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Yang Gao

Publications and source records attributed to Yang Gao.

At least 487 records · Page 27Linked to original sources
arXiv2023

Novel method to extract the femtometer structure of strange baryons using the vacuum polarization effect

One of the fundamental goals of particle physics is to gain microscopic understanding of the strong interaction. Electromagnetic form factors quantify the structure of hadrons in terms of charge and magnetization distributions. While the nucleon structure has been investigated extensively, data on hyperons is still scarce. It has recently been demonstrated that electron-positron annihilations into hyperon-antihyperon pairs provide a powerful tools to investigate their inner structure. We present a novel method useful for hyperon-antihyperon pairs of different types which exploits the cross section enhancement due to the vacuum polarization effect at the $J/ψ$ resonance. Using the 10 billion $J/ψ$ events collected with the BESIII detector, this allows a thorough determination of the hyperon structure . The result is essentially a precise snapshot of a $\barΛΣ^0$~($Λ\barΣ^0$) pair in the making, encoded in the form factor ratio and the phase. Their values are measured to be $R = 0.860\pm0.029({\rm stat.})\pm0.010({\rm syst.})$, $ΔΦ_1=(1.011\pm0.094({\rm stat.})\pm0.010({\rm syst.}))~\rm rad$ for $\barΛΣ^0$ and $ΔΦ_2=(2.128\pm0.094({\rm stat.})\pm0.010({\rm syst.}))~\rm rad$ for $Λ\barΣ^0$, respectively. Furthermore, charge-parity (CP) breaking is investigated for the first time in this reaction and found to be consistent with CP symmetry.

hep-ex↗
arXiv2023

Search for a scalar partner of the $X(3872)$ via $ψ(3770)$ decays into $γηη'$ and $γπ^{+}π^{-}J/ψ$

Using a data sample corresponding to an integrated luminosity of 2.93 fb$^{-1}$ collected at a center-of-mass energy of 3.773~GeV with the BESIII detector at the BEPCII collider, we search for a scalar partner of the $X(3872)$, denoted as $X(3700)$, via $ψ(3770)\to γηη'$ and $γπ^{+}π^{-}J/ψ$ processes. No significant signals are observed and the upper limits of the product branching fractions $ {\cal B}(ψ(3770)\toγX(3700))\cdot {\cal B}(X(3700)\to ηη')$ and ${\cal B}(ψ(3770)\toγX(3700))\cdot {\cal B}(X(3700)\toπ^{+}π^{-}J/ψ)$ are determined at the 90\% confidence level, for the narrow $X(3700)$ with a mass ranging from 3710 to 3740 MeV/$c^2$, which are from 0.8 to 1.8 $(\times 10^{-5})$ and 0.9 to 3.4 $(\times 10^{-5})$, respectively.

hep-ex↗
arXiv2023

A coupled-channel analysis of the $X(3872)$ lineshape with BESIII data

We perform a study of the $X(3872)$ lineshape using the data samples of $e^+e^-\toγX(3872)$, $X(3872)\to D^0\bar{D}^0 π^0$ and $π^+π^- J/ψ$ collected with the BESIII detector. The effects of the coupled-channels and the off-shell $D^{*0}$ are included in the parameterization of the lineshape. The lineshape mass parameter is obtained to be $M_{X}=(3871.63\pm 0.13^{+0.06}_{-0.05})$ MeV. Two poles are found on the first and second Riemann sheets corresponding to the $D^{*0}\bar{D}^0$ branch cut. The pole location on the first sheet is much closer to the $D^{*0}\bar{D}^0$ threshold than the other, and is determined to be $7.04\pm0.15^{+0.07}_{-0.08}$ MeV above the $D^0\bar{D}^0π^0$ threshold with an imaginary part $-0.19\pm0.08^{+0.14}_{-0.19}$ MeV.

