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Low-phase-noise microwave-frequency integrated oscillators provide compact solutions for various applications in signal processing, communications, and sensing. Surface acoustic waves (SAW), featuring orders-of-magnitude shorter wavelength than electromagnetic waves at the same frequency, enable integrated microwave-frequency systems with much smaller footprint on chip. SAW devices also allow higher quality (Q) factors than electronic components at room temperature. Here, we demonstrate a low-phase-noise gigahertz-frequency SAW oscillator on 128{\deg}Y-cut lithium niobate, where the SAW resonator occupies a footprint of 0.05 mm$^2$. Leveraging phononic crystal bandgap-edge modes to balance between Q factors and insertion losses, our 1-GHz SAW oscillator features a low phase noise of -132.5 dBc/Hz at a 10 kHz offset frequency and an overlapping Hadamard deviation of $6.5\times10^{-10}$ at an analysis time of 64 ms. The SAW resonator-based oscillator holds high potential in developing low-noise sensors and acousto-optic integrated circuits.

In this paper, we study the tensor products of irreducible highest weight modules with irreducible loop modules over the affine-Virasoro algebra with aid of the ``shifting technique" established for the Virasoro algebra in [H. Chen, X. Guo, K. Zhao, Tensor product weight modules over the Virasoro algebra, J. Lond. Math. Soc. 88(2013), 829-844.]. All such tensor product modules are indecomposable modules with infinite-dimensional weight spaces. Moreover, we obtain the necessary and sufficient conditions for such tensor product modules to be irreducible. Therefore, we obtain a class of new irreducible weight modules over the affine-Virasoro algebra. Finally, the necessary and sufficient conditions for any two such tensor product modules to be isomorphic are also determined., Comment: 23 pages

Deep models often struggle with out-of-distribution (OOD) generalization, limiting their real-world applicability beyond controlled laboratory settings. Invariant risk minimization (IRM) addresses this issue by learning invariant features and minimizing the risk across different domains. Thus, it avoids the pitfalls of pseudo-invariant features and spurious causality associated with empirical risk minimization (ERM). However, according to the support overlap theorem, ERM and IRM may fail to address the OOD problem when pseudo-invariant features have insufficient support overlap. To this end, we propose a novel method to enlarge feature support overlap for domain generalization. Specifically, we introduce Bayesian random semantic data augmentation to increase sample diversity and overcome the deficiency of IRM. Experiments on several challenging OOD generalization benchmarks demonstrate that our approach surpasses existing models, delivering superior performance and robustness. The code is available at \url{https://github.com/YaoyaoZhu19/BSDG}.

In recent years, Transformers have become the de-facto architecture for long-term sequence forecasting (LTSF), but faces challenges such as quadratic complexity and permutation invariant bias. A recent model, Mamba, based on selective state space models (SSMs), has emerged as a competitive alternative to Transformer, offering comparable performance with higher throughput and linear complexity related to sequence length. In this study, we analyze the limitations of current Mamba in LTSF and propose four targeted improvements, leading to MambaTS. We first introduce variable scan along time to arrange the historical information of all the variables together. We suggest that causal convolution in Mamba is not necessary for LTSF and propose the Temporal Mamba Block (TMB). We further incorporate a dropout mechanism for selective parameters of TMB to mitigate model overfitting. Moreover, we tackle the issue of variable scan order sensitivity by introducing variable permutation training. We further propose variable-aware scan along time to dynamically discover variable relationships during training and decode the optimal variable scan order by solving the shortest path visiting all nodes problem during inference. Extensive experiments conducted on eight public datasets demonstrate that MambaTS achieves new state-of-the-art performance.

Two-dimensional (2D) materials are promising candidates for spintronic applications. Maintaining their atomically smooth interfaces during integration of ferromagnetic (FM) electrodes is crucial since conventional metal deposition tends to induce defects at the interfaces. Meanwhile, the difficulties in picking up FM metals with strong adhesion and in achieving conductance match between FM electrodes and spin transport channels make it challenging to fabricate high-quality 2D spintronic devices using metal transfer techniques. Here, we report a solvent-free magnetic electrode transfer technique that employs a graphene layer to assist in the transfer of FM metals. It also serves as part of the FM electrode after transfer for optimizing spin injection, which enables the realization of spin valves with excellent performance based on various 2D materials. In addition to two-terminal devices, we demonstrate that the technique is applicable for four-terminal spin valves with nonlocal geometry. Our results provide a promising future of realizing 2D spintronic applications using the developed magnetic electrode transfer technique.

