arXiv subjects

Huimin Peng

Publications and source records attributed to Huimin Peng.

11 recordsLinked to original sources

arXiv2026

N\'eel-order-dependent transverse transport in noncoplanar antiferromagnet $\text{MnTe}_{2}$

Antiferromagnets hold appealing potential in next-generation spintronic devices with higher frequency and scalability, thanks to their alternating spin orientations that cancel out net magnetization. However, the lack of a nonzero magnetization makes the detection of the magnetic configuration of antiferromagnet difficult, hampering the applications of antiferromagnets. Here, we report a new transverse transport effect in noncoplanar antiferromagnet $\text{MnTe}_{2}$. This effect is antisymmetric in both magnetic field and N\'eel order, but symmetric in its two indices. It can be understood in terms of the contribution induced by both magnetic field and geometric quantities, as confirmed by our theoretical calculations. Our discovery of a new N\'eel-order-dependent transverse transport effect provides opportunities to the advancing antiferromagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2026

Nonlinear Hall quantum oscillations to probe topological Brown-Zak fermions in graphene moir\'e systems

Due to the deep connection with the quantum geometry of electronic Bloch wavefunctions, the second-order nonlinear Hall effect (NLHE) has been an attractive topic since its proposal. However, studies on NLHE under a magnetic field have been lacking. Given that quantum oscillations in the linear response regime have been proven to be useful tools in investigating electronic systems, searching for quantum oscillations in NLHE is of great interest and is expected to provide new avenues to unveil rich quantum geometric properties of novel quasiparticles. Here, we propose a new type of NLHE quantum oscillations and experimentally probe it in graphene moir\'e systems. It stems from the alternation of the dominant NLHE mechanisms with recurring Bloch states under magnetic field, which enables sensitive detection of Brown-Zak fermions, giving an onset field as low as 0.5 T. Most importantly, when the commensurability condition is satisfied, the nonlinear transport of Brown-Zak fermions is mainly governed by quantum geometric contributions. Our findings not only establish a new type of quantum oscillations, but also demonstrate the first experimental detection of the topological nature of Brown-Zak fermions, shedding light on the exploration of novel topological quasiparticles.

cond-mat.mes-hall↗

arXiv2023

Effective manipulation and realization of a colossal nonlinear Hall effect in an electric-field tunable moiré system

The second-order nonlinear Hall effect illuminates a frequency-doubling transverse current emerging in quantum materials with broken inversion symmetry even when time-reversal symmetry is preserved. This nonlinear response originates from both the Berry curvature dipole and the chiral Bloch electron skew scatterings, reflecting various information of the lattice symmetries, band dispersions, and topology of the electron wavefunctions. Even though many efforts have been put in detecting the nonlinear Hall effect in diverse condensed matter systems, effective manipulation of the two principal mechanisms in a single system has been lacking, and the reported response is relatively weak. Here, we report effective manipulation of the nonlinear Hall effect and realization of a colossal second-order Hall conductivity, $\sim500 μmSV^{-1}$, orders of magnitudes higher than the reported values, in AB-BA stacked twisted double bilayer graphene. A Berry-curvature-dipole-dominated nonlinear Hall effect, as well as its controllable transition to skew-scattering-dominated response, is identified near the band edge. The colossal response, on the other hand, is detected near the van Hove singularities, mainly determined by the skew scattering of the chiral Bloch electrons. Our findings establish electrically tunable moiré systems promising for nonlinear Hall effect manipulations and applications.

cond-mat.mes-hall↗

arXiv2022

Giant second-order nonlinearity in twisted bilayer graphene

In the second-order response regime, the Hall voltage can be nonzero without breaking the time-reversal symmetry, as long as the system is noncentrosymmetric. There are multiple mechanisms with different scaling rules that contribute to the nonlinear Hall effect (NLHE). The intrinsic contribution is closely related to the Berry curvature dipole and has been extensively investigated recently. The study of the extrinsic contribution, however, is scarce, although it can enter the NLHE even in the leading order. Here, we report a giant nonlinear transport response in TBG, in which the intrinsic mechanism is forbidden. The magnitude and direction of the second-order nonlinearity can be effectively tuned by the gate voltage. The peak value of the second-order Hall conductivity close to the full filling of the moiré band reaches 8.76 $μmSV^{-1}$, four-order larger than those detected in $WTe_2$. The observed giant second-order nonlinearity can be understood from the collaboration of the asymmetric scattering of electrons off the static (Coulomb impurities) and dynamic disorders (phonons) in noncentrosymmetric crystals. It is mainly determined by the skew-scattering contribution from impurities at 1.7 K. The skew-scattering from phonons has a much larger coupling coefficient as suggested by the scaling results, and becomes as important as the impurity contribution as the temperature rises. Our observations demonstrate the potential of TBG in studying nonlinear response and possible rectification applications.

cond-mat.mes-hall↗

arXiv2021

A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning

This paper briefly reviews the connections between meta-learning and self-supervised learning. Meta-learning can be applied to improve model generalization capability and to construct general AI algorithms. Self-supervised learning utilizes self-supervision from original data and extracts higher-level generalizable features through unsupervised pre-training or optimization of contrastive loss objectives. In self-supervised learning, data augmentation techniques are widely applied and data labels are not required since pseudo labels can be estimated from trained models on similar tasks. Meta-learning aims to adapt trained deep models to solve diverse tasks and to develop general AI algorithms. We review the associations of meta-learning with both generative and contrastive self-supervised learning models. Unlabeled data from multiple sources can be jointly considered even when data sources are vastly different. We show that an integration of meta-learning and self-supervised learning models can best contribute to the improvement of model generalization capability. Self-supervised learning guided by meta-learner and general meta-learning algorithms under self-supervision are both examples of possible combinations.

