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With the advance of diffusion models, various personalized image generation methods have been proposed. However, almost all existing work only focuses on either subject-driven or style-driven personalization. Meanwhile, state-of-the-art methods face several challenges in realizing compositional personalization, i.e., composing different subject and style concepts, such as concept disentanglement, unified reconstruction paradigm, and insufficient training data. To address these issues, we introduce FreeTuner, a flexible and training-free method for compositional personalization that can generate any user-provided subject in any user-provided style (see Figure 1). Our approach employs a disentanglement strategy that separates the generation process into two stages to effectively mitigate concept entanglement. FreeTuner leverages the intermediate features within the diffusion model for subject concept representation and introduces style guidance to align the synthesized images with the style concept, ensuring the preservation of both the subject's structure and the style's aesthetic features. Extensive experiments have demonstrated the generation ability of FreeTuner across various personalization settings.

On-chip integrated metasurface driven by in-plane guided waves is of great interests in various light field manipulation applications such as colorful augmented reality and holographic display. However, it remains a challenge to design colorful multichannel holography by a single on-chip metasurface. Here we present metasurfaces integrated on top of guided-wave photonic slab that achieves multi-channel colorful holographic light display. An end-to-end scheme is used to inverse design the metasurface for projecting off-chip preset multiple patterns. Particular examples are presented for customized patterns that were encoded into the metasurface with a single-cell meta-atom, working simultaneously at RGB color channels and for several different diffractive distance, with polarization dependence. Holographic images are generated at 18 independent channels with such a single-cell metasurface. The proposed design scheme is easy to implement and the resulting device is viable to fabrication, promising a plenty of applications in nanophotonics., Comment: 18 pages and 11 figures

There is currently strong interest in improving visual object tracking by augmenting the RGB modality with the output of a visual event camera that is particularly informative about the scene motion. However, existing approaches perform event feature extraction for RGB-E tracking using traditional appearance models, which have been optimised for RGB only tracking, without adapting it for the intrinsic characteristics of the event data. To address this problem, we propose an Event backbone (Pooler), designed to obtain a high-quality feature representation that is cognisant of the innate characteristics of the event data, namely its sparsity. In particular, Multi-Scale Pooling is introduced to capture all the motion feature trends within event data through the utilisation of diverse pooling kernel sizes. The association between the derived RGB and event representations is established by an innovative module performing adaptive Mutually Guided Fusion (MGF). Extensive experimental results show that our method significantly outperforms state-of-the-art trackers on two widely used RGB-E tracking datasets, including VisEvent and COESOT, where the precision and success rates on COESOT are improved by 4.9% and 5.2%, respectively. Our code will be available at https://github.com/SSSpc333/TENet.

The significance of network structures in promoting group cooperation within social dilemmas has been widely recognized. Prior studies attribute this facilitation to the assortment of strategies driven by spatial interactions. Although reinforcement learning has been employed to investigate the impact of dynamic interaction on the evolution of cooperation, there remains a lack of understanding about how agents develop neighbour selection behaviours and the formation of strategic assortment within an explicit interaction structure. To address this, our study introduces a computational framework based on multi-agent reinforcement learning in the spatial Prisoner's Dilemma game. This framework allows agents to select dilemma strategies and interacting neighbours based on their long-term experiences, differing from existing research that relies on preset social norms or external incentives. By modelling each agent using two distinct Q-networks, we disentangle the coevolutionary dynamics between cooperation and interaction. The results indicate that long-term experience enables agents to develop the ability to identify non-cooperative neighbours and exhibit a preference for interaction with cooperative ones. This emergent self-organizing behaviour leads to the clustering of agents with similar strategies, thereby increasing network reciprocity and enhancing group cooperation., Comment: Accepted at IJCAI 2024 (33rd International Joint Conference on Artificial Intelligence - Jeju)

Second-order topological insulators can be characterized by their bulk polarization, which is believed to be intrinsically connected to the center of the Wannier function. In this study, we demonstrate the existence of second-order topological insulators that feature a pair of partially degenerate photonic bands. These arise from the nonsymmorphic glide symmetry in an all-dielectric photonic crystal. The center of the maximally localized Wannier function (MLWF) is consistently located at the origin but is not equivalent with respect to the sum of constituent polarizations. As a result, topological corner modes can be identified by the distinctly hybridized MLWFs that truncate at the sample boundary. Through full-wave numerical simulations paired with microwave experiments, the second-order topology is clearly confirmed and characterized. These topological corner states exhibit notably unique modal symmetries, which are made possible by the inversion of the Wannier bands. Our results provide an alternative approach to explore higher-order topological physics with significant potential for applications in integrated and quantum photonics.

