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In this work, the soft rescattering parameters in the $B^\pm\rightarrow \pi^\pm\pi^+\pi^-$ and $B^\pm\rightarrow K^\pm\pi^+\pi^-$ decays with the light scalar meson $f_0(500)$ as the intermediate resonance are studied within the QCD factorization. Considering the interference effect between $\rho(770)^0$ and $f_0(500)$, we utilize the experimentally more direct event yields for fitting and get the soft rescattering parameters $|\rho_k^{SP}|=3.29\pm1.01$ and $|\rho_k^{PS}|=2.33\pm0.73$ in $B\rightarrow PS$ and $B\rightarrow SP$ decays ($P$ and $S$ denote pseudoscalar and scalar mesons, respectively), respectively. We also study the branching ratios and $CP$ asymmetries in the decay modes involving other scalar mesons, including $f_0(980)$, $a_0(980)$, $a_0(1450)$ and $K_0^*(1430)$, to test the rationality of the values of $|\rho_k^{SP}|$ and $|\rho_k^{PS}|$. Meanwhile, the wealth of experimental data facilitate the examination of the forward-backward asymmetry induced $CP$ asymmetries (FB-CPAs), and the localized $CP$ asymmetries (LACPs). We investigate these asymmetries resulting from the interference between $\rho(770)^0$ and $f_0(500)$ for $B^\pm\rightarrow \pi^\pm\pi^+\pi^-$ and $B^\pm\rightarrow K^\pm\pi^+\pi^-$ decays when the invariant mass of $\pi^+\pi^-$ locates in the low-energy region $0.445\mathrm{GeV}

We study the problem of robust matrix completion (RMC), where the partially observed entries of an underlying low-rank matrix is corrupted by sparse noise. Existing analysis of the non-convex methods for this problem either requires the explicit but empirically redundant regularization in the algorithm or requires sample splitting in the analysis. In this paper, we consider a simple yet efficient nonconvex method which alternates between a projected gradient step for the low-rank part and a thresholding step for the sparse noise part. Inspired by leave-one out analysis for low rank matrix completion, it is established that the method can achieve linear convergence for a general class of thresholding functions, including for example soft-thresholding and SCAD. To the best of our knowledge, this is the first leave-one-out analysis on a nonconvex method for RMC. Additionally, when applying our result to low rank matrix completion, it improves the sampling complexity of existing result for the singular value projection method.

Polarized attosecond $\gamma-$rays may offer excitation and hyperfine tracking of reactions relevant to nuclear physics, astrophysics, high-energy physics, etc. However, unfortunately, generation of a feasible and easy-to-deploy source is still a great challenge. Here, we put forward a novel method for producing ultra-collimated high-brilliance polarized attosecond $\gamma-$rays via the interaction of an unpolarized electron beam with a solid-density plasma. As a relativistic electron beam enters a solid-density plasma, it can be modulated into high-density clusters via the self-modulation instability of itself and further into attosecond slices due to its own hosing instability. This is accompanied by the generation of similar pulse-width $\gamma-$slices via nonlinear Compton scattering. The severe hosing instability breaks the symmetry of the excited electromagnetic fields, resulting in net linear polarization of $\gamma-$slices, which challenges the conventional perception that the interaction of an axially symmetric unpolarized electron beam with a uniform plasma cannot generate polarized radiation. In addition, we also obtain high-quality electron microbunches which may serve as an alternative source for prebunched free-electron lasers.

Projected policy gradient under the simplex parameterization, policy gradient and natural policy gradient under the softmax parameterization, are fundamental algorithms in reinforcement learning. There have been a flurry of recent activities in studying these algorithms from the theoretical aspect. Despite this, their convergence behavior is still not fully understood, even given the access to exact policy evaluations. In this paper, we focus on the discounted MDP setting and conduct a systematic study of the aforementioned policy optimization methods. Several novel results are presented, including 1) global linear convergence of projected policy gradient for any constant step size, 2) sublinear convergence of softmax policy gradient for any constant step size, 3) global linear convergence of softmax natural policy gradient for any constant step size, 4) global linear convergence of entropy regularized softmax policy gradient for a wider range of constant step sizes than existing result, 5) tight local linear convergence rate of entropy regularized natural policy gradient, and 6) a new and concise local quadratic convergence rate of soft policy iteration without the assumption on the stationary distribution under the optimal policy. New and elementary analysis techniques have been developed to establish these results.

