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Video generation requires modeling a vast spatiotemporal space, which demands significant computational resources and data usage. To reduce the complexity, the prevailing approaches employ a cascaded architecture to avoid direct training with full resolution. Despite reducing computational demands, the separate optimization of each sub-stage hinders knowledge sharing and sacrifices flexibility. This work introduces a unified pyramidal flow matching algorithm. It reinterprets the original denoising trajectory as a series of pyramid stages, where only the final stage operates at the full resolution, thereby enabling more efficient video generative modeling. Through our sophisticated design, the flows of different pyramid stages can be interlinked to maintain continuity. Moreover, we craft autoregressive video generation with a temporal pyramid to compress the full-resolution history. The entire framework can be optimized in an end-to-end manner and with a single unified Diffusion Transformer (DiT). Extensive experiments demonstrate that our method supports generating high-quality 5-second (up to 10-second) videos at 768p resolution and 24 FPS within 20.7k A100 GPU training hours. All code and models will be open-sourced at https://pyramid-flow.github.io.

Several pulsars with unusually long periods were discovered recently, comprising a potential population of ultra long period pulsars (ULPPs). The origin of their long periodicity is not well understood, but may be related to magnatars spun down by surrounding fallback disks. While there are few systematic investigations on the fallback disk-assisted evolution of magnetars, the instability in the disk has received little attention, which determines the lifetime of the disk. In this work we simulate the evolution of the magnetic field, spin period, and magnetic inclination angle of magnetars with a supernova fallback disk. We find that thermal viscous instability in the disk could significantly affect the formation of ULPPs. Our simulation results also reveal that a large fraction of ULPPs seem to be nearly aligned and orthogonal rotators. This might help place ULPPs above the death line in the pulse period - period derivative plane. However, some extra mechanisms seem to be required to account for radio emission of ULPPs., Comment: 15 pages, 8 figures, accepted for publication in ApJ

This paper introduces an effcient class of adaptive stencil extension reconstruction methods based on a discontinuity feedback factor, addressing the challenges of weak robustness and high computational cost in high-order schemes, particularly those of 7th-order or above. Two key innovations are presented: The accuracy order adaptively increases from the lowest level based on local stencil smoothness, contrasting with conventional methods like Weighted Essentially Non-Oscillatory (WENO) and Monotonic Upstream-Centered Scheme for Conservation Laws (MUSCL)limiters, which typically reduce order from the highest level. The Discontinuity Feedback Factor (DF) serves a dual purpose: detecting sub-cell discontinuity strength and explicitly incorporating into the reconstruction process as a local smoothness measure. This approach eliminates the need for computationally expensive smoothness indicators often required in very high-order schemes, such as 9th-order schemes, and can be easily generalized to arbitrary high-order schemes. Rigorous test cases, including a Mach 20000 jet, demonstrate the exceptional robustness of this approach., Comment: submitted to Communications in Computational Physics

Ammonia is a key component of fertilizer and a potential clean fuel and hydrogen carrier. The Haber-Bosch process for ammonia synthesis consumes more than half of industrial hydrogen and contributes up to ~3% of global greenhouse gas emissions. Light-driven reactions via surface plasmon resonances offer a less energy-intensive pathway for ammonia production by altering reaction intermediates. Here, we report gold-ruthenium plasmonic bimetallic alloys for ammonia synthesis at room temperature and pressure, driven by visible light. We use colloidal synthesis to create AuRu$_x$ alloys (x=0.1, 0.2, 0.3) and disperse these nanoparticles on MgO supports for gas-phase ammonia synthesis. We observe a ~60 $\mu$mol/g/h reactivity and ~0.12% external quantum efficiency on a AuRu$_0$$_.$$_2$ sample under 100 mW/cm$^2$ visible light. In-situ diffuse reflective infrared Fourier transform spectroscopic measurements show that hydrogenation of nitrogen adsorbates is accelerated under light compared to thermocatalysis. Combining wavelength-dependent reactivity and spectroscopic findings with semi-classical electromagnetic modeling, we show plasmonic bimetallic alloys expedite ammonia synthesis by aiding hydrogenation of adsorbed nitrogen species via plasmon-mediated hot electrons. Quantum mechanical calculations reveal hydrogen-assisted N$_2$ splitting in the excited state is key to activating the reaction under ambient conditions. Therefore, light or H$_2$ alone cannot dissociate N$_2$ -- the key bottleneck to breaking N$_2$'s triple bond. Our findings are consistent with recent hypotheses on how nitrogenase enzymes catalyze ammonia production at mild conditions and provide insights for sustainable photochemical transformations., Comment: 21 pages, 4 figures, journal article submission soon

