arXiv subjects

Yang Gao

Publications and source records attributed to Yang Gao.

At least 397 records · Page 22Linked to original sources

arXiv2024

Improved measurements of the Dalitz decays $η/η'\rightarrowγe^{+}e^{-}$

Based on a data sample of 10 billion $J/ψ$ events collected with the BESIII detector, improved measurements of the Dalitz decays $η/η'\rightarrowγe^+e^-$ are performed, where the $η$ and $η'$ are produced through the radiative decays $J/ψ\rightarrowγη/η'$. The branching fractions of $η\rightarrowγe^+e^-$ and $η'\rightarrowγe^+e^-$ are measured to be $(7.07 \pm 0.05 \pm 0.23)\times10^{-3}$ and $(4.83\pm0.07\pm0.14)\times10^{-4}$, respectively. Within the single pole model, the parameter of electromagnetic transition form factor for $η\rightarrowγe^+e^-$ is determined to be $Λ_η=(0.749 \pm 0.027 \pm 0.007)~ {\rm GeV}/c^{2}$. Within the multi-pole model, we extract the electromagnetic transition form factors for $η'\rightarrowγe^+e^-$ to be $Λ_{η'} = (0.802 \pm 0.007\pm 0.008)~ {\rm GeV}/c^{2}$ and $γ_{η'} = (0.113\pm0.010\pm0.002)~ {\rm GeV}/c^{2}$. The results are consistent with both theoretical predictions and previous measurements. The characteristic sizes of the interaction regions for the $η$ and $η'$ are calculated to be $(0.645 \pm 0.023 \pm 0.007 )~ {\rm fm}$ and $(0.596 \pm 0.005 \pm 0.006)~ {\rm fm}$, respectively. In addition, we search for the dark photon in $η/η^\prime\rightarrowγe^{+}e^{-}$, and the upper limits of the branching fractions as a function of the dark photon are given at 90\% confidence level.

hep-ex↗

arXiv2024

Evidence of the $h_c\to K_S^0 K^+π^-+c.c.$ decay

Based on $(2.712\pm0.014)\times10^9$ $ψ(3686)$ events collected by the BESIII collaboration, evidence of the hadronic decay $h_c\to K_S^0K^+π^-+c.c.$ is found with a significance of $4.3σ$ in the $ψ(3686)\toπ^0 h_c$ process. The branching fraction of $h_c\to K_S^0 K^+π^- +c.c.$ is measured to be $(7.3\pm0.8\pm1.8)\times10^{-4}$, where the first and second uncertainties are statistical and systematic, respectively. Combining with the exclusive decay width of $η_c\to K\bar{K}π$, our result indicates inconsistencies with both pQCD and NRQCD predictions.

hep-ex↗

arXiv2024

Amplitude Analysis of the Decays $D^0\toπ^+π^-π^+π^-$ and $π^+π^-π^0π^0$

Using $e^+e^-$ annihilation data corresponding to an integrated luminosity of 2.93 $\rm fb^{-1}$ taken at the center-of-mass energy $\sqrt{s}=3.773$~GeV with the BESIII detector, a joint amplitude analysis is performed on the decays $D^0\toπ^+π^-π^+π^-$ and $D^0\toπ^+π^-π^0π^0$(non-$η$). The fit fractions of individual components are obtained, and large interferences among the dominant components of $D^{0}\to a_{1}(1260)π$, $D^{0}\toπ(1300)π$, $D^{0}\toρ(770)ρ(770)$ and $D^{0}\to2(ππ)_{S}$ are found in both channels. With the obtained amplitude model, the $CP$-even fractions of $D^0\to π^+π^-π^+π^-$ and $D^0\toπ^+π^-π^0π^0$(non-$η$) are determined to be $(75.2\pm1.1_{\rm stat.}\pm1.5_{\rm syst.})\%$ and $(68.9\pm1.5_{\rm stat.}\pm 2.4_{\rm syst.})\%$, respectively. The branching fractions of $D^0\to π^+π^-π^+π^-$ and $D^0\toπ^+π^-π^0π^0$(non-$η$) are measured to be $(0.688\pm0.010_{\rm stat.}\pm 0.010_{\rm syst.})\%$ and $(0.951\pm0.025_{\rm stat.}\pm 0.021_{\rm syst.})\%$, respectively. The amplitude analysis provides an important model for binning strategy in the measurements of the strong phase parameters of $D^0 \to 4π$ when used to determine the CKM angle $γ(ϕ_{3})$ via the $B^{-}\to D K^{-}$ decay.