Conflict-Driven Clause Learning (CDCL) is the mainstream framework for solving the Satisfiability problem (SAT), and CDCL solvers typically rely on various heuristics, which have a significant impact on their performance. Modern CDCL solvers, such as MiniSat and Kissat, commonly incorporate several heuristics and select one to use according to simple rules, requiring significant time and expert effort to fine-tune in practice. The pervasion of Large Language Models (LLMs) provides a potential solution to address this issue. However, generating a CDCL solver from scratch is not effective due to the complexity and context volume of SAT solvers. Instead, we propose AutoSAT, a framework that automatically optimizes heuristics in a pre-defined modular search space based on existing CDCL solvers. Unlike existing automated algorithm design approaches focusing on hyperparameter tuning and operator selection, AutoSAT can generate new efficient heuristics. In this first attempt at optimizing SAT solvers using LLMs, several strategies including the greedy hill climber and (1+1) Evolutionary Algorithm are employed to guide LLMs to search for better heuristics. Experimental results demonstrate that LLMs can generally enhance the performance of CDCL solvers. A realization of AutoSAT outperforms MiniSat on 9 out of 12 datasets and even surpasses the state-of-the-art hybrid solver Kissat on 4 datasets.

Natural policy gradient (NPG) and its variants are widely-used policy search methods in reinforcement learning. Inspired by prior work, a new NPG variant coined NPG-HM is developed in this paper, which utilizes the Hessian-aided momentum technique for variance reduction, while the sub-problem is solved via the stochastic gradient descent method. It is shown that NPG-HM can achieve the global last iterate $\epsilon$-optimality with a sample complexity of $\mathcal{O}(\epsilon^{-2})$, which is the best known result for natural policy gradient type methods under the generic Fisher non-degenerate policy parameterizations. The convergence analysis is built upon a relaxed weak gradient dominance property tailored for NPG under the compatible function approximation framework, as well as a neat way to decompose the error when handling the sub-problem. Moreover, numerical experiments on Mujoco-based environments demonstrate the superior performance of NPG-HM over other state-of-the-art policy gradient methods.

Projected policy gradient (PPG) is a basic policy optimization method in reinforcement learning. Given access to exact policy evaluations, previous studies have established the sublinear convergence of PPG for sufficiently small step sizes based on the smoothness and the gradient domination properties of the value function. However, as the step size goes to infinity, PPG reduces to the classic policy iteration method, which suggests the convergence of PPG even for large step sizes. In this paper, we fill this gap and show that PPG admits a sublinear convergence for any constant step sizes. Due to the existence of the state-wise visitation measure in the expression of policy gradient, the existing optimization-based analysis framework for a preconditioned version of PPG (i.e., projected Q-ascent) is not applicable, to the best of our knowledge. Instead, we proceed the proof by computing the state-wise improvement lower bound of PPG based on its inherent structure. In addition, the finite iteration convergence of PPG for any constant step size is further established, which is also new.

In this paper, we consider a distributed stochastic non-convex optimization problem, which is about minimizing a sum of $n$ local cost functions over a network with only zeroth-order information. A novel single-loop Decentralized Zeroth-Order Variance Reduction algorithm, called DZOVR, is proposed, which combines two-point gradient estimation, momentum-based variance reduction technique, and gradient tracking. Under mild assumptions, we show that the algorithm is able to achieve $\mathcal{O}(dn^{-1}\epsilon^{-3})$ sampling complexity at each node to reach an $\epsilon$-accurate stationary point and also exhibits network-independent and linear speedup properties. To the best of our knowledge, this is the first stochastic decentralized zeroth-order algorithm that achieves this sampling complexity. Numerical experiments demonstrate that DZOVR outperforms the other state-of-the-art algorithms and has network-independent and linear speedup properties.