To effectively mitigate the influence of atmospheric turbulence, a novel discrete-time analog transmission free-space optical (DTAT-FSO) communication scheme is proposed. It directly maps information sources to discrete-time analog symbols via joint source-channel coding and modulation. Differently from traditional digital free space optical (TD-FSO) schemes, the proposed DTAT-FSO approach can automatically adapt to the variation of the channel state, with no need to adjust the specific modulation and coding scheme. The performance of the DTAT-FSO system was evaluated in both intensity modulation/direct detection (IM/DD) and coherent FSO systems for high-resolution image transmission. The results show that the DTAT-FSO reliably transmits images at low received optical powers (ROPs) and automatically enhances quality at high ROPs, while the TD-FSO experiences cliff and leveling effects when the channel state varies. With respect to the TD-FSO scheme, the DTAT-FSO scheme improved receiver sensitivity by 2.5 dB in the IM/DD FSO system and 0.8 dB in the coherent FSO system, and it achieved superior image fidelity under the same ROP. The automatic adaptation feature and improved performance of the DTAT-FSO suggest its potential for terrestrial, airborne, and satellite optical networks, addressing challenges posed by atmospheric turbulence.

This paper presents a kinetic model for the coupled evolution of radiation, electrons, and ions in a radiation plasma system. The model is solved using two methods. The gas-kinetic scheme (GKS) for electron and ion hydrodynamics and the unified gas-kinetic scheme (UGKS) for non-equilibrium radiative transfer. The UGKS accurately captures multiscale photon transport from free streaming to diffusion across varying fluid opacities. This approach enables the scheme to model equilibrium plasma with non-equilibrium radiation transport. The model is validated through several test cases, including radiative transfer in kinetic and diffusion regimes, Marshak wave, Radiative shock, 3T(three-temperature) double lax shock tube problem, two-dimensional Sedov blast wave, and two-dimensional tophat based problem. These tests demonstrate the current scheme's capability to handle diverse radiation plasma scenarios.

We propose a novel mechanism that the spin alignment of vector meson $\phi$ and $K^{*0}$ in most central heavy ion collisions is induced by intrinsic QCD dynamics without external vortical/ magnetic fields. The local spin density fluctuation of quarks due to the axial-vector and tensor interactions between quarks can induce the spin alignments of vector meson. It is found that axial-vector interaction results in $\rho_{00}<1/3$ for the spin alignment of $\phi$ meson, while tensor interaction results in $\rho_{00}>1/3$. We argue that the spin alignment of $K^{*0}$ due to the spin density fluctuation from flavor-mixing would be a signature or hint of instanton., Comment: 9 pages,4 figures

The unified gas-kinetic wave-particle (UGKWP) method is a hybrid method for multiscale flow simulations, in which the contributions to the whole gas evolution from deterministic hydrodynamic wave and stochastic particle transport are combined simultaneously. Originally, the UGKWP method was developed as a direct modeling approach at discrete level. In this work, we revisit the time evolution of each part of the involved simulation particles and wave molecules in UGKWP, and present the corresponding kinetic equations. The resultant kinetic system can be viewed as a collision decomposition of the original kinetic equation, which can serve as a basis for developing other kinetic methods for flows in all flow regimes., Comment: 16 pages, 3 figures

