arXiv subjects

Yang Gao

Publications and source records attributed to Yang Gao.

At least 343 records · Page 19Linked to original sources

arXiv2024

Search for di-photon decays of an axion-like particle in radiative $J/ψ$ decays

We search for the di-photon decay of a light pseudoscalar axion-like particle, $a$, in radiative $J/ψ$ decays, using 10 billion $J/ψ$ events collected with the BESIII detector. We find no evidence of a signal and set upper limits at the $95\%$ confidence level on the product branching fraction $\mathcal{B}(J/ψ\to γa) \times \mathcal{B}(a \to γγ)$ and the axion-like particle photon coupling constant $g_{a γγ}$ in the ranges of $(3.7-48.5) \times 10^{-8}$ and $(2.2 -101.8)\times 10^{-4}$ GeV$^{-1}$, respectively, for $0.18 \le m_a \le 2.85$ GeV/$c^2$. These are the most stringent limits to date in this mass region.

hep-ex↗

arXiv2024

The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation

Despite the recent success of domain generalization in medical image segmentation, voxel-wise annotation for all source domains remains a huge burden. Semi-supervised domain generalization has been proposed very recently to combat this challenge by leveraging limited labeled data along with abundant unlabeled data collected from multiple medical institutions, depending on precisely harnessing unlabeled data while improving generalization simultaneously. In this work, we observe that domain shifts between medical institutions cause disparate feature statistics, which significantly deteriorates pseudo-label quality due to an unexpected normalization process. Nevertheless, this phenomenon could be exploited to facilitate unseen domain generalization. Therefore, we propose 1) multiple statistics-individual branches to mitigate the impact of domain shifts for reliable pseudo-labels and 2) one statistics-aggregated branch for domain-invariant feature learning. Furthermore, to simulate unseen domains with statistics difference, we approach this from two aspects, i.e., a perturbation with histogram matching at image level and a random batch normalization selection strategy at feature level, producing diverse statistics to expand the training distribution. Evaluation results on three medical image datasets demonstrate the effectiveness of our method compared with recent SOTA methods. The code is available at https://github.com/qiumuyang/SIAB.

cs.CV↗

arXiv2024

Measurement of the cross sections of $e^+e^-\to K^{-}\barΞ^{+}Λ/Σ^{0}$ at center-of-mass energies between 3.510 and 4.914 GeV

Using $e^+e^-$ collision data collected with the BESIII detector at the BEPCII collider at center-of-mass energies between 3.510 and 4.914GeV, corresponding to an integrated luminosity of 25 fb$^{-1}$, we measure the Born cross sections for the process $e^+e^-\to K^-\barΞ^+Λ/Σ^{0}$ at thirty-five energy points with a partial-reconstruction strategy. By fitting the dressed cross sections of $e^+e^-\to K^-\barΞ^+Λ/Σ^{0}$, evidence for $ψ(4160) \to K^{-}\barΞ^{+}Λ$ is found for the first time with a significance of 4.4$σ$, including systematic uncertainties. No evidence for other possible resonances is found. In addition, the products of electronic partial width and branching fraction for all assumed resonances decaying into $K^{-}\barΞ^{+}Λ/Σ^{0}$ are determined.

hep-ex↗

arXiv2024

Search for $η_{c}(2S)\to K^+ K^- η^{\prime}$ decay

Using $(2.712\pm0.014)\times10^{9}$ $ψ(3686)$ events collected with the BESIII detector operating at the BEPCII, we find an evidence of the $η_{c}(2S)\to K^+ K^- η^{\prime}$ decay with a statistical significance of 3.1$σ$. Its decay branching fraction is measured to be $(12.24\pm4.60(\mathrm{stat.})\pm2.37(\mathrm{syst.})\pm4.68(\mathrm{extr.}))\times 10^{-4}$, where the first uncertainty is statistical, the second is systematic, and the third uncertainty is from the branching fraction of the $ψ(3686)\toγη_{c}(2S)$ decay. The upper limit on the product branching fraction $B[ψ(3686)\toγη_{c}(2S)] \times$ $B[η_{c}(2S)\to K^+ K^- η^{\prime}]$ is set to be $1.14 \times 10^{-6}$ at $90\%$ confidence level. In addition, the branching fractions of $χ_{c1}\to K^+ K^- η^{\prime}$ and $χ_{c2}\to K^+ K^- η^{\prime}$ are updated to be $(8.47\pm0.09(\mathrm{stat.})\pm0.47(\mathrm{syst.}))\times 10^{-4}$ and $(1.53\pm0.04(\mathrm{stat.})\pm0.08(\mathrm{syst.}))\times 10^{-4}$, respectively. The precision is improved by twofold.