hep-ex↗

arXiv2024

Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation

Dataset distillation has emerged as a promising approach in deep learning, enabling efficient training with small synthetic datasets derived from larger real ones. Particularly, distribution matching-based distillation methods attract attention thanks to its effectiveness and low computational cost. However, these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets, reducing class discrimination, and an exclusive focus on mean feature consistency, lacking precision and comprehensiveness. To address these challenges, we introduce two novel constraints: a class centralization constraint and a covariance matching constraint. The class centralization constraint aims to enhance class discrimination by more closely clustering samples within classes. The covariance matching constraint seeks to achieve more accurate feature distribution matching between real and synthetic datasets through local feature covariance matrices, particularly beneficial when sample sizes are much smaller than the number of features. Experiments demonstrate notable improvements with these constraints, yielding performance boosts of up to 6.6% on CIFAR10, 2.9% on SVHN, 2.5% on CIFAR100, and 2.5% on TinyImageNet, compared to the state-of-the-art relevant methods. In addition, our method maintains robust performance in cross-architecture settings, with a maximum performance drop of 1.7% on four architectures. Code is available at https://github.com/VincenDen/IID.

cs.CV↗

arXiv2024

First observation of the decay $Λ^+_c\to nK^{0}_{S}π^+π^0$

Based on 4.5 fb$^{-1}$ of $e^{+}e^{-}$ collision data accumulated at center-of-mass energies between $4599.53$ MeV and $4698.82$ MeV with the BESIII detector, the decay $Λ_{c}^{+}\to nK_{S}^{0}π^+π^0$ is observed for the first time with a significance of $9.2σ$. The branching fraction is measured to be $(0.85\pm0.13\pm0.03)\%$, where the first uncertainty is statistical and the second systematic, which differs from the theoretical prediction based on isospin by 4.4$σ$. This indicates that there may be resonant contributions or some unknown dynamics in this decay.

hep-ex↗

arXiv2024

Observation of $D^{+}\to K_{S}^{0}a_{0}(980)^{+}$ in the amplitude analysis of $D^{+} \to K_{S}^{0}π^+η$

We perform for the first time an amplitude analysis of the decay $D^{+}\to K_{S}^{0}π^+η$ and report the observation of the decay $D^{+}\to K_{S}^{0}a_{0}(980)^{+}$ using 2.93 fb$^{-1}$ of $e^+e^-$ collision data taken at a center-of-mass energy of 3.773 GeV with the BESIII detector. As the only W-annihilation free decay among $D$ to $a_{0}(980)$-pseudoscalar, $D^{+}\to K_{S}^{0}a_{0}(980)^{+}$ is the ideal decay to extract the contributions of the external and internal $W$-emission amplitudes involving $a_{0}(980)$ and study the final-state interactions. The absolute branching fraction of $D^{+}\to K_{S}^{0}π^+η$ is measured to be $(1.27\pm0.04_{\rm stat.}\pm0.03_{\rm syst.})\%$. The product branching fractions of $D^{+}\to K_{S}^{0}a_{0}(980)^{+}$ with $a_{0}(980)^{+}\to π^+η$ and $D^{+}\to π^+ K_0^*(1430)^0$ with $K_0^*(1430)^0\to K_{S}^{0}η$ are measured to be $(1.33\pm0.05_{\rm stat.}\pm0.04_{\rm syst.})\%$ and $(0.14\pm0.03_{\rm stat.}\pm0.01_{\rm syst.})\%$, respectively.

hep-ex↗

arXiv2024

Observation of the semileptonic decays $D^0\rightarrow K_S^0π^-π^0 e^+ ν_e$ and $D^+\rightarrow K_S^0π^+π^- e^+ ν_e$

By analyzing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 2.93 $\rm fb^{-1}$ collected at a center-of-mass energy of 3.773 GeV with the \text{BESIII} detector, the first observation of the semileptonic decays $D^0\rightarrow K_S^0π^-π^0 e^+ ν_e$ and $D^+\rightarrow K_S^0π^+π^- e^+ ν_e$ is reported. With a dominant hadronic contribution from $K_1(1270)$, the branching fractions are measured to be $\mathcal{B}(D^0\rightarrow {K}_1(1270)^-(\to K^0_Sπ^-π^0)e^+ν_e)=(1.69^{+0.53}_{-0.46}\pm0.15)\times10^{-4}$ and $\mathcal{B}(D^+\to \bar{K}_1(1270)^0(\to K^0_Sπ^+π^-)e^+ν_e)=(1.47^{+0.45}_{-0.40}\pm0.20)\times10^{-4}$ with statistical significance of 5.4$σ$ and 5.6$σ$, respectively. When combined with measurements of the $K_1(1270)\to K^+π^-π$ decays, the absolute branching fractions are determined to be $\mathcal{B}(D^0\to K_1(1270)^-e^+ν_e)=(1.05^{+0.33}_{-0.28}\pm0.12\pm0.12)\times10^{-3}$ and $\mathcal{B}(D^+\to \bar{K}_1(1270)^0e^+ν_e)=(1.29^{+0.40}_{-0.35}\pm0.18\pm0.15)\times10^{-3}$. The first and second uncertainties are statistical and systematic, respectively, and the third uncertainties originate from the assumed branching fractions of the $K_1(1270)\to Kππ$ decays.