RGBT tracking draws increasing attention due to its robustness in multi-modality warranting (MMW) scenarios, such as nighttime and bad weather, where relying on a single sensing modality fails to ensure stable tracking results. However, the existing benchmarks predominantly consist of videos collected in common scenarios where both RGB and thermal infrared (TIR) information are of sufficient quality. This makes the data unrepresentative of severe imaging conditions, leading to tracking failures in MMW scenarios. To bridge this gap, we present a new benchmark, MV-RGBT, captured specifically in MMW scenarios. In contrast with the existing datasets, MV-RGBT comprises more object categories and scenes, providing a diverse and challenging benchmark. Furthermore, for severe imaging conditions of MMW scenarios, a new problem is posed, namely \textit{when to fuse}, to stimulate the development of fusion strategies for such data. We propose a new method based on a mixture of experts, namely MoETrack, as a baseline fusion strategy. In MoETrack, each expert generates independent tracking results along with the corresponding confidence score, which is used to control the fusion process. Extensive experimental results demonstrate the significant potential of MV-RGBT in advancing RGBT tracking and elicit the conclusion that fusion is not always beneficial, especially in MMW scenarios. Significantly, the proposed MoETrack method achieves new state-of-the-art results not only on MV-RGBT, but also on standard benchmarks, such as RGBT234, LasHeR, and the short-term split of VTUAV (VTUAV-ST). More information of MV-RGBT and the source code of MoETrack will be released at https://github.com/Zhangyong-Tang/MoETrack.

Pooling is a crucial operation in computer vision, yet the unique structure of skeletons hinders the application of existing pooling strategies to skeleton graph modelling. In this paper, we propose an Improved Graph Pooling Network, referred to as IGPN. The main innovations include: Our method incorporates a region-awareness pooling strategy based on structural partitioning. The correlation matrix of the original feature is used to adaptively adjust the weight of information in different regions of the newly generated features, resulting in more flexible and effective processing. To prevent the irreversible loss of discriminative information, we propose a cross fusion module and an information supplement module to provide block-level and input-level information respectively. As a plug-and-play structure, the proposed operation can be seamlessly combined with existing GCN-based models. We conducted extensive evaluations on several challenging benchmarks, and the experimental results indicate the effectiveness of our proposed solutions. For example, in the cross-subject evaluation of the NTU-RGB+D 60 dataset, IGPN achieves a significant improvement in accuracy compared to the baseline while reducing Flops by nearly 70%; a heavier version has also been introduced to further boost accuracy.

Audio-driven visual scene editing endeavors to manipulate the visual background while leaving the foreground content unchanged, according to the given audio signals. Unlike current efforts focusing primarily on image editing, audio-driven video scene editing has not been extensively addressed. In this paper, we introduce AudioScenic, an audio-driven framework designed for video scene editing. AudioScenic integrates audio semantics into the visual scene through a temporal-aware audio semantic injection process. As our focus is on background editing, we further introduce a SceneMasker module, which maintains the integrity of the foreground content during the editing process. AudioScenic exploits the inherent properties of audio, namely, audio magnitude and frequency, to guide the editing process, aiming to control the temporal dynamics and enhance the temporal consistency. First, we present an audio Magnitude Modulator module that adjusts the temporal dynamics of the scene in response to changes in audio magnitude, enhancing the visual dynamics. Second, the audio Frequency Fuser module is designed to ensure temporal consistency by aligning the frequency of the audio with the dynamics of the video scenes, thus improving the overall temporal coherence of the edited videos. These integrated features enable AudioScenic to not only enhance visual diversity but also maintain temporal consistency throughout the video. We present a new metric named temporal score for more comprehensive validation of temporal consistency. We demonstrate substantial advancements of AudioScenic over competing methods on DAVIS and Audioset datasets.