hep-ex↗

arXiv2024

Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization

Domain generalization (DG) aims to avoid the performance degradation of the model when the distribution shift between the limited training data and unseen test data occurs. Recently, foundation models with enormous parameters have been pre-trained with huge datasets, demonstrating strong generalization ability and showing promising direction for solving the DG problem. However, fully Fine-Tuning (FT) the foundation models results in unsatisfactory out-of-distribution accuracy due to the destroyed pre-trained generalized features. Recently, Parameter-Efficient Fine-Tuning (PEFT) alleviates the above problem by fine-tuning a small portion of the model parameters while keeping the rest frozen, which achieves better generalization performance compared to FT. Nevertheless, PEFT still suffers from the issue of overfitting to the training domains. To address the above issue, we propose Parameter-Efficient Group with Orthogonal regularization (PEGO) for vision transformers, which effectively preserves the generalization ability of the pre-trained network and learns more diverse knowledge compared with conventional PEFT. Specifically, we inject a group of trainable Low-Rank Adaptation (LoRA) modules into the pre-trained model and propose an orthogonal regularization loss to enhance the generalization ability of the model. Our framework achieves SOTA performance on five DG benchmarks, while only requiring training a small number of parameters without adding additional testing cost.

cs.CV↗

arXiv2024

Strong and weak $CP$ tests in sequential decays of polarized $Σ^0$ hyperons

The $J/ψ, ψ(3686) \to Σ^0 \barΣ^{0}$ processes and subsequent decays are studied using the world's largest $J/ψ$ and $ψ(3686)$ data samples collected with the BESIII detector. The strong-$CP$ symmetry is tested in the decays of the $Σ^0$ hyperons for the first time by measuring the decay parameters, $α_{Σ^0} = -0.0017 \pm 0.0021 \pm 0.0018$ and $\barα_{Σ^0} = 0.0021 \pm 0.0020 \pm 0.0022$. The weak-$CP$ test is performed in the subsequent decays of their daughter particles $Λ$ and $\barΛ$. Also for the first time, the transverse polarizations of the $Σ^0$ hyperons in $J/ψ$ and $ψ(3686)$ decays are observed with opposite directions, and the ratios between the S-wave and D-wave contributions of the $J/ψ, ψ(3686) \to Σ^0 \barΣ^{0}$ decays are obtained. These results are crucial to understand the decay dynamics of the charmonium states and the production mechanism of the $Σ^0-\barΣ^0$ pairs.

hep-ex↗

arXiv2024

Study of $e^+e^-\toωX(3872)$ and $γX(3872)$ from 4.66 to 4.95 GeV

Using data samples with an integrated luminosity of $4.5~\text{fb}^{-1}$ collected by the BESIII detector at center-of-mass energies ranging from 4.66 to 4.95 GeV, we study the processes of $e^+e^-\toωX(3872)$ and $e^+e^-\toγX(3872)$. With the $e^+e^-\toωX(3872)$ process, the branching fraction ratio $R\equiv\frac{\mathcal{B}(X(3872)\toγJ/ψ)}{\mathcal{B}(X(3872)\toπ^+π^- J/ψ)}$ is measured to be $0.38\pm0.20_\text{stat.}\pm0.01_\text{syst.}$ ($R< 0.83$ at 90\% confidence level). In addition, we measure the ratio of the average cross section of $e^+e^-\toωX(3872)$ to $e^+e^-\toωχ_{c1}(ωχ_{c2})$ to be $σ_{ωX(3872)}/σ_{ωχ_{c1}}~(σ_{ωX(3872)}/σ_{ωχ_{c2}})=5.2\pm1.0_\text{stat.}\pm1.9_\text{syst.}~ (5.5\pm1.1_\text{stat.}\pm2.4_\text{syst.})$. Finally, we search for the process of $e^+e^-\toγX(3872)$, and no obvious signal is observed. The upper limit on the ratio of the average cross section of $e^+e^-\toγX(3872)$ to $e^+e^-\toωX(3872)$ is set as $σ_{γX(3872)}/σ_{ωX(3872)}<0.23$ at 90\% confidence level.