Photonic integrated circuits (PICs) offer ultra-broad optical bandwidths that enable unprecedented data throughputs for signal processing applications. Dynamic reconfigurability enables compensation of fabrication flaws and fluctuating external environments, tuning for adaptive equalization and training of optical neural networks. The initial step in PIC reconfiguration entails measuring its dynamic performance, often described by its frequency response. While measuring the amplitude response is straightforward, e.g. using a tunable laser and optical power meter, measuring the phase response presents challenges due to various factors, including phase variations in test connections and instrumentation limitations. To address these challenges, a universal and robust characterization technique is proposed, which uses an on-chip reference path coupled to the signal processing core (SPC), with a delay larger or smaller than the total delay across the signal processing paths. A Fourier transform of the chip's power response reveals the SPC's impulse response. The method is more robust against low reference-path power and imprecise delays. Experiments using a finite-impulse-response (FIR) structure demonstrate rapid SPC training, overcoming thermal crosstalk and device imperfections. This approach offers a promising solution for PIC characterization, facilitating expedited physical parameter training for advanced applications in communications and optical neural networks.

This paper introduces a memory-reduction third-order compact gas-kinetic scheme (CGKS) for solving compressible Euler and Navier-Stokes equations on 3D unstructured meshes. The scheme utilizes a time-evolution gas distribution function to provide a time-evolution solution at cell interfaces, enabling the implementation of Hermite WENO techniques for high-order reconstruction. However, the HWENO method needs to store a coefficients matrix for the quadratic polynomial to achieve third-order accuracy, resulting in high memory usage. A novel reconstruction method, built upon HWENO reconstruction, has been designed to enhance computational efficiency and reduce memory usage compared to the original CGKS. The simple idea is that the first-order and second-order terms of the quadratic polynomials are determined in a two-step way. In the first step, the second-order terms are obtained from the reconstruction of a linear polynomial of the first-order derivatives by only using the cell-averaged slopes, since the second-order derivatives are nothing but the "derivatives of derivatives". Subsequently, the first-order terms left can be determined by the linear reconstruction only using cell-averaged values. Thus, we successfully split one quadratic least-square regression into several linear least-square regressions, which are commonly used in a second-order finite volume code. Since only a small matrix inversion is needed in a 3-D linear least-square regression, the computational cost for the new reconstruction is dramatically reduced and the storage of the reconstruction-coefficient matrix is no longer necessary. The proposed new reconstruction technique can reduce the overall computational cost by about 20 to 30 percent. The challenging large-scale unsteady numerical simulation is performed, which demonstrates that the current improvement brings the CGKS to a new level for industrial applications., Comment: arXiv admin note: substantial text overlap with arXiv:2402.02075

Nonequilibrium flows have been frequently encountered in various aerospace engineering applications. To understand nonequilibrium physics, multiscale effects, and the dynamics in these applications, an effective and reliable multiscale scheme for all flow regimes is required. Following the direct modeling methodology, the adaptive unified gas-kinetic scheme employs discrete velocity space (DVS) to accurately capture the non-equilibrium physics, recovering the original unified gas-kinetic scheme (UGKS), and adaptively employs continuous distribution functions based on the Chapman-Enskog expansion to achieve better efficiency. Different regions are dynamically coupled at the cell interface through the fluxes from the discrete and continuous gas distribution functions, thereby avoiding any buffer zone between them. In the current study, an implicit adaptive unified gas-kinetic scheme (IAUGKS) is constructed to further enhance the efficiency of steady-state solutions. The current scheme employs implicit macroscopic governing equations and couples them with implicit microscopic governing equations within the non-equilibrium region, resulting in high convergence efficiency in all flow regimes. A series of numerical tests were conducted for high Mach number flows around diverse geometries such as a cylinder, a sphere, an X-38-like vehicle, and a space station. The current scheme can capture the non-equilibrium physics and provide accurate predictions of surface quantities. In comparison with the original UGKS, the velocity space adaptation, unstructured DVS, and implicit iteration significantly improve the efficiency by one or two orders of magnitude. Given its exceptional efficiency and accuracy, the IAUGKS serves as an effective tool for nonequilibrium flow simulations.