Maintaining temporal stability is crucial in multi-agent trajectory prediction. Insufficient regularization to uphold this stability often results in fluctuations in kinematic states, leading to inconsistent predictions and the amplification of errors. In this study, we introduce a framework called Multi-Agent Trajectory prediction via neural interaction Energy (MATE). This framework assesses the interactive motion of agents by employing neural interaction energy, which captures the dynamics of interactions and illustrates their influence on the future trajectories of agents. To bolster temporal stability, we introduce two constraints: inter-agent interaction constraint and intra-agent motion constraint. These constraints work together to ensure temporal stability at both the system and agent levels, effectively mitigating prediction fluctuations inherent in multi-agent systems. Comparative evaluations against previous methods on four diverse datasets highlight the superior prediction accuracy and generalization capabilities of our model.

Benefiting from strong generalization ability, pre-trained vision language models (VLMs), e.g., CLIP, have been widely utilized in zero-shot scene understanding. Unlike simple recognition tasks, grounded situation recognition (GSR) requires the model not only to classify salient activity (verb) in the image, but also to detect all semantic roles that participate in the action. This complex task usually involves three steps: verb recognition, semantic role grounding, and noun recognition. Directly employing class-based prompts with VLMs and grounding models for this task suffers from several limitations, e.g., it struggles to distinguish ambiguous verb concepts, accurately localize roles with fixed verb-centric template1 input, and achieve context-aware noun predictions. In this paper, we argue that these limitations stem from the mode's poor understanding of verb/noun classes. To this end, we introduce a new approach for zero-shot GSR via Language EXplainer (LEX), which significantly boosts the model's comprehensive capabilities through three explainers: 1) verb explainer, which generates general verb-centric descriptions to enhance the discriminability of different verb classes; 2) grounding explainer, which rephrases verb-centric templates for clearer understanding, thereby enhancing precise semantic role localization; and 3) noun explainer, which creates scene-specific noun descriptions to ensure context-aware noun recognition. By equipping each step of the GSR process with an auxiliary explainer, LEX facilitates complex scene understanding in real-world scenarios. Our extensive validations on the SWiG dataset demonstrate LEX's effectiveness and interoperability in zero-shot GSR.

Leveraging generative retrieval (GR) techniques to enhance search systems is an emerging methodology that has shown promising results in recent years. In GR, a text-to-text model maps string queries directly to relevant document identifiers (docIDs), dramatically simplifying the retrieval process. However, when applying most GR models in large-scale E-commerce for personalized item search, we must face two key problems in encoding and decoding. (1) Existing docID generation methods ignore the encoding of efficiency information, which is critical in E-commerce. (2) The positional information is important in decoding docIDs, while prior studies have not adequately discriminated the significance of positional information or well exploited the inherent interrelation among these positions. To overcome these problems, we introduce an efficient Hierarchical encoding-decoding Generative retrieval method (Hi-Gen) for large-scale personalized E-commerce search systems. Specifically, we first design a representation learning model using metric learning to learn discriminative feature representations of items to capture semantic relevance and efficiency information. Then, we propose a category-guided hierarchical clustering scheme that makes full use of the semantic and efficiency information of items to facilitate docID generation. Finally, we design a position-aware loss to discriminate the importance of positions and mine the inherent interrelation between different tokens at the same position. This loss boosts the performance of the language model used in the decoding stage. Besides, we propose two variants of Hi-Gen (Hi-Gen-I2I and Hi-Gen-Cluster) to support online real-time large-scale recall in the online serving process. Hi-Gen gets 3.30% and 4.62% improvements over SOTA for Recall@1 on the public and industry datasets, respectively., Comment: Accepted by ICDM 2024

Spin-mechanical coupling is vital in diverse fields including spintronics, sensing and quantum transduction. Two-dimensional (2D) magnetic materials provide a unique platform for investigating spin-mechanical coupling, attributed to their mechanical flexibility and novel spin orderings. However, studying spin-mechanical coupling in 2D magnets presents challenges in probing mechanical deformation and thermodynamic properties change at nanoscale. Here we use nano opto-electro-mechanical interferometry to mechanically detect the phase transition and magnetostriction effect in multilayer CrSBr, an air-stable antiferromagnets with large magnon-exciton coupling. The transitions among antiferromagnetism, spin-canted ferromagnetism and paramagnetism are visualized by optomechanical frequency anomalies. Nontrivial magnetostriction coefficient 2.3x10^(-5) and magnetoelastic coupling strength on the order of 10^6 J/m^3 have been found. Moreover, we demonstrate the substantial tunability of the magnetoelastic constant by nearly 50% via gate-induced strain. Our findings demonstrate the strong spin-mechanical coupling in CrSBr and paves the way for developing sensitive magnetic sensing and efficient quantum transduction at atomically thin limit.

