arXiv subjects

Minghui Liu

Publications and source records attributed to Minghui Liu.

At least 37 records · Page 2Linked to original sources

arXiv2025

A $p_T$-ratio observable for studies of intrinsic transverse momentum of partons from Drell-Yan $p_T$ spectra

The determination of the intrinsic transverse momentum distribution of partons is central both for applications of parton shower Monte Carlo generators and for QCD studies of transverse momentum dependent (TMD) parton densities. Valuable information on this distribution is provided by experimental measurements of Drell-Yan transverse momentum $p_T$, in the region of low transverse momenta, with fine binning in $p_T$. However, such fine-binning measurements are challenging, as they require an extremely delicate control of systematic uncertainties. We suggest a $p_T$ observable based on measuring ratios between cross sections of suitably defined low-$p_T$ and high-$p_T$ regions. This observable does not rely on any dedicated partition of bins and has lower systematic uncertainties, and is shown to provide a good sensitivity to the intrinsic transverse momentum.

hep-ph↗

arXiv2024

Coarse-grained binning in Drell-Yan transverse momentum spectra

We report a study of the determination of the intrinsic transverse momentum of partons, the intrinsic $k_T$, from the dilepton transverse momentum $p_T$ in Drell-Yan (DY) production at hadron colliders. The result shows that a good sensitivity to the intrinsic $k_T$ distribution is achieved by measuring relative ratios between the cross sections of suitably defined low-$p_T$ and high-$p_T$ regions. The study is performed through both a pseudo-data test and an extraction from measurements of the DY process by the CMS collaboration. Since the methodology does not rely on any dedicated partition of bins, this $p_T$-ratio observable requires less special treatment in very low $p_T$ regions, and propagates lower systematic uncertainties induced from unfolding or momentum migration, in contrast with previous proposals of using a fine-binning measurement of the differential cross section.

hep-ph↗

arXiv2024

Learning Normal Flow Directly From Event Neighborhoods

Event-based motion field estimation is an important task. However, current optical flow methods face challenges: learning-based approaches, often frame-based and relying on CNNs, lack cross-domain transferability, while model-based methods, though more robust, are less accurate. To address the limitations of optical flow estimation, recent works have focused on normal flow, which can be more reliably measured in regions with limited texture or strong edges. However, existing normal flow estimators are predominantly model-based and suffer from high errors. In this paper, we propose a novel supervised point-based method for normal flow estimation that overcomes the limitations of existing event learning-based approaches. Using a local point cloud encoder, our method directly estimates per-event normal flow from raw events, offering multiple unique advantages: 1) It produces temporally and spatially sharp predictions. 2) It supports more diverse data augmentation, such as random rotation, to improve robustness across various domains. 3) It naturally supports uncertainty quantification via ensemble inference, which benefits downstream tasks. 4) It enables training and inference on undistorted data in normalized camera coordinates, improving transferability across cameras. Extensive experiments demonstrate our method achieves better and more consistent performance than state-of-the-art methods when transferred across different datasets. Leveraging this transferability, we train our model on the union of datasets and release it for public use. Finally, we introduce an egomotion solver based on a maximum-margin problem that uses normal flow and IMU to achieve strong performance in challenging scenarios.

cs.CV↗

arXiv2024

New test on contact interactions in the data at $\sqrt{s}$ 130-207GeV by Bhabha scattering process $e^+e^-\to e^+e^-$

We used data mainly collected by OPAL experiment to test the signal significance of a parameter $\varepsilon$, which is equal to zero in SM. For total cross sections, we obtained no derivation of enough significance. But for differential cross sections, some derivation over 3$σ$ are observed(statistical uncertainty only), indicating that this contact interaction might exist.

hep-ex↗

arXiv2024

Quandle Coloring Quivers of general Torus links by dihedral quandles

We completely characterize the coloring quivers of general torus links by dihedral quandles by first exhausting all possible numbers of colorings, followed by determining the interconnections between colorings in each case. The quiver is obtained as function of the number of colorings. The quiver always contains complete subgraphs, in particular a complete subgraph corresponding to the trivial colorings, but the total number of subgraphs in the quiver and the weights of their edges varies depending on the number of colorings.