The Unified Gas-Kinetic Wave-Particle (UGKWP) method is constructed for partially ionized plasma (PIP). This method possesses both multiscale and unified preserving (UP) properties. The multiscale property allows the method to capture a wide range of plasma physics, from the particle transport in the kinetic regime to the two-fluid and magnetohydrodynamics (MHD) in the near continuum regimes, with the variation of local cell Knudsen number and normalized Larmor radius.The unified preserving property ensures that the numerical time step is not limited by the particle collision time in the continuum regime for the capturing of dissipative macroscopic solutions of the resistivity, Hall-effect, and all the way to the ideal MHD equations.The UGKWP is clearly distinguishable from the classical single scale Particle-in-Cell/Monte Carlo Collision (PIC/MCC) methods.The UGKWP method combines the evolution of microscopic velocity distribution with the evolution of macroscopic mean field quantities, granting it UP properties. Moreover, the time step in UGKWP is not constrained by the plasma cyclotron period through the Crank-Nicolson scheme for fluid and electromagnetic field interactions. The momentum and energy exchange between different species is approximated by the Andries-Aoki-Perthame (AAP) model. Overall, the UGKWP method enables a smooth transition from the PIC method in the rarefied regime to the MHD solvers in the continuum regime. This method has been extensively tested on a variety of phenomena ranging from kinetic Landau damping to the macroscopic flow problems, such as the Brio-Wu shock tube, Orszag-Tang vortex, and Geospace Environmental Modeling (GEM) magnetic reconnection. These tests demonstrate that the proposed method can capture the fundamental features of PIP across different scales seamlessly.

We proposed a low-complexity SVM-based signal recovery algorithm and evaluated it in 100G-PON with 25G-class devices. For the first time, it experimentally achieved 24 dB power budget @ FEC threshold 1E-3 over 40 km SMF, improving receiver sensitivity over 2 dB compared to FFE&DFE.

Understanding individual ions in solutions is essential for advancing our knowledge of complex chemical systems. However, tracking and detecting ions at the single-ion level in liquid environments remains a challenge. We introduce a strategy for visualization and differentiation of different ions in liquid environment via point defects in hexagonal boron nitride (h-BN) as the ion sensor. Ions interacting with the optically active point defects in h-BN alter emission properties, allowing us to capture these changes and visualize single ions. Using Li+ in organic electrolytes as a model, we observed a spectral shift of over 10 nm upon Li+ addition, and an over 50 nm red shift with applied electric fields due to reactions between Li+ and h-BN point defects. Frequency domain analysis further revealed the rapid dynamics of ion migration and the slow electrochemical reactions. We further spectroscopically differentiated various ions (H+, Li+, Na+, K+, Zn2+, Al3+) in aqueous solution. Each ion, with its distinct electron cloud configuration, interacts distinctively with the electron clouds of h-BN defects, resulting in specific and identifiable spectroscopic signatures. This ion sensing platform enables the direct visualization and differentiation of individual ions in a liquid environment, offering insights into chemical reactions at the single-ion level. This capability presents potential applications in various fields involving ions in liquids, including but not limited to biology, battery technology, and environmental science.

We proposed and experimentally demonstrated a look-up table boosted fast CDR and equalization scheme for the burst-mode 50/100 Gbps bandwidth-limited flexible PON, requiring no preamble for convergence and achieved the same bit error rate performance as in the case of long preambles.

In this paper, we study a class of mean-field reflected backward stochastic differential equations (MF-RBSDEs) driven by a marked point process and also analyze MF-RBSDEs driven by a poission process. Based on a g-expectation representation lemma, we give the existence and uniqueness of the particle system of MF-RBSDEs driven by a marked point process under Lipschitz generator conditions and obtain a convergence of this system. We also establish the well-posedness of the MF-RBSDEs driven by a poission process and the rate of of convergence of the corresponding particle system towards the solution to the MF-RBSDEs driven by a poission process under bounded terminal, bounded obstacle conditions.