hep-ex↗
arXiv2023

Observation of $χ_{cJ}\to Λ\bar Λη$

By analyzing $(448.1\pm2.9)\times10^6$ $ψ(3686)$ events collected with the BESIII detector operating at the BEPCII collider, the decays of $χ_{cJ} \to Λ\bar Λη$ ($J=0$, 1 and 2) are observed for the first time with statistical significances of $13.9σ$, $6.7σ$, and $8.2σ$, respectively. The product branching fractions of $ψ(3686)\toγχ_{cJ}$ and $χ_{cJ}\to Λ\bar Λη$ are measured. Dividing by the world averages of the branching fractions of $ψ(3686)\toγχ_{cJ}$, the branching fractions of $χ_{cJ}\to Λ\bar Λη$ decays are determined to be $(2.31\pm0.30\pm0.21)\times10^{-4}$, $(5.86\pm1.38\pm0.68)\times10^{-5}$, and $(1.05\pm0.21\pm0.15)\times10^{-4}$ for $J=0$, 1 and 2, respectively, where the first uncertainties are statistical and the second systematic.

hep-ex↗
arXiv2023

Measurements of the branching fractions of the inclusive decays $D^{0}(D^{+})\to π^+π^+π^-X$

Using $e^+e^-$ annihilation data corresponding to an integrated luminosity of 2.93 fb$^{-1}$ taken at a center-of-mass energy of 3.773 GeV with the BESIII detector, we report the first measurements of the branching fractions of the inclusive decays $D^0\to π^+π^+π^-X$ and $D^+\to π^+π^+π^-X$, where pions from $K^0_S$ decays have been excluded from the $π^+π^+π^-$ system and $X$ denotes any possible particle combination. The branching fractions of $D^0(D^+)\to π^+π^+π^-X$ are determined to be ${\mathcal B}(D^0\to π^+π^+π^-X)=(17.60\pm0.11\pm0.22)\%$ and ${\mathcal B}(D^+\to π^+π^+π^-X)=(15.25\pm0.09\pm0.18)\%$, where the first uncertainties are statistical and the second systematic.

hep-ex↗
arXiv2023

Search for $\barΛ$-$Λ$ oscillations in the decay $J/ψ\to p K^- \barΛ+c.c.$

We report the first search for $\barΛ$--$Λ$ oscillations in the decay $J/ψ\to p K^- \barΛ + c.c.$ by analyzing $1.31\times10^9$ $J/ψ$ events accumulated with the BESIII detector at the BEPCII collider. The $J/ψ$ events are produced using $e^+e^-$ collisions at a center of mass energy $\sqrt{s}= 3.097$~GeV. No evidence for hyperon oscillations is observed. The upper limit for the oscillation rate of $\barΛ$ to $Λ$ hyperons is determined to be $\mathcal{P}(Λ)=\frac{\mathcal{B}(J/ψ\to pK^-Λ+c.c.)}{\mathcal{B}(J/ψ\to pK^-\barΛ+c.c.)}<4.4\times10^{-6}$ corresponding to an oscillation parameter $δm_{Λ\barΛ}$ of less than $3.8\times10^{-18}$~GeV at the 90\% confidence level.

hep-ex↗
arXiv2023

Measurement of branching fractions of $Λ_{c}^{+}$ decays to $Σ^{+} K^{+} K^{-}$, $Σ^{+}ϕ$ and $Σ^{+} K^{+} π^{-}(π^{0})$

Based on 4.5 fb$^{-1}$ data taken at seven center-of-mass energies ranging from 4.600 to 4.699 GeV with the BESIII detector at the BEPCII collider, we measure the branching fractions of $Λ_{c}^{+}\rightarrowΣ^{+}+hadrons$ relative to $Λ_{c}^{+}\rightarrow Σ^+ π^+ π^-$. Combining with the world average branching fraction of $Λ_{c}^{+}\rightarrow Σ^+ π^+ π^-$, their branching fractions are measured to be $(0.377\pm0.042\pm0.018\pm0.021)\%$ for $Λ_{c}^{+}\rightarrowΣ^{+} K^{+} K^{-}$, $(0.200\pm0.023\pm0.010\pm0.011)\%$ for $Λ_{c}^{+}\rightarrowΣ^{+} K^{+} π^{-}$, $(0.414\pm0.080\pm0.029\pm0.023)\%$ for $Λ_{c}^{+}\rightarrowΣ^{+}ϕ$ and $(0.197\pm0.036\pm0.008\pm0.011)\%$ for $Λ_{c}^{+}\rightarrowΣ^{+}K^{+} K^{-}$(non-$ϕ$). In all the above results, the first uncertainties are statistical, the second are systematic and the third are from external input of the branching fraction of $Λ_{c}^{+}\rightarrow Σ^+ π^+ π^-$. Since no signal for $Λ_{c}^{+}\rightarrowΣ^{+} K^{+} π^{-}π^{0}$ is observed, the upper limit of its branching fraction is determined to be 0.11\% at the 90$\%$ confidence level.