In recent astronomical observations, an almost dark galaxy, designated as Nube, has unveiled an intriguing anomaly in its stellar distribution. Specifically, Nube exhibits an exceptionally low central brightness, with the 2D half-light radius of its stars far exceeding the typical values found in dwarf galaxies, and even surpassing those observed in ultra-diffuse galaxies (UDGs). This phenomenon is difficult to explain within the framework of cold dark matter (CDM). Meanwhile, due to its ultralight particle mass, fuzzy dark matter (FDM) exhibits a de Broglie wavelength on the order of kiloparsecs under the typical velocities of galaxies. The interference between different modes of the FDM wave gives rise to fluctuations in the gravitational field, which can lead to the dynamical heating of stars within galaxies, resulting in an expansion of their spatial distribution. In this paper, we aim to interpret the anomalous stellar distribution observed in Nube as a consequence of the dynamical heating effect induced by FDM. Our findings suggest that a FDM particle mass around $1-2\times 10^{-23}$ eV can effectively account for this anomaly. And we propose that the FDM dynamical heating effect provides a new insight into understanding the formation of field UDGs., Comment: 13 pages, 4 figures

Human motion prediction is consisting in forecasting future body poses from historically observed sequences. It is a longstanding challenge due to motion's complex dynamics and uncertainty. Existing methods focus on building up complicated neural networks to model the motion dynamics. The predicted results are required to be strictly similar to the training samples with L2 loss in current training pipeline. However, little attention has been paid to the uncertainty property which is crucial to the prediction task. We argue that the recorded motion in training data could be an observation of possible future, rather than a predetermined result. In addition, existing works calculate the predicted error on each future frame equally during training, while recent work indicated that different frames could play different roles. In this work, a novel computationally efficient encoder-decoder model with uncertainty consideration is proposed, which could learn proper characteristics for future frames by a dynamic function. Experimental results on benchmark datasets demonstrate that our uncertainty consideration approach has obvious advantages both in quantity and quality. Moreover, the proposed method could produce motion sequences with much better quality that avoids the intractable shaking artefacts. We believe our work could provide a novel perspective to consider the uncertainty quality for the general motion prediction task and encourage the studies in this field. The code will be available in https://github.com/Motionpre/Adaptive-Salient-Loss-SAGGB., Comment: Accepted by AAAI2024

Recently LHAASO Collaboration gives precise measurements of cosmic rays (CR) all particle energy spectrum and mean logarithmic mass $\left \langle \ln A \right \rangle$ from 0.3 PeV to 30 PeV. Combining the CR measurements by AMS-02 and DAMPE in space and that by LHAASO and Auger on the ground we construct a model to recover all these measurements from tens of GeV to tens of EeV. We find the LHAASO measurement is crucial in the model construction by connecting the Galactic component to the extragalactic component. The precise measurements of CR spectra for individual species by AMS-02 and DAMPE together with the newest LHAASO results clearly indicates three Galactic CR components, that is, a soft low energy background, a hard high energy component, and a local source contribution. However, the LHAASO data show that above $\sim 10^{16}$ eV a nonnegligible extragalactic component must be included. Combining the Auger results and the LHAASO results we figure out the extragalactic CRs which need at least two components at lower and higher energies. Thanks to the precise measurements by LHAASO the constraints on the model parameters are quite stringent. The spectra features and mass measurements in all energy range are all well reproduced in the model., Comment: 11 pages, 2 figures, 4 tables

Observations have revealed a significant dark matter deficit in the ultra-diffuse galaxy NGC1052-DF2 (DF2). It is widely accepted that the formation of this unique galaxy can be attributed to the tidal stripping of its host galaxy, NGC1052. In this study, we simulate the evolution of a satellite system containing globular clusters (GCs) within an accreting host halo in the framework of self-interacting dark matter (SIDM). Our simulation results suggest that the heightened tidal stripping resulting from DM self-interactions can give rise to the transformation of a conventional dwarf galaxy into a dark matter deficit galaxy resembling DF2. By comparing the simulation results with identical initial conditions in both the standard cold dark matter (CDM) and SIDM models, we find that the latter is more likely to replicate the properties of DF2. Furthermore, we demonstrate that a DF2 analog can also be produced on an orbit with a greater pericenter distance by increasing the strength of DM self-interactions. This suggests that the issue of extreme orbital parameters can be mitigated by implementing the SIDM model. The distributions of the GC population derived in our SIDM simulation are consistent with the observed characteristics of DF2. For comparison, we also explored the potential for achieving GC distributions in the context of CDM., Comment: 12 pages, 7 figures