The mass and decay of the $s\bar{s}$ member of the $1^{3}F_{4}$ meson nonet are investigated in the framework of the Regge phenomenology and the $^{3}P_{0}$ model. We propose, based on the results, that the assignment of the $s\bar{s}$ member of the $1^{3}F_{4}$ meson nonet will require additional testing in the future. Our results also provide information for future studies of the $1^{3}F_{4}$ meson nonet., Comment: 11 pages

We put forward a novel method for producing ultrarelativistic high-density high-polarization positrons through a single-shot interaction of a strong laser with a tilted solid foil. In our method, the driving laser ionizes the target, and the emitted electrons are accelerated and subsequently generate abundant $\gamma$ photons via the nonlinear Compton scattering, dominated by the laser. These $\gamma$ photons then generate polarized positrons via the nonlinear Breit-Wheeler process, dominated by a strong self-generated quasi-static magnetic field $\mathbf{B}^{\rm S}$. We find that placing the foil at an appropriate angle can result in a directional orientation of $\mathbf{B}^{\rm S}$, thereby polarizing positrons. Manipulating the laser polarization direction can control the angle between the $\gamma$ photon polarization and $\mathbf{B}^{\rm S}$, significantly enhancing the positron polarization degree. Our spin-resolved quantum electrodynamics particle-in-cell simulations demonstrate that employing a laser with a peak intensity of about $10^{23}$ W/cm$^2$ can obtain dense ($\gtrsim$ 10$^{18}$ cm$^{-3}$) polarized positrons with an average polarization degree of about 70\% and a yield of above 0.1 nC per shot. Moreover, our method is feasible using currently available or upcoming laser facilities and robust with respect to the laser and target parameters. Such high-density high-polarization positrons hold great significance in laboratory astrophysics, high-energy physics and new physics beyond the Standard Model.

The convergence of deterministic policy gradient under the Hadamard parametrization is studied in the tabular setting and the global linear convergence of the algorithm is established. To this end, we first show that the error decreases at an $O(\frac{1}{k})$ rate for all the iterations. Based on this result, we further show that the algorithm has a faster local linear convergence rate after $k_0$ iterations, where $k_0$ is a constant that only depends on the MDP problem and the step size. Overall, the algorithm displays a linear convergence rate for all the iterations with a loose constant than that for the local linear convergence rate.

The Sinkhorn algorithm is a widely used method for solving the optimal transport problem, and the Greenkhorn algorithm is one of its variants. While there are modified versions of these two algorithms whose computational complexities are $O({n^2\|C\|_\infty^2\log n}/{\varepsilon^2})$ to achieve an $\varepsilon$-accuracy, to the best of our knowledge, the existing complexities for the vanilla versions are $O({n^2\|C\|_\infty^3\log n}/{\varepsilon^3})$. In this paper we fill this gap and show that the complexities of the vanilla Sinkhorn and Greenkhorn algorithms are indeed $O({n^2\|C\|_\infty^2\log n}/{\varepsilon^2})$. The analysis relies on the equicontinuity of the dual variables for the discrete entropic regularized optimal transport problem, which is of independent interest.

In this work, the mass spectrum of the first radial excitation axial vector meson nonet is considered in the framework of the nonrelativistic constituent quark model and Regge phenomenology. After that, we investigate the strong decay characteristics of these states within the $^{3}P_{0}$ model. The results are compared to values from other phenomenological models, and they may be beneficial in the prospective search for $2^{3}P_{1}$ meson nonets., Comment: 11 pages

Color centers in silicon carbide have become potentially versatile quantum sensors. Particularly, wide temperature range temperature sensing has been realized in recent years. However, the sensitivity is limited due to the short dephasing time of the color centers. In this work, we realize a high sensitivity silicon carbide divacancy-based thermometer using the thermal Carr-Purcell-Meiboom-Gill (TCPMG) method. First, the zero field splitting D of PL6 divacancy as a function of temperature is measured with a linear slope of -99.7 kHz/K. The coherence times of TCPMG pulses linearly increase with the pulse number and the longest coherence time is about 21 us, which is ten times larger than dephasing time. The corresponding temperature sensing sensitivity is 13.4 mK/Hz1/2, which is about 15 times higher than previous results. Finally, we monitor the laboratory temperature variations for 24 hours using the TCMPG pulse. The experiments pave the way for the applications of silicon carbide-based high sensitivity thermometer in the semiconductor industry, biology, and materials sciences.

