arXiv subjects

Yang Gao

Publications and source records attributed to Yang Gao.

At least 631 records · Page 35Linked to original sources

arXiv2022

Measurement of the $C\!P$-even fraction of $D^{0}\toπ^{+}π^{-}π^{+}π^{-}$

A measurement of the $C\!P$-even fraction of the decay $D^{0}\toπ^{+}π^{-}π^{+}π^{-}$ is performed with a quantum-correlated $ψ(3770)\to D\bar{D}$ data sample collected by the BESIII experiment, corresponding to an integrated luminosity of 2.93 $\textrm{fb}^{-1}$. Using a combination of $C\!P$ eigenstates, $D \to π^+π^-π^0$ and $D \to K_{S,L}^0π^{+}π^{-}$ as tagging modes, the $C\!P$-even fraction is measured to be $F_{+}^{4π} = 0.735 \pm 0.015 \pm 0.005$, where the first uncertainty is statistical and the second is systematic. This is the most precise determination of this quantity to date. It provides valuable model-independent input for the measurement of the CKM angle $γ$ with $B^\pm\to D K^\pm$ decays, and for time-dependent studies of $C\!P$ violation and mixing in the $D^{0}$-$\bar{D}^{0}$ system.

hep-ex↗

arXiv2022

Amplitude analysis and branching fraction measurement of the decay $D_{s}^{+} \to K^+π^+π^-$

Using $6.32$ fb$^{-1}$ of $e^{+}e^{-}$ collision data collected at the center-of-mass energies between 4.178 and 4.226 GeV with the BESIII detector, we perform an amplitude analysis of the decay $D^+_s \to K^+π^+π^-$ and determine the amplitudes of the various intermediate states. The absolute branching fraction of $D^+_s\to K^+π^+π^-$ is measured to be ($6.11\pm0.18_{\rm stat.}\pm0.11_{\rm syst.})\times 10^{-3}$. The branching fractions of the dominant intermediate processes $D_{s}^{+} \to K^+ρ^0, ρ^0 \to π^+π^-$ and $D_{s}^{+} \to K^*(892)^0π^+, K^*(892)^0 \to K^+π^-$ are determined to be $(1.96\pm0.19_{\rm stat.}\pm0.23_{\rm syst.})\times 10^{-3}$ and $(1.85\pm0.12_{\rm stat.}\pm0.13_{\rm syst.})\times 10^{-3}$, respectively. The intermediate resonances $f_0(500)$, $f_0(980)$, and $f_0(1370)$ are observed for the first time in this channel.

hep-ex↗

arXiv2022

Transformers in Medical Image Analysis: A Review

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis.

cs.CV↗

arXiv2022

Study of the resonance structures in $e^{+}e^{-} \rightarrow π^{+}π^{-}J/ψ$ process

Using about 23 $\mathrm{fb^{-1}}$ of data collected with the BESIII detector operating at the BEPCII storage ring, a precise measurement of the $e^{+}e^{-} \rightarrow π^{+}π^{-}J/ψ$ Born cross section is performed at center-of-mass energies from 3.7730 to 4.7008 GeV. Two structures, identified as the $Y(4220)$ and the $Y(4320)$ states, are observed in the energy-dependent cross section with a significance larger than $10σ$. The masses and widths of the two structures are determined to be ($M, Γ$) = ($4221.4\pm1.5\pm2.0$ MeV/$c^{2}$, $41.8\pm2.9\pm2.7$ MeV) and ($M, Γ$) = ($4298\pm12\pm26$ MeV/$c^{2}$, $127\pm17\pm10$ MeV), respectively. A small enhancement around 4.5 GeV with a significance about $3σ$, compatible with the $ψ(4415)$, might also indicate the presence of an additional resonance in the spectrum. The inclusion of this additional contribution in the fit to the cross section affects the resonance parameters of the $Y(4320)$ state.

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arXiv2022

Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization

With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention. Previous DG models usually require a sufficient quantity of annotated samples from observed source domains during training. In this paper, we relax this requirement about full annotation and investigate semi-supervised domain generalization (SSDG) where only one source domain is fully annotated along with the other domains totally unlabeled in the training process. With the challenges of tackling the domain gap between observed source domains and predicting unseen target domains, we propose a novel deep framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels. Concretely, to predict accurate pseudo-labels under domain shift, a domain-aware pseudo-labeling module is developed. Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process. When accurate pseudo-labels are generated for unlabeled source domains, the domain mixup operation is applied to augment new domains between labeled and unlabeled domains, which is beneficial for boosting the generalization capability of the model. Extensive results on publicly available DG benchmark datasets show the efficacy of our proposed SSDG method.

