WikiEdge:ArXiv速递/2025-03-04

摘要

• 原文标题：The subpath number of cactus graphs
• 中文标题：仙人掌图的子路径数
• 发布日期：2025-03-04 14:55:49+00:00
• 作者：Martin Knor, Jelena Sedlar, Riste Škrekovski, Yu Yang
• 分类：math.CO, 05C30, 05C38
• 原文链接：http://arxiv.org/abs/2503.02683v1

原文摘要：The subpath number of a graph G is defined as the total number of subpaths in G, and it is closely related to the number of subtrees, a well-studied topic in graph theory. This paper is a continuation of our previous paper [5], where we investigated the subpath number and identified extremal graphs within the classes of trees, unicyclic graphs, bipartite graphs, and cycle chains. Here, we focus on the subpath number of cactus graphs and characterize all maximal and minimal cacti with n vertices and k cycles. We prove that maximal cacti are cycle chains in which all interior cycles are triangles, while the two end-cycles differ in length by at most one. In contrast, minimal cacti consist of k triangles, all sharing a common vertex, with the remaining vertices forming a tree attached to this joint vertex. By comparing extremal cacti with respect to the subpath number to those that are extremal for the subtree number and the Wiener index, we demonstrate that the subpath number does not correlate with either of these quantities, as their corresponding extremal graphs differ. 中文摘要：图的子路径数定义为图中所有子路径的总数，它与子树数密切相关，后者是图论中一个被广泛研究的主题。本文是我们之前论文[5]的延续，在那篇论文中我们研究了子路径数，并在树、单环图、二分图和环链等图类中识别了极值图。本文中，我们专注于仙人掌图的子路径数，并刻画了所有具有n个顶点和k个环的极大和极小仙人掌图。我们证明了极大仙人掌图是环链，其中所有内部环都是三角形，而两个端环的长度最多相差一。相反，极小仙人掌图由k个三角形组成，这些三角形共享一个公共顶点，其余顶点形成一个附着于该公共顶点的树。通过比较子路径数的极值仙人掌图与子树数和维纳指数的极值图，我们证明了子路径数与这两个量不相关，因为它们的极值图不同。

摘要

• 原文标题：Enhancing the charging performance of an atomic quantum battery
• 中文标题：提升原子量子电池的充电性能
• 发布日期：2025-03-04 15:46:20+00:00
• 作者：Ming-Liang Hu, Ting Gao, Heng Fan
• 分类：quant-ph
• 原文链接：http://arxiv.org/abs/2503.02727v1

原文摘要：We study a quantum battery (QB) model composed of two atoms, where the charger and battery elements are coupled to a multimode vacuum field that serves as a mediator for energy transfer. Different figures of merit such as ergotropy, charging time, and charging efficiency are analyzed, putting emphasis on the role of various control parameters on the charging performance. It is found that there is a range of angle between the transition dipole moments and interatomic axis in which the QB can be charged. The optimal charging performance is achieved if the atomic dipole moments are perpendicular or parallel to the interatomic axis. The charging performance also improves with the decrease of the interatomic distance. Besides, the charged ergotropy can be enhanced by increasing the initial ergotropy of the charger and it is beneficial to charge the QB starting from a passive state. 中文摘要：我们研究了一个由两个原子组成的量子电池（QB）模型，其中充电器和电池元件耦合到一个多模真空场，该场作为能量转移的媒介。我们分析了诸如功容量、充电时间和充电效率等不同的性能指标，重点研究了各种控制参数对充电性能的影响。研究发现，在过渡偶极矩和原子间轴之间存在一定角度范围内，量子电池可以被充电。如果原子偶极矩垂直于或平行于原子间轴，则可以实现最佳充电性能。充电性能还随着原子间距离的减小而提高。此外，通过增加充电器的初始功容量可以增强充电后的功容量，并且从被动状态开始充电对量子电池是有益的。