Testing conditional independence has many applications, such as in Bayesian network learning and causal discovery. Different test methods have been proposed. However, existing methods generally can not work when only discretized observations are available. Specifically, consider $X_1$, $\tilde{X}_2$ and $X_3$ are observed variables, where $\tilde{X}_2$ is a discretization of latent variables $X_2$. Applying existing test methods to the observations of $X_1$, $\tilde{X}_2$ and $X_3$ can lead to a false conclusion about the underlying conditional independence of variables $X_1$, $X_2$ and $X_3$. Motivated by this, we propose a conditional independence test specifically designed to accommodate the presence of such discretization. To achieve this, we design the bridge equations to recover the parameter reflecting the statistical information of the underlying latent continuous variables. An appropriate test statistic and its asymptotic distribution under the null hypothesis of conditional independence have also been derived. Both theoretical results and empirical validation have been provided, demonstrating the effectiveness of our test methods.

The Sun stands out as the closest and clearest astrophysical accelerator of cosmic rays, while other objects within and beyond the galaxy remain enigmatic. It is probable that the cosmic ray spectrum and mass components from these celestial sources share similarities, offering a novel approach to study their origin. In this study, we analyze of spectra and mass in the energy range from MeV to 10~EeV. We find: (1) the mean-logarithmic mass $\rm\left\langle lnA \right\rangle$ distribution with energy exhibits much clearer feature structures than the spectra; (2) a 100~TeV bump is presented in the $\rm\left\langle lnA \right\rangle$ distribution; (3) for protons, the knee is located at $\sim2$ PeV, the boundary between the galaxy and extra-galaxy occurs at $\sim30$ PeV, marked by a sharp dip; (4) the all-particle spectrum exhibits hardening at $\sim30$~PeV due to the contribution of nearby galaxies, and the extra-galactic dominate $\sim0.7$~EeV. We hope the LHAASO experiment can perform spectral measurements of individual species to validate our results., Comment: 6 pages, 3 figures

Radiation therapy is crucial in cancer treatment. Experienced experts typically iteratively generate high-quality dose distribution maps, forming the basis for excellent radiation therapy plans. Therefore, automated prediction of dose distribution maps is significant in expediting the treatment process and providing a better starting point for developing radiation therapy plans. With the remarkable results of diffusion models in predicting high-frequency regions of dose distribution maps, dose prediction methods based on diffusion models have been extensively studied. However, existing methods mainly utilize CNNs or Transformers as denoising networks. CNNs lack the capture of global receptive fields, resulting in suboptimal prediction performance. Transformers excel in global modeling but face quadratic complexity with image size, resulting in significant computational overhead. To tackle these challenges, we introduce a novel diffusion model, MD-Dose, based on the Mamba architecture for predicting radiation therapy dose distribution in thoracic cancer patients. In the forward process, MD-Dose adds Gaussian noise to dose distribution maps to obtain pure noise images. In the backward process, MD-Dose utilizes a noise predictor based on the Mamba to predict the noise, ultimately outputting the dose distribution maps. Furthermore, We develop a Mamba encoder to extract structural information and integrate it into the noise predictor for localizing dose regions in the planning target volume (PTV) and organs at risk (OARs). Through extensive experiments on a dataset of 300 thoracic tumor patients, we showcase the superiority of MD-Dose in various metrics and time consumption.

Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical image classification. Mainstream data augmentation (DA) methods are usually applied at the image level. Due to the specificity and diversity of medical imaging, expertise is often required to design effective DA strategies, and improper augmentation operations can degrade model performance. Although automatic augmentation methods exist, they are computationally intensive. Semantic data augmentation can implemented by translating features in feature space. However, over-translation may violate the image label. To address these issues, we propose \emph{Bayesian Random Semantic Data Augmentation} (BSDA), a computationally efficient and handcraft-free feature-level DA method. BSDA uses variational Bayesian to estimate the distribution of the augmentable magnitudes, and then a sample from this distribution is added to the original features to perform semantic data augmentation. We performed experiments on nine 2D and five 3D medical image datasets. Experimental results show that BSDA outperforms current DA methods. Additionally, BSDA can be easily assembled into CNNs or Transformers as a plug-and-play module, improving the network's performance. The code is available online at \url{https://github.com/YaoyaoZhu19/BSDA}.