cs.CV↗

arXiv2022

CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation

Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO. This gap exists largely because commonly used random crop augmentations obtain semantically inconsistent content in crowded scene images of diverse objects. Previous works use preprocessing pipelines to localize salient objects for improved cropping, but an end-to-end solution is still elusive. In this work, we propose a framework which accomplishes this goal via joint learning of representations and segmentation. We leverage segmentation masks to train a model with a mask-dependent contrastive loss, and use the partially trained model to bootstrap better masks. By iterating between these two components, we ground the contrastive updates in segmentation information, and simultaneously improve segmentation throughout pretraining. Experiments show our representations transfer robustly to downstream tasks in classification, detection and segmentation.

cs.CV↗

arXiv2022

MVDG: A Unified Multi-view Framework for Domain Generalization

To generalize the model trained in source domains to unseen target domains, domain generalization (DG) has recently attracted lots of attention. Since target domains can not be involved in training, overfitting source domains is inevitable. As a popular regularization technique, the meta-learning training scheme has shown its ability to resist overfitting. However, in the training stage, current meta-learning-based methods utilize only one task along a single optimization trajectory, which might produce a biased and noisy optimization direction. Beyond the training stage, overfitting could also cause unstable prediction in the test stage. In this paper, we propose a novel multi-view DG framework to effectively reduce the overfitting in both the training and test stage. Specifically, in the training stage, we develop a multi-view regularized meta-learning algorithm that employs multiple optimization trajectories to produce a suitable optimization direction for model updating. We also theoretically show that the generalization bound could be reduced by increasing the number of tasks in each trajectory. In the test stage, we utilize multiple augmented images to yield a multi-view prediction to alleviate unstable prediction, which significantly promotes model reliability. Extensive experiments on three benchmark datasets validate that our method can find a flat minimum to enhance generalization and outperform several state-of-the-art approaches.

cs.CV↗

arXiv2022

Measurement of the branching fraction for $ψ(3686)\to ωK^0_SK^0_S$

Analyzing $(448.1\pm2.9)\times10^6$ $ψ(3686)$ events collected with the BESIII detector at the BEPCII collider, the $ψ(3686)\to ωK_{S}^{0}K_{S}^{0}$ decay is observed for the first time. The branching fraction for this decay is determined to be $\mathcal{B}_{ψ(3686)\to ωK_{S}^{0}K^{0}_{S}}$=$(7.04\pm0.39\pm0.36)$$\times10^{-5}$, where the first uncertainty is statistical and the second is systematic.

hep-ex↗

arXiv2022

Search for baryon and lepton number violating decays $D^{0}\to \bar{p}e^{+}$ and $D^{0}\to pe^{-}$

Using an electron-positron collision data sample corresponding to an integrated luminosity of 2.93~fb$^{-1}$ collected with the BESIII detector at a center-of-mass energy of 3.773 GeV, we search for the baryon and lepton number violating decays $D^{0}\to \bar{p}e^{+}$ and $D^{0}\to pe^{-}$. No obvious signals are found with the current statistics. The upper limits on the branching fractions for $D^{0}\to \bar{p}e^{+}$ and $D^{0}\to pe^{-}$ are set to be $1.2\times 10^{-6}$ and $2.2\times 10^{-6}$ at 90\% confidence level, respectively.

hep-ex↗

arXiv2022

Measurements of Absolute Branching Fractions of $D^0\to K_L^0ϕ$, $K_L^0η$, $K_L^0ω$, and $K_L^0η^{\prime}$

We report the first measurements of the absolute branching fractions of $D^0\to K_L^0ϕ$, $D^0\to K_L^0η$, $D^0\to K_L^0ω$, and $D^0\to K_L^0η^{\prime}$, obtained by analyzing $2.93\,\rm fb^{-1}$ of $e^+e^-$ collision data taken at a center-of-mass energy of 3.773 GeV with the BESIII detector. Taking the world averages of the branching fractions of $D^0\to K_S^0ϕ$, $D^0\to K_S^0η$, $D^0\to K_S^0ω$, and $D^0\to K_S^0η^{\prime}$, the $K_S^0$-$K_L^0$ asymmetry $\mathcal{R}(D^0)$ in these decay modes are obtained. The CP asymmetries in these decays are also determined. No significant $CP$ violation is observed.

hep-ex↗

arXiv2022

Measurements of the absolute branching fractions of hadronic $D$-meson decays involving kaons and pions