The fallback disk model is widely accepted to explain long-period neutron stars (NSs) which can't be simulated by magnetic dipole radiation. However, no confirmed detection of disk was found from the newly discovered long period pulsars GLEAM-X 162759.5-523504.3, GPM J1839-10 and the known slowest isolated NSs 1E 161348-5055. This might be that the disks have either been in noninteracting/inactive state where its emission is too weak to be detected or have been disrupted. In this work, we conduct simulations to examine the lifetime of supernova fallback disks around isolated neutron stars. We assume that the disk's mass varies in a self-similar way and its interaction with the NS occurs only in interacting/active state. Our results reveal that nearly all the interacting lifetimes for the disk are shorter than 0.1 Myr while the existence lifetimes are considerably longer., Comment: Accepted in ApJ, comments are welcome

Magnification serves as an independent and complementary gravitational lensing measurement to shear. We develop a novel method to achieve an accurate and robust magnification measurement around BOSS CMASS galaxies across physical scales of $0.016h^{-1}{\rm Mpc} < r_{\rm p} < 10h^{-1}{\rm Mpc}$. We first measure the excess total flux density $\delta M$ of the source galaxies in deep DECaLS photometric catalog that are lensed by CMASS galaxies. We convert $\delta M$ to magnification $\mu$ by establishing the $\delta \mu-\delta M$ relation using a deeper photometric sample. By comparing magnification measurements in three optical bands ($grz$), we constrain the dust attenuation curve and its radial distribution, discovering a steep attenuation curve in the circumgalactic medium of CMASS galaxies. We further compare dust-corrected magnification measurements to model predictions from high-resolution dark matter-only (DMO) simulations in WMAP and Planck cosmologies, as well as the hydrodynamic simulation \texttt{TNG300-1}, using precise galaxy-halo connections from the Photometric objects Around Cosmic webs method and the accurate ray-tracing algorithm \texttt{P3MLens}. For $r_{\rm p} > 70h^{-1}$ kpc, our magnification measurements are in good agreement with both WMAP and Planck cosmologies, resulting in an estimation of the matter fluctuation amplitude of $S_8=0.816\pm0.024$. However, at $r_{\rm p} < 70h^{-1}$ kpc, we observe an excess magnification signal, which is higher than the DMO model in Planck cosmology at $2.8\sigma$ and would be exacerbated if significant baryon feedback is included. Implications of the potential small scale discrepancy for the nature of dark matter and for the processes governing galaxy formation are discussed., Comment: 25 pages, 19 figures. Main results in Figure 9 (dust) and Figure 18 (matter). Published in ApJ

Semantic communications, a promising approach for agent-human and agent-agent interactions, typically operate at a feature level, lacking true semantic understanding. This paper explores understanding-level semantic communications (ULSC), transforming visual data into human-intelligible semantic content. We employ an image caption neural network (ICNN) to derive semantic representations from visual data, expressed as natural language descriptions. These are further refined using a pre-trained large language model (LLM) for importance quantification and semantic error correction. The subsequent semantic importance-aware communications (SIAC) aim to minimize semantic loss while respecting transmission delay constraints, exemplified through adaptive modulation and coding strategies. At the receiving end, LLM-based semantic error correction is utilized. If visual data recreation is desired, a pre-trained generative artificial intelligence (AI) model can regenerate it using the corrected descriptions. We assess semantic similarities between transmitted and recovered content, demonstrating ULSC's superior ability to convey semantic understanding compared to feature-level semantic communications (FLSC). ULSC's conversion of visual data to natural language facilitates various cognitive tasks, leveraging human knowledge bases. Additionally, this method enhances privacy, as neither original data nor features are directly transmitted.

Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require training on extensive domain-specific images to achieve identity preservation, which lacks flexibility across different use cases. To address this issue, we exploit classifier guidance, a training-free technique that steers diffusion models using an existing classifier, for personalized image generation. Our study shows that based on a recent rectified flow framework, the major limitation of vanilla classifier guidance in requiring a special classifier can be resolved with a simple fixed-point solution, allowing flexible personalization with off-the-shelf image discriminators. Moreover, its solving procedure proves to be stable when anchored to a reference flow trajectory, with a convergence guarantee. The derived method is implemented on rectified flow with different off-the-shelf image discriminators, delivering advantageous personalization results for human faces, live subjects, and certain objects. Code is available at https://github.com/feifeiobama/RectifID.