hep-ex↗

arXiv2024

InternLM2 Technical Report

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack" test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution.

cs.CL↗

arXiv2024

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms.

eess.IV↗

arXiv2024

Observation of structures in the processes $e^+e^-\rightarrowωχ_{c1}$ and $ωχ_{c2}$

We present measurements of the Born cross sections for the processes $e^+e^-\rightarrowωχ_{c1}$ and $ωχ_{c2}$ at center-of-mass energies $\sqrt{s}$ from 4.308 to 4.951 GeV. The measurements are performed with data samples corresponding to an integrated luminosity of 11.0 $\rm{fb}^{-1}$ collected with the BESIII detector operating at the BEPCII storage ring. Assuming the $e^+e^-\rightarrowωχ_{c2}$ signals come from a single resonance, the mass and width are determined to be $M=(4413.6\pm9.0\pm0.8)$ MeV/$c^2$ and $Γ=(110.5\pm15.0\pm2.9)$ MeV, respectively, which is consistent with the parameters of the well-established resonance $ψ(4415)$. In addition, we also use one single resonance to describe the $e^+e^-\rightarrowωχ_{c1}$ lineshape, and determine the mass and width to be $M=(4544.2\pm18.7\pm1.7)$ MeV/$c^2$ and $Γ=(116.1\pm33.5\pm1.7)$ MeV, respectively. The structure of this lineshape, observed for the first time, requires further understanding.

hep-ex↗

arXiv2024

Measurement of $Λ$ transverse polarization in $e^{+}e^{-}$ collisions at $\sqrt{s}= 3.68-3.71$ GeV

With data samples collected with the BESIII detector at seven energy points at $\sqrt{s}= 3.68 - 3.71$ GeV, corresponding to an integrated luminosity of 333 pb$^{-1}$, we present a study of the $Λ$ transverse polarization in the $e^+e^-\toΛ\barΛ$ reaction. The significance of polarization by combining the seven energy points is found to be 2.6$σ$ including the systematic uncertainty, which implies a non-zero phase between the transition amplitudes of the $Λ\barΛ$ helicity states. The modulus ratio and the relative phase of EM-$psionic$ form factors combined with all energy points are measured to be $R^Ψ =$ 0.71$^{+0.10}_{-0.10}$ $\pm$ 0.03 and $ΔΦ^Ψ$ = (23$^{+8.8}_{-8.0}$ $\pm$ 1.6$)^\circ$, where the first uncertainties are statistical and the second systematic.

hep-ex↗

arXiv2024

InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules

Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for INStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations. This novel design allows for more accurate and efficient representations of complex appearances and geometries. Experiments show that this method not only achieves superior generalization performance but also provides a flexible pathway for integration with other NeRF-like systems, even in sparse input settings. Code will be available https://github.com/bbbbby-99/InsertNeRF.

cs.CV↗

arXiv2024

MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment

The advent of deep reinforcement learning (DRL) has significantly advanced the field of robotics, particularly in the control and coordination of quadruped robots. However, the complexity of real-world tasks often necessitates the deployment of multi-robot systems capable of sophisticated interaction and collaboration. To address this need, we introduce the Multi-agent Quadruped Environment (MQE), a novel platform designed to facilitate the development and evaluation of multi-agent reinforcement learning (MARL) algorithms in realistic and dynamic scenarios. MQE emphasizes complex interactions between robots and objects, hierarchical policy structures, and challenging evaluation scenarios that reflect real-world applications. We present a series of collaborative and competitive tasks within MQE, ranging from simple coordination to complex adversarial interactions, and benchmark state-of-the-art MARL algorithms. Our findings indicate that hierarchical reinforcement learning can simplify task learning, but also highlight the need for advanced algorithms capable of handling the intricate dynamics of multi-agent interactions. MQE serves as a stepping stone towards bridging the gap between simulation and practical deployment, offering a rich environment for future research in multi-agent systems and robot learning. For open-sourced code and more details of MQE, please refer to https://ziyanx02.github.io/multiagent-quadruped-environment/ .

cs.RO↗

arXiv2024

Precise measurement of the $e^+e^-\to D_s^+D_s^-$ cross sections at center-of-mass energies from threshold to 4.95 GeV