hep-ex↗

arXiv2024

Any-point Trajectory Modeling for Policy Learning

Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning. However, the high cost of collecting demonstration data is a significant bottleneck. Videos, as a rich data source, contain knowledge of behaviors, physics, and semantics, but extracting control-specific information from them is challenging due to the lack of action labels. In this work, we introduce a novel framework, Any-point Trajectory Modeling (ATM), that utilizes video demonstrations by pre-training a trajectory model to predict future trajectories of arbitrary points within a video frame. Once trained, these trajectories provide detailed control guidance, enabling the learning of robust visuomotor policies with minimal action-labeled data. Across over 130 language-conditioned tasks we evaluated in both simulation and the real world, ATM outperforms strong video pre-training baselines by 80% on average. Furthermore, we show effective transfer learning of manipulation skills from human videos and videos from a different robot morphology. Visualizations and code are available at: \url{https://xingyu-lin.github.io/atm}.

cs.RO↗

arXiv2024

InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output

We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that supports long-contextual input and output. IXC-2.5 excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities with merely 7B LLM backend. Trained with 24K interleaved image-text contexts, it can seamlessly extend to 96K long contexts via RoPE extrapolation. This long-context capability allows IXC-2.5 to excel in tasks requiring extensive input and output contexts. Compared to its previous 2.0 version, InternLM-XComposer-2.5 features three major upgrades in vision-language comprehension: (1) Ultra-High Resolution Understanding, (2) Fine-Grained Video Understanding, and (3) Multi-Turn Multi-Image Dialogue. In addition to comprehension, IXC-2.5 extends to two compelling applications using extra LoRA parameters for text-image composition: (1) Crafting Webpages and (2) Composing High-Quality Text-Image Articles. IXC-2.5 has been evaluated on 28 benchmarks, outperforming existing open-source state-of-the-art models on 16 benchmarks. It also surpasses or competes closely with GPT-4V and Gemini Pro on 16 key tasks. The InternLM-XComposer-2.5 is publicly available at https://github.com/InternLM/InternLM-XComposer.

cs.CV↗

arXiv2024

Improved measurement of the semileptonic decay $D^+_{s}\to K^0 e^+ν_e$

Analyzing $e^+e^-$ collision data corresponding to an integrated luminosity of $7.33~\mathrm{fb}^{-1}$ collected at center-of-mass energies between 4.128 and 4.226~GeV with the BESIII detector, we measure the branching fraction of the semileptonic decay $D^+_{s}\to K^0 e^+ν_e$ to be $(2.98\pm0.23\pm0.12)\times10^{-3}$. The $D_s^+\to K^0$ hadronic form factor is determined from the differential decay rate of $D^+_s\to K^0 e^+ν_e$ to be $f^{K^0}_+(0)=0.636\pm0.049\pm0.013$. For both measurements, the first uncertainty is statistical and the second systematic. The branching fraction and form factor measurements are factors of 1.6 and 1.7 more precise than the previous world averages, respectively.

hep-ex↗

arXiv2024

Word Matters: What Influences Domain Adaptation in Summarization?

Domain adaptation aims to enable Large Language Models (LLMs) to generalize domain datasets unseen effectively during the training phase. However, factors such as the size of the model parameters and the scale of training data are general influencers and do not reflect the nuances of domain adaptation performance. This paper investigates the fine-grained factors affecting domain adaptation performance, analyzing the specific impact of `words' in training data on summarization tasks. We propose quantifying dataset learning difficulty as the learning difficulty of generative summarization, which is determined by two indicators: word-based compression rate and abstraction level. Our experiments conclude that, when considering dataset learning difficulty, the cross-domain overlap and the performance gain in summarization tasks exhibit an approximate linear relationship, which is not directly related to the number of words. Based on this finding, predicting a model's performance on unknown domain datasets is possible without undergoing training.