hep-ex↗
arXiv2023

Parameter-Efficient Transfer Learning for Audio-Visual-Language Tasks

The pretrain-then-finetune paradigm has been widely used in various unimodal and multimodal tasks. However, finetuning all the parameters of a pre-trained model becomes prohibitive as the model size grows exponentially. To address this issue, the adapter mechanism that freezes the pre-trained model and only finetunes a few extra parameters is introduced and delivers promising results. Most studies on adapter architectures are dedicated to unimodal or bimodal tasks, while the adapter architectures for trimodal tasks have not been investigated yet. This paper introduces a novel Long Short-Term Trimodal Adapter (LSTTA) approach for video understanding tasks involving audio, visual, and language modalities. Based on the pre-trained from the three modalities, the designed adapter module is inserted between the sequential blocks to model the dense interactions across the three modalities. Specifically, LSTTA consists of two types of complementary adapter modules, namely the long-term semantic filtering module and the short-term semantic interaction module. The long-term semantic filtering aims to characterize the temporal importance of the video frames and the short-term semantic interaction module models local interactions within short periods. Compared to previous state-of-the-art trimodal learning methods pre-trained on a large-scale trimodal corpus, LSTTA is more flexible and can inherit any powerful unimodal or bimodal models. Experimental results on four typical trimodal learning tasks show the effectiveness of LSTTA over existing state-of-the-art methods.

cs.MM↗
arXiv2023

Measurement of the branching fraction of $D_s^+\to τ^+ ν_τ$ via $τ^+ \to μ^+ ν_μ\bar{ν_τ}$

Utilizing $7.33~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data taken at the center-of-mass energies of 4.128, 4.157, 4.178, 4.189, 4.199, 4.209, 4.219, and 4.226 GeV with the BESIII detector, the branching fraction of the leptonic decay $D_s^+\toτ^+ν_τ$ via $τ^+\toμ^+ν_μ\bar ν_τ$ is measured to be $\mathcal{B}_{D_s^+\toτ^+ν_τ}=(5.34\pm0.16_{\rm stat}\pm0.10_{\rm syst})\%$. Combining this branching fraction with the world averages of the measurements of the masses of $τ^+$ and $D_s^+$ as well as the lifetime of $D_s^+$, we extract the product of the decay constant of $D_s^+$ and the $c\to s$ Cabibbo-Kobayashi-Maskawa matrix element to be $f_{D_s^+}|V_{cs}|=(246.2\pm3.7_{\rm stat}\pm2.5_{\rm syst})~\mathrm{MeV}$. Taking $|V_{cs}|$ from a global fit in the standard model we obtain $f_{D_s^+}=(252.7\pm3.8_{\rm stat}\pm2.6_{\rm syst})$ MeV. Conversely, taking $f_{D_s^+}$ from lattice quantum chromodynamics calculations, we obtain $|V_{cs}| = 0.984\pm0.015_{\rm stat}\pm0.010_{\rm syst}$.

hep-ex↗
arXiv2023

Improved measurement of the branching fractions for $J/ψ\toγπ^0$, $γη$ and $γη^\prime$

Using a data sample of $(1.0087\pm 0.0044)\times 10^{10}$ $J/ψ$ events collected with the BESIII detector, the decays of $J/ψ\toγπ^{0} (η, η^\prime)\toγγγ$ are studied. Newly measured branching fractions are $\mathcal{B}$$(J/ψ\toγπ^{0})$=$(3.34\pm 0.02\pm 0.09)\times 10^{-5}$, $\mathcal{B}$$(J/ψ\toγη)$=$(1.096\pm 0.001\pm0.019)\times 10^{-3}$ and $\mathcal{B}$$(J/ψ\toγη^\prime)$=$(5.40\pm 0.01\pm0.11)\times 10^{-3}$, where the first uncertainties are statistical and the second are systematic. These results are consistent with the world average values within two standard deviations. The ratio of partial widths $Γ(J/ψ\toγη^\prime)/Γ(J/ψ\toγη)$ is measured to be $4.93 \pm 0.13$. The singlet-octet pseudoscalar mixing angle $θ_P$ is determined to be $θ_P = -(22.11 \pm0.26)^\circ$ or $-(19.34 \pm 0.34)^\circ$ with two different phenomenological models.

