arXiv subjects

Yang Gao

Publications and source records attributed to Yang Gao.

At least 685 records · Page 38Linked to original sources

arXiv2022

Search for a $CP$-odd light Higgs boson in $J/ψ\to γA^0$

Using $J/ψ$ radiative decays from 9.0 billion $J/ψ$ events collected by the BESIII detector, we search for di-muon decays of a $CP$-odd light Higgs boson ($A^0$), predicted by many new physics models beyond the Standard Model, including the Next-to-Minimal Supersymmetric Standard Model. No evidence for the $CP$-odd light Higgs production is found, and we set $90\%$ confidence level upper limits on the product branching fraction $\mathcal{B}(J/ψ\to γA^0)\times \mathcal{B}(A^0 \to μ^+μ^-)$ in the range of $(1.2-778.0)\times 10^{-9}$ for $0.212 \le m_{A^0} \le 3.0$ GeV/$c^2$. The new measurement is a 6-7 times improvement over our previous measurement, and is also slightly better than the BaBar measurement in the low-mass region for $\tan β=1$.

hep-ex↗

arXiv2022

Observation of the electromagnetic Dalitz decay $D^{\ast 0}\to D^{0}e^{+}e^{-}$

Based on 3.19 fb$^{-1}$ of $e^+e^-$ collision data accumulated at the center-of-mass energy 4.178 GeV with the BESIII detector operating at the BEPCII collider, the electromagnetic Dalitz decay $D^{\ast 0}\to D^{0}e^{+}e^{-}$ is observed for the first time with a statistical significance of $13.2σ$. The ratio of the branching fraction of $D^{\ast 0}\to D^{0}e^{+}e^{-}$ to that of $D^{\ast 0}\to D^{0} γ$ is measured to be $(11.08\pm0.76\pm0.49)\times 10^{-3}$. By using the world average value of the branching fraction of $D^{\ast 0}\to D^{0} γ$, the branching fraction of $D^{\ast 0}\to D^{0}e^{+}e^{-}$ is determined to be $(3.91\pm0.27\pm0.17\pm0.10)\times 10^{-3}$, where the first uncertainty is statistical, the second systematic and the third external branching fractions.

hep-ex↗

arXiv2021

Observation of $J/ψ$ Electromagnetic Dalitz Decays to $X(1835)$, $X(2120)$ and $X(2370)$

Using a sample of about 10 billion $J/ψ$ events collected at a center-of-mass energy $\sqrt s = 3.097$ GeV with the BESIII detector, the electromagnetic Dalitz decays $J/ψ\to e^+e^- π^+ π^- η'$, with $η'\toγπ^+ π^-$ and $η'\toπ^+π^-η$, have been studied. The decay $J/ψ\to e^+ e^- X(1835)$ is observed with a significance of $15σ$, and the transition form factor of $J/ψ\to e^+e^-X(1835)$ is presented for the first time. The intermediate states $X(2120)$ and $X(2370)$ are also observed in the $π^+ π^- η'$ invariant mass spectrum with significances of $5.3σ$ and $7.3σ$. The corresponding product branching fractions for $J/ψ\to e^+e^-X$, $X\toπ^+π^-η'$ $(X=X(1835), X(2120)$ and $X(2370))$, are reported.

hep-ex↗

arXiv2021

MW-GAN: Multi-Warping GAN for Caricature Generation with Multi-Style Geometric Exaggeration

Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo. It requires simultaneous style transfer and shape exaggeration with rich diversity, and meanwhile preserving the identity of the input. To address this challenging problem, we propose a novel framework called Multi-Warping GAN (MW-GAN), including a style network and a geometric network that are designed to conduct style transfer and geometric exaggeration respectively. We bridge the gap between the style and landmarks of an image with corresponding latent code spaces by a dual way design, so as to generate caricatures with arbitrary styles and geometric exaggeration, which can be specified either through random sampling of latent code or from a given caricature sample. Besides, we apply identity preserving loss to both image space and landmark space, leading to a great improvement in quality of generated caricatures. Experiments show that caricatures generated by MW-GAN have better quality than existing methods.

cs.CV↗

arXiv2021

Cross section measurements of the $e^+e^-\to D^{+}D^{-}$ and $e^+e^-\to D^{*+}D^{-}$ processes at center-of-mass energies from 4.085 to 4.600 GeV

The Born cross sections of the $e^+e^-\to D^{*+}D^{*-}$ and $e^+e^-\to D^{*+}D^{-}$ processes are measured using $e^+e^-$ collision data collected with the BESIII experiment at center-of-mass energies from 4.085 to 4.600 GeV, corresponding to an integrated luminosity of $15.7~{\rm fb}^{-1}$. The results are consistent with and more precise than the previous measurements by the Belle, Babar and CLEO collaborations. The measurements are essential for understanding the nature of vector charmonium and charmonium-like states.