cs.CL↗

arXiv2024

IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases. However, the ill-conditioned nature of dipole inversion makes QSM reconstruction from the tissue field prone to noise and artifacts. In this work, we propose a novel deep learning-based IR2QSM method for QSM reconstruction. It is designed by iterating four times of a reverse concatenations and middle recurrent modules enhanced U-net, which could dramatically improve the efficiency of latent feature utilization. Simulated and in vivo experiments were conducted to compare IR2QSM with several traditional algorithms (MEDI and iLSQR) and state-of-the-art deep learning methods (U-net, xQSM, and LPCNN). The results indicated that IR2QSM was able to obtain QSM images with significantly increased accuracy and mitigated artifacts over other methods. Particularly, IR2QSM demonstrated on average the best NRMSE (27.59%) in simulated experiments, which is 15.48%, 7.86%, 17.24%, 9.26%, and 29.13% lower than iLSQR, MEDI, U-net, xQSM, LPCNN, respectively, and led to improved QSM results with fewer artifacts for the in vivo data.

eess.IV↗

arXiv2024

How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment

Recent studies have demonstrated that In-Context Learning (ICL), through the use of specific demonstrations, can align Large Language Models (LLMs) with human preferences known as In-Context Alignment (ICA), indicating that models can comprehend human instructions without requiring parameter adjustments. However, the exploration of the mechanism and applicability of ICA remains limited. In this paper, we begin by dividing the context text used in ICA into three categories: format, system prompt, and example. Through ablation experiments, we investigate the effectiveness of each part in enabling ICA to function effectively. We then examine how variants in these parts impact the model's alignment performance. Our findings indicate that the example part is crucial for enhancing the model's alignment capabilities, with changes in examples significantly affecting alignment performance. We also conduct a comprehensive evaluation of ICA's zero-shot capabilities in various alignment tasks. The results indicate that compared to parameter fine-tuning methods, ICA demonstrates superior performance in knowledge-based tasks and tool-use tasks. However, it still exhibits certain limitations in areas such as multi-turn dialogues and instruction following.

cs.CL↗

arXiv2024

Observation of $η_{c}$(1S, 2S) and $χ_{cJ}$ decays to 2$(π^{+}π^{-})η$ via $ψ$(3686) radiative transitions

Based on $2.7 \times 10^9~ψ(3686)$ decays collected with the BESIII detector, the radiative decay $ψ(3686)\to\gamma2(π^{+}π^{-})η$ is investigated to measure properties of S- and P-wave charmonium states. The branching fraction of the decay $η_{c}(1S) \to 2(π^{+}π^{-})η$, which is found to have a strong dependence on the interference pattern between $η_c(1S)$ and non-$η_c(1S)$ processes, is measured in both destructive and constructive interference scenarios for the first time. The mass and width of the $η_{c}(1S)$ are measured to be $M=(2984.14 \pm 0.13 \pm 0.38)$ MeV/$c^{2}$ and $Γ=(28.82 \pm 0.11 \pm 0.82)$ MeV, respectively. Clear signals for the decays of the $χ_{cJ}(J=0,1,2)$ and the $η_{c}(2S)$ to $2(π^{+}π^{-})η$ are also observed for the first time, and the corresponding branching fractions are measured. The ratio of the branching fractions between the $η_{c}(2S)$ and $η_{c}(1S)$ decays is significantly lower than the theoretical prediction, which might suggest different dynamics in their decays.

hep-ex↗

arXiv2024

Combinatorial Optimization with Automated Graph Neural Networks

In recent years, graph neural networks (GNNs) have become increasingly popular for solving NP-hard combinatorial optimization (CO) problems, such as maximum cut and maximum independent set. The core idea behind these methods is to represent a CO problem as a graph and then use GNNs to learn the node/graph embedding with combinatorial information. Although these methods have achieved promising results, given a specific CO problem, the design of GNN architectures still requires heavy manual work with domain knowledge. Existing automated GNNs are mostly focused on traditional graph learning problems, which is inapplicable to solving NP-hard CO problems. To this end, we present a new class of \textbf{AUTO}mated \textbf{G}NNs for solving \textbf{NP}-hard problems, namely \textbf{AutoGNP}. We represent CO problems by GNNs and focus on two specific problems, i.e., mixed integer linear programming and quadratic unconstrained binary optimization. The idea of AutoGNP is to use graph neural architecture search algorithms to automatically find the best GNNs for a given NP-hard combinatorial optimization problem. Compared with existing graph neural architecture search algorithms, AutoGNP utilizes two-hop operators in the architecture search space. Moreover, AutoGNP utilizes simulated annealing and a strict early stopping policy to avoid local optimal solutions. Empirical results on benchmark combinatorial problems demonstrate the superiority of our proposed model.

