these weaknesses, we propose a novel graph convolution network architecture, Hierarchical Graph Networks (HGN). It is based on denser graph topology generated by our multi-scale graph structure building strategy, thus providing more delicate geometric information. The proposed architecture contains three sparse-to-fine representation subnetworks organized in parallel, in which multi-scale graph-structured features are processed and exchange information through a novel feature fusion strategy, leading to rich hierarchical representations. We also introduce a 3D coarse mesh constraint to further boost detail-related feature learning. Extensive experiments demonstrate that our HGN achieves the state-of-the art performance with reduced network parameters. Code is released at https://github.com/qingshi9974/BMVC2021-Hierarchical-Graph-Networks-for-3D-Human-Pose-Estimation.

cs.CV↗

arXiv2021

A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization

Matrix multiplication is the bedrock in Deep Learning inference application. When it comes to hardware acceleration on edge computing devices, matrix multiplication often takes up a great majority of the time. To achieve better performance in edge computing, we introduce a low-power Multi-layer Perceptron (MLP) accelerator based on a pipelined matrix multiplication scheme and a nonuniform quantization methodology. The implementation is running on Field-programmable Gate Array (FPGA) devices and tested its performance on handwritten digit classification and Q-learning tasks. Results show that our method can achieve better performance with fewer power consumption.

cs.LG↗

arXiv2020

Weakly nonlinear topological gap solitons in Su-Schrieffer-Heeger photonic lattices

We study both theoretically and experimentally the effect of nonlinearity on topologically protected linear interface modes in a photonic Su-Schrieffer-Heeger (SSH) lattice. It is shown that under either focusing or defocusing nonlinearity, this linear topological mode of the SSH lattice turns into a family of topological gap solitons. These solitons are stable. However, they exhibit only a low amplitude and power and are thus weakly nonlinear, even when the bandgap of the SSH lattice is wide. As a consequence, if the initial beam has modest or high power, it will either delocalize, or evolve into a soliton not belonging to the family of topological gap solitons. These theoretical predictions are observed in our experiments with optically induced SSH-type photorefractive lattices.

physics.optics↗

arXiv2020

Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans

Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\% better than the insurance carrier's existing pricing model, and identified 84\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.

cs.CY↗

arXiv2020

Electron Transmission Across Normal Metal-Strained Graphene-Normal Metal Junctions

The transmission of the electron across the single normal metal-graphene (NG) and normal-metal-graphene-normal-metal (NGN) junctions has been investigated. For the single NG junction, the profile of the maximum transmission which has been plotted against the dimensionless interface hopping respectively bears similarity to that of the conductance of the system. The minor effect of the incidence energy on transmission can also be found in conductance of the single NG junction whose tunneling behavior poses a striking difference from that of the NGN junction. Concerning with NGN junction, the transmission and conductance show more abundant structures when subjected to different incidence energies, interface hopping, and strain strengths. The increase of strain strength always induces more resonance peaks at different angles in transmission and can therefore enhance the conductance. The increase of length of the middle graphene segment can accommodate more quasi-resonance states, leading to the more resonance peaks and richer structures in transmission. In both single NG and NGN junctions, the increase of the wavefunction period on metal side(s) can be observed due to the enhancement of strain strength, which can serve as the sensor for the detection of the strain strength in graphene.

cond-mat.mes-hall↗