The Runge--Kutta discontinuous Galerkin (RKDG) method is a high-order technique for addressing hyperbolic conservation laws, which has been refined over recent decades and is effective in handling shock discontinuities. Despite its advancements, the RKDG method faces challenges, such as stringent constraints on the explicit time-step size and reduced robustness when dealing with strong discontinuities. On the other hand, the Gas-Kinetic Scheme (GKS) based on a high-order gas evolution model also delivers significant accuracy and stability in solving hyperbolic conservation laws through refined spatial and temporal discretizations. Unlike RKDG, GKS allows for more flexible CFL number constraints and features an advanced flow evolution mechanism at cell interfaces. Additionally, GKS' compact spatial reconstruction enhances the accuracy of the method and its ability to capture stable strong discontinuities effectively. In this study, we conduct a thorough examination of the RKDG method using various numerical fluxes and the GKS method employing both compact and non-compact spatial reconstructions. Both methods are applied under the framework of explicit time discretization and are tested solely in inviscid scenarios. We will present numerous numerical tests and provide a comparative analysis of the outcomes derived from these two computational approaches.

Recently, it discovered two ultra-long period radio transients GLEAM-X J162759.5-523504.3 (J1627) and GPM J1839$-$10 (J1839) with spin periods longer than 1000 s. The origin of these two ultra-long period radio transients is intriguing in understanding the spin evolution of neutron stars (NSs). In this work, we diagnose whether the interaction between strong magnetized NSs and fallback disks can spin NSs down to the observed ultra-long period. Our simulations found that the magnetar+fallback disk model can account for the observed period, period derivative, and X-ray luminosity of J1627 in the quasi-spin-equilibrium stage. To evolve to the current state of J1627, the initial mass-accretion rate of the fallback disk and the magnetic field of the NS are in the range of $(1.1-30)\times10^{24}~\rm g\,s^{-1}$ and $(2-5)\times10^{14}~\rm G$, respectively. In an active lifetime of fallback disk, J1839 is impossible to achieve the observed upper limit of period derivative. Therefore, we propose that J1839 may be in the second ejector phase after the fallback disk becomes inactive. Those NSs with a magnetic field of $(2-6)\times10^{14}~\rm G$ and a fallback disk with an initial mass-accretion rate of $\sim10^{24}-10^{26}~\rm g\,s^{-1}$ are the possible progenitors of J1839., Comment: 12 pages, 10 figures, ApJ in press

In this paper, we study the mean reflected backward stochastic differential equations with jump (BSDEJs). We extend the work of Briand and Hibon on the propagation of chaos for mean reflected BSDEs \cite{briand2021particles} to the jump framework. Besides, we study the reflections for the particle system and obtain the rate of of convergence of the particle system towards the deterministic flat solution to the mean reflected BSDEJ.

In the SMASH-CLVisc-hybrid framework, including SMASH for the initial conditions and the hadronic rescattering stage, and CLVisc for the quark gluon plasma (QGP) evolution, we investigate net baryon number fluctuations via considering the equation of state (EoS) with and without a critical end point (CEP) in the QCD phase transition. Specifically, two distinct QCD EoS are utilized: one with smooth crossover derived from NEOS and another with a critical end point sourced from the rPNJL model. Our results show that non-monotonic behavior of $\kappa\sigma^2$ is not observed above the collision energy $7.7 {\rm GeV}$, nor are there explicit differences between the EoS characterized by crossover and that by CEP. This could be attributed to the significant deviation of the freeze-out line from the location of CEP. It is also found that the pure SMASH result of $\kappa \sigma^2$ is positive and close to zero at 3 GeV, which is different from the negative value observed from STAR.

In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty. Unlike traditional MoE approaches that rely on fixed Top-K routing, which activates a predetermined number of experts regardless of the input's complexity, our method dynamically selects experts based on the confidence level in expert selection for each input. This allows for a more efficient utilization of computational resources, activating more experts for complex tasks requiring advanced reasoning and fewer for simpler tasks. Through extensive evaluations, our dynamic routing method demonstrates substantial improvements over conventional Top-2 routing across various benchmarks, achieving an average improvement of 0.7% with less than 90% activated parameters. Further analysis shows our model dispatches more experts to tasks requiring complex reasoning skills, like BBH, confirming its ability to dynamically allocate computational resources in alignment with the input's complexity. Our findings also highlight a variation in the number of experts needed across different layers of the transformer model, offering insights into the potential for designing heterogeneous MoE frameworks. The code and models are available at https://github.com/ZhenweiAn/Dynamic_MoE.