math.GT↗

arXiv2023

Measurement of the effective weak mixing angle at the CEPC

We present a study of the measurement of the effective weak mixing angle parameter ($\sin^2θ^{\ell}_\text{eff}$) at the Circular Electron Positron Collider (CEPC). As a fundamental physics parameter, $\sin^2θ^{\ell}_\text{eff}$ plays a key role not only in the global test of the standard model electroweak sector, but also in constraining the potential beyond standard model new physics at high energy frontier. CEPC proposes a two year running period around the Z boson mass pole at high instataneous luminosity, providing a large data sample with $4\times 10^{12}$ $Z$ candidates generated in total. It allows a high precision measurement of $\sin^2θ^{\ell}_\text{eff}$ both in the lepton and quark final states, of which the uncertainty can be one order of magnitude lower than any previous measurement at the LEP, SLC, Tevatron and LHC. It will not only improve the overall precision of the $\sin^2θ^\ell_\text{eff}$ experimental determination to be comparable to the preicision of the theoretical calculation with two-loop radiative corrections, but also provide direct comparisons between different final states. In this paper, we also study the measurement of $\sin^2θ^{\ell}_\text{eff}$ at high mass region. With one month data taken, the precision of $\sin^2θ^{\ell}_\text{eff}$ measured at 130 GeV from $b$ quark final state is 0.00010, which will be an important experimental observation on the energy-running effect of $\sin^2θ^{\ell}_\text{eff}$.

hep-ex↗

arXiv2022

EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph

Web and artificial intelligence technologies, especially semantic web and knowledge graph (KG), have recently raised significant attention in educational scenarios. Nevertheless, subject-specific KGs for K-12 education still lack sufficiency and sustainability from knowledge and data perspectives. To tackle these issues, we propose EDUKG, a heterogeneous sustainable K-12 Educational Knowledge Graph. We first design an interdisciplinary and fine-grained ontology for uniformly modeling knowledge and resource in K-12 education, where we define 635 classes, 445 object properties, and 1314 datatype properties in total. Guided by this ontology, we propose a flexible methodology for interactively extracting factual knowledge from textbooks. Furthermore, we establish a general mechanism based on our proposed generalized entity linking system for EDUKG's sustainable maintenance, which can dynamically index numerous heterogeneous resources and data with knowledge topics in EDUKG. We further evaluate EDUKG to illustrate its sufficiency, richness, and variability. We publish EDUKG with more than 252 million entities and 3.86 billion triplets. Our code and data repository is now available at https://github.com/THU-KEG/EDUKG.

cs.CL↗

arXiv2022

Measurement of the proton structure parameters in the forward-backward charge asymmetry

The forward-backward asymmetry ($A_{FB}$) in the Drell-Yan process $pp/p\bar p \to Z/γ^* \to \ell^+\ell^-$ is sensitive to the proton structure information. Such information has been factorized into well-defined proton structure parameters which can be regarded as experimental observables. In this paper, we extract the structure parameters from the $A_{FB}$ distributions reported by the CMS collaboration in $pp$ collisions at $\sqrt{s} = 8$ TeV, and by the D0 collaboration in $p\bar p$ collisions at $\sqrt{s} = 1.96$ TeV. It is the first time that the unique parton information in the $A_{FB}$ spectrum can be decoupled from the electroweak calculation and measured as standalone observables, which can be used as new data constraints in the global quantum chromodynamics analysis of the parton distribution functions (PDFs). Although the parton information in the $pp$ and $p\bar p$ collisions are different, and the precisions of the measured structure parameters are statistically limited, the results from both the hadron colliders indicate that the down quark contribution might be higher than the theoretical predictions with the current PDFs at the relevant momentum fraction range.

hep-ex↗

arXiv2022

Boost Asymmetry of the diboson productions in pp collisions

We propose the boost asymmetry of the diboson productions in pp collisions as a new experimental observable, which can provide unique information on the proton structure. The boost asymmetry rises as the difference in the kinematics of the two bosons, that are coupled to the two different quark and antiquark initial states, respectively, and thus reflects different features of the quark and antiquark parton densities. By comparing the kinematics of the two bosons, such as the boson energy or rapidity, the diboson events with antiquark having higher energy than quark can be distinguished from those with quark having higher energy than antiquark. This would provide unique information in some special parton momentum fraction regions, which cannot be directly proved by current W and Z measurements at the Large Hadron Collider or other deep inelastic scattering experients.