摘要

• 原文标题：First Measurement of the Decay Dynamics in the Semileptonic Transition of the $D^{+(0)}$ into the Axial-vector Meson $\bar K_1(1270)$
• 中文标题：$D^{+(0)}$ 到轴矢量介子 $\bar K_1(1270)$ 的半轻衰变中衰变动力学的首次测量
• 发布日期：2025-03-04 02:09:02+00:00
• 作者：BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, Z. K. Chen, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Y. N. Gao, Y. Y. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. J. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, C. K. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, M. R. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Y. P. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, L. Q. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, W. T. Liu, X. Liu, X. Liu, X. Y. Liu, Y. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, F. M. Melendi, Y. H. Meng, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, T. Z. Song, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, L. F. Tang, M. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, B. Wang, B. Wang, Bo Wang, C. Wang, Cong Wang, D. Y. Wang, H. J. Wang, J. J. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, D. H. Wei, H. R. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. Wu, X. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. 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Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Z. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Q. Zhou, J. Y. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, J. H. Zou, J. Zu
• 分类：hep-ex
• 原文链接：http://arxiv.org/abs/2503.02196v1

原文摘要：Using $e^+e^-$ collision data taken at the center-of-mass energy of 3.773 GeV with the BESIII detector, corresponding to an integrated luminosity of 20.3 fb$^{-1}$, we report the first amplitude and angular analyses of the semileptonic decays $D^{+(0)}\to K^-\pi^+\pi^{0(-)} e^+\nu_e$. From the amplitude analysis, we determine for the first time the hadronic form factors of the semileptonic $D$ decays into the axial-vector meson $\bar{K}_1(1270)$ to be $r_A=(-11.2\pm1.0\pm0.9)\times10^{-2}$ and $r_V = (-4.3\pm 1.0\pm2.4)\times 10^{-2}$. The angular analysis yields an up-down asymmetry $\mathcal{A}^\prime_{ud} = 0.01\pm0.11$, which is consistent with the Standard Model prediction. 中文摘要：使用BESIII探测器在3.773 GeV质心能量下采集的$e^+e^-$对撞数据，对应积分亮度为20.3 fb$^{-1}$，我们首次报告了半轻子衰变$D^{+(0)}\to K^-\pi^+\pi^{0(-)} e^+\nu_e$的振幅和角分布分析。通过振幅分析，我们首次确定了半轻子$D$衰变到轴矢量介子$\bar{K}_1(1270)$的强子形状因子为$r_A=(-11.2\pm1.0\pm0.9)\times10^{-2}$和$r_V = (-4.3\pm 1.0\pm2.4)\times 10^{-2}$。角分布分析得到的上下不对称性$\mathcal{A}^\prime_{ud} = 0.01\pm0.11$，与标准模型的预测一致。

摘要

• 原文标题：Super-Linear Growth and Rising Inequality in Online Social Communities: Insights from Reddit
• 中文标题：在线社交社区中的超线性增长与不平等加剧：来自Reddit的洞察
• 发布日期：2025-03-04 14:22:45+00:00
• 作者：Guilherme Machado, Diogo Pacheco, Ronaldo Menezes, Gareth Baxter
• 分类：physics.soc-ph
• 原文链接：http://arxiv.org/abs/2503.02661v1

原文摘要：We study the effect of the number of users on the activity of communities within the online content sharing and discussion platform Reddit, called subreddits. We found that comment activity on Reddit has a heavy-tailed distribution, where a large fraction of the comments are made by a small set of users. Furthermore, as subreddits grow in size, this behavior becomes stronger, with activity (measured by the comments made in a subreddit) becoming even more centralised in a (relatively) smaller core of users. We verify that these changes are not explained by finite size nor by sampling effects. Instead, we observe a systematic change of the distribution with subreddit size. To quantify the centralisation and inequality of activity in a subreddit, we used the Gini coefficient. We found that as subreddits grow in users, so does the Gini coefficient, seemingly as a natural effect of the scaling. We found that the excess number of comments (the total number of comments minus the total number of users) follows a power law with exponent 1.27. For each subreddit we considered a snapshot of one month of data, as a compromise between statistical relevance and change in the system's dynamics. We show results over the whole year 2021 (with each subreddit having twelve snapshots, at most), nevertheless all results were consistent when using a single month or different years. 中文摘要：我们研究了用户数量对在线内容分享和讨论平台Reddit（称为subreddits）中社区活动的影响。我们发现，Reddit上的评论活动呈现出重尾分布，即大部分评论由一小部分用户完成。此外，随着subreddits规模的扩大，这种行为变得更加明显，活动（通过subreddit中的评论数量衡量）更加集中在（相对）较小的核心用户群体中。我们验证了这些变化不能通过有限规模或抽样效应来解释。相反，我们观察到分布随着subreddit规模的系统性变化。为了量化subreddit中活动的集中度和不平等性，我们使用了基尼系数。我们发现，随着subreddits用户数量的增加，基尼系数也随之增加，这似乎是规模扩展的自然结果。我们发现，评论的过剩数量（总评论数减去总用户数）遵循指数为1.27的幂律分布。对于每个subreddit，我们考虑了一个月的数据快照，作为统计相关性和系统动态变化之间的折衷。我们展示了2021年全年的结果（每个subreddit最多有十二个快照），然而，使用单个月份或不同年份时，所有结果都是一致的。