Facial Attribute Classification (FAC) holds substantial promise in widespread applications. However, FAC models trained by traditional methodologies can be unfair by exhibiting accuracy inconsistencies across varied data subpopulations. This unfairness is largely attributed to bias in data, where some spurious attributes (e.g., Male) statistically correlate with the target attribute (e.g., Smiling). Most of existing fairness-aware methods rely on the labels of spurious attributes, which may be unavailable in practice. This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation. Initially, we identify the potential spurious attributes based on generative models. Notably, it enhances interpretability by explicitly showing the spurious attributes in image space. Following this, for each image, we first edit the spurious attributes with a random degree sampled from a uniform distribution, while keeping target attribute unchanged. Then we train a fair FAC model by fostering model invariance to these augmentation. Extensive experiments on three common datasets demonstrate the effectiveness of our method in promoting fairness in FAC without compromising accuracy. Codes are in https://github.com/heqianpei/DiGA., Comment: CVPR 2024

In the centers of galaxies, stars that orbit supermassive black hole binaries (SMBHBs) can undergo tidal disruptions due to the Lidov-Kozai mechanism. Nevertheless, most previous researches have predominantly focused on full tidal disruption events (FTDEs). In this study, we employ N-body simulations to investigate partial tidal disruption events (PTDEs) induced by intermediate-mass black holes (IMBHs) in SMBH-IMBH binaries, taking into account consideration the IMBH's mass, semi-major axis, and eccentricity of the outer orbit. Our findings indicate that, in comparison to FTDEs, the majority of tidal disruption events are actually PTDEs. Furthermore, we find that a significant number of stars experiencing partial disruption ultimately get captured by the IMBH, potentially leading to repeating flares. By comparing the period of the periodic eruptions observed in ASASSN-14ko, we find that PTDEs in a specific SMBH-IMBH binary system can align with the observed period if the SMBH has a mass of $10^7\rm{\ M_\odot}$, the IMBH has a mass smaller than approximately $10^5\rm{\ M_\odot}$, the eccentricity of the SMBH-IMBH binary exceeds approximately $0.5$, and the semi-major axis of the SMBH-IMBH binary is larger than approximately $0.001\rm{\ pc}$. Moreover, our model effectively accounts for the observed period derivative for ASASSN-14ko ($\dot{P}=-0.0026\pm 0.0006$), and our results also imply that some quasi-periodic eruptions may be attributed to PTDEs occurring around SMBH-IMBH binaries., Comment: 11 pages, 8 figures

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in ABSA lies in precisely determining the aspects' boundaries (start and end indices), especially for long ones, due to users' colloquial expressions. We propose DiffusionABSA, a novel diffusion model tailored for ABSA, which extracts the aspects progressively step by step. Particularly, DiffusionABSA gradually adds noise to the aspect terms in the training process, subsequently learning a denoising process that progressively restores these terms in a reverse manner. To estimate the boundaries, we design a denoising neural network enhanced by a syntax-aware temporal attention mechanism to chronologically capture the interplay between aspects and surrounding text. Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. Our code is publicly available at https://github.com/Qlb6x/DiffusionABSA., Comment: Accepted to LREC-COLING 2024, submission version

The excesses in the electron and positron spectra observed by many experiments, such as PAMELA and AMS-02, have sparked significant theoretical investigation. It is not easy to distinguish the two primary hypotheses dark matter annihilation/decay and pulsars from the spectral features. Should pulsars be the source of this excess, the expected variability in their distribution may introduce distinct irregularities in the positron energy spectrum. In this study, we use an irregularity estimator to detect these potential features in the positron energy spectrum of AMS-02. Our analysis of the current AMS-02 data reveals these spectral irregularities with a statistical significance of $1.75\sigma$. However, our projection indicates that, with AMS-02 data collected over a period of 20 years, such irregularities could be identified with a confidence level of $3\sigma$ level in 71\% of our simulations., Comment: 6 pages, 6 figures

The generation of the narrowband strong-correlated biphotons via spontaneous four-wave mixing can be effectively controlled and enhanced by an additional driving field which drives a transition with its upper level being a Rydberg state. We study the properties of the noise of the generated biphotons and show that in the region of weak pumping and low atomic density, a high degree of the photon correlation is maintained with the photon-pair generation rate siginificantly enhanced.