By analyzing an electron-positron collision data sample corresponding to an integrated luminosity of $2.93\,\rm fb^{-1}$ taken at the center-of-mass energy of 3.773 GeV with the BESIII detector, we obtain for the first time the absolute branching fractions for seven $D^0$ and $D^+$ hadronic decay modes and search for the hadronic decay $D^0\to K^0_S K^0_Sπ^0$ with much improved sensitivity. The results are ${\mathcal B}(D^0\to K^0_Sπ^0π^0π^0 )=( 7.64\pm 0.30\pm 0.29)\times 10^{-3}$, ${\mathcal B}(D^0\to K^-π^+π^0π^0π^0 )=( 9.54\pm 0.30\pm 0.31)\times 10^{-3}$, ${\mathcal B}(D^0\to K^0_Sπ^+π^-π^0π^0)=(12.66\pm 0.45\pm 0.43)\times 10^{-3}$, ${\mathcal B}(D^+\to K^0_Sπ^+π^0π^0 )=(29.04\pm 0.62\pm 0.87)\times 10^{-3}$, ${\mathcal B}(D^+\to K^0_Sπ^+π^+π^-π^0)=(15.28\pm 0.57\pm 0.60)\times 10^{-3}$, ${\mathcal B}(D^+\to K^0_Sπ^+π^0π^0π^0)=( 5.54\pm 0.44\pm 0.32)\times 10^{-3}$, ${\mathcal B}(D^+\to K^-π^+π^+π^0π^0 )=( 4.95\pm 0.26\pm 0.19)\times 10^{-3}$, ${\mathcal B}({D^0\to K^0_S K^0_Sπ^0}) < 1.57 \times 10^{-4}$ at the 90\% confidence level. Here the first uncertainties are statistical and the second ones systematic. The newly studied decays greatly enrich the knowledge of the $D\to \bar Kπππ$ and $D\to \bar Kππππ$ hadronic decays, and open a bridge to access more two-body hadronic $D$ decays containing scalar, vector, axial and tensor mesons in the charm sector.

hep-ex↗

arXiv2022

TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing

Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient's symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD. Our dataset contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZY-BERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.

cs.CL↗

arXiv2022

Measurement of $e^{+}e^{-}\rightarrowπ^{+}π^{-}D^{+}D^{-}$ cross sections at center-of-mass energies from 4.190 to 4.946 GeV

Using data samples collected with the BESIII detector operating at the BEPCII storage ring, we measure the cross sections of the $e^{+}e^{-}\rightarrowπ^{+}π^{-}D^{+}D^{-}$ process at center-of-mass energies from 4.190 to 4.946 GeV with a partial reconstruction method. Two resonance structures are seen and the resonance parameters are determined from a fit to the cross section line shape. The first resonance we observe has a mass of (4373.1 $\pm$ 4.0 $\pm$ 2.2) MeV/$c^2$ and a width of (146.5 $\pm$ 7.4 $\pm$ 1.3) MeV, in agreement with those of the $Y(4390)$ state; the other resonance has a mass of (4706 $\pm$ 11 $\pm$ 4) MeV/$c^2$, a width of (45 $\pm$ 28 $\pm$ 9) MeV, and a statistical significance of $4.1$ standard deviations ($σ$). This is the first evidence for a vector state at this mass value. The spin-$3$ $D$-wave charmonium state $X(3842)$ is searched for through the $e^{+}e^{-}\rightarrowπ^{+}π^{-}X(3842)\rightarrowπ^{+}π^{-}D^{+}D^{-}$ process, and evidence with a significance of $4.2σ$ is found in the data samples with center-of-mass energies from 4.600 to 4.700 GeV.

hep-ex↗

arXiv2022

$Λ_{c}^{+}$ decays at BESIII

BESIII has made great progress in taking data which is the largest data samples near the $Λ_{c}^{+}\barΛ_{c}^{-}$ threshold. We have measured the branching fraction of $Λ_{c}^{+}\to nπ^{+}$ to be $(6.6\pm 1.2_{\mathrm{stat.}}\pm 0.4_{\mathrm{syst.}})\times 10^{-4}$ with the statistical significance of 7.3 $σ$ firstly using $3.9\mathrm{fb}^{-1}$ $e^{+}e^{-}$ collision collected with BESIII detector at six center-of-mass energies between 4.612 and 4.699 GeV. With the result of the branching fraction of $Λ_{c}^{+}\to pπ^{0}$ from Belle, the ratio of the branching fractions between $Λ_{c}^{+}\to nπ^{+}$ and $Λ_{c}^{+}\to pπ^{0}$ is measured to be larger than 7.2 at 90% confidence level. The branching fractions of $Λ_{c}^{+}\to Λπ^{+}$ and $Λ_{c}^{+}\to Σ^{0} π^{+}$ are measured to be $(1.31\pm 0.08_{\mathrm{stat.}}\pm 0.05_{\mathrm{syst.}})\times 10^{-2}$ and $(1.22\pm 0.08_{\mathrm{stat.}}\pm 0.07_{\mathrm{syst.}})\times 10^{-2}$, respectively, which are consistent with previous BESIII results. More results of $Λ_{c}^{+}$ decays will be published with better precision this year using $6.4\mathrm{fb}^{-1}$ $e^{+}e^{-}$ collision data samples between 4.600 and 4.946 GeV.