The detection of GW170817/GRB170817A implied the strong association between short gamma-ray bursts (SGRBs) and binary neutron star (BNS) mergers which produce gravitational waves (GWs). More evidence is needed to confirm the association and reveal the physical processes of BNS mergers. The upcoming High Altitude Detection of Astronomical Radiation (HADAR) experiment, excelling in a wide field of view (FOV) and a large effective area above tens of GeV, is a hope for the prompt detection of very-high-energy (VHE; > 10 GeV) SGRBs. The aim of this paper is to simulate and analyse GW/SGRB joint detections by future GW detector networks in synergy with HADAR, including the second generation LIGO, Virgo and KAGRA and the third generation ET and CE. We provide a brief introduction of the HADAR experiment for SGRB simulations and its expected SGRB detections. For GW simulations, we adopt a phenomenological model to describe GWs produced by BNS mergers and introduce the signal-noise ratios (SNRs) as detector responses. Following a theoretical analysis we compute the redshift-dependent efficiency functions of GW detector networks. We then construct the simulation of GW detection by Monte Carlo sampling. We compare the simulated results of LIGO-Virgo O2 and O3 runs with their actual detections as a check. The combination of GW and SGRB models is then discussed for joint detection, including parameter correlations, triggered SNRs and efficiency skymaps. The estimated joint detection rates are 0.09-2.52 per year for LHVK network with HADAR under different possible configurations, and approximately 0.27-7.89 per year for ET+CE network with HADAR.

In this note, we investigate Jacobian conjecture through investigation of automorphisms of polynomial rings in characteristic $p$. Making use of the technique of inverse limits, we show that under Jacobian condition for a given homomorphism $\varphi$ of the polynomial ring $\mathbf{k}[x_1,\ldots,x_n]$, if $\varphi$ preserves the maximal ideals, then $\varphi$ is an automorphism., Comment: 8 pages, to appcar in JAA

The AMS-02 experiment has observed new properties of primary cosmic rays (CRs) categorized into two groups: He-C-O-Fe and Ne-Mg-Si-S, which are independent of CR propagation. In this study, we investigate the unexpected properties of these nuclei using a spatial propagation model. All nuclei spectra are accurately reproduced and separated into primary and secondary contributions. Our findings include: 1. Primary CR spectra are identical. 2. Our calculations align with AMS-02 results for primary-dominated nuclei within a 10\% difference, but show significant discrepancies for the secondary-dominated nuclei. 3. The primary element abundance is presented for the first time. We anticipate that the DAMPE and future HERD experiments will provide observations of nuclei spectra above TeV energy., Comment: 8 pages, 8 figures. accepted by PRD

Radiation therapy serves as an effective and standard method for cancer treatment. Excellent radiation therapy plans always rely on high-quality dose distribution maps obtained through repeated trial and error by experienced experts. However, due to individual differences and complex clinical situations, even seasoned expert teams may need help to achieve the best treatment plan every time quickly. Many automatic dose distribution prediction methods have been proposed recently to accelerate the radiation therapy planning process and have achieved good results. However, these results suffer from over-smoothing issues, with the obtained dose distribution maps needing more high-frequency details, limiting their clinical application. To address these limitations, we propose a dose prediction diffusion model based on SwinTransformer and a projector, SP-DiffDose. To capture the direct correlation between anatomical structure and dose distribution maps, SP-DiffDose uses a structural encoder to extract features from anatomical images, then employs a conditional diffusion process to blend noise and anatomical images at multiple scales and gradually map them to dose distribution maps. To enhance the dose prediction distribution for organs at risk, SP-DiffDose utilizes SwinTransformer in the deeper layers of the network to capture features at different scales in the image. To learn good representations from the fused features, SP-DiffDose passes the fused features through a designed projector, improving dose prediction accuracy. Finally, we evaluate SP-DiffDose on an internal dataset. The results show that SP-DiffDose outperforms existing methods on multiple evaluation metrics, demonstrating the superiority and generalizability of our method.

The rapid development of functional graphene-like nanoribbons with high-quality has become increasingly reliant on multiple nanofabrication platforms while traditional methods are facing mounting limitations in this regard. Consequently, the demand for novel techniques to explore and manipulate graphene-like nanoribbons has surged. Herein, we report an on-surface synthesis of graphene-like nanoribbons on pentacene/picene monomers via a series of self-assembly and annealing on the one-dimensional(1D) Au(110) substrate. Our scanning tunneling microscope(STM) research reveals that four-/eight- membered rings are formed between adjacent molecules. Furthermore, we demonstrate the technique to manipulate the pentacene dimer without breaking its structure by operating an STM tip. Our results exhibit a possible platform for developing next generation graphene-based quantum computing designs and a technique to obtain multiple functional graphene-like nanoribbons with high-precision.