We propose a scheme for realizing broadband and tunable transmission non-reciprocity by utilizing two-photon near-resonant transitions in thermal atoms as single-photon far-detuned transitions can be eliminated. Our basic idea is to largely reduce the Doppler broadenings on a pair of two-photon, probe and coupling, transitions and meanwhile make the only four-photon transition Doppler-free (velocity-dependent) for a forward (backward) probe field. One main advantage of this scheme lies in that the transmission non-reciprocity can be realized and manipulated in a frequency range typically exceeding $200$ MHz with isolation ratio above $20$ dB and insertion loss below $1.0$ dB by modulating an assistant field in frequency and amplitude. The intersecting angle between four applied fields also serves as an effective control knob to optimize the nonreciprocal transmission of a forward or backward probe field, e.g. in a much wider frequency range approaching $1.4$ GHz.

The Be isotopic measurements preliminarily reported by the AMS-02 Collaboration have reached an unprecedented energy of 12 GeV/$n$. As secondary cosmic rays (CRs), the Be isotopes include both stable and unstable species, which are crucial for constraining the propagation parameters of Galactic CRs. However, uncertainties in their production cross sections can skew the interpretation of the CR data, especially when cross-section measurements are of significantly lower quality than CR measurements. In this work, we consider the uncertainties of the cross sections to interpret the Be isotopic data by adopting a cross-section parametrization that fully utilizes the available experimental data. Owing to the high-quality measurements of the $^7$Be production cross section, we innovatively employ $^7$Be instead of $^9$Be to constrain propagation parameters. Notably, the diffusion halo thickness is constrained to $5.67\pm0.76$~kpc, representing a moderate value compared to previous analogous works. Combining the well-constrained CR propagation model and the precise CR measurements of $^9$Be, we conversely constrain the major production cross section of $^9$Be and find that it ought to be remarkably lower than previously thought. Our analysis also questions the reliability of certain cross sections measured by some experiments, potentially marking the first time CR data has been used to identify dubious nucleon production cross sections. The method presented in this work holds promise for analyzing upcoming isotopic data from other nuclei., Comment: 15 pages, 9 figures

Recently, signatures of superconductivity with critical temperature from 20 to 30 K have been reported in pressured trilayer nickelate La$_4$Ni$_3$O$_{10}$ through a pressure-induced structure transition. Here we explore the evolution of electronic structures and electronic correlations in different phases of La$_4$Ni$_3$O$_{10}$ under corresponding pressure regions, by using density functional theory (DFT) combined with dynamical mean-field theory (DMFT). Similar to bilayer superconductor La$_3$Ni$_2$O$_{7}$, the electronic bands in superconducting La$_4$Ni$_3$O$_{10}$ are dominated by Ni-3$d_{x^2-y^2}$ and 3$d_{z^2}$ orbits near the Fermi level, in contrast, the inner Ni-O plane in La$_4$Ni$_3$O$_{10}$ generates a doublet hole-pocket Fermi surfaces around the Brillouin-zone corner, meanwhile one branch of the Ni-$3d_{z^2}$ bands is pushed very close above the Fermi level, which can induce an electron pocket through small electron doping. The DFT+DMFT simulations suggest that the electronic correlations only give minor modification to the Fermi surfaces, meanwhile the Ni-$3d_{z^2}$ and 3$d_{x^2-y^2}$ states on outer Ni-O layers have considerable greater mass enhancements than on the inner layer. The sensitiveness of electronic structure under doping and unique layer dependence of correlation suggest a distinct superconducting mechanism with respect to bilayer La$_3$Ni$_2$O$_{7}$. Based on the DFT and DFT+DMFT simulations, we eventually derive a trilayer effective tight-binding model, which can produce rather precise electronic bands and Fermi surfaces, hence can serve as an appropriate model to further study the superconducting mechanism and paring symmetry in trilayer La$_4$Ni$_3$O$_{10}$., Comment: 8 pages, 10 figures