摘要

• 原文标题：Inferring Galactic Parameters from Chemical Abundances with Simulation-Based Inference
• 中文标题：基于模拟推理从化学丰度推断银河系参数
• 发布日期：2025-03-04 10:05:58+00:00
• 作者：Tobias Buck, Berkay Günes, Giuseppe Viterbo, William H. Oliver, Sven Buder
• 分类：astro-ph.GA, astro-ph.IM, physics.comp-ph, physics.data-an, physics.space-ph
• 原文链接：http://arxiv.org/abs/2503.02456v1

原文摘要：Galactic chemical abundances provide crucial insights into fundamental galactic parameters, such as the high-mass slope of the initial mass function (IMF) and the normalization of Type Ia supernova (SN Ia) rates. Constraining these parameters is essential for advancing our understanding of stellar feedback, metal enrichment, and galaxy formation processes. However, traditional Bayesian inference techniques, such as Hamiltonian Monte Carlo (HMC), are computationally prohibitive when applied to large datasets of modern stellar surveys. We leverage simulation-based-inference (SBI) as a scalable, robust, and efficient method for constraining galactic parameters from stellar chemical abundances and demonstrate its the advantages over HMC in terms of speed, scalability, and robustness against model misspecifications. We combine a Galactic Chemical Evolution (GCE) model, CHEMPY, with a neural network emulator and a Neural Posterior Estimator (NPE) to train our SBI pipeline. Mock datasets are generated using CHEMPY, including scenarios with mismatched nucleosynthetic yields, with additional tests conducted on data from a simulated Milky Way-like galaxy. SBI results are benchmarked against HMC-based inference, focusing on computational performance, accuracy, and resilience to systematic discrepancies. SBI achieves a $\sim75,600\times$ speed-up compared to HMC, reducing inference runtime from $\gtrsim42$ hours to mere seconds for thousands of stars. Inference on $1,000$ stars yields precise estimates for the IMF slope ($\alpha_{\rm IMF} = -2.298 \pm 0.002$) and SN Ia normalization ($\log_{10}(N_{\rm Ia}) = -2.885 \pm 0.003$), deviating less than 0.05% from the ground truth. SBI also demonstrates similar robustness to model misspecification than HMC, recovering accurate parameters even with alternate yield tables or data from a cosmological simulation. (shortened...) 中文摘要：银河化学丰度为基本银河参数提供了关键的见解，例如初始质量函数（IMF）的高质量斜率和Ia型超新星（SN Ia）速率的归一化。约束这些参数对于推进我们对恒星反馈、金属富集和星系形成过程的理解至关重要。然而，传统的贝叶斯推断技术，如哈密顿蒙特卡洛（HMC），在处理现代恒星调查的大数据集时计算上是不切实际的。我们利用基于模拟的推断（SBI）作为一种可扩展、稳健且高效的方法，从恒星化学丰度中约束银河参数，并展示了其在速度、可扩展性和对模型错误设定的鲁棒性方面相对于HMC的优势。我们将银河化学演化（GCE）模型CHEMPY与神经网络模拟器和神经后验估计器（NPE）结合，训练我们的SBI管道。使用CHEMPY生成模拟数据集，包括核合成产量不匹配的情景，并在模拟的类似银河系的数据上进行额外测试。SBI结果与基于HMC的推断进行基准测试，重点关注计算性能、准确性和对系统差异的恢复能力。SBI实现了与HMC相比约75,600倍的加速，将数千颗恒星的推断运行时间从超过42小时减少到仅几秒钟。对1,000颗恒星的推断得出了IMF斜率（$\alpha_{\rm IMF} = -2.298 \pm 0.002$）和SN Ia归一化（$\log_{10}(N_{\rm Ia}) = -2.885 \pm 0.003$）的精确估计，与真实值的偏差小于0.05%。SBI还展示了与HMC相似的模型错误设定鲁棒性，即使使用替代的产量表或来自宇宙学模拟的数据，也能恢复准确的参数。（简化...）