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Self-organizing systems consist of autonomous agents that can perform complex tasks and adapt to dynamic environments without a central controller. Prior research often relies on reinforcement learning to enable agents to gain the skills needed for task completion, such as in the box-pushing environment. However, when agents push from opposing directions during exploration, they tend to exert equal and opposite forces on the box, resulting in minimal displacement and inefficient training. This paper proposes a model called Shared Pool of Information (SPI), which enables information to be accessible to all agents and facilitates coordination, reducing force conflicts among agents and enhancing exploration efficiency. Through computer simulations, we demonstrate that SPI not only expedites the training process but also requires fewer steps per episode, significantly improving the agents' collaborative effectiveness., Comment: 17 pages, 16 figures

We present the determination of the charm quark mass, the masses, and decay constants of charmed mesons using thirteen 2+1 flavor gauge ensembles at five different lattice spacings $a\in[0.05,0.11]$ fm, 8 pion masses $m_{\pi}\in(130,360)$ MeV, and several values of the strange quark mass, which facilitate us to do the chiral and continuum extrapolation. These ensembles are generated through the stout smeared clover fermion action and Symanzik gauge actions with the tadpole improvement. By absorbing the discretization errors into the masses and field normalization of the charm quark, we manage to suppress the discretization error of the charmed meson mass and all the S-wave open charmed meson decay constants to a few percent or even less at lattice spacing \( a \sim 0.1 \) fm. Moreover, discretization errors for other quantities are also significantly reduced. The continuum extrapolated charm quark mass, $m_c(m_c)=1.2933(72)(95)$ GeV in $\overline{\textrm{MS}}$ scheme, is determined using QED-subtracted $D_s$ meson mass and non-perturbative renormalization. Predictions of the open and close charm mesons using this charm quark mass agree with the experimental values at 0.1-0.5\% level uncertainty. We obtained $D_{(s)}$ decay constants and also by far the most precise $D_{(s)}^*$ decay constants $f_{D^*}=0.2292(26)(17)$ GeV and $f_{D^*_s}=0.2691(30)(03)$ GeV., Comment: 24 pages, 15 figures, revised version based on the comments

As a generalization of the well-known Bell state measurement (BSM), the elegant joint measurement (EJM) is a kind of novel two-qubit joint measurement, parameterized by a subtle phase factor $\theta \in [0,\pi/2]$. We explore quantum teleportation based on the EJM, inspired by Gisin's idea that quantum entanglement not only provides quantum channel and also quantum joint measurement for quantum teleportation. It is a probabilistic teleportation caused by undesired nonunitary quantum evolution. There are two interesting features in the present scenario. First, it goes beyond the conventional teleportation scenario, which can be included in the present scenario. Second, different from the BSM being single input and four outcomes, it can provide an adjustable input setting or even multiple measurement settings for the sender (or the controller). Moreover, we show in detail the feasible quantum circuits to realize the present scenario, where a few unitary operations and a nonunitary quantum gate are being utilized., Comment: 8 pages, 3 figures

We present the light-flavor quark masses and low energy constants using the 2+1 flavor full-QCD ensembles with stout smeared clover fermion action and Symanzik gauge actions. Both the fermion and gauge actions are tadpole improved self-consistently. The simulations are performed on 11 ensembles at 3 lattice spacings $a\in[0.05,0.11]$ fm, 4 spatial sizes $L\in[2.5, 5.1]$ fm, 7 pion masses $m_{\pi}\in[135,350]$ MeV, and several values of the strange quark mass. The quark mass is defined through the partially conserved axial current (PCAC) relation and renormalized to $\overline{\mathrm{MS}}$ 2 GeV through the intermediate regularization independent momentum subtraction (RI/MOM) scheme. The systematic uncertainty of using the symmetric momentum subtraction (SMOM) scheme is also included. Eventually, we predict $m_u=2.45(22)(20)$ MeV, $m_d=4.74(11)(09)$ MeV, and $m_s=98.8(2.9)(4.7)$ MeV with the systematic uncertainties from lattice spacing determination, continuum extrapolation and renormalization constant included. We also obtain the chiral condensate $\Sigma^{1/3}=268.6(3.6)(0.7)$ MeV and the pion decay constant $F=86.6(7)(1.4) $ MeV in the $N_f=2$ chiral limit, and the next-to-leading order low energy constants $\ell_3=2.43(54)(05)$ and $\ell_4=4.322(75)(96)$., Comment: Version accepted by PRD. 7 pages, 4 figures, with more details in the appendix