hep-ex↗

arXiv2022

Semantic-Aware Fine-Grained Correspondence

Establishing visual correspondence across images is a challenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representations for visual correspondence. However, we find that these methods often fail to leverage semantic information and over-rely on the matching of low-level features. In contrast, human vision is capable of distinguishing between distinct objects as a pretext to tracking. Inspired by this paradigm, we propose to learn semantic-aware fine-grained correspondence. Firstly, we demonstrate that semantic correspondence is implicitly available through a rich set of image-level self-supervised methods. We further design a pixel-level self-supervised learning objective which specifically targets fine-grained correspondence. For downstream tasks, we fuse these two kinds of complementary correspondence representations together, demonstrating that they boost performance synergistically. Our method surpasses previous state-of-the-art self-supervised methods using convolutional networks on a variety of visual correspondence tasks, including video object segmentation, human pose tracking, and human part tracking.

cs.CV↗

arXiv2022

Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction

Imitation learning is a widely used policy learning method that enables intelligent agents to acquire complex skills from expert demonstrations. The input to the imitation learning algorithm is usually composed of both the current observation and historical observations since the most recent observation might not contain enough information. This is especially the case with image observations, where a single image only includes one view of the scene, and it suffers from a lack of motion information and object occlusions. In theory, providing multiple observations to the imitation learning agent will lead to better performance. However, surprisingly people find that sometimes imitation from observation histories performs worse than imitation from the most recent observation. In this paper, we explain this phenomenon from the information flow within the neural network perspective. We also propose a novel imitation learning neural network architecture that does not suffer from this issue by design. Furthermore, our method scales to high-dimensional image observations. Finally, we benchmark our approach on two widely used simulators, CARLA and MuJoCo, and it successfully alleviates the copycat problem and surpasses the existing solutions.

cs.CV↗

arXiv2022

EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer

Most existing methods view makeup transfer as transferring color distributions of different facial regions and ignore details such as eye shadows and blushes. Besides, they only achieve controllable transfer within predefined fixed regions. This paper emphasizes the transfer of makeup details and steps towards more flexible controls. To this end, we propose Exquisite and locally editable GAN for makeup transfer (EleGANt). It encodes facial attributes into pyramidal feature maps to preserves high-frequency information. It uses attention to extract makeup features from the reference and adapt them to the source face, and we introduce a novel Sow-Attention Module that applies attention within shifted overlapped windows to reduce the computational cost. Moreover, EleGANt is the first to achieve customized local editing within arbitrary areas by corresponding editing on the feature maps. Extensive experiments demonstrate that EleGANt generates realistic makeup faces with exquisite details and achieves state-of-the-art performance. The code is available at https://github.com/Chenyu-Yang-2000/EleGANt.

cs.CV↗

arXiv2022

Evidence for a neutral near-threshold structure in the $K^{0}_{\rm S}$ recoil-mass spectra in $e^+e^-\rightarrow K^{0}_{\rm S}D_s^+D^{-}$ and $e^+e^-\rightarrow K^{0}_{\rm S}D_s^{+}D^{-}$

We study the processes $e^+e^-\rightarrow K^{0}_{\rm S}D_s^+D^{*-}$ and $e^+e^-\rightarrow K^{0}_{\rm S}D_s^{*+}D^{-}$, as well as their charge conjugated processes, at five center-of-mass energies between 4.628~GeV and 4.699~GeV, using data samples corresponding to an integrated luminosity of $3.8~\rm{fb^{-1}}$ collected by the BESIII detector at the BEPCII storage ring. Based on a partial reconstruction technique, we find evidence of a structure near the thresholds for $D_s^+D^{*-}$ and $D_s^{*+}D^{-}$ production in the $K^{0}_{\rm S}$ recoil-mass spectrum, which we refer to as the $ Z_{cs}(3985)^0$. Fitting with a Breit-Wigner line shape, we find the mass of the structure to be $(3992.2\pm{1.7}\pm{1.6})~\rm{MeV}/c^2$ and the width to be $(7.7_{-3.8}^{+4.1}\pm{4.3})~\rm{MeV}$, where the first uncertainties are statistical and the second are systematic. The significance of the $ Z_{cs}(3985)^0$ signal is found to be $4.6σ$ including both the statistical and systematic uncertainty. We report the Born cross section multiplied by the branching fraction at different energy points. The mass of the $Z_{cs}(3985)^0$ is close to that of the $Z_{cs}(3985)^+$. Assuming SU(3) symmetry, the cross section of the neutral channel is consistent with that of the charged one. Hence, we conclude that the $Z_{cs}(3985)^0$ is the isospin partner of the $Z_{cs}(3985)^+$.

hep-ex↗