cs.LG↗

arXiv2024

Measurements of the branching fractions of the $P$-wave charmonium spin-singlet state $h_c(^1P_1) \to h^+ h^-π^0/η$

Based on $(2712.4\pm 14.3)\times10^{6}$ $ψ(3686)$ events, we investigate four hadronic decay modes of the $P$-wave charmonium spin-singlet state $h_c(^1P_1) \to h^+ h^- π^0/η$ ($h=π$ or $K$) via the process $ψ(3686) \to π^{0}h_c$ at BESIII. The $h_c \to π^+ π^- π^0$ decay is observed with a significance of 9.6$σ$ after taking into account systematic uncertainties. Evidences for $h_c \to K^+ K^- π^0$ and $h_c \to K^+ K^- η$ are found with significances of $3.5σ$ and $3.3σ$, respectively, after considering the systematic uncertainties. The branching fractions of these decays are measured to be $\mathcal{B}(h_c \to π^+ π^- π^0)=(1.36\pm0.16\pm0.14)\times10^{-3}$, $\mathcal{B}(h_c \to K^+ K^- π^0)=(3.26\pm0.84\pm0.36)\times10^{-4}$, and $\mathcal{B}(h_c \to K^+ K^- η)=(3.13\pm1.08\pm0.38)\times10^{-4}$, where the first uncertainties are statistical and the second are systematic. No significant signal of $h_c\toπ^+π^-η$ is found, and the upper limit of its decay branching fraction is determined to be $\mathcal{B}(h_c\toπ^+π^-η) < 4.0 \times 10^{-4}$ at 90% confidence level.

hep-ex↗

arXiv2024

Evidence of the Singly Cabibbo Suppressed decay $Λ_c^+\to pπ^0$

Evidence for the singly Cabibbo suppressed decay $Λ_c^+\to pπ^0$ is reported for the first time with a statistical significance of $3.7σ$ based on 6.0 $\mathrm{fb}^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies between 4.600 and 4.843 GeV with the BESIII detector at the BEPCII collider. The absolute branching fraction of $Λ_c^+\to pπ^0$ is measured to be $(1.56^{+0.72}_{-0.58}\pm0.20)\times 10^{-4}$. Combining with the branching fraction of $Λ_c^+\to nπ^+$, $(6.6\pm1.3)\times10^{-4}$, the ratio of the branching fractions of $Λ_c^+\to nπ^+$ and $Λ_c^+\to pπ^0$ is calculated to be $3.2^{+2.2}_{-1.2}$. As an important input for the theoretical models describing the decay mechanisms of charmed baryons, our result indicates that the non-factorizable contributions play an essential role and their interference with the factorizable contributions should not be significant. In addition, the absolute branching fraction of $Λ_c^+\to pη$ is measured to be $(1.63\pm0.31_{\rm stat}\pm0.11_{\rm syst}) \times10^{-3}$.

hep-ex↗

arXiv2024

Topology-Aware Blending Method for Implicit Heterogeneous Porous Model Design

Porous structures are materials consisting of minuscule pores, where the microstructure morphology significantly impacts their macroscopic properties. Integrating different porous structures through a blending method is indispensable to cater to diverse functional regions in heterogeneous models. Previous studies on blending methods for porous structures have mainly focused on controlling the shape of blending regions, yet they have fallen short in effectively addressing topological errors in blended structures. This paper introduces a new blending method that successfully addresses this issue. Initially, a novel initialization method is proposed, which includes distinct strategies for blending regions of varying complexities. Subsequently, we formulate the challenge of eliminating topological errors as an optimization problem based on persistent homology. Through iterative updates of control coefficients, this optimization problem is solved to generate a blended porous structure. Our approach not only avoids topological errors but also governs the shape and positioning of the blending region while remaining unchanged in the structure outside blending region. The experimental outcomes validate the effectiveness of our method in producing high-quality blended porous structures. Furthermore, these results highlight potential applications of our blending method in biomimetics and the design of high-stiffness mechanical heterogeneous models.

cs.GR↗