Using the $e^+e^-$ collision data collected with the BESIII detector operating at the BEPCII collider, at center-of-mass energies from the threshold to $4.95$~GeV, we present precise measurements of the cross sections for the process $e^+e^-\to D_s^+D_s^-$ using a single tag method. The resulting cross section lineshape exhibits several new structures, thereby offering an input for coupled channel analysis and model tests, which are critical to understand vector charmonium-like states with masses between 4 and 5~GeV.

hep-ex↗

arXiv2024

QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping

Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While supervised deep learning methods have shown success in specific QSM tasks, their generalizability across different acquisition scenarios remains constrained. Recent developments in diffusion models have demonstrated potential for solving 2D medical imaging inverse problems. However, their application to 3D modalities, such as QSM, remains challenging due to high computational demands. In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks. QSMDiff adopts unsupervised 3D image patch training and full-size measurement guidance during inference for controlled image generation. Evaluation on simulated and in-vivo human brains, using gradient-echo and echo-planar imaging sequences across different acquisition parameters, demonstrates superior performance. The method proposed in QSMDiff also holds promise for impacting other 3D medical imaging applications beyond QSM.

eess.IV↗

arXiv2024

Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization

Combining offline and online reinforcement learning (RL) is crucial for efficient and safe learning. However, previous approaches treat offline and online learning as separate procedures, resulting in redundant designs and limited performance. We ask: Can we achieve straightforward yet effective offline and online learning without introducing extra conservatism or regularization? In this study, we propose Uni-o4, which utilizes an on-policy objective for both offline and online learning. Owning to the alignment of objectives in two phases, the RL agent can transfer between offline and online learning seamlessly. This property enhances the flexibility of the learning paradigm, allowing for arbitrary combinations of pretraining, fine-tuning, offline, and online learning. In the offline phase, specifically, Uni-o4 leverages diverse ensemble policies to address the mismatch issues between the estimated behavior policy and the offline dataset. Through a simple offline policy evaluation (OPE) approach, Uni-o4 can achieve multi-step policy improvement safely. We demonstrate that by employing the method above, the fusion of these two paradigms can yield superior offline initialization as well as stable and rapid online fine-tuning capabilities. Through real-world robot tasks, we highlight the benefits of this paradigm for rapid deployment in challenging, previously unseen real-world environments. Additionally, through comprehensive evaluations using numerous simulated benchmarks, we substantiate that our method achieves state-of-the-art performance in both offline and offline-to-online fine-tuning learning. Our website: https://lei-kun.github.io/uni-o4/ .

cs.LG↗

arXiv2024

AMSP: Reducing Communication Overhead of ZeRO for Efficient LLM Training

Training large language models (LLMs) encounters challenges in GPU memory consumption due to the high memory requirements of model states. The widely used Zero Redundancy Optimizer (ZeRO) addresses this issue through strategic sharding but introduces communication challenges at scale. To tackle this problem, we propose AMSP, a system designed to optimize ZeRO for scalable LLM training. AMSP incorporates three flexible sharding strategies: Full-Replica, Full-Sharding, and Partial-Sharding, and allows each component within the model states (Parameters, Gradients, Optimizer States) to independently choose a sharding strategy as well as the device mesh. We conduct a thorough analysis of communication costs, formulating an optimization problem to discover the optimal sharding strategy. Additionally, AMSP optimizes distributed LLM training by efficiently overlapping communication with computation. Evaluations demonstrate up to 52\% Model FLOPs Utilization (MFU) when training the LLaMA-based model on 1024 GPUs, resulting in a 1.56 times improvement in training throughput compared to newly proposed systems like MiCS and ZeRO++.

cs.DC↗

arXiv2024

SpaceOctopus: An Octopus-inspired Motion Planning Framework for Multi-arm Space Robot

Space robots have played a critical role in autonomous maintenance and space junk removal. Multi-arm space robots can efficiently complete the target capture and base reorientation tasks due to their flexibility and the collaborative capabilities between the arms. However, the complex coupling properties arising from both the multiple arms and the free-floating base present challenges to the motion planning problems of multi-arm space robots. We observe that the octopus elegantly achieves similar goals when grabbing prey and escaping from danger. Inspired by the distributed control of octopuses' limbs, we develop a multi-level decentralized motion planning framework to manage the movement of different arms of space robots. This motion planning framework integrates naturally with the multi-agent reinforcement learning (MARL) paradigm. The results indicate that our method outperforms the previous method (centralized training). Leveraging the flexibility of the decentralized framework, we reassemble policies trained for different tasks, enabling the space robot to complete trajectory planning tasks while adjusting the base attitude without further learning. Furthermore, our experiments confirm the superior robustness of our method in the face of external disturbances, changing base masses, and even the failure of one arm.

cs.RO↗