cs.LG↗

arXiv2021

User-Oriented Smart General AI System under Causal Inference

General AI system solves a wide range of tasks with high performance in an automated fashion. The best general AI algorithm designed by one individual is different from that devised by another. The best performance records achieved by different users are also different. An inevitable component of general AI is tacit knowledge that depends upon user-specific comprehension of task information and individual model design preferences that are related to user technical experiences. Tacit knowledge affects model performance but cannot be automatically optimized in general AI algorithms. In this paper, we propose User-Oriented Smart General AI System under Causal Inference, abbreviated as UOGASuCI, where UOGAS represents User-Oriented General AI System and uCI means under the framework of causal inference. User characteristics that have a significant influence upon tacit knowledge can be extracted from observed model training experiences of many users in external memory modules. Under the framework of causal inference, we manage to identify the optimal value of user characteristics that are connected with the best model performance designed by users. We make suggestions to users about how different user characteristics can improve the best model performance achieved by users. By recommending updating user characteristics associated with individualized tacit knowledge comprehension and technical preferences, UOGAS helps users design models with better performance.

cs.LG↗

arXiv2021

A Brief Survey of Associations Between Meta-Learning and General AI

This paper briefly reviews the history of meta-learning and describes its contribution to general AI. Meta-learning improves model generalization capacity and devises general algorithms applicable to both in-distribution and out-of-distribution tasks potentially. General AI replaces task-specific models with general algorithmic systems introducing higher level of automation in solving diverse tasks using AI. We summarize main contributions of meta-learning to the developments in general AI, including memory module, meta-learner, coevolution, curiosity, forgetting and AI-generating algorithm. We present connections between meta-learning and general AI and discuss how meta-learning can be used to formulate general AI algorithms.

cs.AI↗

arXiv2020

A Comprehensive Overview and Survey of Recent Advances in Meta-Learning

This article reviews meta-learning also known as learning-to-learn which seeks rapid and accurate model adaptation to unseen tasks with applications in highly automated AI, few-shot learning, natural language processing and robotics. Unlike deep learning, meta-learning can be applied to few-shot high-dimensional datasets and considers further improving model generalization to unseen tasks. Deep learning is focused upon in-sample prediction and meta-learning concerns model adaptation for out-of-sample prediction. Meta-learning can continually perform self-improvement to achieve highly autonomous AI. Meta-learning may serve as an additional generalization block complementary for original deep learning model. Meta-learning seeks adaptation of machine learning models to unseen tasks which are vastly different from trained tasks. Meta-learning with coevolution between agent and environment provides solutions for complex tasks unsolvable by training from scratch. Meta-learning methodology covers a wide range of great minds and thoughts. We briefly introduce meta-learning methodologies in the following categories: black-box meta-learning, metric-based meta-learning, layered meta-learning and Bayesian meta-learning framework. Recent applications concentrate upon the integration of meta-learning with other machine learning framework to provide feasible integrated problem solutions. We briefly present recent meta-learning advances and discuss potential future research directions.

cs.LG↗

arXiv2020

Holding-Based Evaluation upon Actively Managed Stock Mutual Funds in China

We analyze actively managed mutual funds in China from 2005 to 2017. We develop performance measures for asset allocation and selection. We find that stock selection ability from holding-based model is positively correlated with selection ability estimated from Fama-French three-factor model, which is price-based regression model. We also find that industry allocation from holding-based model is positively correlated with timing ability estimated from price-based Treynor-Mazuy model most of the time. We conclude that most actively managed funds have positive stock selection ability but not asset allocation ability, which is due to the difficulty in predicting policy changes.

q-fin.PM↗

arXiv2020

Measurement Error in Nutritional Epidemiology: A Survey

This article reviews bias-correction models for measurement error of exposure variables in the field of nutritional epidemiology. Measurement error usually attenuates estimated slope towards zero. Due to the influence of measurement error, inference of parameter estimate is conservative and confidence interval of the slope parameter is too narrow. Bias-correction in estimators and confidence intervals are of primary interest. We review the following bias-correction models: regression calibration methods, likelihood based models, missing data models, simulation based methods, nonparametric models and sampling based procedures.

stat.ME↗

arXiv2018

Post-Lasso Inference for High-Dimensional Regression

Among the most popular variable selection procedures in high-dimensional regression, Lasso provides a solution path to rank the variables and determines a cut-off position on the path to select variables and estimate coefficients. In this paper, we consider variable selection from a new perspective motivated by the frequently occurred phenomenon that relevant variables are not completely distinguishable from noise variables on the solution path. We propose to characterize the positions of the first noise variable and the last relevant variable on the path. We then develop a new variable selection procedure to control over-selection of the noise variables ranking after the last relevant variable, and, at the same time, retain a high proportion of relevant variables ranking before the first noise variable. Our procedure utilizes the recently developed covariance test statistic and Q statistic in post-selection inference. In numerical examples, our method compares favorably with other existing methods in selection accuracy and the ability to interpret its results.

stat.ME↗