Solitons have garnered significant attention across various fields, yet a contentious debate persists regarding the precise structure of solitons on indium chains. Currently, multiple forms of solitons in one-dimensional atomic chains have been reported. STM provides an effective means to study the precise atomic structure of solitons, particularly their dynamics and interactions. However, limited research has been conducted on soliton interactions and soliton-chain interactions, despite their profound impact on relative soliton motions and the overall physical properties of the system. In this work, we characterized the structures of the soliton dimer and trimer, observed the displacements induced by the soliton entity and statisticized the dynamic behaviors of soliton dimers over time evolution or temperature. To reveal the soliton mechanism, we further utilized STM to investigate the CDWs between two solitons when two monomers were encountered. Additionally, we achieved the manipulation of the monomer on the indium chain by the STM tip. Our work serves as an important approach to elucidate interactions in correlated electronic systems and advance the development of potential topological soliton computers.

Self-supervised learning has been known for learning good representations from data without the need for annotated labels. We explore the simple siamese (SimSiam) architecture for representation learning on strong gravitational lens images. Commonly used image augmentations tend to change lens properties; for example, zoom-in would affect the Einstein radius. To create image pairs representing the same underlying lens model, we introduce a lens augmentation method to preserve lens properties by fixing the lens model while varying the source galaxies. Our research demonstrates this lens augmentation works well with SimSiam for learning the lens image representation without labels, so we name it LenSiam. We also show that a pre-trained LenSiam model can benefit downstream tasks. We open-source our code and datasets at https://github.com/kuanweih/LenSiam ., Comment: 5 pages, 2 figures. Accepted by NeurIPS 2023 AI for Science Workshop

In conventional optical microscopes, image contrast of objects mainly results from the differences in light intensity and/or color. Muller matrix optical microscopes (MMMs), on the other hand, can provide significantly enhanced image contrast and rich information about objects by analyzing their interactions with polarized light. However, state-of-art MMMs are fundamentally limited by bulky and slow polarization state generators and analyzers. Here, we demonstrated the feasibility of applying metasurfaces to enable a fast and compact MMM, i.e., Meta-MMM. We developed a dual-color MMM, in both reflection and transmission modes, based on a chip-integrated high-speed (>20fps) metasurface polarization state analyzer (Meta-PSA) and realized high measurement accuracy for Muller matrix (MM) imaging. We then applied our Meta-MMM to nanostructure characterization, surface morphology analysis and discovered birefringent structures in honeybee wings. Our meta-MMMs hold the promise to revolutionize various applications from biological imaging, medical diagnosis, material characterization to industry inspection and space exploration.

Talking face generation has a wide range of potential applications in the field of virtual digital humans. However, rendering high-fidelity facial video while ensuring lip synchronization is still a challenge for existing audio-driven talking face generation approaches. To address this issue, we propose HyperLips, a two-stage framework consisting of a hypernetwork for controlling lips and a high-resolution decoder for rendering high-fidelity faces. In the first stage, we construct a base face generation network that uses the hypernetwork to control the encoding latent code of the visual face information over audio. First, FaceEncoder is used to obtain latent code by extracting features from the visual face information taken from the video source containing the face frame.Then, HyperConv, which weighting parameters are updated by HyperNet with the audio features as input, will modify the latent code to synchronize the lip movement with the audio. Finally, FaceDecoder will decode the modified and synchronized latent code into visual face content. In the second stage, we obtain higher quality face videos through a high-resolution decoder. To further improve the quality of face generation, we trained a high-resolution decoder, HRDecoder, using face images and detected sketches generated from the first stage as input.Extensive quantitative and qualitative experiments show that our method outperforms state-of-the-art work with more realistic, high-fidelity, and lip synchronization. Project page: https://semchan.github.io/HyperLips Project/

The HADAR experiment, which will be constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high sensitivity advantages of focused Cherenkov detectors. Its physics objective is to observe transient sources such as gamma-ray bursts and counterparts of gravitational waves. The aim of this study is to utilize the latest AI technology to enhance the sensitivity of the HADAR experiment. We have built training datasets and models with distinctive creativity by incorporating relevant physical theories for various applications. They are able to determine the kind, energy, and direction of incident particles after careful design. We have obtained a background identification accuracy of 98.6%, a relative energy reconstruction error of 10.0%, and an angular resolution of 0.22-degrees in a test dataset at 10 TeV. These findings demonstrate the enormous potential for enhancing the precision and dependability of detector data analysis in astrophysical research. Thanks to deep learning techniques, the HADAR experiment's observational sensitivity to the Crab Nebula has surpassed that of MAGIC and H.E.S.S. at energies below 0.5 TeV and remains competitive with conventional narrow-field Cherenkov telescopes at higher energies. Additionally, our experiment offers a fresh approach to dealing with strongly connected scattered data.