We calculate the soft function using lattice QCD in the framework of large momentum effective theory incorporating the one-loop perturbative contributions. The soft function is a crucial ingredient in the lattice determination of light cone objects using transverse-momentum-dependent (TMD) factorization. It consists of a rapidity-independent part called intrinsic soft function and a rapidity-dependent part called Collins-Soper kernel. We have adopted appropriate normalization when constructing the pseudo-scalar meson form factor that is needed in the determination of the intrinsic part and applied Fierz rearrangement to suppress the higher-twist effects. In the calculation of CS kernel we consider a CLS ensemble other than the MILC ensemble used in a previous study. We have also compared the applicability of determining the CS kernel using quasi TMDWFs and quasi TMDPDFs. As an example, the determined soft function is used to obtain the physical TMD wave functions (WFs) of pion and unpolarized iso-vector TMD parton distribution functions (PDFs) of proton., Comment: 24 pages, 19 figures, published version

We present a first lattice QCD calculation of the transverse-momentum-dependent wave functions (TMDWFs) of the pion using large-momentum effective theory. Numerical simulations are based on one ensemble with 2+1+1 flavors of highly improved staggered quarks action with lattice spacing $a=0.121$~fm from the MILC Collaboration, and one with 2 +1 flavor clover fermions and tree-level Symanzik gauge action generated by the CLS Collaboration with $a=0.098$~fm. As a key ingredient, the soft function is first obtained by incorporating the one-loop perturbative contributions and a proper normalization. Based on this and the equal-time quasi-TMDWFs simulated on the lattice, we extract the light-cone TMDWFs. The results are comparable between the two lattice ensembles and a comparison with phenomenological parametrization is made. Our studies provide a first attempt of $ab$ $initio$ calculation of TMDWFs which will eventually lead to crucial theory inputs for making predictions for exclusive processes under QCD factorization.

When an unexpected metro disruption occurs, metro managers need to reschedule timetables to avoid trains going into the disruption area, and transport passengers stranded at disruption stations as quickly as possible. This paper proposes a two-stage optimization model to jointly make decisions for two tasks. In the first stage, the timetable rescheduling problem with cancellation and short-turning strategies is formulated as a mixed integer linear programming (MILP). In particular, the instantaneous parameters and variables are used to describe the accumulation of time-varying passenger flow. In the second one, a system-optimal dynamic traffic assignment (SODTA) model is employed to dynamically schedule response vehicles, which is able to capture the dynamic traffic and congestion. Numerical cases of Beijing Metro Line 9 verify the efficiency and effectiveness of our proposed model, and results show that: (1) when occurring a disruption event during peak hours, the impact on the normal timetable is greater, and passengers in the direction with fewer train services are more affected; (2) if passengers stranded at the terminal stations of disruption area are not transported in time, they will rapidly increase at a speed of more than 300 passengers per minute; (3) compared with the fixed shortest path, using the response vehicles reduces the total travel time about 7%. However, it results in increased travel time for some passengers., Comment: 32 pages, 21 figures

The utilization of digital health has increased recently, and these services provide extensive guidance to encourage users to exercise frequently by setting daily exercise goals to promote a healthy lifestyle. These comprehensive guides evolved from the consideration of various personalized behavioral factors. Nevertheless, existing approaches frequently neglect the users dynamic behavior and the changing in their health conditions. This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory. We conducted a methodological study by designing a deep reinforcement learning algorithm to evaluate exercise performance, considering fitness-fatigue effects. The deep reinforcement learning algorithm combines deep learning techniques to analyse time series data and infer user exercise behavior. In addition, we use the asynchronous advantage actor-critic algorithm for reinforcement learning to determine the optimal exercise intensity through exploration and exploitation. The personalized exercise data and biometric data used in this study were collected from publicly available datasets, encompassing walking, sports logs, and running. In our study, we conducted The statistical analyses/inferential tests to compare the effectiveness of machine learning approach in exercise goal setting across different exercise goal setting strategies.