Based on the mass relations from the Regge phenomenology, we revisit the mass spectrum of $1^{3}D_{1}$ meson nonet. The masses of kaon and $s\bar{s}$ member of $1^{3}D_{1}$ ($1^{3}D_{1}(s\bar{s})$) meson nonet are obtained and the results are compared with the values from different theoretical models. Moreover, the strong decay properties of the kaon and $1^{3}D_{1}(s\bar{s})$ are presented in the $^{3}P_{0}$ model. On the basis of results, we suggest the assignment of $1^{3}D_{1}$ meson nonet need further tested in the new experiment in the future. Our results also provide mass constraints for the study of these states.

In this work, we investigate the mass spectrum of $1^{1}D_{2}$ and $1^{3}D_{2}$ meson nonets in the framework of the meson mass matrix and Regge phenomenology. The results are compared with the values from different phenomenological models and may be useful for the assignment of the $1^{1}D_{2}$ and $1^{3}D_{2}$ meson nonets in the future., Comment: 13 pages, 6 figures

This paper investigates the low-rank tensor completion problem, which is about recovering a tensor from partially observed entries. We consider this problem in the tensor train format and extend the preconditioned metric from the matrix case to the tensor case. The first-order and second-order quotient geometry of the manifold of fixed tensor train rank tensors under this metric is studied in detail. Algorithms, including Riemannian gradient descent, Riemannian conjugate gradient, and Riemannian Gauss-Newton, have been proposed for the tensor completion problem based on the quotient geometry. It has also been shown that the Riemannian Gauss-Newton method on the quotient geometry is equivalent to the Riemannian Gauss-Newton method on the embedded geometry with a specific retraction. Empirical evaluations on random instances as well as on function-related tensors show that the proposed algorithms are competitive with other existing algorithms in terms of recovery ability, convergence performance, and reconstruction quality., Comment: The manuscript has been adjusted in several places

This paper studies a policy optimization problem arising from collaborative multi-agent reinforcement learning in a decentralized setting where agents communicate with their neighbors over an undirected graph to maximize the sum of their cumulative rewards. A novel decentralized natural policy gradient method, dubbed Momentum-based Decentralized Natural Policy Gradient (MDNPG), is proposed, which incorporates natural gradient, momentum-based variance reduction, and gradient tracking into the decentralized stochastic gradient ascent framework. The $\mathcal{O}(n^{-1}\epsilon^{-3})$ sample complexity for MDNPG to converge to an $\epsilon$-stationary point has been established under standard assumptions, where $n$ is the number of agents. It indicates that MDNPG can achieve the optimal convergence rate for decentralized policy gradient methods and possesses a linear speedup in contrast to centralized optimization methods. Moreover, superior empirical performance of MDNPG over other state-of-the-art algorithms has been demonstrated by extensive numerical experiments.

Silicon vacancy in silicon carbide has drawn much attention for various quantum sensing. However, most of the previous experiments are achieved using confocal scanning systems, which limit its applications in practical applications. In this work, we demonstrate a compact fiber-integrated silicon carbide silicon vacancy-based vector magnetometer at room temperature. First, we effectively couple the silicon vacancy in a tiny silicon carbide slice to an optical fiber tip and realize the readout of the spin signal through the fiber at the same time. We then study the optically detected magnetic resonance spectra at different laser and microwave powers, obtaining an optimized magnetic field sensitivity of 12.3 {\mu}T/Hz1/2. Based on this, the magnetometer is performed to measure the strength and polar angle of an external magnetic field, respectively. Through these experiments, we have paved the way for fiber-integrated silicon vacancy-based magnetometer applications in practical environments such as geophysics and biomedical sensing.