For the simulations of unsteady flow, the global time step becomes really small with a large variation of local cell size. In this paper, an implicit high-order gas-kinetic scheme (HGKS) is developed to remove the restrictions on the time step for unsteady simulations. In order to improve the efficiency and keep the high-order accuracy, a two-stage third-order implicit time-accurate discretization is proposed. In each stage, an artificial steady solution is obtained for the implicit system with the pseudo-time iteration. In the iteration, the classical implicit methods are adopted to solve the nonlinear system, including the lower-upper symmetric Gauss-Seidel (LUSGS) and generalized minimum residual (GMRES) methods. To achieve the spatial accuracy, the HGKSs with both non-compact and compact reconstructions are constructed. For the non-compact scheme, the weighted essentially non-oscillatory (WENO) reconstruction is used. For the compact one, the Hermite WENO (HWENO) reconstruction is adopted due to the updates of both cell-averaged flow variables and their derivatives. The expected third-order temporal accuracy is achieved with the two-stage temporal discretization. For the smooth flow, only a single artificial iteration is needed. For uniform meshes, the efficiency of the current implicit method improves significantly in comparison with the explicit one. For the flow with discontinuities, compared with the well-known Crank-Nicholson method, the spurious oscillations in the current schemes are well suppressed. The increase of the artificial iteration steps introduces extra reconstructions associating with a reduction of the computational efficiency. Overall, the current implicit method leads to an improvement in efficiency over the explicit one in the cases with a large variation of mesh size., Comment: arXiv admin note: text overlap with arXiv:2304.09485

In the framework of Nambu--Jona-Lasinio model beyond mean field approximation, the effects of pion fluctuations on (inverse) magnetic catalysis and magnetic susceptibility are studied. The negative magnetic susceptibility at low temperature is observed when contributions from both neutral and charged pions are taken into account. In weak field approximation, it is observed that at finite temperature, the magnetic inhibition effect in the chiral limit, resulting from the difference between the transverse and longitudinal velocities of neutral pions, converts to weak magnetic catalysis when considering a non-zero current quark mass. Moreover, the magnetic catalysis is amplified by the charged pions., Comment: 16 pages, 8 figures

The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images. Recent works leverage image-caption datasets to train MLLMs, achieving state-of-the-art performance on image-to-text tasks. However, there are few studies exploring which layers of MLLMs make the most effort to the global image information, which plays vital roles in multimodal comprehension and generation. In this study, we find that the intermediate layers of models can encode more global semantic information, whose representation vectors perform better on visual-language entailment tasks, rather than the topmost layers. We further probe models regarding local semantic representations through object recognition tasks. We find that the topmost layers may excessively focus on local information, leading to a diminished ability to encode global information. Our code and data are released via https://github.com/kobayashikanna01/probing_MLLM_rep., Comment: Accepted by LREC-COLING 2024 as a short paper (Camera Ready)

By using the extrapolation method, a polarized Polykov-loop potential at finite real angular velocity is constructed from the lattice results at finite imaginary angular velocity. The chiral and deconfinement phase transitions under rotation have been simultaneously investigated in the Polarized-Polyakov-loop Nambu-Jona-Lasinio (PPNJL) model. It is observed that both critical temperatures of deconfinement and chiral phase transition increase with the angular velocity, which is in consistent with lattice results. The spin alignment of vector meson has the negative deviation of $\rho_{00} -1/3$ under rotation, and the deviation in the PPNJL model is much more significant than that in the NJL model and the quark coalescence model, which revealing the important role of rotating gluons on the quark polarization., Comment: 10 pages, 6 figures