hep-ph↗

arXiv2022

Factorization of the forward-backward charge asymmetry and measurements of the weak mixing angle and proton structure at hadron colliders

The forward-backward charge asymmetry (AFB) at hadron colliders is sensitive to both the electroweak (EW) symmetry breaking represented by the effective weak mixing angle, and the proton structure information in the initial state modeled by the parton distribution functions (PDFs). Due to their strong correlation, the precisions of the determination on the weak mixing angle and PDFs using the measured AFB spectrum are limited. In this paper, we define a set of structure parameters which factorize the unique proton information of the relative difference between quarks and antiquarks in the AFB observation. Other than the conventional way of extracting the weak mixing angle fro the convolution of PDF and EW calculations, we propose a new method to simultaneously determine the value of the weak mixing angle and the proton structure terms by fitting to the observed AFB distribution, and point out the necessity of specifying additional observations to further reduce the uncertainties on the proton structure terms respectively, so that the model-independent high precision measurements can be achieved at the future LHC experiments.

hep-ph↗

arXiv2022

On Schur Algebras and Derivations of Free Lie Algebras

We investigate the action of Schur algebra on the Lie algebras of derivations of free Lie algebras and operad structures constructed from it. We also show that the Lie algebra of derivations is generated by quadratic derivations together with the action of the Schur operad. Applications to certain subgroups of the automorphism group of a finitely generated free group are given as well.

math.GR↗

arXiv2022

Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency

Image resolution that has close relations with accuracy and computational cost plays a pivotal role in network training. In this paper, we observe that the reduced image retains relatively complete shape semantics but loses extensive texture information. Inspired by the consistency of the shape semantics as well as the fragility of the texture information, we propose a novel training strategy named Temporally Resolution Decrement. Wherein, we randomly reduce the training images to a smaller resolution in the time domain. During the alternate training with the reduced images and the original images, the unstable texture information in the images results in a weaker correlation between the texture-related patterns and the correct label, naturally enforcing the model to rely more on shape properties that are robust and conform to the human decision rule. Surprisingly, our approach greatly improves both the training and inference efficiency of convolutional neural networks. On ImageNet classification, using only 33\% calculation quantity (randomly reducing the training image to 112$\times$112 within 90\% epochs) can still improve ResNet-50 from 76.32\% to 77.71\%. Superimposed with the strong training procedure of ResNet-50 on ImageNet, our method achieves 80.42\% top-1 accuracy with saving 37.5\% calculation overhead. To the best of our knowledge this is the highest ImageNet single-crop accuracy on ResNet-50 under 224$\times$224 without extra data or distillation.

cs.CV↗

arXiv2022

White Paper Assistance: A Step Forward Beyond the Shortcut Learning

The promising performances of CNNs often overshadow the need to examine whether they are doing in the way we are actually interested. We show through experiments that even over-parameterized models would still solve a dataset by recklessly leveraging spurious correlations, or so-called 'shortcuts'. To combat with this unintended propensity, we borrow the idea of printer test page and propose a novel approach called White Paper Assistance. Our proposed method involves the white paper to detect the extent to which the model has preference for certain characterized patterns and alleviates it by forcing the model to make a random guess on the white paper. We show the consistent accuracy improvements that are manifest in various architectures, datasets and combinations with other techniques. Experiments have also demonstrated the versatility of our approach on fine-grained recognition, imbalanced classification and robustness to corruptions.

cs.CV↗

arXiv2022

Selective Output Smoothing Regularization: Regularize Neural Networks by Softening Output Distributions

In this paper, we propose Selective Output Smoothing Regularization, a novel regularization method for training the Convolutional Neural Networks (CNNs). Inspired by the diverse effects on training from different samples, Selective Output Smoothing Regularization improves the performance by encouraging the model to produce equal logits on incorrect classes when dealing with samples that the model classifies correctly and over-confidently. This plug-and-play regularization method can be conveniently incorporated into almost any CNN-based project without extra hassle. Extensive experiments have shown that Selective Output Smoothing Regularization consistently achieves significant improvement in image classification benchmarks, such as CIFAR-100, Tiny ImageNet, ImageNet, and CUB-200-2011. Particularly, our method obtains 77.30% accuracy on ImageNet with ResNet-50, which gains 1.1% than baseline (76.2%). We also empirically demonstrate the ability of our method to make further improvements when combining with other widely used regularization techniques. On Pascal detection, using the SOSR-trained ImageNet classifier as the pretrained model leads to better detection performances.