In this study, we focus on a black hole immersed in a cosmological Chaplygin-like dark fluid (CDF), characterized by the equation of state $p=-B/\rho$ and an additional parameter $q$ influencing the energy density of the fluid. We investigate the geodesic structure, shadow, and optical appearance of such a black hole. Through analysis on the effective potential and the epicyclic frequencies, it is found that the existence of innermost/outermost stable circular orbits for a timelike particle is governed by the CDF parameters. The behaviors of the orbital conserved quantities and Keplerian frequency are also examined. Due to the existence of pseudo-cosmological horizon, the determination of the shadow radius depends significantly on the position of the observer. By placing the static observer at an approximately flat position between the event and pseudo-cosmological horizons, we constrain the CDF parameters using EHT observations. We investigate the effect of CDF on the shadows and optical images of the black hole, surrounded by various profiles of accretions. For the thin disk accretion, the light trajectories are categorized into direct emission, lensing ring, and photon ring based on impact parameters. Due to the existence of outermost stable circular orbits, outer edges could exist in the direct and lensing ring images. The observed brightness is mainly due to direct emission, with a minor contribution from the lensing ring, while the contribution from the photon ring is negligible due to extreme demagnetization. In the case of spherical accretion, we consider both static and infalling accretion models. The images obtained under infalling accretion are slightly darker than those under static accretion, attributed to the Doppler effect. Throughout the study, we analyze the influence of the parameters $B$ and $q$ on the results., Comment: 35 pages, 15 figures. Discussions added

We propose a novel approach to nonlinear functional regression, called the Mapping-to-Parameter function model, which addresses complex and nonlinear functional regression problems in parameter space by employing any supervised learning technique. Central to this model is the mapping of function data from an infinite-dimensional function space to a finite-dimensional parameter space. This is accomplished by concurrently approximating multiple functions with a common set of B-spline basis functions by any chosen order, with their knot distribution determined by the Iterative Local Placement Algorithm, a newly proposed free knot placement algorithm. In contrast to the conventional equidistant knot placement strategy that uniformly distributes knot locations based on a predefined number of knots, our proposed algorithms determine knot location according to the local complexity of the input or output functions. The performance of our knot placement algorithms is shown to be robust in both single-function approximation and multiple-function approximation contexts. Furthermore, the effectiveness and advantage of the proposed prediction model in handling both function-on-scalar regression and function-on-function regression problems are demonstrated through several real data applications, in comparison with four groups of state-of-the-art methods.

This research investigates the impact of dynamic, time-varying interactions on cooperative behaviour in social dilemmas. Traditional research has focused on deterministic rules governing pairwise interactions, yet the impact of interaction frequency and synchronization in groups on cooperation remains underexplored. Addressing this gap, our work introduces two temporal interaction mechanisms to model the stochastic or periodic participation of individuals in public goods games, acknowledging real-life variances due to exogenous temporal factors and geographical time differences. We consider that the interaction state significantly influences both game payoff calculations and the strategy updating process, offering new insights into the emergence and sustainability of cooperation. Our results indicate that maximum game participation frequency is suboptimal under a stochastic interaction mechanism. Instead, an intermediate activation probability maximizes cooperation, suggesting a vital balance between interaction frequency and inactivity security. Furthermore, local synchronization of interactions within specific areas is shown to be beneficial, as time differences hinder the spread of cross-structures but promote the formation of dense cooperative clusters with smoother boundaries. We also note that stronger clustering in networks, larger group sizes and lower noise increase cooperation. This research contributes to understanding the role of node-based temporality and probabilistic interactions in social dilemmas, offering insights into fostering cooperation., Comment: Accepted at Physical Review E

Interactive Video Object Segmentation (iVOS) is a challenging task that requires real-time human-computer interaction. To improve the user experience, it is important to consider the user's input habits, segmentation quality, running time and memory consumption.However, existing methods compromise user experience with single input mode and slow running speed. Specifically, these methods only allow the user to interact with one single frame, which limits the expression of the user's intent.To overcome these limitations and better align with people's usage habits, we propose a framework that can accept multiple frames simultaneously and explore synergistic interaction across frames (SIAF). Concretely, we designed the Across-Frame Interaction Module that enables users to annotate different objects freely on multiple frames. The AFI module will migrate scribble information among multiple interactive frames and generate multi-frame masks. Additionally, we employ the id-queried mechanism to process multiple objects in batches. Furthermore, for a more efficient propagation and lightweight model, we design a truncated re-propagation strategy to replace the previous multi-round fusion module, which employs an across-round memory that stores important interaction information. Our SwinB-SIAF achieves new state-of-the-art performance on DAVIS 2017 (89.6%, J&F@60). Moreover, our R50-SIAF is more than 3 faster than the state-of-the-art competitor under challenging multi-object scenarios., Comment: Accepted by TCSVT