The detection of gravitational wave events has stimulated theoretical modeling of the formation and evolution of double compact objects (DCOs). However, even for the most studied isolated binary evolution channel, there exist large uncertainties in the input parameters and treatments of the binary evolution process. So far, double neutron stars (DNSs) are the only DCOs for which direct observations are available through traditional electromagnetic astronomy. In this work, we adopt a population synthesis method to investigate the formation and evolution of Galactic DNSs. We construct 324 models for the formation of Galactic DNSs, taking into account various possible combinations of critical input parameters and processes such as mass transfer efficiency, supernova type, common envelope efficiency, neutron star kick velocity, and pulsar selection effect. We employ Bayesian analysis to evaluate the adopted models by comparing with observations. We also compare the expected DNS merger rate in the Galaxy with that inferred from the known Galactic population of Pulsar-NS systems. Based on these analyses we derive favorable range of the aforementioned key parameters., Comment: 29 pages, 9 figures, 2 tables, accepted by ApJ

In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos. Compared to static images, video poses unique challenges for effective large-scale pre-training due to the modeling of its spatiotemporal dynamics. In this paper, we address such limitations in video-language pre-training with an efficient video decomposition that represents each video as keyframes and temporal motions. These are then adapted to an LLM using well-designed tokenizers that discretize visual and temporal information as a few tokens, thus enabling unified generative pre-training of videos, images, and text. At inference, the generated tokens from the LLM are carefully recovered to the original continuous pixel space to create various video content. Our proposed framework is both capable of comprehending and generating image and video content, as demonstrated by its competitive performance across 13 multimodal benchmarks in image and video understanding and generation. Our code and models are available at https://video-lavit.github.io.

In this paper, we present an advanced high-order compact gas-kinetic scheme (CGKS) for 3D unstructured mixed-element meshes, augmented with a geometric multigrid technique to accelerate steady-state convergence. The scheme evolves cell-averaged flow variables and their gradients on the original mesh. Mesh coarsening employs a two-step parallel agglomeration algorithm using a random hash for cell interface selection and a geometric skewness metric for deletion confirmation, ensuring both efficiency and robustness. For the coarser meshes, first-order kinetic flux vector splitting (KFVS) schemes with explicit or implicit time-stepping are used. The proposed multigrid CGKS is tested across various flow regimes on hybrid unstructured meshes, demonstrating significant improvements. A three-layer V-cycle multigrid strategy, coupled with an explicit forward Euler method on coarser levels, results in a convergence rate up to ten times faster than standard CGKS. In contrast, the implicit lower-upper symmetric Gauss-Seidel (LU-SGS) method offers limited convergence acceleration. Our findings indicate that the explicit multigrid CGKS is highly scalable and effective for large-scale computations, marking a substantial step forward in computational fluid dynamics.

The roles that mass and environment play in the galaxy quenching are still under debate. Leveraging the Photometric objects Around Cosmic webs (PAC) method, we analyze the excess surface distribution $\bar{n}_2w_{\rm{p}}(r_{\rm{p}})$ of photometric galaxies in different color (rest-frame $u-r$) within the stellar mass range of $10^{9.0}M_{\odot}\sim10^{11.0}M_{\odot}$ around spectroscopic massive central galaxies ($10^{10.9}\sim10^{11.7}M_{\odot}$) at the redshift interval $0

In this paper, a robustness-enhanced reconstruction for the high-order finite volume scheme is constructed on the 2-D structured mesh, and both the high-order gas-kinetic scheme(GKS) and the Lax-Friedrichs(L-F) flux solver are considered to verify the validity of this algorithm. The strategy of the successful WENO reconstruction is adopted to select the smooth sub-stencils. However, there are cases where strong discontinuities exist in all sub-stencils of the WENO reconstruction, which leads to a decrease in the robustness. To improve the robustness of the algorithm in discontinuous regions in two-dimensional space, the hybrid reconstruction based on a combination of discontinuity feedback factor(DF) \cite{ji2021gradient} and WENO reconstruction is developed to deal with the possible discontinuities. Numerical results from smooth to extreme cases have been presented and validate that the new finite volume scheme is effective for robustness enhancement and maintains high resolution compared to the WENO scheme., Comment: submitted to Journal of Scientific Computing