In autonomous driving tasks, scene understanding is the first step towards predicting the future behavior of the surrounding traffic participants. Yet, how to represent a given scene and extract its features are still open research questions. In this study, we propose a novel text-based representation of traffic scenes and process it with a pre-trained language encoder. First, we show that text-based representations, combined with classical rasterized image representations, lead to descriptive scene embeddings. Second, we benchmark our predictions on the nuScenes dataset and show significant improvements compared to baselines. Third, we show in an ablation study that a joint encoder of text and rasterized images outperforms the individual encoders confirming that both representations have their complementary strengths.

Recent observations of cosmic rays (CRs) have revealed a two-component anomaly in the spectra of primary and secondary particles, as well as their ratios, prompting investigation into their common origin. In this study, we incorporate the identification of slow diffusion zones around sources as a common phenomenon into our calculations, which successfully reproduces all previously described anomalies except for the positron spectrum. Crucially, our research offers a clear physical picture of the origin of CR: while high-energy ($\textrm{>200~GV}$, including the knee) particles are primarily produced by fresh accelerators and are confined to local regions, low energy ($\textrm{<200~GV}$) components come from distant sources and travel through the outer diffusive zone outside of the galactic disk. This scenario can be universally applied in the galactic disk, as evidenced by ultra-high energy diffuse $\rm\gamma$-ray emissions detected by the AS$\rm\gamma$ experiment. Furthermore, our results predict that the spectrum of diffuse $\rm\gamma$-ray is spatial-dependent, resting with local sources, which can be tested by LHAASO experiment., Comment: 9 pages, 7 figures, accepted by PhysRevD

Owing to its atomically thin thickness, layer-dependent tunable band gap, flexibility, and CMOS compatibility, MoS$_2$ is a promising candidate for photodetection. However, mono-layer MoS2-based photodetectors typically show poor optoelectronic performances, mainly limited by their low optical absorption. In this work, we hybridized CVD-grown monolayer MoS$_2$ with a gold nanodisk (AuND) array to demonstrate a superior visible photodetector through a synergetic effect. It is evident from our experimental results that there is a strong light-matter interaction between AuNDs and monolayer MoS$_2$, which results in better photodetection due to a surface trap state passivation with a longer charge carrier lifetime compared to pristine MoS$_2$. In particular, the AuND/MoS$_2$ system demonstrated a photoresponsivity of $8.7 \times 10^{4}$ A/W, specific detectivity of $6.9 \times 10^{13}$ Jones, and gain $1.7 \times 10^{5}$ at $31.84 \mu W/cm^{2}$ illumination power density of 632 nm wavelength with an applied voltage of 4.0 V for an AuND/MoS$_2$-based photodetector. To our knowledge, these optoelectronic responses are one order higher than reported results for CVD MoS$_2$-based photodetector in the literature., Comment: 30 pages, 14 figures

Cone Beam CT (CBCT) plays a crucial role in Adaptive Radiation Therapy (ART) by accurately providing radiation treatment when organ anatomy changes occur. However, CBCT images suffer from scatter noise and artifacts, making relying solely on CBCT for precise dose calculation and accurate tissue localization challenging. Therefore, there is a need to improve CBCT image quality and Hounsfield Unit (HU) accuracy while preserving anatomical structures. To enhance the role and application value of CBCT in ART, we propose an energy-guided diffusion model (EGDiff) and conduct experiments on a chest tumor dataset to generate synthetic CT (sCT) from CBCT. The experimental results demonstrate impressive performance with an average absolute error of 26.87$\pm$6.14 HU, a structural similarity index measurement of 0.850$\pm$0.03, a peak signal-to-noise ratio of the sCT of 19.83$\pm$1.39 dB, and a normalized cross-correlation of the sCT of 0.874$\pm$0.04. These results indicate that our method outperforms state-of-the-art unsupervised synthesis methods in accuracy and visual quality, producing superior sCT images.