hep-ex↗

arXiv2021

Mastering Atari Games with Limited Data

Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billions) of environment steps to train. Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal. We propose a sample efficient model-based visual RL algorithm built on MuZero, which we name EfficientZero. Our method achieves 194.3% mean human performance and 109.0% median performance on the Atari 100k benchmark with only two hours of real-time game experience and outperforms the state SAC in some tasks on the DMControl 100k benchmark. This is the first time an algorithm achieves super-human performance on Atari games with such little data. EfficientZero's performance is also close to DQN's performance at 200 million frames while we consume 500 times less data. EfficientZero's low sample complexity and high performance can bring RL closer to real-world applicability. We implement our algorithm in an easy-to-understand manner and it is available at https://github.com/YeWR/EfficientZero. We hope it will accelerate the research of MCTS-based RL algorithms in the wider community.

cs.LG↗

arXiv2021

Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration

Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems. In this paper, we introduce a novel Episodic Multi-agent reinforcement learning with Curiosity-driven exploration, called EMC. We leverage an insight of popular factorized MARL algorithms that the "induced" individual Q-values, i.e., the individual utility functions used for local execution, are the embeddings of local action-observation histories, and can capture the interaction between agents due to reward backpropagation during centralized training. Therefore, we use prediction errors of individual Q-values as intrinsic rewards for coordinated exploration and utilize episodic memory to exploit explored informative experience to boost policy training. As the dynamics of an agent's individual Q-value function captures the novelty of states and the influence from other agents, our intrinsic reward can induce coordinated exploration to new or promising states. We illustrate the advantages of our method by didactic examples, and demonstrate its significant outperformance over state-of-the-art MARL baselines on challenging tasks in the StarCraft II micromanagement benchmark.

cs.LG↗

arXiv2021

Discovering Non-monotonic Autoregressive Orderings with Variational Inference

The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generated. One strategy to recover this information is to decode both the content and ordering of tokens. Existing approaches supervise content and ordering by designing problem-specific loss functions and pre-training with an ordering pre-selected. Other recent works use iterative search to discover problem-specific orderings for training, but suffer from high time complexity and cannot be efficiently parallelized. We address these limitations with an unsupervised parallelizable learner that discovers high-quality generation orders purely from training data -- no domain knowledge required. The learner contains an encoder network and decoder language model that perform variational inference with autoregressive orders (represented as permutation matrices) as latent variables. The corresponding ELBO is not differentiable, so we develop a practical algorithm for end-to-end optimization using policy gradients. We implement the encoder as a Transformer with non-causal attention that outputs permutations in one forward pass. Permutations then serve as target generation orders for training an insertion-based Transformer language model. Empirical results in language modeling tasks demonstrate that our method is context-aware and discovers orderings that are competitive with or even better than fixed orders.

cs.CL↗

arXiv2021

Intrinsic nonlinear Hall effect in antiferromagnetic tetragonal CuMnAs

Detecting the orientation of the Néel vector is a major research topic in antiferromagnetic spintronics. Here we recognize the intrinsic nonlinear Hall effect, which is independent of the relaxation time, as a prominent contribution to the time-reversal-odd second order conductivity and can be used to detect the flipping of the Néel vector. In contrast, the Berry-curvature-dipole-induced nonlinear Hall effect depends linear on relaxation time and is time-reversal-even. We study the intrinsic nonlinear Hall effect in an antiferromagnetic metal: tetragonal CuMnAs, and show that its nonlinear Hall conductivity can reach the order of mA/V$^2$. The dependence on the chemical potential of such nonlinear Hall conductivity can be qualitatively explained by a tilted massive Dirac model. Moreover, we demonstrate its strong temperature dependence and briefly discuss its competition with the second order Drude conductivity. Finally, a complete survey of magnetic point groups are presented, providing guidelines for finding more antiferromagnetic materials with the intrinsic nonlinear Hall effect.

cond-mat.mes-hall↗

arXiv2021

NAS-FCOS: Efficient Search for Object Detection Architectures

Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures. What is noteworthy is that as of now, object detection is less touched by NAS algorithms despite its significant importance in computer vision. To the best of our knowledge, most of the recent NAS studies on object detection tasks fail to satisfactorily strike a balance between performance and efficiency of the resulting models, let alone the excessive amount of computational resources cost by those algorithms. Here we propose an efficient method to obtain better object detectors by searching for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely, FCOS [36], using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms, and strategies for evaluating network quality, we are able to find top-performing detection architectures within 4 days using 8 V100 GPUs. The discovered architectures surpass state-of-the-art object detection models (such as Faster R-CNN, Retina-Net and, FCOS) by 1.0% to 5.4% points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS method for object detection. Code is available at https://github.com/Lausannen/NAS-FCOS.

cs.CV↗

arXiv2021

Berry Phase origin of the planar spin Hall effect

The Berry phase origin is elaborated for the recent-discovered planar spin Hall effect which features current-induced spin polarization within the plane of the Hall deflection. We unravel a spin-repulsion vector governing the planar spin Hall effect, providing a transparent criteria for identifying intrinsic planar spin Hall materials. Finite spin-repulsion vector is found permitted in 13 crystalline point groups, revealing a big number of unexplored planar spin Hall systems. Our result can be used for the quantitative calculation of the planar spin Hall effect, facilitating further material hunting and experimental investigation.