High-energy photons may oscillate with axion-like particles (ALPs) when they propagate through the Milky Way's magnetic field, resulting in an alteration in the observed photon energy spectrum. The ultra-high energy gamma-ray spectra, measured by the Large High Altitude Air Shower Observatory (LHAASO) up to $\mathcal{O}(1)~\mathrm{PeV}$, provide a promising opportunity to investigate the ALP-photon oscillation effect. In this study, we utilize the gamma-ray spectra of four Galactic sources measured by LHAASO, including the Crab Nebula, LHAASO J2226+6057, LHAASO J1908+0621, and LHAASO J1825-1326, to explore this effect. We employ the $\rm CL_s$ method to set constraints on the ALP parameters. Combing the observations of the four sources, our analysis reveals that the ALP-photon coupling $g_{a\gamma}$ is constrained to be smaller than $1.4\times10^{-10}$ ${\rm GeV}^{-1}$ for the ALP mass of $\sim 4\times10^{-7} ~\mathrm{eV}$ at the 95\% C.L. By combing the observations of the Crab Nebula from LHAASO and other experiments, we find that the ALP-photon coupling could be set to be about $7.2\times10^{-11}$ ${\rm GeV}^{-1}$ for the ALP mass $\sim 4 \times10^{-7}~\mathrm{eV}$ , which is in close proximity to the CAST constraint.

Advanced image fusion methods are devoted to generating the fusion results by aggregating the complementary information conveyed by the source images. However, the difference in the source-specific manifestation of the imaged scene content makes it difficult to design a robust and controllable fusion process. We argue that this issue can be alleviated with the help of higher-level semantics, conveyed by the text modality, which should enable us to generate fused images for different purposes, such as visualisation and downstream tasks, in a controllable way. This is achieved by exploiting a vision-and-language model to build a coarse-to-fine association mechanism between the text and image signals. With the guidance of the association maps, an affine fusion unit is embedded in the transformer network to fuse the text and vision modalities at the feature level. As another ingredient of this work, we propose the use of textual attention to adapt image quality assessment to the fusion task. To facilitate the implementation of the proposed text-guided fusion paradigm, and its adoption by the wider research community, we release a text-annotated image fusion dataset IVT. Extensive experiments demonstrate that our approach (TextFusion) consistently outperforms traditional appearance-based fusion methods. Our code and dataset will be publicly available at https://github.com/AWCXV/TextFusion., Comment: v2 version, 13 pages, 16 figures, with the code repository link

In some quantum gravity (QG) theories, Lorentz symmetry may be broken above the Planck scale. The Lorentz invariance violation (LIV) may induce observable effects at low energies and be detected at high energy astrophysical measurements. The Large High Altitude Air Shower Observatory(LHAASO) has detected the onset, rise, and decay phases of the afterglow of GRB 221009A, covering a wide energy range of photons approximately from $0.2$ to $18$ TeV. This observation provides an excellent opportunity to study the Lorentz invariance violation effect. In this study, we simultaneously utilize the data from the KM2A and WCDA detectors of LHAASO, and apply two event by event methods, namely the pair view method and maximum likelihood method, to investigate LIV. We obtain stringent constraints on the QG energy scale. For instance, through the maximum likelihood method, we determine the 95$\%$ confidence level lower limits to be $E_{QG,1} > 14.7 (6.5)\times 10^{19}$GeV for the subluminal (superluminal) scenario of $n = 1$, and $E_{QG,2} > 12.0 (7.2)\times 10^{11}$GeV for the subluminal (superluminal) scenario of $n = 2$. We find that the rapid rise and slow decay behaviors of the afterglow can impose strong constraints on the subluminal scenario, while the constraints are weaker for the superluminal scenario., Comment: 11 pages, 6 figures.Accepted for publication in JCAP