hep-ex↗
arXiv2023

Defensive Few-shot Learning

This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting. To address this situation, we develop a general defensive few-shot learning (DFSL) framework to answer the following two key questions: (1) how to transfer adversarial defense knowledge from one sample distribution to another? (2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting? To answer the first question, we propose an episode-based adversarial training mechanism by assuming a task-level distribution consistency to better transfer the adversarial defense knowledge. As for the second question, within each few-shot task, we design two kinds of distribution consistency criteria to narrow the distribution gap between clean and adversarial examples from the feature-wise and prediction-wise perspectives, respectively. Extensive experiments demonstrate that the proposed framework can effectively make the existing few-shot models robust against adversarial attacks. Code is available at https://github.com/WenbinLee/DefensiveFSL.git.

cs.CV↗
arXiv2023

IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization

Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled data will inevitably contain unseen-class outliers not belonging to any of the labeled classes. To deal with the challenging open-set SSL task, the mainstream methods tend to first detect outliers and then filter them out. However, we observe a surprising fact that such approach could result in more severe performance degradation when labels are extremely scarce, as the unreliable outlier detector may wrongly exclude a considerable portion of valuable inliers. To tackle with this issue, we introduce a novel open-set SSL framework, IOMatch, which can jointly utilize inliers and outliers, even when it is difficult to distinguish exactly between them. Specifically, we propose to employ a multi-binary classifier in combination with the standard closed-set classifier for producing unified open-set classification targets, which regard all outliers as a single new class. By adopting these targets as open-set pseudo-labels, we optimize an open-set classifier with all unlabeled samples including both inliers and outliers. Extensive experiments have shown that IOMatch significantly outperforms the baseline methods across different benchmark datasets and different settings despite its remarkable simplicity. Our code and models are available at https://github.com/nukezil/IOMatch.

cs.CV↗
arXiv2023

A Multilayer Framework for Online Metric Learning

Online metric learning has been widely applied in classification and retrieval. It can automatically learn a suitable metric from data by restricting similar instances to be separated from dissimilar instances with a given margin. However, the existing online metric learning algorithms have limited performance in real-world classifications, especially when data distributions are complex. To this end, this paper proposes a multilayer framework for online metric learning to capture the nonlinear similarities among instances. Different from the traditional online metric learning, which can only learn one metric space, the proposed Multi-Layer Online Metric Learning (MLOML) takes an online metric learning algorithm as a metric layer and learns multiple hierarchical metric spaces, where each metric layer follows a nonlinear layers for the complicated data distribution. Moreover, the forward propagation (FP) strategy and backward propagation (BP) strategy are employed to train the hierarchical metric layers. To build a metric layer of the proposed MLOML, a new Mahalanobis-based Online Metric Learning (MOML) algorithm is presented based on the passive-aggressive strategy and one-pass triplet construction strategy. Furthermore, in a progressively and nonlinearly learning way, MLOML has a stronger learning ability than traditional online metric learning in the case of limited available training data. To make the learning process more explainable and theoretically guaranteed, theoretical analysis is provided. The proposed MLOML enjoys several nice properties, indeed learns a metric progressively, and performs better on the benchmark datasets. Extensive experiments with different settings have been conducted to verify these properties of the proposed MLOML.