Relying on large-scale training data with pixel-level labels, previous edge detection methods have achieved high performance. However, it is hard to manually label edges accurately, especially for large datasets, and thus the datasets inevitably contain noisy labels. This label-noise issue has been studied extensively for classification, while still remaining under-explored for edge detection. To address the label-noise issue for edge detection, this paper proposes to learn Pixel-level NoiseTransitions to model the label-corruption process. To achieve it, we develop a novel Pixel-wise Shift Learning (PSL) module to estimate the transition from clean to noisy labels as a displacement field. Exploiting the estimated noise transitions, our model, named PNT-Edge, is able to fit the prediction to clean labels. In addition, a local edge density regularization term is devised to exploit local structure information for better transition learning. This term encourages learning large shifts for the edges with complex local structures. Experiments on SBD and Cityscapes demonstrate the effectiveness of our method in relieving the impact of label noise. Codes are available at https://github.com/DREAMXFAR/PNT-Edge., Comment: Accepted by ACM-MM 2023

Several Pulsar Timing Array (PTA) collaborations have recently provided strong evidence for a nHz Stochastic Gravitational-Wave Background (SGWB). Here we investigate the implications of a first-order phase transition occurring within the early universe's dark quantum chromodynamics (dQCD) epoch, specifically within the framework of the mirror twin Higgs dark sector model. Our analysis indicates a distinguishable SGWB signal originating from this phase transition, which can explain the measurements obtained by PTAs. Remarkably, a significant portion of the parameter space for the SGWB signal also effectively resolves the existing tensions in both the $H_0$ and $S_8$ measurements in Cosmology. This intriguing correlation suggests a possible common origin of these three phenomena for $0.2 < \Delta N_{\rm eff} < 0.5$, where the mirror dark matter component constitutes about $30\%$ of the total dark matter abundance. Next generation CMB experiment such as CMB-S4 is able to test this parameter region., Comment: 7 pages, 4 figures

The origin of interstellar and intergalactic magnetic fields remains largely unknown. One possibility is that they are related to the primordial magnetic fields (PMFs) produced by, for instance, the phase transitions of the early Universe. In this paper, we show that the PMF-induced turbulence generated at around the QCD phase transition epoch--the characteristic magnetic field strength $B_{\rm ch}^* \sim \mathcal{O}(1)~\rm{\mu G}$ and coherent length scale $\ell_{\rm ch}^* \sim \mathcal{O}(1)~\rm{pc}$--can naturally accommodate nanohertz gravitational waves reported by pulsar timing array (PTA) collaborations. Moreover, the evolution of the PMFs to the recombination era with the form of $B_{\rm ch}\sim \ell_{\rm ch}^{-\alpha}$ can induce baryon density inhomogeneities, alter the recombination history, and alleviate the tension of the Hubble parameter $H_0$ and the matter clumpiness parameter $S_8$ between early- and late-time measurements for $0.88\leq \alpha \leq 1.17$ (approximate 95\% credible region based on three PTA likelihoods). The further evolved PMFs may account for the $\sim {\cal O}(10^{-16})$ Gauss extragalactic magnetic field inferred with GRB 221009A., Comment: 7 pages, 3 figures, 1 table

Exploring novel materials with enhanced optical nonlinearities at low power levels with ultrafast response and small footprints is of great interests for information processing, communication, sensing and quantum systems. Recent progress on nonlinear metamaterials and metasurfaces suggests promising solutions to overcome the limitations of nonlinear materials in nature. Here we present a design concept for highly enhanced saturable absorption effect based on subwavelength-thick (<1/5$\lambda$0) hybrid graphene-plasmonic metasurface structures in infrared wavelengths. Our theoretical and experimental results demonstrated that, by exciting nonequilibrium carriers inside nanoscale hotspots, one could not only enhance the saturable absorption in graphene, but also reduce the saturation fluence by over 3 orders of magnitude (from ~ 1 mJ/cm2 to ~100 nJ/cm2). Our pump-probe measurement results suggested an ultrashort saturable absorption recovery time (<60 fs), which is ultimately determined by the relaxation dynamics of photoexcited carriers in graphene. We also observed pulse narrowing effects in our devices based on the autocorrelation measurement results. Such design concepts can be tailored via structure engineering to operate in broader wavelength ranges up to mid- and far- infrared spectral regions. These ultrafast low-saturation fluence saturable absorber designs can enable low-threshold, compact, self-starting mode-locked lasers, laser pulse shaping, and high-speed optical information processing.