cs.CV↗

arXiv2022

Hint for a minimal interaction length in $e^+e^-\toγγ$ annihilation in total cross section of centre-of-mass energies 55-207 GeV

The measurements of the total cross section of the $ e^+e^-\toγγ$ reaction from the VENUS, TOPAS, OPAL, DELPHI, ALEPH and L3 collaborations, collected between 1989 to 2003, are used to perform a $ χ^{2} $ test to search for a finite interaction length in direct contact term. The experimental data of the total cross section compared to the QED cross section of a $ χ^{2} $ test allows, to set limit on a finite interaction length $ r_{e}=(1.25\pm 0.16) \times 10 ^{-17} [cm] $. In the direct contact term annihilation is this interaction lengths a measure for the size of the electron.

hep-ex↗

arXiv2022

Channel Self-Supervision for Online Knowledge Distillation

Recently, researchers have shown an increased interest in the online knowledge distillation. Adopting an one-stage and end-to-end training fashion, online knowledge distillation uses aggregated intermediated predictions of multiple peer models for training. However, the absence of a powerful teacher model may result in the homogeneity problem between group peers, affecting the effectiveness of group distillation adversely. In this paper, we propose a novel online knowledge distillation method, \textbf{C}hannel \textbf{S}elf-\textbf{S}upervision for Online Knowledge Distillation (CSS), which structures diversity in terms of input, target, and network to alleviate the homogenization problem. Specifically, we construct a dual-network multi-branch structure and enhance inter-branch diversity through self-supervised learning, adopting the feature-level transformation and augmenting the corresponding labels. Meanwhile, the dual network structure has a larger space of independent parameters to resist the homogenization problem during distillation. Extensive quantitative experiments on CIFAR-100 illustrate that our method provides greater diversity than OKDDip and we also give pretty performance improvement, even over the state-of-the-art such as PCL. The results on three fine-grained datasets (StanfordDogs, StanfordCars, CUB-200-211) also show the significant generalization capability of our approach.

cs.CV↗

arXiv2021

Reduction of the electroweak correlation in the PDF updating by using the forward-backward asymmetry of Drell-Yan process

We propose a new observable for the measurement of the forward-backward asymmetry $(A_{FB})$ in Drell-Yan lepton production. At hadron colliders, the $A_{FB}$ distribution is sensitive to both the electroweak (EW) fundamental parameter $\sin^2 θ_{W}$, the weak mixing angle, and the parton distribution functions (PDFs). Hence, the determination of $\sin^2 θ_{W}$ and the updating of PDFs by directly using the same $A_{FB}$ spectrum are strongly correlated. This correlation would introduce large bias or uncertainty into both precise measurements of EW and PDF sectors. In this article, we show that the sensitivity of $A_{FB}$ on $\sin^2 θ_{W}$ is dominated by its average value around the $Z$ pole region, while the shape (or gradient) of the $A_{FB}$ spectrum is insensitive to $\sin^2 θ_{W}$ and contains important information on the PDF modeling. Accordingly, a new observable related to the gradient of the spectrum is introduced, and demonstrated to be able to significantly reduce the potential bias on the determination of $\sin^2 θ_{W}$ when updating the PDFs using the same $A_{FB}$ data.

hep-ph↗

arXiv2021

Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network

In this paper, we propose a novel data augmentation strategy named Cut-Thumbnail, that aims to improve the shape bias of the network. We reduce an image to a certain size and replace the random region of the original image with the reduced image. The generated image not only retains most of the original image information but also has global information in the reduced image. We call the reduced image as thumbnail. Furthermore, we find that the idea of thumbnail can be perfectly integrated with Mixed Sample Data Augmentation, so we put one image's thumbnail on another image while the ground truth labels are also mixed, making great achievements on various computer vision tasks. Extensive experiments show that Cut-Thumbnail works better than state-of-the-art augmentation strategies across classification, fine-grained image classification, and object detection. On ImageNet classification, ResNet-50 architecture with our method achieves 79.21\% accuracy, which is more than 2.8\% improvement on the baseline.

cs.CV↗