cond-mat.mes-hall↗

arXiv2021

Study of the Dalitz decay $J/ψ\to e^+e^- η$

We report corrected results on a study of the electromagnetic Dalitz decay $J/ψ\to e^+e^- η$ and search for di-electron decays of a light dark gauge boson ($γ'$) in $J/ψ\to γ' η$ with the two $η$ decay modes $η\rightarrow γγ$ and $η\rightarrow π^+π^-π^0$, using $(1310.6\pm 7.0)\times10^6$ $J/ψ$ events collected with the BESIII detector. In particular we update the measurement of the pole mass of the transition form factor (TFF) of $J/ψ\to e^+e^- η$ to be $Λ= 2.56 \pm 0.04({\rm stat}) \pm 0.03({\rm syst})$ GeV/$c^2$. The branching fraction of $J/ψ\to e^+e^-η$ and the kinetic-mixing strength between the Standard Model photon and $γ'$ are also updated after taking the corrected $Λ$ value into account. The measured branching fraction of $J/ψ\to e^+e^- η$ is updated to be $(1.42 \pm 0.04({\rm stat}) \pm 0.07 ({\rm syst}))\times 10^{-5}$, where the first and second uncertainties are statistical and systematic, respectively.

hep-ex↗

arXiv2021

Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation.

cs.CV↗

arXiv2021

Electric Polarization in Inhomogeneous Crystals

We derive the charge density up to second order in spatial gradient in inhomogeneous crystals using the semiclassical coarse graining procedure based on the wave packet method. It can be recast as divergence of polarization, whose first-order contribution consists of three parts, a perturbative correction to the original Berry connection expression, a topological part that can be written as an integral of the Chern-Simons 3-form, and a previously-unknown, quadrupole-like contribution. The topological part can be related to the quantized fractional charge carried by a vortex in two dimensional systems. We then generalize our results to the multi-band case and show that the quadrupole-like contribution plays an important role, as it makes the total polarization gauge-independent. Finally, we verify our theory in several model systems.

cond-mat.mes-hall↗

arXiv2021

Study of the decay $D_s^+\to K_S^0K_S^0π^+$ and observation an isovector partner to $f_0(1710)$

Using $e^+e^-$ annihilation data corresponding to a total integrated luminosity of 6.32 $\rm fb^{-1}$ collected at center-of-mass energies between 4.178 and 4.226 GeV with the BESIII detector, we perform an amplitude analysis of the decay $D_{s}^{+} \to K_{S}^{0}K_{S}^{0}π^{+}$ for the first time. An enhancement is observed in the $K_{S}^{0}K_{S}^{0}$ mass spectrum near 1.7 GeV/$c^2$, which was not seen in $D_{s}^{+} \to K^+K^-π^{+}$ in an earlier work, implying the existence of an isospin one partner of the $f_0(1710)$. The branching fraction of the decay $D_{s}^{+}\to K_{S}^{0}K_{S}^{0}π^{+}$ is determined to be $\mathcal{B}(D_{s}^{+} \to K_{S}^{0}K_{S}^{0}π^{+})=(0.68\pm0.04_{\rm stat}\pm0.01_{\rm syst})\%$.

hep-ex↗

arXiv2021

The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results

In this paper, we introduce the Eval4NLP-2021shared task on explainable quality estimation. Given a source-translation pair, this shared task requires not only to provide a sentence-level score indicating the overall quality of the translation, but also to explain this score by identifying the words that negatively impact translation quality. We present the data, annotation guidelines and evaluation setup of the shared task, describe the six participating systems, and analyze the results. To the best of our knowledge, this is the first shared task on explainable NLP evaluation metrics. Datasets and results are available at https://github.com/eval4nlp/SharedTask2021.

cs.CL↗

arXiv2021

Mining Latent Classes for Few-shot Segmentation

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e. potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we add an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and foreground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5i and 7.0% mIOU on COCO-20i at the cost of 74% fewer parameters and 2.5x faster inference speed. The source code is available at https://github.com/LiheYoung/MiningFSS.

cs.CV↗

arXiv2021

Amplitude analysis and branching fraction measurement of the decay $D_{s}^{+} \to π^{+}π^{0}π^{0}$

Using a data set corresponding to an integrated luminosity of 6.32~$\rm fb^{-1}$ recorded by the BESIII detector at center-of-mass energies between 4.178 and 4.226~GeV, an amplitude analysis of the decay $D_{s}^{+} \to π^{+}π^{0}π^{0}$ is performed, and the relative fractions and phases of different intermediate processes are determined. The absolute branching fraction of the decay $D_{s}^{+} \to π^{+}π^{0}π^{0}$ is measured to be $(0.50\pm 0.04_{\text{stat}}\pm 0.02_{\text{syst}})\%$. The absolute branching fraction of the intermediate process $D_{s}^{+} \to f_0(980)π^{+}, f_0(980)\toπ^{0}π^{0}$ is determined to be $(0.21\pm 0.03_{\text{stat}}\pm 0.03_{\text{syst}})\%$.

hep-ex↗