cs.CV↗
arXiv2023

Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings

Contrastive self-supervised learning (SSL) methods, such as MoCo and SimCLR, have achieved great success in unsupervised visual representation learning. They rely on a large number of negative pairs and thus require either large memory banks or large batches. Some recent non-contrastive SSL methods, such as BYOL and SimSiam, attempt to discard negative pairs and have also shown remarkable performance. To avoid collapsed solutions caused by not using negative pairs, these methods require non-trivial asymmetry designs. However, in small data regimes, we can not obtain a sufficient number of negative pairs or effectively avoid the over-fitting problem when negatives are not used at all. To address this situation, we argue that negative pairs are still important but one is generally sufficient for each positive pair. We show that a simple Triplet-based loss (Trip) can achieve surprisingly good performance without requiring large batches or asymmetry designs. Moreover, to alleviate the over-fitting problem in small data regimes and further enhance the effect of Trip, we propose a simple plug-and-play RandOm MApping (ROMA) strategy by randomly mapping samples into other spaces and requiring these randomly projected samples to satisfy the same relationship indicated by the triplets. Integrating the triplet-based loss with random mapping, we obtain the proposed method Trip-ROMA. Extensive experiments, including unsupervised representation learning and unsupervised few-shot learning, have been conducted on ImageNet-1K and seven small datasets. They successfully demonstrate the effectiveness of Trip-ROMA and consistently show that ROMA can further effectively boost other SSL methods. Code is available at https://github.com/WenbinLee/Trip-ROMA.

cs.CV↗
arXiv2023

DomainAdaptor: A Novel Approach to Test-time Adaptation

To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that aims to adapt a trained CNN model to unseen domains during the test. To maximumly mine the information in the test data, we propose a unified method called DomainAdaptor for the test-time adaptation, which consists of an AdaMixBN module and a Generalized Entropy Minimization (GEM) loss. Specifically, AdaMixBN addresses the domain shift by adaptively fusing training and test statistics in the normalization layer via a dynamic mixture coefficient and a statistic transformation operation. To further enhance the adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy Minimization loss to better exploit the information in the test data. Extensive experiments show that DomainAdaptor consistently outperforms the state-of-the-art methods on four benchmarks. Furthermore, our method brings more remarkable improvement against existing methods on the few-data unseen domain. The code is available at https://github.com/koncle/DomainAdaptor.

cs.CV↗
arXiv2023

Generalized Bandit Regret Minimizer Framework in Imperfect Information Extensive-Form Game

Regret minimization methods are a powerful tool for learning approximate Nash equilibrium (NE) in two-player zero-sum imperfect information extensive-form games (IIEGs). We consider the problem in the interactive bandit-feedback setting where we don't know the dynamics of the IIEG. In general, only the interactive trajectory and the reached terminal node value $v(z^t)$ are revealed. To learn NE, the regret minimizer is required to estimate the full-feedback loss gradient $\ell^t$ by $v(z^t)$ and minimize the regret. In this paper, we propose a generalized framework for this learning setting. It presents a theoretical framework for the design and the modular analysis of the bandit regret minimization methods. We demonstrate that the most recent bandit regret minimization methods can be analyzed as a particular case of our framework. Following this framework, we describe a novel method SIX-OMD to learn approximate NE. It is model-free and extremely improves the best existing convergence rate from the order of $O(\sqrt{X B/T}+\sqrt{Y C/T})$ to $O(\sqrt{ M_{\mathcal{X}}/T} +\sqrt{ M_{\mathcal{Y}}/T})$. Moreover, SIX-OMD is computationally efficient as it needs to perform the current strategy and average strategy updates only along the sampled trajectory.

cs.LG↗
arXiv2023

Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32% accuracy improvement than supervised deep learning and traditional iterative methods. It is also 33% more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 minutes.

eess.IV↗
arXiv2023

Measurement of $e^{+}e^{-}\toϕπ^{+}π^{-}$ cross sections at center-of-mass energies from 2.00 to 3.08 GeV

Using data corresponding to an integrated luminosity of $651~\mathrm{pb}^{-1}$ accumulated at 22 center-of-mass energies from 2.00 to 3.08 GeV by the BESIII experiment, the process $e^{+}e^{-}\toϕπ^{+}π^{-}$ is studied. The cross sections for $e^{+}e^{-}\toϕπ^{+}π^{-}$ are consistent with previous results, but with improved precision. To measure the mass and width of the structure observed in the cross section line shape, a combine fit is performed after enhancing the contribution from $ϕf_{0}(980)$. The fit reveals a structure with the mass of $M=2178\pm20\pm5~{\rm MeV}/c^2$ and the width of $\varGamma=140\pm36\pm16~{\rm MeV}$, where the first uncertainties are statistical and the second ones are systematic.

hep-ex↗